From 78b41baa33ea3c247fb21af3f6a47b1eb7e5295c Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Tue, 2 Jan 2024 17:08:38 +0000 Subject: [PATCH 01/50] Add skeleton project structure --- .gitignore | 11 + Gemfile | 14 + Gemfile.lock | 273 +++++++++++++++++ LICENCE | 37 +++ README.md | 54 +++- _config.yml | 46 +++ _episodes/01_intro.md | 50 +++ _episodes/99_wrapup.md | 52 ++++ _includes/head-custom.html | 30 ++ _includes/navbar.html | 12 + _includes/prev_next_navbar.html | 16 + _includes/schedule.html | 17 ++ _includes/sortedpages.html | 1 + _includes/toc.html | 4 + _layouts/home.html | 43 +++ _layouts/info_page.html | 43 +++ _layouts/page.html | 65 ++++ _sass/jekyll-theme-slate.scss | 519 ++++++++++++++++++++++++++++++++ acknowledgements.md | 11 + code.md | 64 ++++ contents.md | 10 + contents_off.md | 8 + index.md | 111 +++++++ resources.md | 8 + schedule.md | 12 + setup.md | 9 + 26 files changed, 1518 insertions(+), 2 deletions(-) create mode 100644 .gitignore create mode 100644 Gemfile create mode 100644 Gemfile.lock create mode 100644 LICENCE create mode 100644 _config.yml create mode 100644 _episodes/01_intro.md create mode 100644 _episodes/99_wrapup.md create mode 100644 _includes/head-custom.html create mode 100644 _includes/navbar.html create mode 100644 _includes/prev_next_navbar.html create mode 100644 _includes/schedule.html create mode 100644 _includes/sortedpages.html create mode 100644 _includes/toc.html create mode 100644 _layouts/home.html create mode 100644 _layouts/info_page.html create mode 100644 _layouts/page.html create mode 100644 _sass/jekyll-theme-slate.scss create mode 100644 acknowledgements.md create mode 100644 code.md create mode 100644 contents.md create mode 100644 contents_off.md create mode 100644 index.md create mode 100644 resources.md create mode 100644 schedule.md create mode 100644 setup.md diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..66d8bc6 --- /dev/null +++ b/.gitignore @@ -0,0 +1,11 @@ +# Bundle configuration +.bundle + +# Locally installed Ruby gems +vendor/ + +# RubyGems lock file (only track Gemfile) +Gemfile.lock + +# Built artifacts +_site/ \ No newline at end of file diff --git a/Gemfile b/Gemfile new file mode 100644 index 0000000..78e58d2 --- /dev/null +++ b/Gemfile @@ -0,0 +1,14 @@ +# frozen_string_literal: true + +source 'https://rubygems.org' + +git_source(:github) { |repo_name| "https://github.com/#{repo_name}" } + +# Synchronize with https://pages.github.com/versions +ruby '>=2.6.8' + +gem 'github-pages', group: :jekyll_plugins + +if Gem::Version.new(RUBY_VERSION) >= Gem::Version.new('3.0.0') + gem 'webrick', '>= 1.6.1' +end diff --git a/Gemfile.lock b/Gemfile.lock new file mode 100644 index 0000000..25aa4bf --- /dev/null +++ b/Gemfile.lock @@ -0,0 +1,273 @@ +GEM + remote: https://rubygems.org/ + specs: + activesupport (7.1.2) + base64 + bigdecimal + concurrent-ruby (~> 1.0, >= 1.0.2) + connection_pool (>= 2.2.5) + drb + i18n (>= 1.6, < 2) + minitest (>= 5.1) + mutex_m + tzinfo (~> 2.0) + addressable (2.8.6) + public_suffix (>= 2.0.2, < 6.0) + base64 (0.2.0) + bigdecimal (3.1.5) + coffee-script (2.4.1) + coffee-script-source + execjs + coffee-script-source (1.11.1) + colorator (1.1.0) + commonmarker (0.23.10) + concurrent-ruby (1.2.2) + connection_pool (2.4.1) + dnsruby (1.70.0) + simpleidn (~> 0.2.1) + drb (2.2.0) + ruby2_keywords + em-websocket (0.5.3) + eventmachine (>= 0.12.9) + http_parser.rb (~> 0) + ethon (0.16.0) + ffi (>= 1.15.0) + eventmachine (1.2.7) + execjs (2.9.1) + faraday (2.8.1) + base64 + faraday-net_http (>= 2.0, < 3.1) + ruby2_keywords (>= 0.0.4) + faraday-net_http (3.0.2) + ffi (1.16.3) + forwardable-extended (2.6.0) + gemoji (3.0.1) + github-pages (228) + github-pages-health-check (= 1.17.9) + jekyll (= 3.9.3) + jekyll-avatar (= 0.7.0) + jekyll-coffeescript (= 1.1.1) + jekyll-commonmark-ghpages (= 0.4.0) + jekyll-default-layout (= 0.1.4) + jekyll-feed (= 0.15.1) + jekyll-gist (= 1.5.0) + jekyll-github-metadata (= 2.13.0) + jekyll-include-cache (= 0.2.1) + jekyll-mentions (= 1.6.0) + jekyll-optional-front-matter (= 0.3.2) + jekyll-paginate (= 1.1.0) + jekyll-readme-index (= 0.3.0) + jekyll-redirect-from (= 0.16.0) + jekyll-relative-links (= 0.6.1) + jekyll-remote-theme (= 0.4.3) + jekyll-sass-converter (= 1.5.2) + jekyll-seo-tag (= 2.8.0) + jekyll-sitemap (= 1.4.0) + jekyll-swiss (= 1.0.0) + jekyll-theme-architect (= 0.2.0) + jekyll-theme-cayman (= 0.2.0) + jekyll-theme-dinky (= 0.2.0) + jekyll-theme-hacker (= 0.2.0) + jekyll-theme-leap-day (= 0.2.0) + jekyll-theme-merlot (= 0.2.0) + jekyll-theme-midnight (= 0.2.0) + jekyll-theme-minimal (= 0.2.0) + jekyll-theme-modernist (= 0.2.0) + jekyll-theme-primer (= 0.6.0) + jekyll-theme-slate (= 0.2.0) + jekyll-theme-tactile (= 0.2.0) + jekyll-theme-time-machine (= 0.2.0) + jekyll-titles-from-headings (= 0.5.3) + jemoji (= 0.12.0) + kramdown (= 2.3.2) + kramdown-parser-gfm (= 1.1.0) + liquid (= 4.0.4) + mercenary (~> 0.3) + minima (= 2.5.1) + nokogiri (>= 1.13.6, < 2.0) + rouge (= 3.26.0) + terminal-table (~> 1.4) + github-pages-health-check (1.17.9) + addressable (~> 2.3) + dnsruby (~> 1.60) + octokit (~> 4.0) + public_suffix (>= 3.0, < 5.0) + typhoeus (~> 1.3) + html-pipeline (2.14.3) + activesupport (>= 2) + nokogiri (>= 1.4) + http_parser.rb (0.8.0) + i18n (1.14.1) + concurrent-ruby (~> 1.0) + jekyll (3.9.3) + addressable (~> 2.4) + colorator (~> 1.0) + em-websocket (~> 0.5) + i18n (>= 0.7, < 2) + jekyll-sass-converter (~> 1.0) + jekyll-watch (~> 2.0) + kramdown (>= 1.17, < 3) + liquid (~> 4.0) + mercenary (~> 0.3.3) + pathutil (~> 0.9) + rouge (>= 1.7, < 4) + safe_yaml (~> 1.0) + jekyll-avatar (0.7.0) + jekyll (>= 3.0, < 5.0) + jekyll-coffeescript (1.1.1) + coffee-script (~> 2.2) + coffee-script-source (~> 1.11.1) + jekyll-commonmark (1.4.0) + commonmarker (~> 0.22) + jekyll-commonmark-ghpages (0.4.0) + commonmarker (~> 0.23.7) + jekyll (~> 3.9.0) + jekyll-commonmark (~> 1.4.0) + rouge (>= 2.0, < 5.0) + jekyll-default-layout (0.1.4) + jekyll (~> 3.0) + jekyll-feed (0.15.1) + jekyll (>= 3.7, < 5.0) + jekyll-gist (1.5.0) + octokit (~> 4.2) + jekyll-github-metadata (2.13.0) + jekyll (>= 3.4, < 5.0) + octokit (~> 4.0, != 4.4.0) + jekyll-include-cache (0.2.1) + jekyll (>= 3.7, < 5.0) + jekyll-mentions (1.6.0) + html-pipeline (~> 2.3) + jekyll (>= 3.7, < 5.0) + jekyll-optional-front-matter (0.3.2) + jekyll (>= 3.0, < 5.0) + jekyll-paginate (1.1.0) + jekyll-readme-index (0.3.0) + jekyll (>= 3.0, < 5.0) + jekyll-redirect-from (0.16.0) + jekyll (>= 3.3, < 5.0) + jekyll-relative-links (0.6.1) + jekyll (>= 3.3, < 5.0) + jekyll-remote-theme (0.4.3) + addressable (~> 2.0) + jekyll (>= 3.5, < 5.0) + jekyll-sass-converter (>= 1.0, <= 3.0.0, != 2.0.0) + rubyzip (>= 1.3.0, < 3.0) + jekyll-sass-converter (1.5.2) + sass (~> 3.4) + jekyll-seo-tag (2.8.0) + jekyll (>= 3.8, < 5.0) + jekyll-sitemap (1.4.0) + jekyll (>= 3.7, < 5.0) + jekyll-swiss (1.0.0) + jekyll-theme-architect (0.2.0) + jekyll (> 3.5, < 5.0) + jekyll-seo-tag (~> 2.0) + jekyll-theme-cayman (0.2.0) + jekyll (> 3.5, < 5.0) + jekyll-seo-tag (~> 2.0) + jekyll-theme-dinky (0.2.0) + jekyll (> 3.5, < 5.0) + jekyll-seo-tag (~> 2.0) + jekyll-theme-hacker (0.2.0) + jekyll (> 3.5, < 5.0) + jekyll-seo-tag (~> 2.0) + jekyll-theme-leap-day (0.2.0) + jekyll (> 3.5, < 5.0) + jekyll-seo-tag (~> 2.0) + jekyll-theme-merlot (0.2.0) + jekyll (> 3.5, < 5.0) + jekyll-seo-tag (~> 2.0) + jekyll-theme-midnight (0.2.0) + jekyll (> 3.5, < 5.0) + jekyll-seo-tag (~> 2.0) + jekyll-theme-minimal (0.2.0) + jekyll (> 3.5, < 5.0) + jekyll-seo-tag (~> 2.0) + jekyll-theme-modernist (0.2.0) + jekyll (> 3.5, < 5.0) + jekyll-seo-tag (~> 2.0) + jekyll-theme-primer (0.6.0) + jekyll (> 3.5, < 5.0) + jekyll-github-metadata (~> 2.9) + jekyll-seo-tag (~> 2.0) + jekyll-theme-slate (0.2.0) + jekyll (> 3.5, < 5.0) + jekyll-seo-tag (~> 2.0) + jekyll-theme-tactile (0.2.0) + jekyll (> 3.5, < 5.0) + jekyll-seo-tag (~> 2.0) + jekyll-theme-time-machine (0.2.0) + jekyll (> 3.5, < 5.0) + jekyll-seo-tag (~> 2.0) + jekyll-titles-from-headings (0.5.3) + jekyll (>= 3.3, < 5.0) + jekyll-watch (2.2.1) + listen (~> 3.0) + jemoji (0.12.0) + gemoji (~> 3.0) + html-pipeline (~> 2.2) + jekyll (>= 3.0, < 5.0) + kramdown (2.3.2) + rexml + kramdown-parser-gfm (1.1.0) + kramdown (~> 2.0) + liquid (4.0.4) + listen (3.8.0) + rb-fsevent (~> 0.10, >= 0.10.3) + rb-inotify (~> 0.9, >= 0.9.10) + mercenary (0.3.6) + minima (2.5.1) + jekyll (>= 3.5, < 5.0) + jekyll-feed (~> 0.9) + jekyll-seo-tag (~> 2.1) + minitest (5.20.0) + mutex_m (0.2.0) + nokogiri (1.15.5-x86_64-linux) + racc (~> 1.4) + octokit (4.25.1) + faraday (>= 1, < 3) + sawyer (~> 0.9) + pathutil (0.16.2) + forwardable-extended (~> 2.6) + public_suffix (4.0.7) + racc (1.7.3) + rb-fsevent (0.11.2) + rb-inotify (0.10.1) + ffi (~> 1.0) + rexml (3.2.6) + rouge (3.26.0) + ruby2_keywords (0.0.5) + rubyzip (2.3.2) + safe_yaml (1.0.5) + sass (3.7.4) + sass-listen (~> 4.0.0) + sass-listen (4.0.0) + rb-fsevent (~> 0.9, >= 0.9.4) + rb-inotify (~> 0.9, >= 0.9.7) + sawyer (0.9.2) + addressable (>= 2.3.5) + faraday (>= 0.17.3, < 3) + simpleidn (0.2.1) + unf (~> 0.1.4) + terminal-table (1.8.0) + unicode-display_width (~> 1.1, >= 1.1.1) + typhoeus (1.4.1) + ethon (>= 0.9.0) + tzinfo (2.0.6) + concurrent-ruby (~> 1.0) + unf (0.1.4) + unf_ext + unf_ext (0.0.9.1) + unicode-display_width (1.8.0) + +PLATFORMS + x86_64-linux + +DEPENDENCIES + github-pages + +RUBY VERSION + ruby 2.7.0p0 + +BUNDLED WITH + 2.4.12 diff --git a/LICENCE b/LICENCE new file mode 100644 index 0000000..244787c --- /dev/null +++ b/LICENCE @@ -0,0 +1,37 @@ +Instructional Material +---------------------- + +The instructional material in this course is copyright © 2024 University of Exeter +and is made available under the Creative Commons Attribution 4.0 International +licence (https://creativecommons.org/licenses/by/4.0/). Instructional material +consists of material that is contained within the _episodes, images and downloads +folders in this repository, with the exception of code snippets and example +programs found in files within these folders. Such code snippets and example +programs are considered software for the purposes of this licence. + + +Software +-------- + +Except where otherwise noted, software provided in this repository is made +available under the MIT licence (https://opensource.org/licenses/MIT). + +Copyright © 2024 University of Exeter + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/README.md b/README.md index 9a60a09..abda305 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,52 @@ -# r-data-course -A course on working with data in R using the Tidyverse +# Working with Data in R + +This repository contains source code for the [Working with data in R](https://uniexeterrse.github.io/r-data-course/) +workshop website. This workshop is part of the Coding for Reproducible Research +initiative at the University of Exeter. + +## Local deployment + +### Install dependencies + +This repo is designed to be deployed with GitHub Pages, but you can also deploy +it locally using [Jekyll](https://jekyllrb.com/). Jekyll is a +[gem](https://guides.rubygems.org/) for the Ruby language. The instructions +below assume you have installed: + +* [Ruby](https://www.ruby-lang.org/) (note that installing Ruby also installs + RubyGems for managing gems too). + +* [Bundler](https://bundler.io/) to manage your Ruby gems. Bundler is itself a + gem and can be installed by running `gem install bundler` (note: installing + may require administrative privileges). + +With these installed, run the following from the root folder of this +repository to install Jekyll: + +```bash +# Ensure gems are installed within the vendor/bundle subdirectory of the repo +bundle config set --local path 'vendor/bundle' + +# Install the required gems +bundle install +``` + + +### Run a local server for the site + +To start a local server with Jekyll, run + +```bash +bundle exec jekyll serve +``` + +This will serve the site on . + + +## Acknowledgements + +This repository is based off the University of Exeter `UoE-workshop-template` +template repository. This template was developed with help from this +[tutorial](https://github.com/evanwill/go-go-ghpages). It was inspired and +influenced by [The Carpentries' template](https://github.com/carpentries/workshop-template) +for creating websites for workshops. diff --git a/_config.yml b/_config.yml new file mode 100644 index 0000000..9ab7cfb --- /dev/null +++ b/_config.yml @@ -0,0 +1,46 @@ +# Basic options +title: Working with data in R +short_name: r-data-course +developers: [Thomas Hawes] +description: A course on working with data in R using the Tidyverse. +n_sessions: 2 +prereq: NA +url: https://uniexeterrse.github.io/r-data-course +notes_on: true + +# Specify that things in the episodes collection should be output. +collections: + episodes: + output: true + permalink: /:path/index.html + sort_by: lesson + +# Set the default layout for things in the episodes collection. +defaults: + - values: + root: . + layout: page + - scope: + path: "" + type: episodes + values: + root: .. + layout: episode + - scope: + path: "" + type: extras + values: + root: .. + layout: page + +# Build settings +theme: jekyll-theme-slate +exclude: + - Gemfile + - Gemfile.lock + - node_modules + - vendor/bundle/ + - vendor/cache/ + - vendor/gems/ + - vendor/ruby/ + - README.md diff --git a/_episodes/01_intro.md b/_episodes/01_intro.md new file mode 100644 index 0000000..11db745 --- /dev/null +++ b/_episodes/01_intro.md @@ -0,0 +1,50 @@ +--- +layout: page +title: Introductions +order: 1 +session: 1 +length: 5 +toc: true +adapted: false +--- + +## Welcome + +This workshop was put together by the Research Software Engineering Group. +This is as part of an ongoing initiative to provide training for staff and +students to expand their skill sets and position themselves to confidently +perform the informatics elements of their research projects in an efficient and +reproducible way. The programme and workshops are under constant evolution, and +we are grateful for your feedback which is a core component of this process. + +We will start by introducing the workshop leaders and helpers. We are all here +because we are passionate about sharing our knowledge and supporting the +development of our colleagues. + + +## Housekeeping + +The workshop in organised into episodes of content, which can be seen in the +[workshop schedule]({{ site.url }}/schedule.html). Our aim is to have an official break +every 60-90 minutes, but there will also be periods in-between demonstrations +where you are free to take additional comfort breaks if required. + +This workshop will be delivered mainly by live coding and exercises, with +participants expected to follow along with the worked examples. Our aim is to be +responsive to the needs of the group, both in person and +virtually. Therefore, think of the schedule as a guide rather than a strict +timetable. We welcome questions and queries as we go along: there are helpers +in the room so if you need assistance at any point please do indicate this to +them. There is also a dedicated +helper for the virtual participants so please raise your virtual hand to +attract their attention, or drop a question in the meeting chat if you prefer. + +Please conduct yourselves in line with the +[code of conduct]({{ site.url }}/code.html) — +essentially, be respectful and aware that we all have different backgrounds, +experiences and viewpoints. + + +## Acknowledgement + +TODO \ No newline at end of file diff --git a/_episodes/99_wrapup.md b/_episodes/99_wrapup.md new file mode 100644 index 0000000..752ed3c --- /dev/null +++ b/_episodes/99_wrapup.md @@ -0,0 +1,52 @@ +--- +layout: page +title: Wrap up +order: 99 +session: 2 +length: 5 +toc: true +adapted: false +--- + + + +This workshop was put together by the Research Software Engineering Group. Along +with similar workshops, it is part of an ongoing initiative to provide training +for staff and students to expand their +skill sets and position themselves to confidently perform the informatics +elements of their research projects in an efficient and reproducible way. + +We are grateful to the workshop leaders and helpers for supporting the delivery. + + +## Feedback + +If you have benefited in any way from this course and want to support its long +term sustainability then please take the time to +complete our feedback survey, +recommend it to your colleagues, and enthuse about it to your senior leadership +team.The programme and workshops are under constant evolution, and we are +grateful for your feedback which is a core component of this process. + + +## Follow up + +Course notes and resources are available all year round for you to revisit and +share as you need. + +You will be invited to be part of network of participants where you can reach +out for follow up support. We encourage peer-to-peer support so please feel free +to answer questions posted by other users. diff --git a/_includes/head-custom.html b/_includes/head-custom.html new file mode 100644 index 0000000..93784e5 --- /dev/null +++ b/_includes/head-custom.html @@ -0,0 +1,30 @@ + + + + + + + + + {% if page.title == "Home" %} + {{ site.title }} · {{ site.tagline }} + {% else %} + {{ page.title }} · {{ site.title }} + {% endif %} + + + + + + + + + + + + + + + + + diff --git a/_includes/navbar.html b/_includes/navbar.html new file mode 100644 index 0000000..897aebc --- /dev/null +++ b/_includes/navbar.html @@ -0,0 +1,12 @@ +
+ Home + Code of Conduct + Schedule + Installation Guide + {% if site.notes_on %} + Course Notes + {% else %} + Course Notes + {% endif %} + Resources +
\ No newline at end of file diff --git a/_includes/prev_next_navbar.html b/_includes/prev_next_navbar.html new file mode 100644 index 0000000..5164e9d --- /dev/null +++ b/_includes/prev_next_navbar.html @@ -0,0 +1,16 @@ +{% include sortedpages.html %} +
+ Home + {% for node in sorted_pages %} + {% if page.url == node.url %} + {% unless forloop.first %} + + {% endunless %} + {% unless forloop.last %} + {% assign nextpage = sorted_pages[forloop.index] %} + + {% endunless %} + {% endif %} + {% assign prevpage = node %} +{% endfor %} +
diff --git a/_includes/schedule.html b/_includes/schedule.html new file mode 100644 index 0000000..0d10698 --- /dev/null +++ b/_includes/schedule.html @@ -0,0 +1,17 @@ +{% include sortedpages.html %} + + + + + + + + + {% for node in sorted_pages %} + + + + + +{% endfor %} +
SessionContentDuration (minutes)
{{ node.session }}{{ node.title }}{{ node.length }}
diff --git a/_includes/sortedpages.html b/_includes/sortedpages.html new file mode 100644 index 0000000..0353bdc --- /dev/null +++ b/_includes/sortedpages.html @@ -0,0 +1 @@ +{% assign sorted_pages = site.episodes | where:"toc","true" | sort: "order" %} diff --git a/_includes/toc.html b/_includes/toc.html new file mode 100644 index 0000000..8c2a575 --- /dev/null +++ b/_includes/toc.html @@ -0,0 +1,4 @@ +{% include sortedpages.html %} +{% for node in sorted_pages %} +
  • {{ node.title }}
    • +{% endfor %} diff --git a/_layouts/home.html b/_layouts/home.html new file mode 100644 index 0000000..89b445d --- /dev/null +++ b/_layouts/home.html @@ -0,0 +1,43 @@ + + + + + + + + + +{% seo %} + {% include head-custom.html %} + + + + + +
    +
    +

    {{ site.title | default: site.github.repository_name }}

    +

    {{ site.description | default: site.github.project_tagline }}

    + + {% include navbar.html %} +
    +
    + + +
    +
    + {{ content }} +
    +
    + + + + + diff --git a/_layouts/info_page.html b/_layouts/info_page.html new file mode 100644 index 0000000..89b445d --- /dev/null +++ b/_layouts/info_page.html @@ -0,0 +1,43 @@ + + + + + + + + + +{% seo %} + {% include head-custom.html %} + + + + + +
    +
    +

    {{ site.title | default: site.github.repository_name }}

    +

    {{ site.description | default: site.github.project_tagline }}

    + + {% include navbar.html %} +
    +
    + + +
    +
    + {{ content }} +
    +
    + + + + + diff --git a/_layouts/page.html b/_layouts/page.html new file mode 100644 index 0000000..c1ad6f4 --- /dev/null +++ b/_layouts/page.html @@ -0,0 +1,65 @@ + + + + + + + + + +{% seo %} + {% include head-custom.html %} + + + + +
    +
    +

    {{ page.title | default: site.github.repository_name }}

    + {% include prev_next_navbar.html %} +
    +
    + + + + +
    +
    + {{ content }} +
    +
    + + + + + diff --git a/_sass/jekyll-theme-slate.scss b/_sass/jekyll-theme-slate.scss new file mode 100644 index 0000000..3e532b2 --- /dev/null +++ b/_sass/jekyll-theme-slate.scss @@ -0,0 +1,519 @@ +@import "rouge-github"; + +/******************************************************************************* +MeyerWeb Reset +*******************************************************************************/ + +html, body, div, span, applet, object, iframe, +h1, h2, h3, h4, h5, h6, p, blockquote, pre, +a, abbr, acronym, address, big, cite, code, +del, dfn, em, img, ins, kbd, q, s, samp, +small, strike, strong, sub, sup, tt, var, +b, u, i, center, +dl, dt, dd, ol, ul, li, +fieldset, form, label, legend, +table, caption, tbody, tfoot, thead, tr, th, td, +article, aside, canvas, details, embed, +figure, figcaption, footer, header, hgroup, +menu, nav, output, ruby, section, summary, +time, mark, audio, video { + margin: 0; + padding: 0; + border: 0; + font: inherit; + vertical-align: baseline; +} + +/* HTML5 display-role reset for older browsers */ +article, aside, details, figcaption, figure, +footer, header, hgroup, menu, nav, section { + display: block; +} + +ol, ul { + list-style: none; +} + +table { + border-collapse: collapse; + border-spacing: 0; +} + +/******************************************************************************* +Theme Styles +*******************************************************************************/ + +body { + box-sizing: border-box; + color:#373737; + background: #212121; + font-size: 16px; + font-family: 'Myriad Pro', Calibri, Helvetica, Arial, sans-serif; + line-height: 1.5; + -webkit-font-smoothing: antialiased; +} + +h1, h2, h3, h4, h5, h6 { + margin: 10px 0; + font-weight: 700; + color:#222222; + font-family: 'Lucida Grande', 'Calibri', Helvetica, Arial, sans-serif; + letter-spacing: -1px; +} + +h1 { + font-size: 36px; + font-weight: 700; +} + +h2 { + padding-bottom: 10px; + font-size: 32px; + background: url('../images/bg_hr.png') repeat-x bottom; +} + +h3 { + font-size: 24px; +} + +h4 { + font-size: 21px; +} + +h5 { + font-size: 18px; +} + +h6 { + font-size: 16px; +} + +p { + margin: 10px 0 15px 0; +} + +footer p { + color: #f2f2f2; +} + +a { + text-decoration: none; + color: #0F79D0; + text-shadow: none; + + transition: color 0.5s ease; + transition: text-shadow 0.5s ease; + -webkit-transition: color 0.5s ease; + -webkit-transition: text-shadow 0.5s ease; + -moz-transition: color 0.5s ease; + -moz-transition: text-shadow 0.5s ease; + -o-transition: color 0.5s ease; + -o-transition: text-shadow 0.5s ease; + -ms-transition: color 0.5s ease; + -ms-transition: text-shadow 0.5s ease; +} + +a:hover, a:focus { + text-decoration: underline; +} + +footer a { + color: #F2F2F2; + text-decoration: underline; +} + +em, cite { + font-style: italic; +} + +strong { + font-weight: bold; +} + +img { + position: relative; + margin: 0 auto; + max-width: 739px; + padding: 5px; + margin: 10px 0 10px 0; + border: 1px solid #ebebeb; + + box-shadow: 0 0 5px #ebebeb; + -webkit-box-shadow: 0 0 5px #ebebeb; + -moz-box-shadow: 0 0 5px #ebebeb; + -o-box-shadow: 0 0 5px #ebebeb; + -ms-box-shadow: 0 0 5px #ebebeb; +} + +p img { + display: inline; + margin: 0; + padding: 0; + vertical-align: middle; + text-align: center; + border: none; +} + +pre, code { + color: #222; + background-color: #fff; + + font-family: Monaco, "Bitstream Vera Sans Mono", "Lucida Console", Terminal, monospace; + font-size: 0.875em; + + border-radius: 2px; + -moz-border-radius: 2px; + -webkit-border-radius: 2px; +} + +pre { + padding: 10px; + box-shadow: 0 0 10px rgba(0,0,0,.1); + overflow: auto; +} + +code { + padding: 3px; + margin: 0 3px; + box-shadow: 0 0 10px rgba(0,0,0,.1); +} + +pre code { + display: block; + box-shadow: none; +} + +blockquote { + color: #666; + margin-bottom: 20px; + padding: 0 0 0 20px; + border-left: 3px solid #bbb; +} + + +ul, ol, dl { + margin-bottom: 15px +} + +ul { + list-style-position: inside; + list-style: disc; + padding-left: 20px; +} + +ol { + list-style-position: inside; + list-style: decimal; + padding-left: 20px; +} + +dl dt { + font-weight: bold; +} + +dl dd { + padding-left: 20px; + font-style: italic; +} + +dl p { + padding-left: 20px; + font-style: italic; +} + +hr { + height: 1px; + margin-bottom: 5px; + border: none; + background: url('../images/bg_hr.png') repeat-x center; +} + +table { + border: 1px solid #373737; + margin-bottom: 20px; + text-align: left; + } + +th { + font-family: 'Lucida Grande', 'Helvetica Neue', Helvetica, Arial, sans-serif; + padding: 10px; + background: #373737; + color: #fff; + } + +td { + padding: 10px; + border: 1px solid #373737; + } + +tr:nth-child(even) { + background-color: #D1CECE; +} + +form { + background: #f2f2f2; + padding: 20px; +} + +kbd { + background-color: #fafbfc; + border: 1px solid #c6cbd1; + border-bottom-color: #959da5; + border-radius: 3px; + box-shadow: inset 0 -1px 0 #959da5; + color: #444d56; + display: inline-block; + font-size: 11px; + line-height: 11px; + padding: 3px 5px; + vertical-align: middle; +} + +/* Add a black background color to the top navigation */ +.topnav { + background-color: #333; + overflow: hidden; +} + +/* Style the links inside the navigation bar */ +.topnav a { + float: left; + color: #f2f2f2; + text-align: center; + padding: 14px 16px; + text-decoration: none; + font-size: 17px; +} + +/* Change the color of links on hover */ +.topnav a:hover { + background-color: #ddd; + color: black; +} + +/* Add a color to the active/current link */ +.topnav a.active { + background-color: #04AA6D; + color: white; +} +/******************************************************************************* +Full-Width Styles +*******************************************************************************/ + +.outer { + width: 100%; +} + +.inner { + position: relative; + max-width: 900px; + padding: 20px 10px; + margin: 0 auto; +} + +#forkme_banner { + display: block; + position: absolute; + top:0; + right: 10px; + z-index: 10; + padding: 10px 50px 10px 10px; + color: #fff; + background: url('../images/blacktocat.png') #0090ff no-repeat 95% 50%; + font-weight: 700; + box-shadow: 0 0 10px rgba(0,0,0,.5); + border-bottom-left-radius: 2px; + border-bottom-right-radius: 2px; +} + +#header_wrap { + background: #212121; + background: -moz-linear-gradient(top, #373737, #212121); + background: -webkit-linear-gradient(top, #373737, #212121); + background: -ms-linear-gradient(top, #373737, #212121); + background: -o-linear-gradient(top, #373737, #212121); + background: linear-gradient(to top, #373737, #212121); +} + +#header_wrap .inner { + padding: 50px 10px 30px 10px; +} + +#project_title { + margin: 0; + color: #fff; + font-size: 42px; + font-weight: 700; + text-shadow: #111 0px 0px 10px; +} + +#project_tagline { + color: #fff; + font-size: 24px; + font-weight: 300; + background: none; + text-shadow: #111 0px 0px 10px; +} + +#downloads { + position: absolute; + width: 210px; + z-index: 10; + bottom: -40px; + right: 0; + height: 70px; + background: url('../images/icon_download.png') no-repeat 0% 90%; +} + +.zip_download_link { + display: block; + float: right; + width: 90px; + height:70px; + text-indent: -5000px; + overflow: hidden; + background: url(../images/sprite_download.png) no-repeat bottom left; +} + +.tar_download_link { + display: block; + float: right; + width: 90px; + height:70px; + text-indent: -5000px; + overflow: hidden; + background: url(../images/sprite_download.png) no-repeat bottom right; + margin-left: 10px; +} + +.zip_download_link:hover { + background: url(../images/sprite_download.png) no-repeat top left; +} + +.tar_download_link:hover { + background: url(../images/sprite_download.png) no-repeat top right; +} + +#main_content_wrap { + background: #f2f2f2; + border-top: 1px solid #111; + border-bottom: 1px solid #111; +} + +#main_content { + padding-top: 40px; +} + +#footer_wrap { + background: #212121; +} + + + +/******************************************************************************* +Small Device Styles +*******************************************************************************/ + +@media screen and (max-width: 992px) { + img { + max-width: 100%; + } +} + +@media screen and (max-width: 480px) { + body { + font-size:14px; + } + + #downloads { + display: none; + } + + .inner { + min-width: 320px; + max-width: 480px; + } + + #project_title { + font-size: 32px; + } + + h1 { + font-size: 28px; + } + + h2 { + font-size: 24px; + } + + h3 { + font-size: 21px; + } + + h4 { + font-size: 18px; + } + + h5 { + font-size: 14px; + } + + h6 { + font-size: 12px; + } + + code, pre { + font-size: 11px; + } + +} + +@media screen and (max-width: 320px) { + body { + font-size:14px; + } + + #downloads { + display: none; + } + + .inner { + min-width: 240px; + max-width: 320px; + } + + #project_title { + font-size: 28px; + } + + h1 { + font-size: 24px; + } + + h2 { + font-size: 21px; + } + + h3 { + font-size: 18px; + } + + h4 { + font-size: 16px; + } + + h5 { + font-size: 14px; + } + + h6 { + font-size: 12px; + } + + code, pre { + min-width: 240px; + max-width: 320px; + font-size: 11px; + } + +} diff --git a/acknowledgements.md b/acknowledgements.md new file mode 100644 index 0000000..7f7bd16 --- /dev/null +++ b/acknowledgements.md @@ -0,0 +1,11 @@ +--- +layout: info_page +title: Acknowledgements +--- + +This workshop was developed by +Dr Thomas Hawes +with the help of TODO. + +This course has been adapted from TODO + diff --git a/code.md b/code.md new file mode 100644 index 0000000..cfea7b9 --- /dev/null +++ b/code.md @@ -0,0 +1,64 @@ +--- +layout: info_page +title: Code of Conduct +--- + + + + +## Code of Conduct + +This course is under continual review to ensure that it is fit for purpose. + +Our ethos is to provide a welcoming and supportive environment for all people, +regardless of background or identity. By registering to attend this workshop, +participants are agreeing to abide by the Researcher Development +Code of Conduct. + +Our goal is to support you to develop your programming skill sets to enable you +to do cutting edge research. We want to create a positive and professional +learning environment and therefore encourage the following kinds of behaviours: + +- Show courtesy and respect towards all who attend a workshop or engage in + community events. + +- Be respectful of different viewpoints and experiences. + +- Gracefully accept constructive criticism. + +- Be patient if there are technical glitches. While we know something about how + to use computers we are not immune to internet or hardware issues. + +Thank you for taking the time to read our Code of Conduct. We look forward to +welcoming you onto our course. diff --git a/contents.md b/contents.md new file mode 100644 index 0000000..1c74eef --- /dev/null +++ b/contents.md @@ -0,0 +1,10 @@ +--- +layout: info_page +title: Workshop contents +--- + +## Workshop contents + +
      +{% include toc.html %} +
    diff --git a/contents_off.md b/contents_off.md new file mode 100644 index 0000000..1694360 --- /dev/null +++ b/contents_off.md @@ -0,0 +1,8 @@ +--- +layout: info_page +title: Workshop contents +--- + + +The materials for this workshop are unavailable at this time. Please check back +again soon. diff --git a/index.md b/index.md new file mode 100644 index 0000000..f5df3af --- /dev/null +++ b/index.md @@ -0,0 +1,111 @@ +--- +layout: home +title: Course Information +--- + + +## Overview + +TODO + + +## Course objectives + +On completion of this series of workshops, participants will be able to: + +TODO + + +## Pre-requisite knowledge + +TODO + + +## Workshop format + +This is a hybrid workshop that runs over 2 sessions. The +[schedule can be found here]({{ site.url }}/schedule.html). _Please note that you will be +expected to attend both sessions._ + + +## Upcoming course dates + +The sessions will be delivered according to the following timetable. + +| Session | Dates | Time | Venue | +| --- |--- |--- |--- | --- | +| Session 1 | TODO DATE | TODO TIME | TODO LOCATION | +| Session 2 | TODO DATE | TODO TIME | TODO LOCATION | + + +Registration for this course is handled by +University of Exeter Researcher Development (see the Software Training section). + + +## Set-up instructions for attendees + +If you have registered for this course you can find the +[instructions for setting up your computer here]({{ site.url }}/setup.html). Also take a look +at [additional resources and supplementary material]({{ site.url }}/resources.html) for the +course. + + +## Course notes + +{% if site.notes_on %} +If you are unable to attend any of the dates above, the +course notes are freely available +for you to work through. Please note that you will need to have completed the +set up instructions linked above. +{% else %} +If you are unable to attend any of the dates above, the +course notes are freely available +for you to work through. Please note that you will need to have completed the +set up instructions linked above. +{% endif %} + + +## Follow-up courses + +On completion of this course you will be invited to join a network of other +participants. This is a supportive forum for you to ask follow-up questions and +receive invites to follow-on courses. + + +## Feedback + +Please remember to complete a feedback survey at the end of the course. Our +workshops are under continual review and your experiences, suggestions and +criticism are invaluable for shaping the direction of this initiative. This is +especially important if you complete the materials in your own time, as it +enables us to measure impact. If you have ideas on other courses you would like +to see then please contact Eilis Hannon: . + + +## Join us + +If you are interested in becoming part of our community of workshop helpers, +leaders and developers please contact Eilis Hannon: . + + +## Acknowledgements + +This course was developed by +[members]({{ site.url }}/acknowledgements.html) of the +University of Exeter's Research Software Engineering Group +who are enthusiastic about sharing their skills with the wider research +community. + +If you have benefited in any way from this course and want to support its long term +sustainability then please take the time to complete our feedback survey, +recommend it to your colleagues, and enthuse about it to your senior leadership +team. + +This workshop is brought to you by: + +- The University of Exeter Research Software Engineering Group +- The Institute of Data Science and Artificial Intelligence +- The University of Exeter Researcher Development Programme +- The University of Exeter Doctoral College + +This course has been adapted from TODO diff --git a/resources.md b/resources.md new file mode 100644 index 0000000..5d1509c --- /dev/null +++ b/resources.md @@ -0,0 +1,8 @@ +--- +layout: info_page +title: Resources +--- + +## Resources for learning R and the Tidyverse + +TODO diff --git a/schedule.md b/schedule.md new file mode 100644 index 0000000..9f998d8 --- /dev/null +++ b/schedule.md @@ -0,0 +1,12 @@ +--- +layout: info_page +title: Workshop Schedule +--- + +The schedule of material that will be covered in workshops is given below. Note +that these are approximate timings that will adapt to participants' +needs during the workshops, and allows time for breaks. + +
      +{% include schedule.html %} +
    diff --git a/setup.md b/setup.md new file mode 100644 index 0000000..4dfab9b --- /dev/null +++ b/setup.md @@ -0,0 +1,9 @@ +--- +layout: info_page +title: Installation Guide +--- + +In order to fully participate in this course, you should make sure you +have the following in place **before** the first workshop. + +TODO From e36f15b191120903f76053f88c40a2122e3ec669 Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Wed, 3 Jan 2024 13:47:00 +0000 Subject: [PATCH 02/50] Remove licence file Will be added when licensing requirements are confirmed. --- LICENCE | 37 ------------------------------------- 1 file changed, 37 deletions(-) delete mode 100644 LICENCE diff --git a/LICENCE b/LICENCE deleted file mode 100644 index 244787c..0000000 --- a/LICENCE +++ /dev/null @@ -1,37 +0,0 @@ -Instructional Material ----------------------- - -The instructional material in this course is copyright © 2024 University of Exeter -and is made available under the Creative Commons Attribution 4.0 International -licence (https://creativecommons.org/licenses/by/4.0/). Instructional material -consists of material that is contained within the _episodes, images and downloads -folders in this repository, with the exception of code snippets and example -programs found in files within these folders. Such code snippets and example -programs are considered software for the purposes of this licence. - - -Software --------- - -Except where otherwise noted, software provided in this repository is made -available under the MIT licence (https://opensource.org/licenses/MIT). - -Copyright © 2024 University of Exeter - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. From 7d296e37b6e2fd2558ebee2a26a8e2f6325080d5 Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Thu, 4 Jan 2024 12:58:29 +0000 Subject: [PATCH 03/50] Add intro to tidyverse episode Also includes attributions of source material. --- LICENCE | 437 ++++++++++++++++++++++++++++++++ README.md | 13 +- _episodes/02_tidyverse_intro.md | 37 +++ _layouts/page.html | 40 +-- acknowledgements.md | 8 +- index.md | 7 +- 6 files changed, 517 insertions(+), 25 deletions(-) create mode 100644 LICENCE create mode 100644 _episodes/02_tidyverse_intro.md diff --git a/LICENCE b/LICENCE new file mode 100644 index 0000000..5fc78dc --- /dev/null +++ b/LICENCE @@ -0,0 +1,437 @@ +Attribution-NonCommercial-ShareAlike 4.0 International + +======================================================================= + +Creative Commons Corporation ("Creative Commons") is not a law firm and +does not provide legal services or legal advice. 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For +the avoidance of doubt, this paragraph does not form part of the +public licenses. + +Creative Commons may be contacted at creativecommons.org. diff --git a/README.md b/README.md index abda305..e5469d2 100644 --- a/README.md +++ b/README.md @@ -45,8 +45,17 @@ This will serve the site on . ## Acknowledgements -This repository is based off the University of Exeter `UoE-workshop-template` -template repository. This template was developed with help from this +The course material is adapted from Eliza Wood's +Tidyverse: Data wrangling & visualization +course, which is licensed under +Creative Commons BY-NC-SA 4.0. +This in itself is based on material from UC Davis's R-DAVIS course, +which draws heavily on Carpentries R lessons. + +The source code in this repository for generating the +[Working with data in R](https://uniexeterrse.github.io/r-data-course/) +website is based on the University of Exeter `UoE-workshop-template` template +repository. This template was developed with help from this [tutorial](https://github.com/evanwill/go-go-ghpages). It was inspired and influenced by [The Carpentries' template](https://github.com/carpentries/workshop-template) for creating websites for workshops. diff --git a/_episodes/02_tidyverse_intro.md b/_episodes/02_tidyverse_intro.md new file mode 100644 index 0000000..75d404c --- /dev/null +++ b/_episodes/02_tidyverse_intro.md @@ -0,0 +1,37 @@ +--- +layout: page +title: "Introduction to the Tidyverse" +order: 2 +session: 1 +length: TODO +toc: true +adapted: true +attrib_name: "Tidyverse: Data wrangling & visualization - Introduction to the Tidyverse" +attrib_link: https://liza-wood.github.io/tidyverse_intro/introduction-to-the-tidyverse.html +attrib_license: Creative Commons BY-NC-SA 4.0 +attrib_license_link: https://creativecommons.org/licenses/by-nc-sa/4.0/ +--- + +## Learning Objectives + +By the end of this episode participants will be able to: + +TODO: objectives + +## What is the Tidyverse? + +The Tidyverse is self-described as: + +> "...an opinionated collection of R packages designed for data science. All +> packages share an underlying design philosophy, grammar, and data structures'." +> +> — https://www.tidyverse.org/ + +When you install the `tidyverse` package, you install a suite of 9 tidy-related +packages, all of which are designed to help you work with data, from cleaning +and manipulation to plotting and modelling. They are increasingly popular, +have large user bases, and are generally very well-documented. + +TODO: highlight common data structure - dataframe / tibble + +TODO: highlight functional 'pipeline' approach diff --git a/_layouts/page.html b/_layouts/page.html index c1ad6f4..b998de6 100644 --- a/_layouts/page.html +++ b/_layouts/page.html @@ -34,31 +34,31 @@

    {{ page.title | default: site.github.repository_name }} {% include prev_next_navbar.html %}

    The material on this page is © University of Exeter.

    + {% if page.adapted %} - {% if page.attrib_custom %} -

    - {{ page.attrib_custom }} -

    + + {% if page.attrib_custom %} +

    + {{ page.attrib_custom }} +

    + + {% else %} +

    + This material is made available under the Creative Commons BY-NC-SA licence. + It has been adapted from {{ page.attrib_name }} + which is licensed under {{ page.attrib_license }}. +

    + {% endif %} + {% else %} -

    Material other than code snippets and example - programs is licensed under Creative Commons BY 4.0. - It has been adapted from {{ page.attrib_name }} - which is © {{ page.attrib_copywrite }} and licensed under {{ page.attrib_license }}. -

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    Material other than code snippets and example - programs is licensed under Creative Commons BY 4.0. -

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    The code snippets and example programs on this page - are MIT licensed, according to the licence notice found in this website's - code repository. -

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    Published with GitHub Pages

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    diff --git a/acknowledgements.md b/acknowledgements.md index 7f7bd16..55027cc 100644 --- a/acknowledgements.md +++ b/acknowledgements.md @@ -7,5 +7,9 @@ This workshop was developed by Dr Thomas Hawes with the help of TODO. -This course has been adapted from TODO - +The course material is adapted from Eliza Wood's +Tidyverse: Data wrangling & visualization +course, which is licensed under +Creative Commons BY-NC-SA 4.0. +This in itself is based on material from UC Davis's R-DAVIS course, +which draws heavily on Carpentries R lessons. diff --git a/index.md b/index.md index f5df3af..1574b49 100644 --- a/index.md +++ b/index.md @@ -108,4 +108,9 @@ This workshop is brought to you by: - The University of Exeter Researcher Development Programme - The University of Exeter Doctoral College -This course has been adapted from TODO +The course material is adapted from Eliza Wood's +Tidyverse: Data wrangling & visualization +course, which is licensed under +Creative Commons BY-NC-SA 4.0. +This in itself is based on material from UC Davis's R-DAVIS course, +which draws heavily on Carpentries R lessons. From 93024f428a990a9abf595d57d8d229ce94b0f820 Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Thu, 4 Jan 2024 16:28:44 +0000 Subject: [PATCH 04/50] Add episode on example data --- _episodes/03_data.md | 102 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 102 insertions(+) create mode 100644 _episodes/03_data.md diff --git a/_episodes/03_data.md b/_episodes/03_data.md new file mode 100644 index 0000000..979d528 --- /dev/null +++ b/_episodes/03_data.md @@ -0,0 +1,102 @@ +--- +layout: page +title: "Example Data" +order: 3 +session: TODO +length: TODO +toc: true +adapted: true +attrib_name: "Tidyverse: Data wrangling & visualization - Data" +attrib_link: https://liza-wood.github.io/tidyverse_intro/data.html +attrib_license: Creative Commons BY-NC-SA 4.0 +attrib_license_link: https://creativecommons.org/licenses/by-nc-sa/4.0/ +--- + +## Learning Objectives + +By the end of this episode participants will be able to: + +TODO: objectives + + +## Palmer Station penguins + +TODO: add code outputs + +The Palmer Station penguins data is a tidy data set related to three species of +Antarctic penguins from Horst, Hill, and Gorman [[^1]]. + +The data contains size measurements for male and female adult foraging +Adélie, Chinstrap, and Gentoo penguins observed on islands in the Palmer +Archipelago near Palmer Station, Antarctica between 2007-2009. Data were +collected and made available by Dr. Kristen Gorman and the Palmer Station Long +Term Ecological Research (LTER) Program. You can read more about the package +on the +`palmerpenguins` documentation website. + +Let's start by installing the package: + +```r +# Install from CRAN +install.packages('palmerpenguins') +``` + +Now we can load in the package library, which stores the `penguins` dataset. + +```r +library(palmerpenguins) +head(penguins) +``` + +Learn more about each variable in the documentation + +```r +?penguins +``` + +Let's take a look at its structure: + +```r +str(penguins) +``` + + +## Palmer Station weather + +To practice data wrangling skills, this course will also use another data +set from the Antarctic LTER Program -- the 'Daily averaged weather timeseries +at Palmer Station, Antarctica'[[^2]], which includes various weather metrics +measured between 1989-2019. We'll read in these data directly from online and +name the data frame `weather`. + +TODO: replace with `readr` functionality? + +```r +weather <- read.csv("https://pasta.lternet.edu/package/data/eml/knb-lter-pal/28/9/a3b91017602b29acbf60e319d6a5e9bc") +``` + +These data have been made available through the Environmental Data Initiative +and more information can be found on the +EDI Data Portal. + +Let's take a look at the data's structure: + +```r +str(weather) +``` + +Together this tutorial uses data collected from penguins across three islands +in the Palmer archipelago and weather sensing technology at the US Palmer +Station. + + +## References + +[^1]: Horst AM, Hill AP, Gorman KB (2020). palmerpenguins: Palmer Archipelago + (Antarctica) penguin data. R package version 0.1.0. + . doi: 10.5281/zenodo.3960218. + +[^2]: Palmer Station Antarctica LTER. 2023. Daily weather averages for Palmer + Station, Antarctica (1989-2023) ver 9. Environmental Data Initiative. + + (Accessed 2024-01-04). From 57fefd798ecce74fd223f8d1b4d54b21ecde4703 Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Thu, 4 Jan 2024 18:27:00 +0000 Subject: [PATCH 05/50] Add data wrangling basics episode Also add acknowledgement for penguin artwork. --- README.md | 3 +- _episodes/04_basic_wrangling.md | 407 ++++++++++++++++++++++++++++++++ acknowledgements.md | 1 + assets/images/culmen_depth.png | Bin 0 -> 796981 bytes index.md | 1 + 5 files changed, 411 insertions(+), 1 deletion(-) create mode 100644 _episodes/04_basic_wrangling.md create mode 100644 assets/images/culmen_depth.png diff --git a/README.md b/README.md index e5469d2..d9cf54c 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ This repository contains source code for the [Working with data in R](https://uniexeterrse.github.io/r-data-course/) workshop website. This workshop is part of the Coding for Reproducible Research -initiative at the University of Exeter. +initiative at the University of Exeter. ## Local deployment @@ -51,6 +51,7 @@ course, which is licensed under Creative Commons BY-NC-SA 4.0. This in itself is based on material from UC Davis's R-DAVIS course, which draws heavily on Carpentries R lessons. +The penguin artwork is from Allison Horst @allison_horst. The source code in this repository for generating the [Working with data in R](https://uniexeterrse.github.io/r-data-course/) diff --git a/_episodes/04_basic_wrangling.md b/_episodes/04_basic_wrangling.md new file mode 100644 index 0000000..4296b62 --- /dev/null +++ b/_episodes/04_basic_wrangling.md @@ -0,0 +1,407 @@ +--- +layout: page +title: "Data Wrangling Basics" +order: 4 +session: TODO +length: TODO +toc: true +adapted: true +attrib_name: "Tidyverse: Data wrangling & visualization - Data wrangling basics" +attrib_link: https://liza-wood.github.io/tidyverse_intro/data-wrangling-basics.html +attrib_license: Creative Commons BY-NC-SA 4.0 +attrib_license_link: https://creativecommons.org/licenses/by-nc-sa/4.0/ +--- + +## Learning Objectives + +By the end of this episode participants will be able to: + +TODO: objectives + + +## Outline / cheat sheet + +- Reducing data dimensionality: + - `select`: Name the columns to keep, separated by a comma. Using `select` + will reduce the number of columns in your dataframe. + - `filter`: Set conditions by which you want to filter (keep) rows of data. + Using the `filter` function will reduce the number of rows in your dataframe. + - `is.na()`: Specify a column name to get a logical vector indicated whether + each value in that row has an NA value. Useful when paired with `filter`. + +- Combining multiple steps + - `%>%`: Chain together multiple steps by 'piping' the output of one line of + code into the next. + +- Creating new variables + - New columns + - `mutate`: Create a new column by assigning a value (transformed or + otherwise). + - `case_when`: Pair with `mutate` to create a new variable that is based on + one or more conditions, typically for creating categorical variables. + - Summary tables + - `group_by`: Set a column (or columns) by which you want to perform a + summarizing function on. Using this function does apparently nothing on + its own, but is powerful when paired with `summarize` or `mutate`. + - `summarize`: Create a new variable by performing a variety of + summary/transformative functions on an existing column (or set of + columns). When paired with `group_by`, data is reduced to a summary table. + + +## Reducing data dimensionality + +The functions, `select` and `filter`, are what you can use to reduce +dimensionality of your dataframe. That means reducing the number of columns +(with `select`) and/or the number of rows (with `filter`). To use `select`, +name the columns to keep, separated by a comma. For example, let's select the +species, year, sex, and body mass columns. + +```r +library(tidyverse) +library(palmerpenguins) +``` + +```r +# Hint: check your column names first +colnames(penguins) +``` + +```r +# Select species, year, sex, and body mass +penguins_selected <- select(penguins, species, year, sex, body_mass_g) +# Take a look at the 'dimensions' with dim() +dim(penguins_selected) +``` + +The `select` function can also be used to remove columns by using the `-` +operator. For example, we may want to remove only two columns, island and year: + +```r +# Remove the island and year columns by using -. +penguins_unselected <- select(penguins, -island, -year) + +# Take a look at the number of columns +ncol(penguins_unselected) +``` + +Now, let's filter the data based on a certain condition. Creating 'conditions' +in R means writing logical statements with a response that will be either `TRUE` +or `FALSE`. To write conditions we can use +[comparative operators](https://www.w3schools.com/r/r_operators.asp), which give +a logical output: + +| Comparative Operator | Name | Example | +|----------------------|--------------------------|---------| +| == | Equal | x == y | +| != | Not equal | x != y | +| > | Greater than | x > y | +| < | Less than | x < y | +| >= | Greater than or equal to | x >= y | +| <= | Less than or equal to | x <= y | + +```r +head(penguins$year == 2007, n = 75) +``` + +The filter function evaluates the condition and keeps only the rows for which the value is TRUE. For example, let's filter observations so that we only keep those from the year 2007. + +```r +penguins_filtered <- filter(penguins, year == 2007) +dim(penguins_filtered) +``` + +TODO: compare with base R way of filtering on boolean vector + +There are other functions we can use to evaluate columns and get a true/false +output. An important one is `is.na()`. This function evaluates a column and +reports back a `TRUE` value when there is an NA in that column's row. For +example, we can use the `head()` function to look at the top 6 values of the +`sex` column, and see that there is an NA in the fourth row. + +```r +head(penguins$sex, n = 10) +``` + +The `is.na` function evaluates the whole column and gives us `TRUE`s whenever it +sees an NA. Not surprisingly, we see a `TRUE` in the fourth observation. + +```r +head(is.na(penguins$sex), n = 10) +``` + +Since `is.na()` gives us a `TRUE`/`FALSE` vector, it is already a good +candidate for using it as a condition in filter. Below we are asking the +filter function to keep only the observations that have an NA in the sex column. +In other others, evaluate the logical conditions +"are there NAs in the sex column?" then filter out the rows where the condition +is `TRUE`, meaning there are NAs. + +```r +penguins_filtered_nas <- filter(penguins, is.na(sex)) +nrow(penguins_filtered_nas) +nrow(penguins) +``` + +To do the reverse, i.e. keep only the observations that do NOT have an NA in +the sex column, we can use the `!` operator to denote 'not' and reverse the +true/false. + +```r +penguins_filtered_nonas <- filter(penguins, !is.na(sex)) +dim(penguins_filtered_nonas) +``` + +You can combine multiple conditions using +[logical operators](https://www.w3schools.com/r/r_operators.asp): + +| Logical Operator | Name | Example | +|------------------|---------|-------------------| +| & | AND | x == 2 & y == 10 | +| \| | OR | x == 2 \| y == 10 | + +For example, we can filter out the rows where there are NAs in the sex column +AND keep only the rows of the penguin species named Adelie. + +```r +penguins_filtered_2conditions <- filter(penguins, !is.na(sex) & species == "Adelie") +nrow(penguins_filtered_2conditions) +``` + + +## Combining multiple steps + +TODO: rearrange the discussion to first do nested calls, then successive updating, then piping + +There are several ways to combine steps in coding. You can perform one +function at a time and save intermediate objects as you work, you can 'nest' +functions, or you can use 'pipes' (`%>%`). So for example, if you wanted to +select the species, sex, bill length, and bill depth, and filter data so to +only keep data 'Adelie' penguins, with no NAs in the sex column, these first +two options would look as follows: + +```r +# Multiple steps with intermediate objects +penguins_step1 <- select(penguins, species, sex, bill_length_mm, bill_depth_mm) +penguins_step2 <- filter(penguins_step1, species == 'Adelie' & !is.na(sex)) +summary(penguins_step2) +``` + +```r +# Multiple steps with nested functions +penguins_nested <- filter(select(penguins, species, sex, bill_length_mm, bill_depth_mm), + species == 'Adelie' & !is.na(sex)) +summary(penguins_nested) +``` + +The third option is pipes. Pipes let you take the output of one function and +send it directly to the next, which is useful when you need to do many things +to the same data set. Pipes in R look like `%>%` and are made available via the +`magrittr` package, installed automatically with the `tidyverse`. (If you use +RStudio, you can type the pipe with Ctrl + Shift + M if you have a PC or +Cmd + Shift + M if you have a Mac.) + +```r +# Multiple steps with pipes +penguins_piped <- penguins %>% + select(species, sex, bill_length_mm, bill_depth_mm) %>% + filter(species == 'Adelie' & !is.na(sex)) +summary(penguins_piped) +``` + +We will use the piping approach, as it helps make a smoother workflow for other +wrangling functions we'll use. + +TODO: note about how result is entered as first arg and no need to specify. + + +### Exercise + +Use pipes to select only the columns for species, sex, bill_length_mm, and +body_mass_g, and filter out where sex is missing and body_mass_g is greater than +3500. Name this new data frame 'filter_challenge' and look at a summary of it to +check your work. + +
    +Check your answer +```r +filter_challenge <- penguins %>% +select(species, sex, bill_length_mm, body_mass_g) %>% +filter(is.na(sex) & body_mass_g > 3500) +str(filter_challenge) +``` +
    + + +## Creating new variables + +### New columns + +We may often want to make a new column with some updated or transformed value. +We can use the `mutate` function for this, in which you can assign the new +column name and its value. The idea is generally +`mutate(data, new_column_name = value)`. For example, if we wanted to calculate +a new value, the ratio of bill length to bill depth, we could do the following + +```r +# Mutate to create a new column; use penguins_piped so our output is +# easier to view +mutate(penguins_piped, bill_ratio = bill_length_mm / bill_depth_mm) +``` + +Note that in order to save the column in the dataframe, we must assign it as a +new dataframe object. The output of mutate is not just a new column on its own, +but the whole dataframe with the new column appended. So far we have not +overwritten in, and we can see that bill_depth_cm is not in our penguins +dataframe + +```r +colnames(penguins_piped) +``` + +Only once we assign it do we have a dataframe now with the new column appended + +```r +penguins_newcolumn <- mutate(penguins_piped, bill_ratio = bill_length_mm / bill_depth_mm) +colnames(penguins_newcolumn) +``` + +In addition to the mathematical transformations we just tried, we may want to +create new, discrete categories with our data. For example, let's categorize +bill ratio into discrete size groups based on its distribution. + +```{r} +summary(penguins_newcolumn$bill_ratio) +``` + +All values are greater than 1, so all bill lengths are greater than depths. +Still, lower ratio values mean that the bill depth is large relative to its length +(deep bills) and higher values mean that bill depth is small relative to its +length (shallow bills). Let's say we wanted to use these bill ratios to group +penguins into one of three groups: 'deep' 'average' and 'shallow'. + +
    + Diagram of penguin bill measurements +
    + Penguin bills. Artwork by @allison_horst, obtained from + https://allisonhorst.github.io/palmerpenguins/articles/art.html +
    +
    + +The `case_when` function, combined with `mutate`, lets us set multiple +conditions and assign resulting categories. This function uses a series of +two-sided formulas where the left-hand side determines describes the condition, +and the right supplies the result. The final condition should always be +`TRUE`, meaning that when the previous conditions have not been met, assign the +last value. + +Using these functions we can create a depth ratio category variable: + +```r +penguins_newcolumn <- penguins_newcolumn %>% + mutate(bill_depth_cat = case_when( + # Using the 1st Q as cutoff + bill_ratio <= 2.013 ~ 'deep', + # Between 1st and 2rd Q + bill_ratio > 2.013 & bill_ratio <= 2.239 ~ 'average', + T ~ 'shallow' + )) + +head(penguins_newcolumn$bill_depth_cat, n = 75) +``` + +A note: Always be cautious about what might be left out when naming the +conditions. In the previous example, we assume that everything left after the +first two conditions would be shallow. To be on the safe side, I often set every +condition and leave the last condition either an error notice, or NA, so that I +can check my conditions. + +```r +penguins_newcolumn <- penguins_newcolumn %>% + mutate(bill_depth_cat = case_when( + # Using the 1st Q as cutoff + bill_ratio <= 2.013 ~ 'deep', + # Between 1st and 2rd Q + bill_ratio > 2.013 & bill_ratio <= 2.239 ~ 'average', + bill_ratio >= 2.239 ~ 'shallow', + T ~ 'ERROR' + )) +``` + +Then we can use the `unique` function to look at all of the unique values of +that new variable to check out work. + +```r +unique(penguins_newcolumn$bill_depth_cat) +``` + + +### Summary tables + +Often we want to aggregate data at certain levels to better understand +differences across groups. For instance, does flipper length differ by species? +Does body mass change between years? We can combine the `group_by` and +`summarize` functions to help answer these kinds of questions. `group_by` sets a +column (or columns) by which you want to perform a summarizing function on, then +`summarize` creates a new variable by performing a variety of +summary/transformative functions on an existing column. + +First, we can use `summarize` on its own, without any grouping, to get a single +summary about the data frame. For example, if we want to know the mean body +mass: + +```r +# calculate the mean body mass for the whole data frame +summarize(penguins, mean_body_mass_g = mean(flipper_length_mm)) +``` + +If the result is NA, be sure to include the 'na.rm = TRUE' argument to tell the +function to 'remove NAs' before calculating: + +```r +# set na.rm = T +summarize(penguins, mean_flipper_length_mm = mean(flipper_length_mm, na.rm = TRUE)) +``` + +But if we want to know the mean flipper length across certain grouping +variables, such as sex, we want to combine summarize with `group_by` first. + +```r +penguins %>% + group_by(species) %>% + summarize(mean_flipper_length_mm = mean(flipper_length_mm, na.rm = TRUE)) +``` + +These functions are powerful. We can group by multiple columns at once AND +summarize can create several new summary variables within the same summarize +function, just by separating arguments by a comma in both cases. For instance, +we can group by species and sex, and generate two summary variables of mean and +standard deviation flipper length: + +```r +penguins %>% + group_by(species,sex) %>% + summarize(mean_flipper_length_mm = mean(flipper_length_mm, na.rm = TRUE), + sd_flipper_length_mm = sd(flipper_length_mm, na.rm = TRUE)) +``` + + +### Exercise + +Use summarize to calculate the mean and standard deviation for each year-sex +group combination. Before calculating those summaries, filter out the +observations where there is no data for penguin sex. Name this new summary table +'summary_challenge'. What is the mean and standard deviation of the body mass +for male penguins in 2008? + +
    +Check your answer +```r +summary_challenge <- penguins %>% + filter(!is.na(sex)) %>% + group_by(year,sex) %>% + summarize(mean_body_mass_g = mean(body_mass_g, na.rm = T), + sd_body_mass_g = sd(body_mass_g, na.rm = T)) +summary_challenge +# mean: 4632.456 sd: 722.0687 +``` +
    diff --git a/acknowledgements.md b/acknowledgements.md index 55027cc..86a50d5 100644 --- a/acknowledgements.md +++ b/acknowledgements.md @@ -13,3 +13,4 @@ course, which is licensed under Creative Commons BY-NC-SA 4.0. 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z`6;YniN&J8e|f*i^(CcY%lX|((fGz;w^dO8sM=dnx2xdvk7w$i`}MLb7KvfTHDqet z@E2e+xNTUI<^l)C@f@kI%C|1{hjSSp!mSj&ycVzhH)HU<8xhFI`RSRRweWYG#Q*Nn z0t_%D%1!I%zV|Et&Cr@6Lo1Q->6@YbW+8lkrvLc;4{*AMi!1W~=Wl)2N_=&@VhnIf z&mUsr`FES?pYJjs?6;Zd#N)57{?A|kr~4MDf@4jb@6DtC?UYdj!NOejL;8^!v7^B?j2&lB47 zguXuld*%8c?2Wy0{SOZ4|4ZfiFKc`+F};_V{;nVJgN6K;8?jd%`_IX@eg7Yq==*o} ZkBR9jH;9ou-Ut6gg>LCreative Commons BY-NC-SA 4.0. This in itself is based on material from UC Davis's R-DAVIS course, which draws heavily on Carpentries R lessons. +The penguin artwork is from Allison Horst @allison_horst. From a25b4f6a429c0af317faa8fa7fbd618f3bed6bfc Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Fri, 5 Jan 2024 10:22:57 +0000 Subject: [PATCH 06/50] Add further wrangling episode --- _episodes/05_further_wrangling.md | 447 ++++++++++++++++++++++++++++++ 1 file changed, 447 insertions(+) create mode 100644 _episodes/05_further_wrangling.md diff --git a/_episodes/05_further_wrangling.md b/_episodes/05_further_wrangling.md new file mode 100644 index 0000000..06903b4 --- /dev/null +++ b/_episodes/05_further_wrangling.md @@ -0,0 +1,447 @@ +--- +layout: page +title: "More Advanced Data Wrangling" +order: 5 +session: TODO +length: TODO +toc: true +adapted: true +attrib_name: "Tidyverse: Data wrangling & visualization - Data wrangling: beyond basics" +attrib_link: https://liza-wood.github.io/tidyverse_intro/data-wrangling-beyond-basics.html +attrib_license: Creative Commons BY-NC-SA 4.0 +attrib_license_link: https://creativecommons.org/licenses/by-nc-sa/4.0/ +--- + + +## Outline / cheat sheet + +- Manipulating date variables: + - `lubridate` package: Use date format functions (e.g. `ymd`) to specify date + data formats and extract particular date-related data. +- Joining multiple dataframes: + - `*_join` series: Combine two dataframes by one or more shared columns. + Specify with kind of join (`full_join`, `left_join`, etc.) to determine + which observations are kept/dropped. +- Reshaping (lengthening/widening) data: + - `pivot_longer`: Take values that are spread across multiple columns and + 'lengthen' the data by stacking those columns on top of one another + - `pivot_wider`: Take 'long' values stacked into one column and 'widen' the + data by spreading them out +- Working with text: + - `separate`: Divide the contents of one column into multiple based on + particular symbols. + - `stringr` package: Identify patterns using 'regular expressions' and apply a + series of commands (e.g. remove, extract, detect) to manipulate text data. + - `paste`: combine contents from multiple columns (or external text). + + +## Manipulating date variables + +Remember the weather data we loaded in at the start. We have weather data from +the Palmer Station in Antarctica from 1989 - 2019, where data are available for +each day. + +```r +library(tidyverse) +library(palmerpenguins) +weather <- read.csv("https://pasta.lternet.edu/package/data/eml/knb-lter-pal/28/8/375b34051b162d84516ec2d02f864675") +``` + +```r +str(weather) +``` + +However, the structure of the data above indicate that the first variable, Date, +is a 'character'. This means that R is not reading these dates as numbers, and +so it may be hard to manipulate them any further (e.g. extracting certain +years). + +```r +head(weather$Date) +class(weather$Date) +``` + +We want to use the `lubridate` package (loaded in when we load in `tidyverse`) +to convert these variables into dates and create a column for the year. This +package is handy because it has very intuitive function names and argument +structures. For instance, you can convert a character type into a date type by +using a function with letters representing the date format. You can see all of +these formats by specifically calling upon the `lubridate` package and then using +semicolons to call upon the functions, such as those starting with 'y'. + +```r +lubridate::y +``` + +In `lubridate`, y = year, m = month, d = day, h = hour, m = minute, s = second, etc. + +We can see that our data is formatted in the year-month-day format, and so we +can use the `ymd` function to indicate to R what format we want it to detect as +it converts characters to dates. If we use this function and assign the output +to a date vector, we can then evaluate the class to see that it is a date. + +```r +date_vector <- ymd(weather$Date) +class(date_vector) +``` + +It is important to remember that manipulating a column does not mean it +automatically saves into the dataframe. Instead, we need to remember to write +(or overwrite) that column into our dataframe. Remember that we can use the +mutate function to do this, where we can overwrite out date column. Note two +different ways of doing this. First, we could use the `ymd` function directly in +our mutate function: + +```r +weather <- mutate(weather, Date = ymd(Date)) +``` + +Or because we have saved the new date data as its own vector, we can feed that +vector into the dataframe as is. + +```r +weather <- mutate(weather, Date = date_vector) +``` + +Now we have our date data saved in our dataframe. + +```r +class(weather$Date) +``` + +Now that R recognizes these as dates, we can extract certain date-related +features, such as the year, month, and day. The lubridate package has functions +conveniently named `year`, `month`, `day` that can be used with a date argument, +and that element of the date will be extracted. For example: + +```r +head(year(weather$Date), n = 75) +``` + +Again, let's use mutate to assign this as a new column + +```r +weather <- mutate(weather, year = year(Date)) +``` + +### Exercise + +We are going to review our basic wrangling skills to prepare the Palmer Station +weather data to merge with the penguins data. + +We would like to merge a _summary_ of the weather data with the penguins. For +this summary, we want the weather data summarized by year, rather than by day +(as it currently is). Then per year, we want to know the maximum, minimum, and +average temperatures. Create this summary table called `weather_summary`. + +

    +Check your answer + +```r +weather_summary <- weather %>% + group_by(year) %>% + summarize(average_temp = mean(Temperature.Average..C., na.rm = T), + max_temp = max(Temperature.High..C., na.rm = T), + min_temp = min(Temperature.Low..C., na.rm = T)) + +``` + +
    + +## Joining multiple dataframes + +In your data wrangling journey, you will often find yourself wanting to combine +one dataframe with some kind of supplementary or partner dataframe. In our +case, we have the penguins and weather data stored separately, but if we ever +wanted to explore any relationships between them, we'd likely want all of that +data stored in one place so that we can line up data points. This lining up of +data points is called **joining** in the tidyverse language. + +The key to joining is to identify the variables by which you want to join the +data. In other words, which columns in each data are the ones that link them +together? In some cases these may be one-to-one matches (e.g. ID numbers to IDs +numbers), or in other cases there is data at different levels that need to be +lined up. + +By default, `*_join` functions will link the data together based on all of the +shared column names, but usually you'll want to double check to make sure that +this will get you the desired output. Typically you can do this by just checking +out the column names of each dataframe (for this exercise we will use the +`weather_summary` object created in the last exercise). + +```r +colnames(penguins) +``` + +```r +colnames(weather_summary) +``` + +We have one column overlapping, year, which makes it the likely candidate for +joining by. But it is also important to think: what is it that actually links +penguins to weather? Penguins experience weather at any given time, and so we +will need to choose a shared time scale at which penguins are experiencing +weather and the scale of data we collected for weather. In the penguins data set +we only know the year that the observations were taken, so we're interested in +the annual weather summaries. This is why we are looking at the weather summary +object, so that we can align the data relatively well. + +Now, let's join. The join functions take three arguments: the two dataframes +you'd like to join, and the name of the column by which to join. There are also +several types of joins, which we can explore in the exercise, but for now let's +use a `left_join`: + +```r +penguins_leftjoined <- left_join(penguins, weather_summary, by = "year") +dim(penguins_leftjoined) +``` + +We go from 8 columns in the penguins data to 11 now that we've appended the +three weather-related columns to the data. And we stick with 344 rows because +that is the number of rows in our 'left' dataframe, penguins. These dimensions +might change depending on the kind of join you use. + + +### Exercise + +Go ahead and check out the documentation for join by using the help function, a +question mark: + +```r +?join +``` + +You'll see the variety of joins that exist in `dplyr`. Try to experiment with +them: What happens when you use another kind of join? Check how the dimensions +of the data change depending on the join you choose. + +
    +Check your answer + +```r +penguins_fulljoined <- full_join(penguins, weather_summary, by = "year") +dim(penguins_fulljoined) +# We now have more rows. Why? We had weather data for more years than we have penguin data, so we have a lot of weather data that is unmatched to any pengion observation +``` + +
    + + +## Reshaping (lengthening/widening) data + +There are often cases where you'd like to change the shape of your data. For +instance, you might want to turn several columns into two: one with the +measurements or values from the previous columns, and one with a categorical +indicator of what is represented in the new column. This is a case for pivoting +longer. + +Let's say we wanted to take the key measures of our penguins and stack them on +top of one another (this is not actually useful given our data, but it is the +practice that counts). When we stack all of these columns on top of one another +we are going to be creating two new columns: one with the names of the columns +and another with the values of the columns. In the `pivot_longer` arguments, it +looks like this: + +```r +# Pivot the data to make it longer +penguins_longer <- pivot_longer(penguins, + cols = c(bill_length_mm, bill_depth_mm, + flipper_length_mm, body_mass_g), + names_to = "measure", values_to = "value") +``` + +```r +head(penguins_longer) +``` + +The reverse scenario is having data within one column that you'd like to widen +out into several columns. Here we have to identify the column we'd like to +un-stack (`values_from`), how we'd like to name the new columns filled with the +widened values (`names_from`). Note that we don't need to put these column names +in quotes because they already exist in the dataframe! + +```r +penguins_wider <- pivot_wider(penguins_longer, + names_from = measure, + values_from = value) +``` + +BUT, we get a bit of an issue here. Our result has condensed many of the +measures down into list forms, leaving us with only 35 observations and several +repeated measured for each observation. Why? When `dplyr` is widening out the data +it wants to match the new columns to _unique observations_. So the function +created one row for each species-island-sex-year combination, thinking those are +unique. We can fix this by creating an ID column (id_col). + +Here want to specify what it is that actually identifies unique observations in +the data. Our issue is that we don't have unique identifiers in the data. +Whoops. Let's fix that and re-run these functions to see why this matters. + +First, let's assign a unique ID column to the original penguins data and then +re-run our `pivot_longer`: + +```r +penguins$ID <- 1:nrow(penguins) +penguins_longer <- pivot_longer(penguins, + cols = c(bill_length_mm, bill_depth_mm, + flipper_length_mm, body_mass_g), + names_to = "measure", values_to = "value") +``` + +```r +head(penguins_longer) +``` + +Now this will play an important part in the `pivot_wider`. Let's set ID as the +the `id_cols` argument in the function: + +```r +penguins_widerID <- pivot_wider(penguins_longer, + id_cols = ID, + names_from = measure, + values_from = value) +head(penguins_widerID) +``` + +Uh, close, but still some issues. What happened to all of our other columns? +Well, since we said ID was our only id column, it dropped everything else. So we +can actually pile everything into our ID column that we wan to keep. + +```{r} +penguins_widerID_allcols <- pivot_wider(penguins_longer, + id_cols = species:ID, + names_from = "measure", + values_from = "value") +head(penguins_widerID_allcols) +``` + +## Working with text + +Let's back up a bit to the cleaning process. We've been working on getting our +data in summary form, bringing in new data, and changing data shape, all of +which is pretty far down the data wrangling pipeline. But we are going to take +this last moment to talk about an earlier stage task: cleaning text data. + +When we first create and collect data, it may not be in its tidiest form. One +common messy data practice is to have multiple types of data in one column. +For instance, we can look at the 'raw' version of the penguins data, also +available through the package. Go ahead and read that in: + +```r +penguins_raw <- penguins_raw +``` + +Let's look at the third and sixth columns, for instance. + +```r +head(select(penguins_raw, 3,6)) +``` + +The Stage column is a good example of where multiple data points might be +combined into one cell as text. Here we have 'Adult, 1 Egg Stage' reported. But, +we might want to separate out the title of the penguin's stage, Adult, from +their egg stage count. For this we can use the `separate` function to divide the +contents of one column into multiple based on particular symbols. In this case, +there is a convention to separate the word Adult (or other stage) from the egg +count using a comma (,). + +For the `separate` function we need to enter a few arguments: the data, the +column to separate, the names of the new columns, and the separator symbol. Note +that the help file says that the separator symbol is "interpreted as a regular +expression" -- we'll get to to that in a second. + +```r +penguins_raw_sep <- separate(penguins_raw, Stage, c("age_cat", "egg_stage"), ',') +``` + +Now we can see that these two have replaced the previous Stage column which adds +one more column to the dataframe. + +```r +head(select(penguins_raw_sep, 4:7)) +``` + +Now, we could even go one step further. The data here are well organized and +uniform, meaning that separate works quite well. Every Stage column has a comma, +and even now in the egg_stage column there are three words, so we could separate +by a space `" "` and get three new columns. However, when data is messy we might +need some more powerful text manipulation tools. + +Enter: the `stringr` package and regular expressions. `stringr` is a package +providing a set of functions for manipulating "strings", also called text or +character data. Check out the great +stringr cheat sheet. +Part of what makes `stringr` so powerful is that it relies on patterns +interpreted as 'regular expressions' (or regex). Regex is its own pattern +language that allows you to generalize string patterns for great adaptability to +string cleaning. + +We will practice just one example, which is to extract the number from the +egg_stage column. The power of regex is that instead of specifying every +possible number that could exist in the column (1,2,3,4,5,6,7,8,9), there is a +symbol for numbers in the regex language, which is `\\d`. There is also a symbol +to quantify the number of symbols, where `?` is zero or one, `*` is zero or +more, `+` is one or more, etc. By combining `\\d+` we've created a regular +expression for the pattern: one or more digits. We can then use this in one of +the string functions, `str_extract`, with the two arguments it needs: the +character vector and the pattern. + +```r +str_extract(penguins_raw_sep$egg_stage, '\\d+') +``` + +Excellent. Our pattern worked and we've successfully extracted the number +pattern from the column. One tiny tweak is that the numbers are in quotation +marks, meaning that they are still character data. We can coerce them to numeric +once they are all numbers. + +```r +penguins_raw_sep$egg_no <- as.numeric(str_extract(penguins_raw_sep$egg_stage, + '\\d+')) +head(penguins_raw_sep$egg_no) +``` + +Finally, maybe we want to put things back together? The `paste` function is a +straightforward way to paste columns or values back together. The arguments for +paste are as many value you'd like to paste together, separated by a comma. For +instance: + +```r +head(paste(penguins_raw_sep$age_cat, penguins_raw_sep$egg_stage)) +``` + +And you can also mix and match data types and lengths, etc: + +```r +paste("Paste together items of different lengths, for example:", + 1:10, "different items") +``` + +### Exercise + +Try to separate the common name from the scientific name from the 'Species' +column in penguins_raw. E.g. separate 'Adelie Penguin (Pygoscelis adeliae)' into +two columns: 'Adelie Penguin' and 'Pygoscelis adeliae'. You may need to combine +`separate` with a `stringr` function (e.g. `str_remove`) depending on how you'd +like to deal with the parentheses. Note that parentheses in regular expressions +are special characters and will need to be treated as such both in the +`separate` and `stringr` functions -- see the cheat sheet! + +
    +Check your answer + +```r +# Separate by the first opening parentheses +penguins_raw <- separate(penguins_raw, Species, + c("common_name", "sci_name"), "\\(") +# Then remove the second closing parentheses with stringr +penguins_raw$sci_name <- str_remove(penguins_raw$sci_name, "\\)") +# I also like to use the base R function 'trimws' to trim white space +penguins_raw$common_name <- trimws(penguins_raw$common_name) +penguins_raw$sci_name <- trimws(penguins_raw$sci_name) +# Let's take a look +head(select(penguins_raw, common_name, sci_name)) +``` + +
    + From aa3ea0610ace75510238ec65004c1167acec0190 Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Fri, 5 Jan 2024 10:55:48 +0000 Subject: [PATCH 07/50] Tweak licence text --- _layouts/page.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_layouts/page.html b/_layouts/page.html index b998de6..16b4eb3 100644 --- a/_layouts/page.html +++ b/_layouts/page.html @@ -52,7 +52,7 @@

    {{ page.title | default: site.github.repository_name }} {% else %}

    - This material is made available under the Creative Commons BY-NC-SA licence. + This material is made available under the Creative Commons BY-NC-SA 4.0 licence.

    {% endif %} {% if site.github.is_project_page %} From bbd5237074eaab69f621951a414e48f59ea9cd14 Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Fri, 5 Jan 2024 11:21:06 +0000 Subject: [PATCH 08/50] Add note of installation requirements --- setup.md | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/setup.md b/setup.md index 4dfab9b..770580a 100644 --- a/setup.md +++ b/setup.md @@ -6,4 +6,12 @@ title: Installation Guide In order to fully participate in this course, you should make sure you have the following in place **before** the first workshop. -TODO +Install: + +- R +- RStudio +- tidyverse package +- rmarkdown package +- The course notebooks + +Download datasets from TODO. From 6d7e21e402f128dacf1f8612a15f43d01a16ba0b Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Fri, 5 Jan 2024 15:05:13 +0000 Subject: [PATCH 09/50] Add notebooks for course with outlines The intention is that these will be provided to participants so that they can follow through the material with the instructor. --- .gitignore | 7 +- R_notebooks/01_Introducing_Tidyverse.Rmd | 10 + R_notebooks/02_Loading_data.Rmd | 12 + R_notebooks/03_Manipulating_a_dataframe.Rmd | 11 + R_notebooks/04_Strings_and_dates.Rmd | 15 + R_notebooks/05_Combining_datasets.Rmd | 9 + R_notebooks/06_Reshaping_data.Rmd | 10 + R_notebooks/07_Summary.Rmd | 16 + R_notebooks/R_notebooks.Rproj | 13 + R_notebooks/renv.lock | 399 ++++++++++++++++++++ _config.yml | 1 + 11 files changed, 502 insertions(+), 1 deletion(-) create mode 100644 R_notebooks/01_Introducing_Tidyverse.Rmd create mode 100644 R_notebooks/02_Loading_data.Rmd create mode 100644 R_notebooks/03_Manipulating_a_dataframe.Rmd create mode 100644 R_notebooks/04_Strings_and_dates.Rmd create mode 100644 R_notebooks/05_Combining_datasets.Rmd create mode 100644 R_notebooks/06_Reshaping_data.Rmd create mode 100644 R_notebooks/07_Summary.Rmd create mode 100644 R_notebooks/R_notebooks.Rproj create mode 100644 R_notebooks/renv.lock diff --git a/.gitignore b/.gitignore index 66d8bc6..457a9db 100644 --- a/.gitignore +++ b/.gitignore @@ -8,4 +8,9 @@ vendor/ Gemfile.lock # Built artifacts -_site/ \ No newline at end of file +_site/ + +# R project +.Rproj.user +.Rprofile +renv/ \ No newline at end of file diff --git a/R_notebooks/01_Introducing_Tidyverse.Rmd b/R_notebooks/01_Introducing_Tidyverse.Rmd new file mode 100644 index 0000000..102c7f3 --- /dev/null +++ b/R_notebooks/01_Introducing_Tidyverse.Rmd @@ -0,0 +1,10 @@ +--- +title: "Introducing the Tidyverse" +output: html_notebook +--- + +## Outline + +- What is the Tidyverse? +- Dataframes (printing) +- Tidy format diff --git a/R_notebooks/02_Loading_data.Rmd b/R_notebooks/02_Loading_data.Rmd new file mode 100644 index 0000000..8f89ece --- /dev/null +++ b/R_notebooks/02_Loading_data.Rmd @@ -0,0 +1,12 @@ +--- +title: "Loading data into R" +output: html_notebook +--- + +## Outline + +- From a package +- From CSV file + - Into a dataframe + - Into a matrix? +- Other sources (Excel workbook, URL) \ No newline at end of file diff --git a/R_notebooks/03_Manipulating_a_dataframe.Rmd b/R_notebooks/03_Manipulating_a_dataframe.Rmd new file mode 100644 index 0000000..a36375a --- /dev/null +++ b/R_notebooks/03_Manipulating_a_dataframe.Rmd @@ -0,0 +1,11 @@ +--- +title: "Manipulating a dataframe" +output: html_notebook +--- + +## Outline + +- Sub-setting data +- Creating new variables +- Sorting +- Summarising data \ No newline at end of file diff --git a/R_notebooks/04_Strings_and_dates.Rmd b/R_notebooks/04_Strings_and_dates.Rmd new file mode 100644 index 0000000..b14513b --- /dev/null +++ b/R_notebooks/04_Strings_and_dates.Rmd @@ -0,0 +1,15 @@ +--- +title: "Interlude: working with strings and dates" +output: html_notebook +--- + +## Outline + +- Working with strings + - Splitting / pasting strings + - Extracting substrings + - Cleaning strings +- Working with dates + - Converting from strings + - Accessor functions + - Basic arithmetic diff --git a/R_notebooks/05_Combining_datasets.Rmd b/R_notebooks/05_Combining_datasets.Rmd new file mode 100644 index 0000000..f2d3382 --- /dev/null +++ b/R_notebooks/05_Combining_datasets.Rmd @@ -0,0 +1,9 @@ +--- +title: "Combining datasets" +output: html_notebook +--- + +## Outline + +- Concatenating data +- Joins diff --git a/R_notebooks/06_Reshaping_data.Rmd b/R_notebooks/06_Reshaping_data.Rmd new file mode 100644 index 0000000..f7aeb95 --- /dev/null +++ b/R_notebooks/06_Reshaping_data.Rmd @@ -0,0 +1,10 @@ +--- +title: "Reshaping data" +output: html_notebook +--- + +## Outline + +- Matrices +- Converting to/from tidy format +- Completion \ No newline at end of file diff --git a/R_notebooks/07_Summary.Rmd b/R_notebooks/07_Summary.Rmd new file mode 100644 index 0000000..4e6a67d --- /dev/null +++ b/R_notebooks/07_Summary.Rmd @@ -0,0 +1,16 @@ +--- +title: "Summary and next steps" +output: html_notebook +--- + +## Outline + +- Review of Tidyverse philosophy + - Tidy data + - Common interfaces + - Functional style +- Next steps / further resources + - Cheat sheets + - Package help and vignettes + - Visualsation with `ggplot2` + - R for Data Science book \ No newline at end of file diff --git a/R_notebooks/R_notebooks.Rproj b/R_notebooks/R_notebooks.Rproj new file mode 100644 index 0000000..8e3c2eb --- /dev/null +++ b/R_notebooks/R_notebooks.Rproj @@ -0,0 +1,13 @@ +Version: 1.0 + +RestoreWorkspace: Default +SaveWorkspace: Default +AlwaysSaveHistory: Default + +EnableCodeIndexing: Yes +UseSpacesForTab: Yes +NumSpacesForTab: 2 +Encoding: UTF-8 + +RnwWeave: Sweave +LaTeX: pdfLaTeX diff --git a/R_notebooks/renv.lock 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+ "Version": "0.41", + "Source": "Repository", + "Repository": "RSPM", + "Requirements": [ + "stats", + "tools" + ], + "Hash": "460a5e0fe46a80ef87424ad216028014" + }, + "yaml": { + "Package": "yaml", + "Version": "2.3.8", + "Source": "Repository", + "Repository": "RSPM", + "Hash": "29240487a071f535f5e5d5a323b7afbd" + } + } +} diff --git a/_config.yml b/_config.yml index 9ab7cfb..08f7b5a 100644 --- a/_config.yml +++ b/_config.yml @@ -36,6 +36,7 @@ defaults: # Build settings theme: jekyll-theme-slate exclude: + - R_notebooks/ - Gemfile - Gemfile.lock - node_modules From 3c64b708647d6162c338a28c205c1942a408d9c9 Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Fri, 5 Jan 2024 15:12:08 +0000 Subject: [PATCH 10/50] Ignore R notebook HTML outputs --- .gitignore | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 457a9db..e856981 100644 --- a/.gitignore +++ b/.gitignore @@ -13,4 +13,5 @@ _site/ # R project .Rproj.user .Rprofile -renv/ \ No newline at end of file +renv/ +*.nb.html \ No newline at end of file From ea865141138f38921f1d6df4a19bdd544cfc7325 Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Fri, 5 Jan 2024 17:48:27 +0000 Subject: [PATCH 11/50] Tweak R project settings Add max column width and set language to EN-GB. --- R_notebooks/R_notebooks.Rproj | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/R_notebooks/R_notebooks.Rproj b/R_notebooks/R_notebooks.Rproj index 8e3c2eb..6d8b9a6 100644 --- a/R_notebooks/R_notebooks.Rproj +++ b/R_notebooks/R_notebooks.Rproj @@ -11,3 +11,8 @@ Encoding: UTF-8 RnwWeave: Sweave LaTeX: pdfLaTeX + +MarkdownWrap: Column +MarkdownWrapAtColumn: 80 + +SpellingDictionary: en_GB From 2918ec5724d4c3546dc4526430435e67f96b5b81 Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Fri, 5 Jan 2024 17:59:08 +0000 Subject: [PATCH 12/50] Add content for notebook 1 An introduction to the Tidyverse and tidy data. --- R_notebooks/01_Introducing_Tidyverse.Rmd | 135 ++++++++++++++++++++++- 1 file changed, 132 insertions(+), 3 deletions(-) diff --git a/R_notebooks/01_Introducing_Tidyverse.Rmd b/R_notebooks/01_Introducing_Tidyverse.Rmd index 102c7f3..c6b533e 100644 --- a/R_notebooks/01_Introducing_Tidyverse.Rmd +++ b/R_notebooks/01_Introducing_Tidyverse.Rmd @@ -5,6 +5,135 @@ output: html_notebook ## Outline -- What is the Tidyverse? -- Dataframes (printing) -- Tidy format +- What is the Tidyverse? +- Dataframes / tibbles +- Tidy data format + +## What is the Tidyverse? + +The Tidyverse is self-described as: + +> "...an opinionated collection of R packages designed for data science. All +> packages share an underlying design philosophy, grammar, and data +> structures'." +> +> — + +When you install the `tidyverse` package, you install a suite of 9 packages (at +the time of writing). These are as follows; we've marked the ones this course +will introduce with a ☺: + +- ☺ `readr`: Load 'rectangular' data into an R session (e.g. CSV files). +- ☺ `dplyr`: Manipulate data (filtering, computing summaries, etc.) +- ☺ `tidyr`: Reshape data (e.g. into 'tidy' format). +- ☺ `stringr`: Working with strings. +- `ggplot2`: Visualise data. +- `tibble`: A modern refresh of the core R dataframe, used throughout + Tidyverse packages. +- `forcats`: Tools for working with R 'factors', often used for categorical + data. +- `purrr`: Tools for working with R objects in a functional way. + +These are designed to help you work with data, from cleaning and manipulation to +plotting and modelling. The benefits of the Tidyverse include: + +- They are increasingly popular with large user bases (good for support / + advice). +- The packages are generally very well-documented. +- The packages are designed to work together seamlessly and operate well with + many other modern R packages. +- Provide features to help you write expressive code and avoid common pitfalls + when working with data. +- Built around a consistent philosophy of how to structure data for analysis + (the 'tidy' data format). + +**Note**: We'll also be using a couple of other very popular packages that are +part of the 'non-core' Tidyverse: + +- `lubridate`: Working with dates. +- `magrittr`: Operators to make for more readable data manipulation (such as + the 'pipe' operator `%>%`). + +## Dataframes / tibbles + +The key data structure that Tidyverse packages are designed to work with is the +*dataframe*. + +Actually, this is not quite the whole story. As you read the documentation for +Tidyverse packages, you'll inevitably come across the term *tibble*. This is +essentially the same thing as a dataframe, although there are some minor +differences. For the purposes of this course, you can consider dataframes and +tibbles to be interchangeable and mentally think of them as 'the same thing'. + +```{r} +library(palmerpenguins) # loads `penguins` object +penguins +``` + +If we look closely, we see that the class of `penguins` (i.e. the kind of object +it is) is a tibble, indicated by the `"tbl_df"` in the output below: + +```{r} +class(penguins) +``` + +## Tidy data + +*Tidy data* is a convention that specifies how to arrange our data into a table. +It states that + +- The columns of the table should correspond to the variables in the data. +- The rows of the table should correspond to observations (or samples) of the + variables in the data. + +Packages in the Tidyverse are designed to work with tidy data. + +You can read more about the tidy data format in [Hadley Wickham's Tidy +Data](https://doi.org/10.18637/jss.v059.i10) paper[^1]. + +[^1]: Wickham, H. (2014). Tidy Data. *Journal of Statistical Software*, + *59*(10), 1–23. + +### Tidy data: Example + +Suppose we have a pet dog and a pet cat, and we want to study the average weight +of our pets by year, to see if there is some kind of association between the +species and the trend in weight variation over time. We could represent this in +a 'matrix-like' format, with the years corresponding to rows and species +corresponding to columns: + +| Year | Dog_weight_kg | Cat_weight_kg | +|:-----|:--------------|:--------------| +| 2021 | 21.4 | 8.7 | +| 2022 | 20.9 | 8.4 | +| 2023 | 21.8 | 8.1 | + +: Weights of pets by year (non-tidy format) + +However, this is not in a tidy format. The variables in our data we're +interested in studying are the year, animal species and weight, but there the +columns correspond to the weights for each kind of species. Instead, we should +have a separate column for the species: + +| Year | Species | Weight_kg | +|:-----|:--------|:----------| +| 2021 | dog | 21.4 | +| 2021 | cat | 8.7 | +| 2022 | dog | 20.9 | +| 2022 | cat | 8.4 | +| 2023 | dog | 21.8 | +| 2023 | cat | 8.1 | + +: Weights of pets by year (tidy format) + +## Acknowledgement + +The material in this notebook is adapted from Eliza Wood's [Tidyverse: Data +wrangling & visualization](https://liza-wood.github.io/tidyverse_intro/) course, +which is licensed under [Creative Commons BY-NC-SA +4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/). This in itself is +based on material from [UC Davis's R-DAVIS +course](https://gge-ucd.github.io/R-DAVIS/index.html), which draws heavily on +[Carpentries](https://datacarpentry.org/R-ecology-lesson/) R lessons. + +## Footnotes From fc8019b9041f84d4e5937f4b0a157dd8c811e9fa Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Sat, 6 Jan 2024 16:23:24 +0000 Subject: [PATCH 13/50] Add .Rhistory to git ignore file --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index e856981..8d499fb 100644 --- a/.gitignore +++ b/.gitignore @@ -13,5 +13,6 @@ _site/ # R project .Rproj.user .Rprofile +.Rhistory renv/ *.nb.html \ No newline at end of file From 747d232847001244685d6660403a195d3aad75b9 Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Sat, 6 Jan 2024 16:24:24 +0000 Subject: [PATCH 14/50] Add palmerpenguins package to lock file --- R_notebooks/renv.lock | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/R_notebooks/renv.lock b/R_notebooks/renv.lock index 4cf89d7..ef29cef 100644 --- a/R_notebooks/renv.lock +++ b/R_notebooks/renv.lock @@ -254,6 +254,16 @@ ], "Hash": "18e9c28c1d3ca1560ce30658b22ce104" }, + "palmerpenguins": { + "Package": "palmerpenguins", + "Version": "0.1.1", + "Source": "Repository", + "Repository": "RSPM", + "Requirements": [ + "R" + ], + "Hash": "6c6861efbc13c1d543749e9c7be4a592" + }, "rappdirs": { "Package": "rappdirs", "Version": "0.3.3", From a66e221418d4645e6156b3e11f2dda972144d5fe Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Sat, 6 Jan 2024 16:50:33 +0000 Subject: [PATCH 15/50] Add details of penguins data to notebook 1 --- R_notebooks/01_Introducing_Tidyverse.Rmd | 29 +++++++++++++++++++++--- 1 file changed, 26 insertions(+), 3 deletions(-) diff --git a/R_notebooks/01_Introducing_Tidyverse.Rmd b/R_notebooks/01_Introducing_Tidyverse.Rmd index c6b533e..439ff3c 100644 --- a/R_notebooks/01_Introducing_Tidyverse.Rmd +++ b/R_notebooks/01_Introducing_Tidyverse.Rmd @@ -9,6 +9,7 @@ output: html_notebook - Dataframes / tibbles - Tidy data format + ## What is the Tidyverse? The Tidyverse is self-described as: @@ -54,6 +55,7 @@ part of the 'non-core' Tidyverse: - `magrittr`: Operators to make for more readable data manipulation (such as the 'pipe' operator `%>%`). + ## Dataframes / tibbles The key data structure that Tidyverse packages are designed to work with is the @@ -65,6 +67,19 @@ essentially the same thing as a dataframe, although there are some minor differences. For the purposes of this course, you can consider dataframes and tibbles to be interchangeable and mentally think of them as 'the same thing'. +To demonstrate this, let's consider the Palmer Station penguins data related to +three species of Antarctic penguins from Horst, Hill, and Gorman [^1]. We'll +work with this dataset throughout this course. The data contains size +measurements for male and female adult foraging Adélie, Chinstrap, and Gentoo +penguins observed on islands in the Palmer Archipelago near Palmer Station, +Antarctica between 2007-2009. Data were collected and made available by +Dr Kristen Gorman and the Palmer Station Long Term Ecological Research (LTER) +Program. You can read more about the package on the +[`palmerpenguins` documentation website](https://allisonhorst.github.io/palmerpenguins/index.html). + +To load the dataset, we just import the `palmerpenguins` package and look at +the `penguins` object. + ```{r} library(palmerpenguins) # loads `penguins` object penguins @@ -77,6 +92,7 @@ it is) is a tibble, indicated by the `"tbl_df"` in the output below: class(penguins) ``` + ## Tidy data *Tidy data* is a convention that specifies how to arrange our data into a table. @@ -89,10 +105,8 @@ It states that Packages in the Tidyverse are designed to work with tidy data. You can read more about the tidy data format in [Hadley Wickham's Tidy -Data](https://doi.org/10.18637/jss.v059.i10) paper[^1]. +Data](https://doi.org/10.18637/jss.v059.i10) paper[^2]. -[^1]: Wickham, H. (2014). Tidy Data. *Journal of Statistical Software*, - *59*(10), 1–23. ### Tidy data: Example @@ -126,6 +140,7 @@ have a separate column for the species: : Weights of pets by year (tidy format) + ## Acknowledgement The material in this notebook is adapted from Eliza Wood's [Tidyverse: Data @@ -136,4 +151,12 @@ based on material from [UC Davis's R-DAVIS course](https://gge-ucd.github.io/R-DAVIS/index.html), which draws heavily on [Carpentries](https://datacarpentry.org/R-ecology-lesson/) R lessons. + ## Footnotes + +[^1]: Horst AM, Hill AP, Gorman KB (2020). palmerpenguins: Palmer Archipelago + (Antarctica) penguin data. R package version 0.1.0. + . doi: 10.5281/zenodo.3960218. + +[^2]: Wickham, H. (2014). Tidy Data. *Journal of Statistical Software*, + *59*(10), 1–23. From 65c0b867005f7cf1afe737660d8998e6d1af4a94 Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Sat, 6 Jan 2024 18:30:01 +0000 Subject: [PATCH 16/50] Add material on subsetting data --- R_notebooks/03_Manipulating_a_dataframe.Rmd | 208 +++++++++++++++++++- 1 file changed, 206 insertions(+), 2 deletions(-) diff --git a/R_notebooks/03_Manipulating_a_dataframe.Rmd b/R_notebooks/03_Manipulating_a_dataframe.Rmd index a36375a..7875b8e 100644 --- a/R_notebooks/03_Manipulating_a_dataframe.Rmd +++ b/R_notebooks/03_Manipulating_a_dataframe.Rmd @@ -1,5 +1,5 @@ --- -title: "Manipulating a dataframe" +title: "Manipulating a dataframe with `dplyr`" output: html_notebook --- @@ -8,4 +8,208 @@ output: html_notebook - Sub-setting data - Creating new variables - Sorting -- Summarising data \ No newline at end of file +- Summarising data + + +## Introduction + +The package `dplyr` from the Tidyverse contains functions for manipulating +dataframes. We will use the `penguins` dataset contained in the `palmerpenguins` +package. Note that this data is already in tidy format. + +```{r} +library(palmerpenguins) # loads `penguins` data +head(penguins) +``` + + +## Sub-setting data + +For taking subsets of data, we can use the functions `select` and +`filter` from `dplyr`: + +- `dplyr::select` is used to keep only specified columns of the dataframe, i.e. to + 'select' certain variables from the data. +- `dplyr::filter` is used to keep rows that meet some specified condition, i.e. to + 'filter' the observations in the data. + + +### Selecting columns + +To use `select`, name the columns to keep after supplying the dataframe +(or tibble), like so: + +``` +dplyr::select(data, column1, column2, ...) +``` + +**Note**: We _don't_ use strings to specify the columns! Instead, we write them +as if they were variables e.g. `dplyr::select(data, column1)` instead of +`dplyr::select(data, "column1")`. + +Let's first check the column names in our tibble: + +```{r} +colnames(penguins) +``` + +Now let's select the species, year, sex, and body mass columns: + +```{r} +# Select species, year, sex, and body mass +penguins_selected <- dplyr::select(penguins, species, year, sex, body_mass_g) + +head(penguins_selected) +``` + +The `select` function can also be used to _remove_ columns by using the `-` +operator. For example, we may want to remove only two columns, island and year: + +```{r} +# Remove the island and year columns by using `-`. +penguins_deselected <- dplyr::select(penguins, -island, -year) + +# Take a look at the columns +colnames(penguins_deselected) +``` + +### Filtering rows + +The `filter` function from `dplyr` is used to keep rows from a dataframe/tibble +that meet a _predicate condition_ in terms of the column values, i.e. a +statement that is either `TRUE` or `FALSE`. + + +#### Comparitive operators + +One way to create predicate conditions is to use the +[comparative operators](https://www.w3schools.com/r/r_operators.asp), which +should be familiar: + +``` +x == y : equal +x != y : not equal +x > y : greater than +x < y : less than +x >= y : greater than or equal +x <= y : less than or equal +``` + +For example, suppose we want to keep all penguin data only from the year +2008. In base R, we could accomplish this by indexing on a boolean vector +created using the `==` operator: + +```{r} +# Create boolean vector indicating where year = 2008 +is_year_2008 <- penguins$year == 2008 +head(is_year_2008, n = 75) +``` + +```{r} +# Index on this vector +head(penguins[is_year_2008,]) +``` + +The `filter` function works by specifying the predicate condition for filtering +in terms of the column names, like so: + +``` +dplyr::filter(data, condition) # keep rows satisfying condition +``` + +So to filter the penguins data to keep only the 2008 observations: + +```{r} +penguins_2008 <- dplyr::filter(penguins, year == 2008) +head(penguins_2008) +``` + + +#### Logical operators + +We can build more complicated predicate conditions by using the +[logical operators](https://www.w3schools.com/r/r_operators.asp) on columns: + +``` +A & B : A AND B e.g. col1 == 2 & col2 == 10 +A | B : A OR B e.g. col1 > 2 | col2 != 10 +!A : NOT A e.g. !(col1 < 2) +``` + +In addition, `filter` allows us to specify multiple AND operations by listing +out multiple predicate conditions: + +``` +dplyr::filter(data, condition1, condition2, ...) # keep rows satisfying + # condition1 AND condition2 AND ... +``` + +Some examples on the penguin data: + +```{r} +# Observations of male penguins from the year 2008: +print(dplyr::filter(penguins, sex == "male" & year == 2008)) # using & +print(dplyr::filter(penguins, sex == "male", year == 2008)) # listing multiple conditions in filter + +# Observations of penguins with bill length > 40mm or bill depth < 20mm +print(dplyr::filter(penguins, bill_length_mm > 40 | bill_depth_mm < 20)) + +# Observations except those of male penguins on the island of Biscoe +print(dplyr::filter(penguins, !(sex == "male" & island == "Biscoe"))) +``` + + +#### Filtering missing values + +There are other functions we can use to evaluate columns and get a true/false +output. An important one is `is.na()`. This function evaluates a column and +reports back a `TRUE` value when there is an `NA` in that column's row. For +example, we can use the `head()` function to look at the top 6 values of the +`sex` column, and see that there is an NA in the fourth row. + +```{r} +head(penguins$sex) +``` + +The `is.na` function evaluates the whole column and gives us `TRUE`s whenever it +sees an NA. Not surprisingly, we see a `TRUE` in the fourth observation. + +```{r} +head(is.na(penguins$sex)) +``` + +Since `is.na()` gives us a `TRUE`/`FALSE` vector, we can use it with `filter`. +The following gives us all rows where the sex column has missing data (i.e. is +set to `NA`): + +```{r} +dplyr::filter(penguins, is.na(sex)) +``` + +To do the reverse, i.e. keep only the observations where the sex value is not +missing, combine `is.na` with the NOT operator: + +```{r} +dplyr::filter(penguins, !is.na(sex)) +``` + +### Exercise: subsetting data + +Write code to extract data for Gentoo penguins with weight between 4.8kg and +5.2kg (inclusive). Only keep the species, sex and body mass columns in the +output. Give the numbers of resulting observations for male penguins, female +penguins and penguins with unknown sex. + +```{r} +penguins_subset <- dplyr::filter(penguins, + species == "Gentoo", + 4800 <= body_mass_g & body_mass_g <= 5200) +penguins_subset <- dplyr::select(penguins_subset, species, sex, body_mass_g) +penguins_subset +``` + +```{r} +cat("Number of male penguins:", nrow(dplyr::filter(penguins_subset, sex == "male")), "\n") +cat("Number of female penguins:", nrow(dplyr::filter(penguins_subset, sex == "female")), "\n") +cat("Number of penguins of unknown sex:", nrow(dplyr::filter(penguins_subset, is.na(sex))), "\n") +``` \ No newline at end of file From 74fd6d05a3c574528e09e46bea4b47038c5d7af4 Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Mon, 8 Jan 2024 10:45:41 +0000 Subject: [PATCH 17/50] Add material on creating variables --- R_notebooks/03_Manipulating_a_dataframe.Rmd | 56 ++++++++++++++++++++- 1 file changed, 55 insertions(+), 1 deletion(-) diff --git a/R_notebooks/03_Manipulating_a_dataframe.Rmd b/R_notebooks/03_Manipulating_a_dataframe.Rmd index 7875b8e..2421354 100644 --- a/R_notebooks/03_Manipulating_a_dataframe.Rmd +++ b/R_notebooks/03_Manipulating_a_dataframe.Rmd @@ -212,4 +212,58 @@ penguins_subset cat("Number of male penguins:", nrow(dplyr::filter(penguins_subset, sex == "male")), "\n") cat("Number of female penguins:", nrow(dplyr::filter(penguins_subset, sex == "female")), "\n") cat("Number of penguins of unknown sex:", nrow(dplyr::filter(penguins_subset, is.na(sex))), "\n") -``` \ No newline at end of file +``` + + +## Creating new variables + +We may often want to make a new column with some updated or transformed value. +We can use the `mutate` function in `dplyr` for this: + +``` +# value1, value2,... are expressions, possibly involving column names + +dplyr::mutate(data, new_column1 = value1, new_column2 = value2, ...) +``` + +For example, to add a column of IDs for identifying each row in the data: + +```{r} +penguins_with_ids <- dplyr::mutate(penguins, id = 1:nrow(penguins)) + +# Optional: Put the ID column at the beginning of the dataframe +penguins_with_ids <- dplyr::relocate(penguins_with_ids, id, .before = species) + +head(penguins_with_ids) +``` + +Another example: if we wanted to calculate a new value, the ratio of bill length +to bill depth, we could do the following + +```{r} +dplyr::mutate(penguins, bill_ratio = bill_length_mm / bill_depth_mm) +``` + +**Note**: the output of mutate is not just a new column on its own, +but the whole dataframe with the new column appended. To update `penguins` to +have the new column, we need to overwrite it: + +```{r} +penguins <- dplyr::mutate(penguins, bill_ratio = bill_length_mm / bill_depth_mm) +colnames(penguins) +``` + + +### Exercise: creating variables + +Update the `penguins` dataframe so that it contains an ID column _with IDs +represented as strings_. + +```{r} +# Solution 1 +penguins <- dplyr::mutate(penguins, id = as.character(1:nrow(penguins))) + +# Solution 2 +penguins <- dplyr::mutate(penguins, id = 1:nrow(penguins)) # int IDs +penguins <- dplyr::mutate(penguins, id = as.character(id)) # update column to strings +``` From 7b4b1640fc3c109b3520e8c1b1c8924226a3602e Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Mon, 8 Jan 2024 11:34:53 +0000 Subject: [PATCH 18/50] Add material on sorting data --- R_notebooks/03_Manipulating_a_dataframe.Rmd | 34 +++++++++++++++++++++ 1 file changed, 34 insertions(+) diff --git a/R_notebooks/03_Manipulating_a_dataframe.Rmd b/R_notebooks/03_Manipulating_a_dataframe.Rmd index 2421354..39d3d75 100644 --- a/R_notebooks/03_Manipulating_a_dataframe.Rmd +++ b/R_notebooks/03_Manipulating_a_dataframe.Rmd @@ -267,3 +267,37 @@ penguins <- dplyr::mutate(penguins, id = as.character(1:nrow(penguins))) penguins <- dplyr::mutate(penguins, id = 1:nrow(penguins)) # int IDs penguins <- dplyr::mutate(penguins, id = as.character(id)) # update column to strings ``` + + +## Sorting observations + +We can sort the rows of data by the value of a particular column using the +`arrange` function in `dplyr`. By default, the sorting is performed in increasing +order, although we can use the `desc()` function on the column to sort in +descending order: + +``` +dplyr::arrange(data, col) # sort rows by ascending order of values from column + # `col` + +dplyr::arrange(data, dplyr::desc(col)) # sort rows by descending order of + # values from column `col` +``` + +For example, to sort the `penguins` data by descending value of flipper length: + +```{r} +penguins_sorted_flipper <- dplyr::arrange(penguins, dplyr::desc(flipper_length_mm)) +penguins_sorted_flipper +``` + + +### Exercise: sorting + +Sort the `penguins` data by the id column we created in the previous exercise. +Explain the resulting order on the id column. + +```{r} +penguins_sorted_id <- dplyr::arrange(penguins, id) +head(dplyr::select(penguins_sorted_id, id, species, year, bill_length_mm)) +``` From 255b93c9fbc4cccd4d213c4c6094d920a4f513b0 Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Mon, 8 Jan 2024 13:28:40 +0000 Subject: [PATCH 19/50] Add material on grouping and summarising --- R_notebooks/03_Manipulating_a_dataframe.Rmd | 121 +++++++++++++++++++- 1 file changed, 119 insertions(+), 2 deletions(-) diff --git a/R_notebooks/03_Manipulating_a_dataframe.Rmd b/R_notebooks/03_Manipulating_a_dataframe.Rmd index 39d3d75..d3505df 100644 --- a/R_notebooks/03_Manipulating_a_dataframe.Rmd +++ b/R_notebooks/03_Manipulating_a_dataframe.Rmd @@ -256,8 +256,8 @@ colnames(penguins) ### Exercise: creating variables -Update the `penguins` dataframe so that it contains an ID column _with IDs -represented as strings_. +Update the `penguins` dataframe so that it contains an ID column +_with IDs represented as strings_. ```{r} # Solution 1 @@ -301,3 +301,120 @@ Explain the resulting order on the id column. penguins_sorted_id <- dplyr::arrange(penguins, id) head(dplyr::select(penguins_sorted_id, id, species, year, bill_length_mm)) ``` + + +## Summarising / aggregating data + +Often we want to aggregate data at certain levels to better understand +differences across groups. For instance, does flipper length differ by species? +Does body mass change between years? + +First, we can use `summarise` on its own, without any grouping, to get a single +summary about the dataframe. Here are some examples using the body mass variable: + +```{r} +# Count number of observations +print(dplyr::summarise(penguins, num_rows = dplyr::n())) + +# Count number of different years in the data +print(dplyr::summarise(penguins, num_years = dplyr::n_distinct(year))) + +# Get the min and max year +print(dplyr::summarise(penguins, min_year = min(year), max_year = max(year))) + +# calculate the sum of body masses for the whole data frame +print(dplyr::summarise(penguins, sum_flipper_length_mm = sum(flipper_length_mm))) +``` + +If the result is `NA`, it's likely because the column contained missing values. +We include the `na.rm = TRUE` optional argument to tell `summarise` to remove +`NA`s before calculating: + +```{r} +# remove missing values +dplyr::summarise(penguins, sum_flipper_length_mm = sum(flipper_length_mm, na.rm = TRUE)) +``` + +More generally, we can compute summaries for subgroups of the data by combining +the `group_by` and `summarise` (or `summarize`) functions in `dplyr`. The steps +to do this are: + +1. First use `group_by` to declare the column (or columns) that you want to + group the data by. +2. Then use `summarise` to actually do the summarising on each of the subgroups + from step 1. The resulting dataframe will have one summary per subgroup. + +For example, to get the average flipper length of each sex: + +```{r} +# First group the data by sex +penguins_grouped_by_sex <- dplyr::group_by(penguins, sex) + +# Then find the mean flipper length for each sex +dplyr::summarise(penguins_grouped_by_sex, mean_flipper_length_mm = mean(flipper_length_mm, na.rm = TRUE)) +``` + + +These functions are powerful. We can group by multiple columns at once to create +pairwise groups. As we saw above, we can also create several summary variables +at the same time: + +``` +dplyr::group_by(data, col1, col2, ...) # group by combinations of values in + # col1, col2, ... + +# Compute multiple summaries on the same data +dplyr::summarise(grouped_data, summary1 = <...>, summary2 = <...>, ...) +``` + + +### Exercise: grouping and summarising + +Create a summary dataframe that gives the mean, range (i.e. `max - min`) +and standard deviation of the body mass for each species / sex combination, as +well as the number of observations in each group. + +```{r} +penguins_grouped <- dplyr::group_by(penguins, species, sex) +dplyr::summarise(penguins_grouped, + mean_body_mass_g = mean(body_mass_g, na.rm = TRUE), + sd_body_mass_g = sd(body_mass_g, na.rm = TRUE), + range_body_mass_g = max(body_mass_g, na.rm = TRUE) - min(body_mass_g, na.rm = TRUE)) +``` + + +### An unimportant detail: grouped dataframes + +`dplyr::group_by` returns a 'grouped dataframe' (`dplyr::grouped_df`). These +behave just like tibbles/dataframes except they have some extra information +about the grouping attached to them. + +When you group by multiple columns, the result of `summarise` will typically +also be a grouped dataframe. Usually, the groups are given by all columns except +the last one in the grouped dataframe supplied to `summarise`. By default, you +get a console message telling you on which columns the output is grouped by (see +the result of the exercise above). + +The upshot of this is that it allows us to do successive summaries without +having to keep regrouping the data. For example, the code below will produce a +'max of averages': + +```{r} +penguins_grouped <- dplyr::group_by(penguins, sex, species) # grouped by sex and species + +# Average mass for each species / sex combination +# Result is grouped by sex (see console output) +penguins_grouped_mass <- dplyr::summarise(penguins_grouped, + mean_body_mass_g = mean(body_mass_g, na.rm = TRUE)) +print(penguins_grouped_mass) + +# Max of the average mass for each species +penguins_max_species_avg_mass <- dplyr::summarise( + penguins_grouped_mass, + max_species_avg_body_mass_g = max(mean_body_mass_g, na.rm = TRUE) + ) +print(penguins_max_species_avg_mass) +``` + +In practice, you don't have to worry about this detail. If you want to get back +to a plain old tibble/dataframe, use the `dplyr::ungroup` function. From 6c666a5b5c8b5764f84bedfd1b90c6e66b14b09a Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Mon, 8 Jan 2024 14:46:25 +0000 Subject: [PATCH 20/50] Add new notebook on piping --- R_notebooks/04_Piping.Rmd | 9 +++++++++ ...04_Strings_and_dates.Rmd => 05_Strings_and_dates.Rmd} | 0 ..._Combining_datasets.Rmd => 06_Combining_datasets.Rmd} | 0 .../{06_Reshaping_data.Rmd => 07_Reshaping_data.Rmd} | 0 R_notebooks/{07_Summary.Rmd => 08_Summary.Rmd} | 0 5 files changed, 9 insertions(+) create mode 100644 R_notebooks/04_Piping.Rmd rename R_notebooks/{04_Strings_and_dates.Rmd => 05_Strings_and_dates.Rmd} (100%) rename R_notebooks/{05_Combining_datasets.Rmd => 06_Combining_datasets.Rmd} (100%) rename R_notebooks/{06_Reshaping_data.Rmd => 07_Reshaping_data.Rmd} (100%) rename R_notebooks/{07_Summary.Rmd => 08_Summary.Rmd} (100%) diff --git a/R_notebooks/04_Piping.Rmd b/R_notebooks/04_Piping.Rmd new file mode 100644 index 0000000..9c31728 --- /dev/null +++ b/R_notebooks/04_Piping.Rmd @@ -0,0 +1,9 @@ +--- +title: "Piping and functional style" +output: html_notebook +--- + +## Outline + +- The functional philosophy of Tidyverse +- Piping operator diff --git a/R_notebooks/04_Strings_and_dates.Rmd b/R_notebooks/05_Strings_and_dates.Rmd similarity index 100% rename from R_notebooks/04_Strings_and_dates.Rmd rename to R_notebooks/05_Strings_and_dates.Rmd diff --git a/R_notebooks/05_Combining_datasets.Rmd b/R_notebooks/06_Combining_datasets.Rmd similarity index 100% rename from R_notebooks/05_Combining_datasets.Rmd rename to R_notebooks/06_Combining_datasets.Rmd diff --git a/R_notebooks/06_Reshaping_data.Rmd b/R_notebooks/07_Reshaping_data.Rmd similarity index 100% rename from R_notebooks/06_Reshaping_data.Rmd rename to R_notebooks/07_Reshaping_data.Rmd diff --git a/R_notebooks/07_Summary.Rmd b/R_notebooks/08_Summary.Rmd similarity index 100% rename from R_notebooks/07_Summary.Rmd rename to R_notebooks/08_Summary.Rmd From 606dcbf7b6db8b276dbb1ac1cdfe17f0c1fc1e46 Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Mon, 8 Jan 2024 14:58:58 +0000 Subject: [PATCH 21/50] Add acknowledgement to notebook 3 --- R_notebooks/03_Manipulating_a_dataframe.Rmd | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/R_notebooks/03_Manipulating_a_dataframe.Rmd b/R_notebooks/03_Manipulating_a_dataframe.Rmd index d3505df..4445873 100644 --- a/R_notebooks/03_Manipulating_a_dataframe.Rmd +++ b/R_notebooks/03_Manipulating_a_dataframe.Rmd @@ -418,3 +418,14 @@ print(penguins_max_species_avg_mass) In practice, you don't have to worry about this detail. If you want to get back to a plain old tibble/dataframe, use the `dplyr::ungroup` function. + + +## Acknowledgement + +The material in this notebook is adapted from Eliza Wood's [Tidyverse: Data +wrangling & visualization](https://liza-wood.github.io/tidyverse_intro/) course, +which is licensed under [Creative Commons BY-NC-SA +4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/). This in itself is +based on material from [UC Davis's R-DAVIS +course](https://gge-ucd.github.io/R-DAVIS/index.html), which draws heavily on +[Carpentries](https://datacarpentry.org/R-ecology-lesson/) R lessons. \ No newline at end of file From ed7ee081e8a995dab33daa27def0b877679ed1fa Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Mon, 8 Jan 2024 19:00:07 +0000 Subject: [PATCH 22/50] Tweak part of packages introduction --- R_notebooks/01_Introducing_Tidyverse.Rmd | 8 ++------ 1 file changed, 2 insertions(+), 6 deletions(-) diff --git a/R_notebooks/01_Introducing_Tidyverse.Rmd b/R_notebooks/01_Introducing_Tidyverse.Rmd index 439ff3c..b3b7d65 100644 --- a/R_notebooks/01_Introducing_Tidyverse.Rmd +++ b/R_notebooks/01_Introducing_Tidyverse.Rmd @@ -48,12 +48,8 @@ plotting and modelling. The benefits of the Tidyverse include: - Built around a consistent philosophy of how to structure data for analysis (the 'tidy' data format). -**Note**: We'll also be using a couple of other very popular packages that are -part of the 'non-core' Tidyverse: - -- `lubridate`: Working with dates. -- `magrittr`: Operators to make for more readable data manipulation (such as - the 'pipe' operator `%>%`). +**Note**: We'll also be using another very popular package for working with +timestamps, called `lubridate`, that is part of the 'non-core' Tidyverse. ## Dataframes / tibbles From 55ef6781918bc5d1975d0557220c9b4d38f4e38b Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Wed, 10 Jan 2024 15:23:39 +0000 Subject: [PATCH 23/50] Add material on working with text --- R_notebooks/05_Strings_and_dates.Rmd | 316 ++++++++++++++++++++++++++- R_notebooks/data/heads_and_tales.csv | 14 ++ 2 files changed, 328 insertions(+), 2 deletions(-) create mode 100644 R_notebooks/data/heads_and_tales.csv diff --git a/R_notebooks/05_Strings_and_dates.Rmd b/R_notebooks/05_Strings_and_dates.Rmd index b14513b..990e728 100644 --- a/R_notebooks/05_Strings_and_dates.Rmd +++ b/R_notebooks/05_Strings_and_dates.Rmd @@ -1,15 +1,327 @@ --- -title: "Interlude: working with strings and dates" +title: "Working with strings and dates" output: html_notebook --- ## Outline - Working with strings + - Cleaning strings - Splitting / pasting strings - Extracting substrings - - Cleaning strings - Working with dates - Converting from strings - Accessor functions - Basic arithmetic + + +## Introduction + +In this notebook we'll be looking at ways to work with two common kinds of +data: text data and dates/timestamps. When we first create and collect data, it +may not be in its tidiest form, especially when it comes to working with +text data and timestamps. The following packages provide lots of useful +functions for working with strings / timestamps: + +- `stringr`: working with strings (included with `tidyverse`). +- `lubridate`: working with dates/timestamps (a non-core Tidyverse package). + +To demonstrate these packages, we'll be working with two datasets: + +- A toy dataset consisting of some coin flipping experiments (thrilling stuff). +- Weather data from the Palmer Station in Antarctica from 1989 - 2019, where + data are available for each day[^1]. + +**Note** We'll be making use of the pipe operator `|>` throughout; see +the `04_Piping.Rmd` notebook. + + +## Working with text - heads and tails + +We have a toy dataset for getting familiar with working with text. The +`heads_and_tails.csv` file contains data on some coin flipping experiments +performed by two people, Joe Bloggs and Jane Doe. The experiments were performed +on different days and are split up into different parts (coin flipping is +tiring work). + +The data is not in a clean state: +- Joe and Jane's names are recorded in multiple ways +- The results are inconsistently formatted. +- The experiments look pretty consistent, but note that in some rows the date is + separated from the `part n` bit by a colon `:` whereas in other rows it's + separated by a space. + +```{r} +experiments <- readr::read_csv("./data/heads_and_tales.csv") +experiments +``` + +We'll some of the functions in `stringr` to clean up this dataset. + + +### Cleaning whitespace + +Before tidying up the columns, let's first rename the current columns to indicate +they contain the 'raw' values. We can use the function `rename` from the `dplyr` +package to do this: + +``` +dplyr::rename(new_col_name1 = old_col_name1, new_col_name1 = old_col_name1, ...) +``` + +```{r} +experiments <- experiments |> + dplyr::rename(Experiment_raw = Experiment, + Technician_raw = Technician, + Result_raw = Result) +print(colnames(experiments)) +``` + +Let's first tackle cleaning up the technician's names. Look at the unique values +in the Technician column: + +```{r} +unique(experiments$Technician_raw) +``` + +Let's make the case consistent. There are several `stringr` functions we could +use for this, of the form `str_to_*` + +```{r} +print(stringr::str_to_lower("aLbErt EinStEin")) # albert einstein +print(stringr::str_to_upper("aLbErt EinStEin")) # ALBERT EINSTEIN +print(stringr::str_to_title("aLbErt EinStEin")) # Albert Einstein +print(stringr::str_to_sentence("aLbErt EinStEin")) # Albert einstein +``` +Let's convert the technicians' names to title case. Do we now have consistent +names for Joe and Jane? + +```{r} +experiments <- experiments |> + dplyr::mutate(Technician = stringr::str_to_title(Technician_raw)) + +unique(experiments$Technician) +``` + +Not quite. We can replace the two spaces in the middle of "Joe Bloggs" with a +single space by using `stringr::str_squish`: + +```{r} +experiments <- experiments |> + dplyr::mutate(Technician = stringr::str_squish(Technician)) + +unique(experiments$Technician) +``` + +Note that a closely related `stringr` function, `str_trim`, can be used for only +removing leading and trailing whitespace, if for some reason we wanted to keep +all the whitespace between the words: + +```{r} +# \t = tab character, \n = new line character +cat("\tAda Lovelace \n") +cat(stringr::str_trim("\tAda Lovelace \n")) # "Ada Lovelace" +``` + +### Replacing characters / substrings + +Next, let's make the Result entries consistently formatted. We'll remove +the commas from the first few experiments and instead just have a string of +`H`s and `T`s. We can view this as replacing each comma with the empty string, +`""`. + +In general, to perform text replacement, we can use one of the following +`stringr` functions: + +```{r} +# Replace the *first* instance of "--" with ", " +print(stringr::str_replace("a--e--i--o--u", pattern = "--", replacement = ", ")) # a, e--i--o--u + +# Replace *all* instances of "--" with ", " +print(stringr::str_replace_all("a--e--i--o--u", pattern = "--", replacement = ", ")) # a, e, i, o, u +``` + +You may wonder why the parameter name 'pattern' is used for the string we want +to replace. The reason is that we actually need to supply a _regular expression_ +to match on. We'll come back to this. + +Let's now clean up the Result column by removing the commas: + +```{r} +experiments <- experiments |> + dplyr::mutate(Result = stringr::str_replace_all(Result_raw, ",", "")) + +experiments |> + dplyr::select(Result_raw, Result) +``` + + +### Exercise: replacing strings + +Put the Experiment column into a consistent format, so that the experiment is +recorded as `:part ` where `` is a date string and `` is a +number `1`, `2`, etc. + +```{r} +experiments <- experiments |> + dplyr::mutate(Experiment = stringr::str_replace(Experiment_raw, " part", ":part")) + +experiments |> + dplyr::select(Experiment_raw, Experiment) +``` + + +### Splitting and concatenating columns + +With the columns cleaned up, we no longer need the 'raw' columns, so let's +remove them: + +```{r} +experiments <- experiments |> + dplyr::select(-Experiment_raw, -Technician_raw, -Result_raw) +``` + +It would be good to have the Experiment column consist of a unique identifier +for each experiment. We could just give it an integer label, but let's do +something more sophisticated. Each row is uniquely identifiable from the +current Experiment string and the name of the Technician. So let's combine +these together to make a new ID string, as follows: + +``` +Joe Bloggs + 2024-01-04:part 2 --> 2024-01-04:2:JoeBloggs +``` + +Note that we've removed the `part ` string and removed the inner space in the +name. How can we accomplish this transformation in smaller steps? + +- First split the experiment label `2024-01-04:part 2` into two pieces: + `2024-01-04` and `2`. + +- Remove the space from the name, `Joe Bloggs` to `JoeBloggs`. + +- Paste the results together using colons: + `2024-01-04` + `2` + `JoeBloggs` --> `2024-01-04:2:JoeBloggs`. + +We know how to do the second step (use `str_replace`), but steps 1 and 3 are new. + +The trick is to do this column-wise in the dataframe. Se we'll +**make new columns** containing the pieces we want to paste together, and then +create a new column by pasting these two columns together. + +To perform the splitting, we can use the `separate` function from the +`tidyr` package (not `stringr`!) to split a column on a separator. The result is +one column for each piece of the split; we thus need to also provide names for +the new columns: + +``` +tidyr::separate(data, + col, + into = c("NewCol1", "NewCol2", ...), + sep = ) +``` + +To paste columns back together with a custom separator, we can use the +`str_c` function from `stringr`: + +```{r} +stringr::str_c("foo", "bar", "baz", sep = " -- ") # foo -- bar -- baz +``` +### Exercise: splitting and concatenating columns + +Use `tidyr::separate` and `stringr:str_c` to create a unique experiment ID by +pasting together: + +- The experiment date +- The sub-experiment number +- The technician's name (without a space) + +E.g. +``` +Joe Bloggs + 2024-01-04:part 2 --> 2024-01-04:2:JoeBloggs +``` + +Hint: don't try to do this all in one go. Instead, build it up step-by-step and +use the pipe operator `|>` join the steps together. + +```{r} +experiments |> + tidyr::separate(col = Experiment, + into = c("Experiment_date", "Sub_experiment"), + sep = ":part ") |> # don't forget the space! + dplyr::mutate(Experiment_id = stringr::str_c(Experiment_date, + Sub_experiment, + stringr::str_remove(Technician, " "), + sep = ":")) +``` + + +### Finding substrings + +How many of the experiments features a run of 5 heads or 5 tails? + +We can answer this by: +- For each row, determine whether it contains a run of 5 heads or 5 tails +- Filter on the result + +To perform the first step, we can use `str_detect` from `stringr`. This simply +returns `TRUE` or `FALSE` depending on whether it finds a given pattern in +a string: + +``` +stringr::str_detect(a_string, pattern) +``` + +The `pattern` argument is interpreted as a _regular expression_. We mentioned +this earlier when looking at `str_replace` / `str_replace_all`. Regular expressions +are a mini-language for specifying matches on strings. We're not going to go into +this much here, but they are useful to know about, especially if you work with +text data a lot; check out the great +[stringr cheat sheet](https://github.com/rstudio/cheatsheets/blob/main/strings.pdf) +for a summary, or consult the +[chapter on regular expressions in R for Data Science (2e)](https://r4ds.hadley.nz/regexps) +for an introduction. Here are some example regular expressions in action, using +`str_detect` to indicate whether the given string matches or not: + +```{r} +# Match on a string as-is (case sensitive) +stringr::str_detect("Regex is cool.", pattern = "ex") |> print() # true +stringr::str_detect("Regex is cool.", pattern = "COOL") |> print() # false + +# Match on 1 or more instances +stringr::str_detect("Regex is cool.", pattern = "o+") |> print() # true +stringr::str_detect("Regex is cool.", pattern = "coolcool+") |> print() # false + +# Regex-special characters need escaping e.g. to match on a period, use \\. +stringr::str_detect("Regex is cool.", pattern = "\\.") |> print() # true + +# Match on either one pattern or another with pipe | +stringr::str_detect("Regex is cool.", pattern = "cool|meh") |> print() # true +stringr::str_detect("Regex is meh.", pattern = "cool|meh") |> print() # true +stringr::str_detect("Regex is cool.", pattern = "meh|lame") |> print() # false + +# Match at the beginning of the string only with ^ +stringr::str_detect("Regex is cool.", pattern = "^Regex") |> print() # true +stringr::str_detect("Regex is cool.", pattern = "^cool") |> print() # false +``` + + +### Exercise: finding substrings + +With the help of `str_detect` and a suitable regular expression, write code to +work out how many experiments feature a run of at least 5 straight heads or 5 +straight tails. + +```{r} +experiments |> + dplyr::mutate(Has_run_of_5 = stringr::str_detect(Result, pattern = "HHHHH|TTTTT")) |> + dplyr::filter(Has_run_of_5) |> + nrow() +``` + + +## Footnotes + +[^1]: Palmer Station Antarctica LTER. 2023. Daily weather averages for Palmer + Station, Antarctica (1989-2023) ver 9. Environmental Data Initiative. + https://doi.org/10.6073/pasta/3eefb45dbfb784c3cabe3690ea46fe9e (Accessed + 2024-01-08). \ No newline at end of file diff --git a/R_notebooks/data/heads_and_tales.csv b/R_notebooks/data/heads_and_tales.csv new file mode 100644 index 0000000..f933bc3 --- /dev/null +++ b/R_notebooks/data/heads_and_tales.csv @@ -0,0 +1,14 @@ +Experiment,Technician,Result +2024-01-04:part 1,joe bloggs,"H,H,H,T,T,H,H,T,H,H,T,H,H,H,T,T,H,H,T,T" +2024-01-04:part 2,Joe Bloggs,"T,T,H,T,H,T,T,H,T,T,T,T,H,T,H,H,T,H,T,T" +2024-01-04:part 3,Joe Bloggs,"H,T,H,T,T,H,T,H,H,T,H,T,H,T,T,T,H,H,H,T,T,H,T,H,T" +2024-01-04 part 1,jane doe,HTHHTHTHTTHTHTHTTT +2024-01-04 part 2,jane doe,HHTTTTHHHTTHTHHTHHTTT +2024-01-05 part 1,Jane doe,THTTTHHTTTH +2024-01-05 part 2,Jane doe,TTTTTHTHTHTTTTTHTTHT +2024-01-06:part 1,Joe Bloggs,HTHTTTTHHHHTTTHHHHHTHH +2024-01-06:part 2,Joe Bloggs,TTTTTTHTTTHTH +2024-01-06 part 1,Jane Doe,HTTTTHHHTHHTTHHHTTTTTHTTH +2024-01-06 part 2,Jane Doe,TTTTHHTHHTTHTHHHTHTTTH +2024-01-06 part 3,Jane Doe,HHTHTTTTTHTTTHTHTHT +2024-01-07:part 1,joe bloggs,TTHTTHHHHTHHHHTTTHHT From a58fffe4e20251a32fa6cb0b3fb37a2271de6a7f Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Wed, 10 Jan 2024 16:03:26 +0000 Subject: [PATCH 24/50] Add material on working with dates --- R_notebooks/05_Strings_and_dates.Rmd | 117 +- .../data/PalmerStation_Daily_Weather.csv | 12478 ++++++++++++++++ R_notebooks/data/README | 15 + 3 files changed, 12608 insertions(+), 2 deletions(-) create mode 100644 R_notebooks/data/PalmerStation_Daily_Weather.csv create mode 100644 R_notebooks/data/README diff --git a/R_notebooks/05_Strings_and_dates.Rmd b/R_notebooks/05_Strings_and_dates.Rmd index 990e728..4b0f987 100644 --- a/R_notebooks/05_Strings_and_dates.Rmd +++ b/R_notebooks/05_Strings_and_dates.Rmd @@ -11,8 +11,7 @@ output: html_notebook - Extracting substrings - Working with dates - Converting from strings - - Accessor functions - - Basic arithmetic + - Accessing timestamp components ## Introduction @@ -318,6 +317,120 @@ experiments |> nrow() ``` +## Working with dates + +We have weather data from the Palmer Station in Antarctica from 1989 - 2019, +where data are available for each day. + +```{r} +weather <- read.csv("./data/PalmerStation_Daily_Weather.csv") +str(weather) +``` + +However, the structure of the data above indicate that the first variable, Date, +is a 'character'. This means that R is not reading these dates as numbers, and +so it may be hard to manipulate them any further (e.g. extracting certain +years). + +```{r} +head(weather$Date) +class(weather$Date) +``` + +(**Note**: if we'd used `read_csv` from the `readr` package then these would +automatically have been coerced into dates. We're avoiding this for now to +demonstrate how to convert raw strings into dates.) + +We'll use the `lubridate` package (loaded in when we load in `tidyverse`) +to convert these variables into dates and create a column for the year. + + +### Converting from strings + +The `lubridate` package has very intuitive function names and argument +structures. For instance, you can convert a character type into a date type by +using a function with letters representing the date format. In `lubridate`, +y = year, m = month, d = day, h = hour, m = minute, s = second, etc. + +(You can see all of these formats by using the auto-complete box in RStudio, +e.g. to see all functions starting with 'y', begin typing `lubridate::y`.) + +We can see that our data is formatted in the year-month-day format, and so we +can use the `ymd` function to indicate to R what format we want it to detect as +it converts characters to dates. If we use this function and assign the output +to a date vector, we can then evaluate the class to see that it is a date. + +```{r} +date_vector <- lubridate::ymd(weather$Date) +class(date_vector) +``` + +Remember that manipulating a column does not mean it automatically saves into +the dataframe. Instead, we need to remember to write (or overwrite) that column +into our dataframe. Remember that we can use the mutate function to do this, +where we can overwrite out date column: + +```{r} +weather <- weather |> + dplyr::mutate(Date = lubridate::ymd(Date)) + +class(weather$Date) +``` + + +### Accessing timestamp components + +Now that R recognizes these as dates, we can extract certain date-related +features, such as the year, month, and day. The `lubridate` package has functions +conveniently named `year`, `month`, `day` that can be used with a date argument, +and that element of the date will be extracted. For example: + +```{r} +weather <- weather |> + dplyr::mutate(Year = lubridate::year(Date)) + +head(weather) |> + dplyr::select(Date, Year) +``` + + +#### Exercise: working with dates + +We are going to review our basic wrangling skills to prepare the Palmer Station +weather data to merge with the penguins data. + +We would like to merge a _summary_ of the weather data with the penguins. For +this summary, we want the weather data summarized by year, rather than by day +(as it currently is). Then per year, we want to know the maximum, minimum, and +average temperatures. Create this summary table called `weather_summary`. + +```{r} +weather_summary <- weather |> + dplyr::group_by(Year) |> + dplyr::summarize(Average_temp = mean(Temperature.Average..C., na.rm = TRUE), + Max_temp = max(Temperature.High..C., na.rm = TRUE), + Min_temp = min(Temperature.Low..C., na.rm = TRUE)) + +``` + +Finally, we'll write the summary weather data to a new CSV file +`./data/weather_annual.csv`. + +```{r} +readr::write_csv(weather_summary, "./data/weather_annual.csv", na = "") +``` + + +## Acknowledgement + +The material in this notebook is adapted from Eliza Wood's [Tidyverse: Data +wrangling & visualization](https://liza-wood.github.io/tidyverse_intro/) course, +which is licensed under [Creative Commons BY-NC-SA +4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/). This in itself is +based on material from [UC Davis's R-DAVIS +course](https://gge-ucd.github.io/R-DAVIS/index.html), which draws heavily on +[Carpentries](https://datacarpentry.org/R-ecology-lesson/) R lessons. + ## Footnotes diff --git a/R_notebooks/data/PalmerStation_Daily_Weather.csv b/R_notebooks/data/PalmerStation_Daily_Weather.csv new file mode 100644 index 0000000..4b7a462 --- /dev/null +++ b/R_notebooks/data/PalmerStation_Daily_Weather.csv @@ -0,0 +1,12478 @@ +Date,Temperature High (C),Temperature Low (C),Temperature Average (C),Sea Surface Temperature (C),Sea Ice (WMO Code),Pressure High (mbar),Pressure Low (mbar),Pressure Average (mbar),Wind Peak (knots),Wind 5-Sec Peak (knots),Wind 2-Min Peak (knots),Wind Average (knots),Wind Peak Direction,Wind 5-Sec Peak Direction,Wind 2-Min Peak Direction,Wind Prevailing Direction,Precipitation Melted (mm),Precipitation Snow (cm),Depth at Snowstake (cm),Sky Coverage (tenths) +1989-04-01,2.8,-1.0,,,,1003.8,997.6,,18.0,,,4.0,110.0,,,SW,0.0,0,,4.0 +1989-04-02,1.1,-2.7,,,,997.6,994.9,,24.0,,,9.0,30.0,,,NE,0.0,0,,2.0 +1989-04-03,-0.6,-3.5,,,,998.1,995.2,,13.0,,,8.0,60.0,,,NE,0.0,0,,5.0 +1989-04-04,1.1,-4.4,,,,1002.0,998.1,,14.0,,,6.0,210.0,,,NW,0.0,0,,5.0 +1989-04-05,-0.6,-2.9,,,,1001.8,997.2,,14.0,,,4.0,230.0,,,SW,0.0,0,,10.0 +1989-04-06,2.5,-3.1,,,,997.3,994.6,,15.0,,,4.0,180.0,,,W,0.0,0,,2.0 +1989-04-07,-1.4,-3.2,,,,995.4,988.4,,16.0,,,8.0,40.0,,,NE,1.0,T,,9.0 +1989-04-08,-0.8,-4.5,,,,996.8,983.0,,39.0,,,14.0,120.0,,,E,0.0,0,,5.0 +1989-04-09,-1.0,-4.0,,,,1002.9,996.8,,27.0,,,11.0,50.0,,,E,0.0,0,,0.0 +1989-04-10,-1.5,-3.8,,,,1002.8,995.0,,16.0,,,6.0,60.0,,,NE,T,T,,10.0 +1989-04-11,-1.2,-3.3,,,,995.0,988.1,,12.0,,,8.0,360.0,,,W,T,T,,10.0 +1989-04-12,-0.6,-5.8,,,,995.8,981.0,,30.0,,,12.0,260.0,,,SE,T,T,,1.0 +1989-04-13,-3.5,-7.2,,,,1001.8,995.8,,16.0,,,10.0,30.0,,,SW,0.0,0,,4.0 +1989-04-14,-3.0,-6.9,,,,1003.1,1001.1,,13.0,,,6.0,70.0,,,E,0.0,0,,7.0 +1989-04-15,1.7,-5.5,,,,1001.1,988.0,,18.0,,,6.0,130.0,,,SE,T,T,,10.0 +1989-04-16,3.6,0.0,,,,988.0,983.8,,32.0,,,16.0,80.0,,,N,0.0,0,,8.0 +1989-04-17,0.8,-0.5,,,,989.2,985.7,,19.0,,,6.0,250.0,,,N,0.3,1,,10.0 +1989-04-18,-1.1,-2.8,,,,996.1,989.2,,25.0,,,8.0,30.0,,,NE,2.0,T,,10.0 +1989-04-19,-3.3,-5.8,,,,997.0,994.0,,26.0,,,10.0,40.0,,,NE,0.0,0,,6.0 +1989-04-20,-2.8,-6.1,,,,994.8,992.1,,19.0,,,5.0,90.0,,,N,1.0,1,,10.0 +1989-04-21,-0.2,-2.7,,,,992.1,983.1,,10.0,,,4.0,310.0,,,N,2.0,1,,10.0 +1989-04-22,0.7,-1.9,,,,985.1,979.9,,20.0,,,2.0,10.0,,,N,4.0,4,,10.0 +1989-04-23,0.6,-2.8,,,,988.5,979.7,,33.0,,,15.0,30.0,,,N,T,T,,10.0 +1989-04-24,-0.5,-3.9,,,,983.8,979.6,,33.0,,,16.0,360.0,,,E,2.0,9,,10.0 +1989-04-25,-1.1,-2.2,,,,992.5,983.1,,22.0,,,8.0,110.0,,,NW,2.0,4,,10.0 +1989-04-26,-1.1,-3.1,,,,997.9,992.5,,16.0,,,8.0,240.0,,,SW,0.0,0,,10.0 +1989-04-27,-0.3,-3.0,,,,997.0,981.0,,24.0,,,10.0,330.0,,,NW,1.0,2,,10.0 +1989-04-28,-0.8,-1.7,,,,981.1,977.3,,20.0,,,12.0,210.0,,,W,4.0,10,,10.0 +1989-04-29,-0.3,-3.3,,,,995.9,981.1,,17.0,,,12.0,20.0,,,N,0.0,0,,7.0 +1989-04-30,0.5,-1.0,,,,995.0,977.0,,45.0,,,16.0,40.0,,,NE,0.0,0,,9.0 +1989-05-01,-0.3,-1.7,,,,994.8,977.0,,40.0,,,10.0,60.0,,,N,0.8,3,,9.0 +1989-05-02,-0.3,-3.8,,,,1011.4,994.9,,21.0,,,8.0,220.0,,,SW,T,T,,7.0 +1989-05-03,2.9,0.0,,,,1010.9,1000.2,,40.0,,,10.0,40.0,,,N,1.0,3,,10.0 +1989-05-04,0.5,-2.7,,,,1005.8,992.9,,20.0,,,8.0,360.0,,,NW,0.0,0,,10.0 +1989-05-05,2.8,-2.8,,,,993.0,982.0,,42.0,,,22.0,360.0,,,W,T,1,,10.0 +1989-05-06,2.2,-3.3,,,,993.5,970.7,,46.0,,,18.0,30.0,,,S,0.0,0,,10.0 +1989-05-07,3.9,0.8,,,,987.9,972.7,,40.0,,,15.0,30.0,,,E,0.5,T,,10.0 +1989-05-08,3.3,0.8,,,,984.0,971.0,,61.0,,,24.0,30.0,,,N,6.0,0,,10.0 +1989-05-09,0.3,-1.3,,,,973.0,971.4,,18.0,,,5.0,310.0,,,NE,0.0,0,,10.0 +1989-05-10,-2.8,-5.0,,,,978.8,973.0,,52.0,,,28.0,30.0,,,NE,T,T,,10.0 +1989-05-11,-4.2,-6.2,,,,994.1,979.0,,26.0,,,14.0,30.0,,,NE,2.0,2,,8.0 +1989-05-12,-2.2,-6.1,,,,1008.4,994.1,,27.0,,,14.0,220.0,,,W,1.0,2,,10.0 +1989-05-13,1.1,-6.1,,,,1009.7,1003.7,,46.0,,,25.0,10.0,,,NW,2.8,4,,10.0 +1989-05-14,2.2,1.1,,,,1013.1,1008.0,,35.0,,,20.0,10.0,,,NW,0.0,0,,10.0 +1989-05-15,4.4,1.7,,,,1010.9,986.9,,45.0,,,30.0,30.0,,,NE,T,0,,10.0 +1989-05-16,3.0,0.0,,,,989.4,970.2,,52.0,,,18.0,30.0,,,NE,3.6,1,,10.0 +1989-05-17,1.7,-1.4,,,,987.5,978.1,,48.0,,,22.0,10.0,,,NW,T,T,,4.0 +1989-05-18,0.3,-2.8,,,,987.6,979.0,,48.0,,,18.0,340.0,,,NW,0.0,0,,10.0 +1989-05-19,0.5,-2.8,,,,992.8,981.6,,46.0,,,16.0,40.0,,,NE,2.0,1,,10.0 +1989-05-20,2.2,-0.5,,,,989.8,981.9,,44.0,,,16.0,30.0,,,N,0.5,1,,10.0 +1989-05-21,2.2,-0.8,,,,987.7,979.0,,65.0,,,30.0,10.0,,,N,2.6,1,,10.0 +1989-05-22,0.6,-1.7,,,,980.9,974.0,,39.0,,,20.0,350.0,,,NW,2.6,3,,9.0 +1989-05-23,-0.3,-2.8,,,,991.0,979.0,,42.0,,,18.0,20.0,,,NW,0.0,0,,8.0 +1989-05-24,0.0,-1.5,,,,991.0,980.6,,47.0,,,22.0,360.0,,,N,T,1,,10.0 +1989-05-25,1.1,-5.6,,,,982.8,956.0,,55.0,,,30.0,10.0,,,N,11.8,5,,10.0 +1989-05-26,1.0,-3.2,,,,965.0,954.0,,77.0,,,26.0,20.0,,,NW,T,T,,10.0 +1989-05-27,1.6,-5.5,,,,969.9,948.0,,66.0,,,30.0,350.0,,,NW,2.0,1,,10.0 +1989-05-28,1.1,-4.4,,,,961.5,944.0,,66.0,,,32.0,30.0,,,NW,T,T,,10.0 +1989-05-29,-2.2,-6.1,,,,964.3,957.3,,52.0,,,20.0,360.0,,,N,T,T,,10.0 +1989-05-30,-1.1,-10.8,,,,972.2,954.9,,44.0,,,20.0,300.0,,,NW,T,T,,8.0 +1989-05-31,0.0,-5.5,,,,972.2,949.0,,79.0,,,38.0,30.0,,,NW,0.5,5,,10.0 +1989-06-01,-0.2,-4.4,,,,965.8,947.0,,56.0,,,34.0,50.0,,,NW,0.2,2,,10.0 +1989-06-02,-1.1,-5.1,,,,971.1,944.1,,58.0,,,30.0,340.0,,,NW,0.9,2,,10.0 +1989-06-03,-4.5,-8.2,,,,971.9,965.0,,34.0,,,16.0,310.0,,,NE,10.0,10,,8.0 +1989-06-04,-5.0,-6.1,,,,988.0,964.1,,44.0,,,22.0,190.0,,,S,0.3,T,,7.0 +1989-06-05,-3.2,-8.5,,,,1001.6,988.0,,20.0,,,12.0,120.0,,,SW,0.0,0,,6.0 +1989-06-06,-3.1,-6.4,,,,999.5,994.8,,18.0,,,6.0,240.0,,,E,T,0,,8.0 +1989-06-07,-2.2,-4.0,,,,998.9,992.0,,26.0,,,15.0,210.0,,,SW,0.0,0,,6.0 +1989-06-08,-2.7,-9.8,,,,1014.3,998.9,,28.0,,,6.0,190.0,,,NW,0.0,0,,6.0 +1989-06-09,-0.6,-5.0,,,,1018.0,1014.3,,14.0,,,8.0,330.0,,,N,0.0,0,,9.0 +1989-06-10,-0.4,-1.6,,,,1017.0,1005.2,,20.0,,,12.0,330.0,,,N,0.6,2,,10.0 +1989-06-11,-0.2,-6.6,,,,1012.0,1005.0,,34.0,,,16.0,230.0,,,SW,0.0,0,,9.0 +1989-06-12,-0.8,-8.3,,,,1023.6,1012.0,,34.0,,,10.0,40.0,,,E,0.0,0,,8.0 +1989-06-13,-1.3,-5.5,,,,1023.9,1012.0,,34.0,,,14.0,60.0,,,NE,T,T,,8.0 +1989-06-14,0.8,-0.5,,,,1012.0,994.8,,28.0,,,8.0,60.0,,,SE,0.5,1,,10.0 +1989-06-15,4.4,1.0,,,,994.8,978.5,,45.0,,,16.0,30.0,,,NE,0.0,0,,9.0 +1989-06-16,1.6,0.6,,,,979.5,972.3,,34.0,,,10.0,40.0,,,NE,0.0,0,,9.0 +1989-06-17,1.7,-0.5,,,,972.2,966.5,,24.0,,,10.0,70.0,,,E,T,T,,10.0 +1989-06-18,1.1,-1.0,,,,976.3,967.1,,34.0,,,12.0,60.0,,,E,0.1,T,,9.0 +1989-06-19,-1.8,-3.9,,,,983.4,976.3,,34.0,,,8.0,30.0,,,SE,2.6,6,,9.0 +1989-06-20,1.7,-2.3,,,,980.8,957.9,,56.0,,,30.0,30.0,,,NE,1.1,T,,10.0 +1989-06-21,2.0,-1.2,,,,971.2,962.1,,42.0,,,18.0,40.0,,,NE,1.0,2,,10.0 +1989-06-22,-0.5,-3.3,,,,983.0,971.2,,27.0,,,8.0,30.0,,,NE,2.0,2,,10.0 +1989-06-23,5.0,-5.0,,,,982.5,975.8,,52.0,,,18.0,20.0,,,NW,7.2,8,,10.0 +1989-06-24,0.3,-0.5,,,,979.0,964.9,,44.0,,,24.0,350.0,,,N,36.8,36,,10.0 +1989-06-25,1.1,-6.3,,,,984.8,962.7,,50.0,,,20.0,240.0,,,NW,2.0,0,,10.0 +1989-06-26,-3.2,-7.2,,,,973.9,955.0,,46.0,,,20.0,140.0,,,SE,2.5,3,,8.0 +1989-06-27,-0.9,-6.1,,,,976.3,967.1,,34.0,,,18.0,20.0,,,N,0.0,0,,10.0 +1989-06-28,1.1,-2.8,,,,975.1,958.6,,42.0,,,14.0,30.0,,,E,0.0,0,,10.0 +1989-06-29,-2.1,-5.6,,,,963.5,958.0,,31.0,,,12.0,240.0,,,SW,0.6,1,,10.0 +1989-06-30,-1.0,-3.3,,,,989.1,963.5,,29.0,,,10.0,70.0,,,E,0.2,T,,9.0 +1989-07-01,0.6,-5.5,,,,989.5,964.9,,58.0,,,20.0,360.0,,,NE,2.0,2,,10.0 +1989-07-02,0.0,-1.7,,,,972.4,949.7,,60.0,,,30.0,40.0,,,N,T,T,,10.0 +1989-07-03,-1.6,-4.4,,,,989.0,972.4,,35.0,,,10.0,220.0,,,SW,0.0,0,,8.0 +1989-07-04,0.0,-3.9,,,,1001.1,989.0,,33.0,,,12.0,30.0,,,NW,0.3,T,,10.0 +1989-07-05,1.6,-1.1,,,,996.2,990.9,,36.0,,,12.0,20.0,,,NE,6.8,7,,10.0 +1989-07-06,2.2,-0.5,,,,991.0,984.7,,27.0,,,12.0,360.0,,,N,1.0,1,,10.0 +1989-07-07,1.6,0.0,,,,999.9,980.5,,47.0,,,18.0,30.0,,,N,0.3,T,,10.0 +1989-07-08,1.6,-1.6,,,,998.8,983.1,,30.0,,,18.0,300.0,,,NW,T,T,,10.0 +1989-07-09,0.6,-1.7,,,,1002.8,998.0,,24.0,,,14.0,10.0,,,N,1.2,1,,9.0 +1989-07-10,2.8,-1.6,,,,1002.1,976.0,,50.0,,,25.0,60.0,,,NE,0.5,1,,10.0 +1989-07-11,2.7,-0.5,,,,977.2,970.9,,64.0,,,32.0,350.0,,,N,0.3,0,,10.0 +1989-07-12,1.6,-0.6,,,,973.4,969.6,,62.0,,,30.0,330.0,,,N,0.2,T,,10.0 +1989-07-13,-0.5,-3.9,,,,984.8,972.8,,38.0,,,20.0,300.0,,,W,T,T,,10.0 +1989-07-14,-0.5,-5.2,,,,993.5,982.9,,43.0,,,23.0,10.0,,,N,0.0,0,,10.0 +1989-07-15,1.1,-1.6,,,,992.7,966.0,,54.0,,,30.0,60.0,,,NE,2.0,2,,10.0 +1989-07-16,0.5,-5.0,,,,983.0,966.1,,58.0,,,18.0,30.0,,,N,0.3,T,,10.0 +1989-07-17,1.1,-3.9,,,,991.4,976.6,,54.0,,,10.0,10.0,,,NE,T,T,,9.0 +1989-07-18,-0.4,-6.1,,,,1003.1,991.1,,33.0,,,12.0,30.0,,,E,0.0,0,,8.0 +1989-07-19,1.7,-1.1,,,,1002.5,998.2,,52.0,,,25.0,30.0,,,NE,T,T,,10.0 +1989-07-20,2.2,-5.6,,,,1002.5,987.8,,54.0,,,30.0,20.0,,,NW,1.0,1,,8.0 +1989-07-21,0.0,-4.4,,,,1002.3,996.5,,40.0,,,25.0,340.0,,,N,17.0,17,,10.0 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this folder were obtained from the sources listed below. + + +PalmerStation_Daily_Weather.csv +------------------------------- + +Palmer Station Antarctica LTER. 2023. Daily weather averages for Palmer Station, +Antarctica (1989-2023) ver 9. Environmental Data Initiative. +https://doi.org/10.6073/pasta/3eefb45dbfb784c3cabe3690ea46fe9e +(Accessed 2024-01-08). + +Intellectual Rights of the authors (as accessed from the above link on +2024-01-08): + +This information is released under the Creative Commons license - Attribution - CC BY (https://creativecommons.org/licenses/by/4.0/). The consumer of these data ("Data User" herein) is required to cite it appropriately in any publication that results from its use. The Data User should realize that these data may be actively used by others for ongoing research and that coordination may be necessary to prevent duplicate publication. The Data User is urged to contact the authors of these data if any questions about methodology or results occur. Where appropriate, the Data User is encouraged to consider collaboration or co-authorship with the authors. The Data User should realize that misinterpretation of data may occur if used out of context of the original study. While substantial efforts are made to ensure the accuracy of data and associated documentation, complete accuracy of data sets cannot be guaranteed. All data are made available "as is." The Data User should be aware, however, that data are updated periodically and it is the responsibility of the Data User to check for new versions of the data. The data authors and the repository where these data were obtained shall not be liable for damages resulting from any use or misinterpretation of the data. Thank you. From 41b42cae684702f1c6d05063532e3960262e1e58 Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Wed, 10 Jan 2024 18:20:56 +0000 Subject: [PATCH 25/50] Add material on joining datasets --- R_notebooks/06_Combining_datasets.Rmd | 198 +++++++++++++++++++++++++- 1 file changed, 196 insertions(+), 2 deletions(-) diff --git a/R_notebooks/06_Combining_datasets.Rmd b/R_notebooks/06_Combining_datasets.Rmd index f2d3382..6769a6c 100644 --- a/R_notebooks/06_Combining_datasets.Rmd +++ b/R_notebooks/06_Combining_datasets.Rmd @@ -5,5 +5,199 @@ output: html_notebook ## Outline -- Concatenating data -- Joins +- Joining datasets + + +## Joining datasets + +In your data wrangling journey, you will often find yourself wanting to combine +one dataframe with some kind of supplementary or partner dataframe. In our +case, we have the penguins and weather data stored separately, but if we ever +wanted to explore any relationships between them, we'd ideally want it in a +single dataframe / table. This requires lining up the observations and variables +in the datasets appropriately, something which is accomplished by performing +appropriate **joins**. + +The key to joining is to identify the variables by which you want to join the +data. In other words, which columns in each data are the ones that link them +together? In some cases these may be one-to-one matches (e.g. ID numbers to IDs +numbers), or in other cases there is data at different levels that need to be +lined up. + +### Left joins + +There are several kinds of join function in `dplyr`, but we'll just focus on +`left_join` and leave you to explore the others for yourself. + +Like all the join functions, `left_join` takes three arguments: the two dataframes +you'd like to join, and the name of the column (or columns) by which to join. + +``` +dplyr::left_join(data_left, data_right, by = ) +``` + +The way `left_join` works is to match up the columns given in the `by` column +and create a new dataframe by pasting new columns from `data_right` alongside +the columns from `data_left`. We bring in as many rows from `data_right` as we +can. The 'left' in `left_join` indicates that we're keeping everything from the +'left' dataframe (i.e. the first one) and joining the other dataframe onto the +'left' one. + +It's helpful to use small, toy dataframes to explore how joins work (or remind +yourself after a period of time away). + +```{r} +df1 <- data.frame(colA = c(1, 2, 3, 4), + colB = c(2021, 2022, 2023, 2024), + colC = c('a', 'b', 'c', 'd')) + +df2 <- data.frame(colA = c(1, 3), + colD = c('foo', 'bar'), + colE = c('dog', 'cat')) +``` + +```{r} +df1 +``` + +```{r} +df2 +``` + +Now let's join `df2` onto `df1` by the column `colA`: + +```{r} +df1 |> + dplyr::left_join(df2, by = "colA") +``` + +Notice: + +- All the data from the left-hand dataframe, `df1`, is kept (columns `colA` -- `colC`). + +- The data from the right-hand dataframe `df2` has been brought over + for the rows where the 'by' column `colA` has matching values in the + left-hand dataframe `df1`. + +- The rows where there aren't matching values in the 'by' column `colA` have + missing values for the other right-hand dataframe columns (`colD` and `colE` + in this case). + + +### Exercise: left join practice + +Suppose now the right hand dataframe is as follows: + +```{r} +df2 <- data.frame(colA = c(1, 3, 2, 4, 5), + colD = c('foo', 'bar', 'baz', 'qux', 'foo'), + colE = c('dog', 'cat', 'mouse', 'rabbit', 'horse')) +``` + +Can you guess what the output of `dplyr::left_join(df1, df2, by = "colA")` will +be? Check your guess with code below. + +```{r} +df1 |> + dplyr::left_join(df2, by = "colA") +``` + +Now what if you swap the order of `df1` and `df2`? Guess the output of +`dplyr::left_join(df2, df1, by = "colA")` and check your answer below. + +```{r} +df2 |> + dplyr::left_join(df1, by = "colA") +``` + + +### Optional exercise: repeated keys + +The values in the 'by' column(s) are sometimes called _keys_ for the join. The +rules we described in the above example are not the whole story and can be +complicated by the presence of repeated keys. + +What do you think happens if there are repeated keys? For example, try to +guess the output of the following code: + +```{r} +df1 <- data.frame(colA = c(1, 2, 3, 3), # repeated key 3 + colB = c(2021, 2022, 2023, 2024), + colC = c('a', 'b', 'c', 'd')) + +df2 <- data.frame(colA = c(1, 3), + colD = c('foo', 'bar'), + colE = c('dog', 'cat')) + +df1 |> + dplyr::left_join(df2, by = "colA") +``` + +Now what if we swap the roles of `df1` and `df2`? + +```{r} +df2 |> + dplyr::left_join(df1, by = "colA") +``` + +#### Warning about doubles + +It's generally not a good idea to join on a 'by' column (or columns) +that contain doubles. This is because matching in the join will be done +by an exact equality test on the doubles, which can create strange +results due to numerical imprecision and be difficult to reproduce. Example: in +the following, two doubles that look distinct are considered equal to +1 / 3 = 0.333..., so are treated as + +```{r} +x <- data.frame(colA = c(1 / 3, 2 / 3), + colB = c(1000, 2000)) + +y <- data.frame(colA = c(0.33333333333333331, 0.33333333333333334), + colB = c("foo", "bar")) + +dplyr::left_join(x, y, by = "colA") +``` + + +### Exercise: joining penguin and weather data + +Recall the penguin data from the `palmerpenguins` package and the annual weather +data we created at the end of workbook `05_Strings_and_dates.Rmd`: + +```{r} +library(palmerpenguins) # loads `penguins` data +weather <- readr::read_csv("./data/weather_annual.csv") + +penguins +weather +``` + +Use a join to create a single dataframe that has all the penguin data and +weather data combined. Hint: there are some subtleties to be aware of: + +- First think about which column(s) to join on -- a call to `dplyr::rename` + might be in order! + +- We should make sure that the 'by' column(s) to join on are of the same type. + Examine the kind of data in each dataframe closely and coerce if necessary! + +```{r} +weather_cleaned <- weather |> + dplyr::rename(year = Year) |> + dplyr::mutate(year = as.integer(year)) + +penguins |> + dplyr::left_join(weather_cleaned, by = "year") +``` + + +## Acknowledgement + +The material in this notebook is adapted from Eliza Wood's [Tidyverse: Data +wrangling & visualization](https://liza-wood.github.io/tidyverse_intro/) course, +which is licensed under [Creative Commons BY-NC-SA +4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/). This in itself is +based on material from [UC Davis's R-DAVIS +course](https://gge-ucd.github.io/R-DAVIS/index.html), which draws heavily on +[Carpentries](https://datacarpentry.org/R-ecology-lesson/) R lessons. From a6a42719d1f660bdb664277853ed0fc18a5be92f Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Wed, 14 Feb 2024 09:44:39 +0000 Subject: [PATCH 26/50] Tweak formatting --- R_notebooks/06_Combining_datasets.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R_notebooks/06_Combining_datasets.Rmd b/R_notebooks/06_Combining_datasets.Rmd index 6769a6c..94d1d92 100644 --- a/R_notebooks/06_Combining_datasets.Rmd +++ b/R_notebooks/06_Combining_datasets.Rmd @@ -147,7 +147,7 @@ that contain doubles. This is because matching in the join will be done by an exact equality test on the doubles, which can create strange results due to numerical imprecision and be difficult to reproduce. Example: in the following, two doubles that look distinct are considered equal to -1 / 3 = 0.333..., so are treated as +`1 / 3 = 0.333...`, so 'match' on the `1 / 3` entry in data frame `x`. ```{r} x <- data.frame(colA = c(1 / 3, 2 / 3), From 314914d0a6ac484c255c2e18c2db333f36314db2 Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Wed, 14 Feb 2024 10:11:41 +0000 Subject: [PATCH 27/50] Add data used in notebook 6 --- R_notebooks/data/README | 20 ++++++++++++++-- R_notebooks/data/weather_annual.csv | 36 +++++++++++++++++++++++++++++ 2 files changed, 54 insertions(+), 2 deletions(-) create mode 100644 R_notebooks/data/weather_annual.csv diff --git a/R_notebooks/data/README b/R_notebooks/data/README index 8bc57e6..c39196f 100644 --- a/R_notebooks/data/README +++ b/R_notebooks/data/README @@ -7,9 +7,25 @@ PalmerStation_Daily_Weather.csv Palmer Station Antarctica LTER. 2023. Daily weather averages for Palmer Station, Antarctica (1989-2023) ver 9. Environmental Data Initiative. https://doi.org/10.6073/pasta/3eefb45dbfb784c3cabe3690ea46fe9e -(Accessed 2024-01-08). +(accessed 2024-01-08). Intellectual Rights of the authors (as accessed from the above link on 2024-01-08): -This information is released under the Creative Commons license - Attribution - CC BY (https://creativecommons.org/licenses/by/4.0/). The consumer of these data ("Data User" herein) is required to cite it appropriately in any publication that results from its use. The Data User should realize that these data may be actively used by others for ongoing research and that coordination may be necessary to prevent duplicate publication. The Data User is urged to contact the authors of these data if any questions about methodology or results occur. Where appropriate, the Data User is encouraged to consider collaboration or co-authorship with the authors. The Data User should realize that misinterpretation of data may occur if used out of context of the original study. While substantial efforts are made to ensure the accuracy of data and associated documentation, complete accuracy of data sets cannot be guaranteed. All data are made available "as is." The Data User should be aware, however, that data are updated periodically and it is the responsibility of the Data User to check for new versions of the data. The data authors and the repository where these data were obtained shall not be liable for damages resulting from any use or misinterpretation of the data. Thank you. +This information is released under the Creative Commons license - Attribution +- CC BY (https://creativecommons.org/licenses/by/4.0/). The consumer of these +data ("Data User" herein) is required to cite it appropriately in any +publication that results from its use. The Data User should realize that these +data may be actively used by others for ongoing research and that coordination +may be necessary to prevent duplicate publication. The Data User is urged to +contact the authors of these data if any questions about methodology or results +occur. Where appropriate, the Data User is encouraged to consider collaboration +or co-authorship with the authors. The Data User should realize that +misinterpretation of data may occur if used out of context of the original +study. While substantial efforts are made to ensure the accuracy of data and +associated documentation, complete accuracy of data sets cannot be guaranteed. +All data are made available "as is." The Data User should be aware, however, +that data are updated periodically and it is the responsibility of the Data User +to check for new versions of the data. The data authors and the repository where +these data were obtained shall not be liable for damages resulting from any use +or misinterpretation of the data. Thank you. diff --git a/R_notebooks/data/weather_annual.csv b/R_notebooks/data/weather_annual.csv new file mode 100644 index 0000000..0e46759 --- /dev/null +++ b/R_notebooks/data/weather_annual.csv @@ -0,0 +1,36 @@ +Year,Average_temp,Max_temp,Min_temp +1989,,7.8,-11.1 +1990,,8.7,-20.6 +1991,,9.8,-18 +1992,,10.3,-24.7 +1993,,10.1,-17.6 +1994,-7.2,9,-25.6 +1995,0.06129032258064516,9,-26 +1996,-0.6418032786885246,9.6,-22.7 +1997,-2.4315068493150687,9.5,-25.2 +1998,-0.9893150684931507,8.5,-21.7 +1999,-1.4975342465753425,9.2,-23.4 +2000,-1.368032786885246,10.8,-19.6 +2001,-1.5865753424657534,9.5,-17.6 +2002,-2.621917808219178,8.5,-21.6 +2003,-1.1893150684931506,9.4,-19.7 +2004,-1.6286885245901639,9.8,-22.9 +2005,-1.7947945205479452,9.8,-19.9 +2006,-0.9971988795518207,10.6,-15.2 +2007,-1.6287671232876713,9.9,-16.3 +2008,-0.7920765027322404,6.8,-18.9 +2009,-1.788622754491018,7.7,-14.1 +2010,-0.7208219178082191,11.6,-17.7 +2011,-1.8876712328767122,8.1,-19.1 +2012,-1.4284153005464482,8.9,-15.9 +2013,-1.949041095890411,8.7,-24.7 +2014,-1.576164383561644,8.5,-16.3 +2015,-3.318082191780822,8.9,-23.3 +2016,-1.8639344262295081,9.4,-18.7 +2017,-1.5217032967032966,9.3,-20.9 +2018,-1.658082191780822,9.7,-21.9 +2019,-1.810958904109589,9,-21.4 +2020,-1.1293956043956044,10.6,-15.3 +2021,-0.9621917808219178,10.9,-18.3 +2022,-0.2071232876712329,11.6,-9 +2023,0.4325966850828729,8.7,-9.2 From 86551808309c25c8cc4c8011c0ea105874c61770 Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Wed, 14 Feb 2024 10:13:05 +0000 Subject: [PATCH 28/50] Update package versions in lock file --- R_notebooks/renv.lock | 365 ++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 356 insertions(+), 9 deletions(-) diff --git a/R_notebooks/renv.lock b/R_notebooks/renv.lock index ef29cef..c474fac 100644 --- a/R_notebooks/renv.lock +++ b/R_notebooks/renv.lock @@ -29,6 +29,30 @@ ], "Hash": "543776ae6848fde2f48ff3816d0628bc" }, + "bit": { + "Package": "bit", + "Version": "4.0.5", + "Source": "Repository", + "Repository": "CRAN", + "Requirements": [ + "R" + ], + "Hash": "d242abec29412ce988848d0294b208fd" + }, + "bit64": { + "Package": "bit64", + "Version": "4.0.5", + "Source": "Repository", + "Repository": "CRAN", + "Requirements": [ + "R", + "bit", + "methods", + "stats", + "utils" + ], + "Hash": "9fe98599ca456d6552421db0d6772d8f" + }, "bslib": { "Package": "bslib", "Version": "0.6.1", @@ -72,16 +96,71 @@ ], "Hash": "1216ac65ac55ec0058a6f75d7ca0fd52" }, + "clipr": { + "Package": "clipr", + "Version": "0.8.0", + "Source": "Repository", + "Repository": "CRAN", + "Requirements": [ + "utils" + ], + "Hash": "3f038e5ac7f41d4ac41ce658c85e3042" + }, + "cpp11": { + "Package": "cpp11", + "Version": "0.4.7", + "Source": "Repository", + "Repository": "RSPM", + "Requirements": [ + "R" + ], + "Hash": "5a295d7d963cc5035284dcdbaf334f4e" + }, + "crayon": { + "Package": "crayon", + "Version": "1.5.2", + "Source": "Repository", + "Repository": "CRAN", + "Requirements": [ + "grDevices", + "methods", + "utils" + ], + "Hash": "e8a1e41acf02548751f45c718d55aa6a" + }, "digest": { "Package": "digest", - "Version": "0.6.33", + "Version": "0.6.34", "Source": "Repository", "Repository": "RSPM", "Requirements": [ "R", "utils" ], - "Hash": "b18a9cf3c003977b0cc49d5e76ebe48d" + "Hash": "7ede2ee9ea8d3edbf1ca84c1e333ad1a" + }, + "dplyr": { + "Package": "dplyr", + "Version": "1.1.4", + "Source": "Repository", + "Repository": "RSPM", + "Requirements": [ + "R", + "R6", + "cli", + "generics", + "glue", + "lifecycle", + "magrittr", + "methods", + "pillar", + "rlang", + "tibble", + "tidyselect", + "utils", + "vctrs" + ], + "Hash": "fedd9d00c2944ff00a0e2696ccf048ec" }, "ellipsis": { "Package": "ellipsis", @@ -105,6 +184,18 @@ ], "Hash": "daf4a1246be12c1fa8c7705a0935c1a0" }, + "fansi": { + "Package": "fansi", + "Version": "1.0.6", + "Source": "Repository", + "Repository": "RSPM", + "Requirements": [ + "R", + "grDevices", + "utils" + ], + "Hash": "962174cf2aeb5b9eea581522286a911f" + }, "fastmap": { "Package": "fastmap", "Version": "1.1.1", @@ -135,16 +226,27 @@ ], "Hash": "47b5f30c720c23999b913a1a635cf0bb" }, + "generics": { + "Package": "generics", + "Version": "0.1.3", + "Source": "Repository", + "Repository": "CRAN", + "Requirements": [ + "R", + "methods" + ], + "Hash": "15e9634c0fcd294799e9b2e929ed1b86" + }, "glue": { "Package": "glue", - "Version": "1.6.2", + "Version": "1.7.0", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Requirements": [ "R", "methods" ], - "Hash": "4f2596dfb05dac67b9dc558e5c6fba2e" + "Hash": "e0b3a53876554bd45879e596cdb10a52" }, "highr": { "Package": "highr", @@ -157,6 +259,20 @@ ], "Hash": "06230136b2d2b9ba5805e1963fa6e890" }, + "hms": { + "Package": "hms", + "Version": "1.1.3", + "Source": "Repository", + "Repository": "CRAN", + "Requirements": [ + "lifecycle", + "methods", + "pkgconfig", + "rlang", + "vctrs" + ], + "Hash": "b59377caa7ed00fa41808342002138f9" + }, "htmltools": { "Package": "htmltools", "Version": "0.5.7", @@ -223,6 +339,19 @@ ], "Hash": "b8552d117e1b808b09a832f589b79035" }, + "lubridate": { + "Package": "lubridate", + "Version": "1.9.3", + "Source": "Repository", + "Repository": "RSPM", + "Requirements": [ + "R", + "generics", + "methods", + "timechange" + ], + "Hash": "680ad542fbcf801442c83a6ac5a2126c" + }, "magrittr": { "Package": "magrittr", "Version": "2.0.3", @@ -264,6 +393,72 @@ ], "Hash": "6c6861efbc13c1d543749e9c7be4a592" }, + "pillar": { + "Package": "pillar", + "Version": "1.9.0", + "Source": "Repository", + "Repository": "CRAN", + "Requirements": [ + "cli", + "fansi", + "glue", + "lifecycle", + "rlang", + "utf8", + "utils", + "vctrs" + ], + "Hash": "15da5a8412f317beeee6175fbc76f4bb" + }, + "pkgconfig": { + "Package": "pkgconfig", + "Version": "2.0.3", + "Source": "Repository", + "Repository": "CRAN", + "Requirements": [ + "utils" + ], + "Hash": "01f28d4278f15c76cddbea05899c5d6f" + }, + "prettyunits": { + "Package": "prettyunits", + "Version": "1.2.0", + "Source": "Repository", + "Repository": "RSPM", + "Requirements": [ + "R" + ], + "Hash": "6b01fc98b1e86c4f705ce9dcfd2f57c7" + }, + "progress": { + "Package": "progress", + "Version": "1.2.3", + "Source": "Repository", + "Repository": "RSPM", + "Requirements": [ + "R", + "R6", + "crayon", + "hms", + "prettyunits" + ], + "Hash": "f4625e061cb2865f111b47ff163a5ca6" + }, + "purrr": { + "Package": "purrr", + "Version": "1.0.2", + "Source": "Repository", + "Repository": "RSPM", + "Requirements": [ + "R", + "cli", + "lifecycle", + "magrittr", + "rlang", + "vctrs" + ], + "Hash": "1cba04a4e9414bdefc9dcaa99649a8dc" + }, "rappdirs": { "Package": "rappdirs", "Version": "0.3.3", @@ -274,6 +469,29 @@ ], "Hash": "5e3c5dc0b071b21fa128676560dbe94d" }, + "readr": { + "Package": "readr", + "Version": "2.1.5", + "Source": "Repository", + "Repository": "RSPM", + "Requirements": [ + "R", + "R6", + "cli", + "clipr", + "cpp11", + "crayon", + "hms", + "lifecycle", + "methods", + "rlang", + "tibble", + "tzdb", + "utils", + "vroom" + ], + "Hash": "9de96463d2117f6ac49980577939dfb3" + }, "renv": { "Package": "renv", "Version": "1.0.3", @@ -286,14 +504,14 @@ }, "rlang": { "Package": "rlang", - "Version": "1.1.2", + "Version": "1.1.3", "Source": "Repository", "Repository": "RSPM", "Requirements": [ "R", "utils" ], - "Hash": "50a6dbdc522936ca35afc5e2082ea91b" + "Hash": "42548638fae05fd9a9b5f3f437fbbbe2" }, "rmarkdown": { "Package": "rmarkdown", @@ -363,6 +581,75 @@ ], "Hash": "960e2ae9e09656611e0b8214ad543207" }, + "tibble": { + "Package": "tibble", + "Version": "3.2.1", + "Source": "Repository", + "Repository": "CRAN", + "Requirements": [ + "R", + "fansi", + "lifecycle", + "magrittr", + "methods", + "pillar", + "pkgconfig", + "rlang", + "utils", + "vctrs" + ], + "Hash": "a84e2cc86d07289b3b6f5069df7a004c" + }, + "tidyr": { + "Package": "tidyr", + "Version": "1.3.1", + "Source": "Repository", + "Repository": "RSPM", + "Requirements": [ + "R", + "cli", + "cpp11", + "dplyr", + "glue", + "lifecycle", + "magrittr", + "purrr", + "rlang", + "stringr", + "tibble", + "tidyselect", + "utils", + "vctrs" + ], + "Hash": "915fb7ce036c22a6a33b5a8adb712eb1" + }, + "tidyselect": { + "Package": "tidyselect", + "Version": "1.2.0", + "Source": "Repository", + "Repository": "CRAN", + "Requirements": [ + "R", + "cli", + "glue", + "lifecycle", + "rlang", + "vctrs", + "withr" + ], + "Hash": "79540e5fcd9e0435af547d885f184fd5" + }, + "timechange": { + "Package": "timechange", + "Version": "0.3.0", + "Source": "Repository", + "Repository": "RSPM", + "Requirements": [ + "R", + "cpp11" + ], + "Hash": "c5f3c201b931cd6474d17d8700ccb1c8" + }, "tinytex": { "Package": "tinytex", "Version": "0.49", @@ -373,6 +660,27 @@ ], "Hash": "5ac22900ae0f386e54f1c307eca7d843" }, + "tzdb": { + "Package": "tzdb", + "Version": "0.4.0", + "Source": "Repository", + "Repository": "CRAN", + "Requirements": [ + "R", + "cpp11" + ], + "Hash": "f561504ec2897f4d46f0c7657e488ae1" + }, + "utf8": { + "Package": "utf8", + "Version": "1.2.4", + "Source": "Repository", + "Repository": "RSPM", + "Requirements": [ + "R" + ], + "Hash": "62b65c52671e6665f803ff02954446e9" + }, "vctrs": { "Package": "vctrs", "Version": "0.6.5", @@ -387,16 +695,55 @@ ], "Hash": "c03fa420630029418f7e6da3667aac4a" }, + "vroom": { + "Package": "vroom", + "Version": "1.6.5", + "Source": "Repository", + "Repository": "RSPM", + "Requirements": [ + "R", + "bit64", + "cli", + "cpp11", + "crayon", + "glue", + "hms", + "lifecycle", + "methods", + "progress", + "rlang", + "stats", + "tibble", + "tidyselect", + "tzdb", + "vctrs", + "withr" + ], + "Hash": "390f9315bc0025be03012054103d227c" + }, + "withr": { + "Package": "withr", + "Version": "3.0.0", + "Source": "Repository", + "Repository": "RSPM", + "Requirements": [ + "R", + "grDevices", + "graphics" + ], + "Hash": "d31b6c62c10dcf11ec530ca6b0dd5d35" + }, "xfun": { "Package": "xfun", - "Version": "0.41", + "Version": "0.42", "Source": "Repository", "Repository": "RSPM", "Requirements": [ + "grDevices", "stats", "tools" ], - "Hash": "460a5e0fe46a80ef87424ad216028014" + "Hash": "fd1349170df31f7a10bd98b0189e85af" }, "yaml": { "Package": "yaml", From b7b16948aa10ed465e7938f559419d05ea7f521e Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Wed, 21 Feb 2024 16:31:16 +0000 Subject: [PATCH 29/50] Add notebook 2 on reading data in --- R_notebooks/02_Loading_data.Rmd | 128 +++++++++++++++++++++++++-- R_notebooks/data/timeseries_data.csv | 11 +++ 2 files changed, 134 insertions(+), 5 deletions(-) create mode 100644 R_notebooks/data/timeseries_data.csv diff --git a/R_notebooks/02_Loading_data.Rmd b/R_notebooks/02_Loading_data.Rmd index 8f89ece..cfb17fe 100644 --- a/R_notebooks/02_Loading_data.Rmd +++ b/R_notebooks/02_Loading_data.Rmd @@ -5,8 +5,126 @@ output: html_notebook ## Outline -- From a package -- From CSV file - - Into a dataframe - - Into a matrix? -- Other sources (Excel workbook, URL) \ No newline at end of file +- Loading data from a csv file +- Other sources (Excel workbook, URL) + + +## Introduction + +The package `readr` provides functions for reading data from 'flat' files, +such as csv or other delimited files. The functionality provided by `readr` +offers some advantages over the base R functions available for performing the +same task. + + + +## Loading data from a csv file + +You may have used the R function `read.csv` to load data from a csv file into +a dataframe: + +```{r} +timeseries <- read.csv("./data/timeseries_data.csv") +print(timeseries) +``` + +This is fine in a lot of cases, however, note some assumptions have been made. +For example, in the above, the `Id` column is read as an integer and the `Date` +column is read in as a string. In fact, the original data file records the Id +with padded zeros, like so: + +``` +Id,Date,X_variable,Y_variable +001,2024-02-01,1.1967837194583961,7.900464364159182 +002,2024-02-02,-0.7507563344053951,12.690405000912477 +003,2024-02-03,-0.7850492689583279,10.280088829160382 +004,2024-02-04,-0.6185457481359723,10.073199260115578 +005,2024-02-05,0.6188251316696602,9.183892504016947 +006,2024-02-06,-1.295559729374875,4.830591718912964 +007,2024-02-07,-1.0478610080617479,11.950495760617637 +008,2024-02-08,1.2772821688688454,7.008456322119265 +009,2024-02-09,-0.06582643175389392,3.4034822391265145 +010,2024-02-10,-0.07455295463910701,11.6370557133845 +``` + +What we really want to is to read the Id as a string and the Date as a date +object. + +The `read_csv` from the `readr` package is more 'shouty' about assumptions made +when parsing the data, and can have better default interpretations. For example: + +```{r} +timeseries <- readr::read_csv("./data/timeseries_data.csv") +``` + +Notice how it has explicitly told us that + +- `Id` is parsed as a string (which is what we wanted!) +- `Date` is parsed as a date (more about dates later in the course) +- `X_variable` and `Y_variable` are parsed as doubles, as expected. + +In general, it's good practice to be explicit about how we parse data from +files, rather than assuming the defaults will work. For example, we've no way of +knowing whether in the future the `readr` package maintainers will decide in the +future to interpret padded numbers as in the `Id` column as integers. + +We can be explicit about how columns are parsed by using the `col_types` +optional argument. The general way to do this is to supply a list with names +being the column names and values being a _column specifier_, +`readr::col_()`: + +``` +readr::read_csv("path/to/data.csv", + col_types = list(colA = readr::col_logical(), # column colA is a boolean + colB = readr::col_integer(), # column colB is an integer + colC = readr::col_double(), # column colC is a floating point number + colD = readr::col_character(), # column colD is a string + colE = readr::col_date(), # column colE is a date + .default = readr::col_double() # columns are floating point numbers + # unless specified otherwise + )) +``` + +**Note:** For a full list of the available column specifiers, run +`vignette("readr")` in the R console. + +So, to specify that we want to read the `Id` column as a string, the `Date` +column as a date, and the remaining columns as floating point numbers: + +```{r} +timeseries <- readr::read_csv("./data/timeseries_data.csv", + col_types = list(Id = readr::col_character(), + Date = readr::col_date(), + .default = readr::col_double())) +print(timeseries) +``` + + +Notice how the informational message about parsing has gone away, because we +explicitly gave the types. + + +### Exercise: parsing data from files + +Change the above so that the `Id` column is parsed as an integer and the +`Date` column is parsed as a string. + +```{r} +timeseries <- readr::read_csv("./data/timeseries_data.csv", + col_types = list(Id = readr::col_integer(), + Date = readr::col_character(), + .default = readr::col_double())) +print(timeseries) +``` + + +## Other file formats + +There are related packages in the Tidyverse ecosystem that provide support for +reading data from file types other than csv: + +- `readxl`: package for reading data from Excel workbooks. +- `googlesheets4`: package for reading data from Google Sheets spreadsheets. + +We also mention here the `ncdf4` package for reading NetCDF files (not part of +the Tidyverse). \ No newline at end of file diff --git a/R_notebooks/data/timeseries_data.csv b/R_notebooks/data/timeseries_data.csv new file mode 100644 index 0000000..b89135d --- /dev/null +++ b/R_notebooks/data/timeseries_data.csv @@ -0,0 +1,11 @@ +Id,Date,X_variable,Y_variable +001,2024-02-01,1.1967837194583961,7.900464364159182 +002,2024-02-02,-0.7507563344053951,12.690405000912477 +003,2024-02-03,-0.7850492689583279,10.280088829160382 +004,2024-02-04,-0.6185457481359723,10.073199260115578 +005,2024-02-05,0.6188251316696602,9.183892504016947 +006,2024-02-06,-1.295559729374875,4.830591718912964 +007,2024-02-07,-1.0478610080617479,11.950495760617637 +008,2024-02-08,1.2772821688688454,7.008456322119265 +009,2024-02-09,-0.06582643175389392,3.4034822391265145 +010,2024-02-10,-0.07455295463910701,11.6370557133845 From c5c2cdcb8e72938967455cffe4017cbbdbe966c4 Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Wed, 28 Feb 2024 15:32:38 +0000 Subject: [PATCH 30/50] Add notebook on pipe operator --- R_notebooks/04_Piping.Rmd | 123 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 123 insertions(+) diff --git a/R_notebooks/04_Piping.Rmd b/R_notebooks/04_Piping.Rmd index 9c31728..7ada745 100644 --- a/R_notebooks/04_Piping.Rmd +++ b/R_notebooks/04_Piping.Rmd @@ -7,3 +7,126 @@ output: html_notebook - The functional philosophy of Tidyverse - Piping operator + + +## The Functional philosophy of Tidyverse + +The Tidyverse packages are built around a philosophy of having functions that +work well together. Let's look at some of the functions we've seen so far: + +| Package | Function | Data input type | Output type | +|---------|-------------|-----------------|-------------------| +| `tidyr` | `read_csv` | string | dataframe | +| `dplyr` | `filter` | dataframe | dataframe | +| `dplyr` | `select` | dataframe | dataframe | +| `dplyr` | `mutate` | dataframe | dataframe | +| `dplyr` | `relocate` | dataframe | dataframe | +| `dplyr` | `arrange` | dataframe | dataframe | +| `dplyr` | `summarise` | dataframe | dataframe | +| `dplyr` | `group_by` | dataframe | grouped dataframe | + +Notice how consistent these are in accepting a dataframe as input and returning +a dataframe (or something like a dataframe). It is this consistency that makes +it easy to compose functions. + +In addition, it's important to note that none of the functions we've seen so +far modify the input dataframe 'in place', they always return a new dataframe. +(This is even the case for the `mutate` function, despite the name!) This +encourages a 'pipeline' approach, where we successively take the output +of one function and feed it in as the input to another function. + + +## Piping operator + +Since version 4.1, R has had a pipe operator `|>` which facilitates chaining +function calls. + +**Note:** Before a pipe operator was introduced into the base R language, people +used another pipe operator `%>%` from the `magrittr` package. It is still very +common to see people using the `magrittr` pipe and it behaves in a similar way +to base R's `|>` (though there are some minor differences). You can use +whichever you like, though for this course we'll use the base R pipe. + +The pipe operator takes the value in the left hand side and 'feeds it into' the +_first_ argument of a function call on the right hand side. So, using +`dplyr::select` as an example, the following two expressions are equivalent ways +to select two columns `colA` and `colB` from a dataframe `data`: + +``` +dplyr::select(data, colA, colB) data |> dplyr::select(colA, colB) +``` + +Notice how it looks like the dataframe argument to `dplyr::select` is missing +in the second expression. The pipe takes care of putting `data` into the first +argument slot, and the remaining slots are available for all other arguments +(in this case, the column names `colA` and `colB`). + +We can now chain multiple pipe calls together, like so: + +``` +data |> dplyr::select(colA, colB) |> dplyr::filter(colA > 10) +``` + +The result of the whole of the expression to the right of the assignment `<-` +gets stored in the `transformed_data`. In words, the above + +1. Takes a variable `data` +2. Selects columns `colA` and `colB` +3. Takes the result of 2. and filters it to keep rows where `colA > 10` + + +Usually, to make it easier to read, people will put successive pipe calls +on new lines, like so: + +``` +data |> + dplyr::select(colA, colB) |> + dplyr::filter(colA > 10) +``` + +If we want to store the overall result in a new variable `data_transformed`, +we can do so by putting the assignment `data_transformed <- ` at the beginning +of the chain, like so: + +``` +data_transformed <- data |> + dplyr::select(colA, colB) |> + dplyr::filter(colA > 10) +``` + + +### Exercise: piping + +The following code calculates the average bill ratio for penguins observed in +the year 2009. + +```{r} +library(palmerpenguins) # loads `penguins` data + +# Select species, year, bill_length_mm, bill_depth_mm +penguins1 <- dplyr::select(penguins, year, species, bill_length_mm, bill_depth_mm) + +# Filter to keep year 2009 data only +penguins2 <- dplyr::filter(penguins1, year == 2009) + +# Compute new bill_ratio column +penguins3 <- dplyr::mutate(penguins2, bill_ratio = bill_length_mm / bill_depth_mm) + +# Compute average bill ratio +penguins4 <- dplyr::summarise(penguins3, mean_bill_ratio = mean(bill_ratio, na.rm = TRUE)) + +print(penguins4) +``` + +Rewrite this code to use the pipe operator instead of storing each intermediate +dataframe, storing the overall result in a new variable called `summarised`. + +```{r} +summarised <- penguins |> + dplyr::select(year, species, bill_length_mm, bill_depth_mm) |> + dplyr::filter(year == 2009) |> + dplyr::mutate(bill_ratio = bill_length_mm / bill_depth_mm) |> + dplyr::summarise(mean_bill_ratio = mean(bill_ratio, na.rm = TRUE)) + +print(summarised) +``` \ No newline at end of file From 60c4301ceea1daa8636ccf28c7a2132ab4d59346 Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Thu, 29 Feb 2024 08:50:24 +0000 Subject: [PATCH 31/50] Add notebook on reshaping data Also add data from GCB website used in this notebook. --- R_notebooks/07_Reshaping_data.Rmd | 182 +++++++++++++++++- ...onal_Fossil_Carbon_Emissions_2023v1.0.xlsx | Bin 0 -> 737973 bytes R_notebooks/data/README | 24 ++- 3 files changed, 202 insertions(+), 4 deletions(-) create mode 100644 R_notebooks/data/National_Fossil_Carbon_Emissions_2023v1.0.xlsx diff --git a/R_notebooks/07_Reshaping_data.Rmd b/R_notebooks/07_Reshaping_data.Rmd index f7aeb95..fa388ef 100644 --- a/R_notebooks/07_Reshaping_data.Rmd +++ b/R_notebooks/07_Reshaping_data.Rmd @@ -5,6 +5,182 @@ output: html_notebook ## Outline -- Matrices -- Converting to/from tidy format -- Completion \ No newline at end of file +- Converting to/from tidy format + +## Introduction + +There are often cases where you'd like to change the shape of your data, by +which we mean to change the structure of the columns and/or rows. The `tidyr` +package contains functions to support this. + +As an example dataset for reshaping, we'll use data on territorial fossil CO2 +emissions by country (Friedlingstein et al. 2023)[^1]. + +**Note:** To read the data, we'll use a call to the `read_excel` function from +the `readxl` package. We only touched on this in notebook 2, so don't worry +about understanding exactly how the data is read in. Treat this as a glimpse of +seeing the Tidyverse in action 'in the wild'. You can read up about `read_excel` +as extra homework, if you're keen :-) + +```{r} +emissions <- readxl::read_excel("data/National_Fossil_Carbon_Emissions_2023v1.0.xlsx", + sheet = "Territorial Emissions", + skip = 11) |> + dplyr::rename(Year = `...1`) |> # missing column name in data got assigned name `...1` when reading + dplyr::select(Year, World, Africa, Asia, `Central America`, Europe, + `Middle East`, `North America`, Oceania, `South America`, + Bunkers) |> + dplyr::mutate(Year = as.integer(Year)) + +print(emissions) +``` + +## Converting to/from tidy format + +One scenario for reshaping data is when we receive data that is not in tidy +format and want to transform it so that it is tidy. + +In the emissions data above, notice how each column other than 'Year' +corresponds to a region of the world (or the category 'Bunkers'): + +```{r} +colnames(emissions) +``` + +This is not in tidy format. The observations correspond to year-region pairs, +while the variable being measured is territorial emissions. But the data is +structured as a matrix, where to get the emissions value for a region and year +you look up the entry for the appropriate row and column. + +What we'd like to do is create a dataframe with columns 'Year', 'Country' and +'Territorial_emissions' columns, with rows corresponding to year-region pairs. + +### Matrix format to tidy format with `pivot_longer` + +We can use the function `pivot_longer` from the `tidyr` package to help us make +the required transformation. Like most of the Tidyverse functions, there are a +lot of possible parameters to use in this function, but we only need to use it +as follows: + +``` +tidyr::pivot_longer(data, , names_to = "Name_for_gathered_columns", values_to = "Variable_name") +``` + +where + +- `data` is the dataframe to transform. +- `` is a specification of the columns that should be + gathered into a new column. +- `names_to` defines the name of the new column to create, whose values will + be the names in ``. +- `values_to` is the name of a new column that contains the values that were + in the columns `` (i.e. the variable that the original + data was giving). + +**Note**: More specifically, `` is a 'tidy-select'. This is a +way of specifying collections of columns within the Tidyverse. Unfortunately +details of this are outside the scope of this course, but you can learn more by +running `?tidyr::tidyr_tidy_select` in the R console. We will explain our use of +'tidy-select' in code comments below. + +The behaviour of `pivot_longer` is much easier to see with an example. The +following creates a new column called 'Region' that contains all the column +names in `emissions` from 'World' to 'Bunkers'. The values from these columns +are put into a new column called 'Territorial_emissions'. + +```{r} +emissions_tidy <- emissions |> + tidyr::pivot_longer(World:Bunkers, # All columns from 'World' to 'Bunkers' + names_to = "Region", + values_to = "Territorial_emissions") + +print(emissions_tidy) +``` + +Notice how each row corresponds to a year-region pair. The +'Territorial_emissions' values line up with the year and region they +corresponded to in the original data. + +Notice that the number of rows is equal to the number of regions multiplied by +the number of years, as we'd expect: + +```{r} +cat("Number of rows in emissions_tidy:", nrow(emissions_tidy), "\n") + +# Use ncol(emissions) - 1 so not to include the year column +cat("Number of year-region pairs:", length(emissions$Year) * (ncol(emissions) - 1), "\n") +``` + +In general, `pivot_longer` is called such because it make the table of data +'longer' i.e. have more rows. + +#### Exercise: pivoting + +What do you think the output of the following `pivot_longer` will be? + +``` +emissions |> + tidyr::pivot_longer(!Africa, # this means all columns except 'Africa' + names_to = "Names_column", + values_to = "Territorial_emissions?") +``` + +Run the code below to see if you're correct! + +```{r} +emissions |> + tidyr::pivot_longer(!Africa, # this means all columns except 'Africa' + names_to = "Names_column", + values_to = "Territorial_emissions?") +``` + +What do values in the 'Africa' and 'Territorial_emissions?' columns correspond +to in the following output? + +```{r} +emissions |> + tidyr::pivot_longer(!Africa, # this means all columns except 'Africa' + names_to = "Names_column", + values_to = "Territorial_emissions?") |> + dplyr::filter(Names_column == "Year") +``` + +### Tidy format to matrix format with `pivot_wider` + +What if we want to go the other way, i.e. turn a tidy dataframe into a matrix +format? In that case, we can use `pivot_wider` from the `tidyr` package. + +``` +tidyr::pivot_wider(data, names_from = , values_from = ) +``` + +where + +- `data` is the dataframe to transform. +- `names_from` is the column whose values will be the names of new columns in + the new dataframe. +- `values_from` is the name of a column whose values will go into the 'cells' + of the new dataframe. + +To see this in action, the following use of `pivot_wider` will take our tidied +emissions data and put it back into the original 'matrix' format, where regions +corresponded to columns and the entries gave the emissions for each year-region +combination. + +```{r} +emissions_tidy |> + tidyr::pivot_wider(names_from = Region, values_from = Territorial_emissions) +``` + +## Acnowledgements + +We acknowledge the Global Carbon Project, which is responsible for the Global +Carbon Budget and we thank the fossil carbon emissions modelling groups for +producing and making available their model output. + + +## 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z=lQdOA#Z2C3>Z~|Xx3MgQUnSmzqBmWds7$o7Yq9hGy&&fd3SF|Bs|1nX?k0nhvSg) zA|;=4`0z<@)AT~WaYrWaVvzmeXi?%}-!*85L%O5myQiN$wVGM|Ks6v7CNgU#HJ<8Y z*B|W-E?FoviA65qw6K7DsSK#8xtYu#mBoP&UnTjIRB~sfKci^aN8Bd%_$g+$adTFr z{2esGZ;aIt?7WdgRT$(@!>|qzjEPn=O-r-w^2x{L` z<`sWbs16wOkQLH_MYk|Ag1x-Ck%ncL_q`JImF$C|JMB)RCHvmzgo2{*&o0lY#G>
    -Check your answer -```r -filter_challenge <- penguins %>% -select(species, sex, bill_length_mm, body_mass_g) %>% -filter(is.na(sex) & body_mass_g > 3500) -str(filter_challenge) -``` -
    - - -## Creating new variables - -### New columns - -We may often want to make a new column with some updated or transformed value. -We can use the `mutate` function for this, in which you can assign the new -column name and its value. The idea is generally -`mutate(data, new_column_name = value)`. For example, if we wanted to calculate -a new value, the ratio of bill length to bill depth, we could do the following - -```r -# Mutate to create a new column; use penguins_piped so our output is -# easier to view -mutate(penguins_piped, bill_ratio = bill_length_mm / bill_depth_mm) -``` - -Note that in order to save the column in the dataframe, we must assign it as a -new dataframe object. The output of mutate is not just a new column on its own, -but the whole dataframe with the new column appended. So far we have not -overwritten in, and we can see that bill_depth_cm is not in our penguins -dataframe - -```r -colnames(penguins_piped) -``` - -Only once we assign it do we have a dataframe now with the new column appended - -```r -penguins_newcolumn <- mutate(penguins_piped, bill_ratio = bill_length_mm / bill_depth_mm) -colnames(penguins_newcolumn) -``` - -In addition to the mathematical transformations we just tried, we may want to -create new, discrete categories with our data. For example, let's categorize -bill ratio into discrete size groups based on its distribution. - -```{r} -summary(penguins_newcolumn$bill_ratio) -``` - -All values are greater than 1, so all bill lengths are greater than depths. -Still, lower ratio values mean that the bill depth is large relative to its length -(deep bills) and higher values mean that bill depth is small relative to its -length (shallow bills). Let's say we wanted to use these bill ratios to group -penguins into one of three groups: 'deep' 'average' and 'shallow'. - -
    - Diagram of penguin bill measurements -
    - Penguin bills. Artwork by @allison_horst, obtained from - https://allisonhorst.github.io/palmerpenguins/articles/art.html -
    -
    - -The `case_when` function, combined with `mutate`, lets us set multiple -conditions and assign resulting categories. This function uses a series of -two-sided formulas where the left-hand side determines describes the condition, -and the right supplies the result. The final condition should always be -`TRUE`, meaning that when the previous conditions have not been met, assign the -last value. - -Using these functions we can create a depth ratio category variable: - -```r -penguins_newcolumn <- penguins_newcolumn %>% - mutate(bill_depth_cat = case_when( - # Using the 1st Q as cutoff - bill_ratio <= 2.013 ~ 'deep', - # Between 1st and 2rd Q - bill_ratio > 2.013 & bill_ratio <= 2.239 ~ 'average', - T ~ 'shallow' - )) - -head(penguins_newcolumn$bill_depth_cat, n = 75) -``` - -A note: Always be cautious about what might be left out when naming the -conditions. In the previous example, we assume that everything left after the -first two conditions would be shallow. To be on the safe side, I often set every -condition and leave the last condition either an error notice, or NA, so that I -can check my conditions. - -```r -penguins_newcolumn <- penguins_newcolumn %>% - mutate(bill_depth_cat = case_when( - # Using the 1st Q as cutoff - bill_ratio <= 2.013 ~ 'deep', - # Between 1st and 2rd Q - bill_ratio > 2.013 & bill_ratio <= 2.239 ~ 'average', - bill_ratio >= 2.239 ~ 'shallow', - T ~ 'ERROR' - )) -``` - -Then we can use the `unique` function to look at all of the unique values of -that new variable to check out work. - -```r -unique(penguins_newcolumn$bill_depth_cat) -``` - - -### Summary tables - -Often we want to aggregate data at certain levels to better understand -differences across groups. For instance, does flipper length differ by species? -Does body mass change between years? We can combine the `group_by` and -`summarize` functions to help answer these kinds of questions. `group_by` sets a -column (or columns) by which you want to perform a summarizing function on, then -`summarize` creates a new variable by performing a variety of -summary/transformative functions on an existing column. - -First, we can use `summarize` on its own, without any grouping, to get a single -summary about the data frame. For example, if we want to know the mean body -mass: - -```r -# calculate the mean body mass for the whole data frame -summarize(penguins, mean_body_mass_g = mean(flipper_length_mm)) -``` - -If the result is NA, be sure to include the 'na.rm = TRUE' argument to tell the -function to 'remove NAs' before calculating: - -```r -# set na.rm = T -summarize(penguins, mean_flipper_length_mm = mean(flipper_length_mm, na.rm = TRUE)) -``` - -But if we want to know the mean flipper length across certain grouping -variables, such as sex, we want to combine summarize with `group_by` first. - -```r -penguins %>% - group_by(species) %>% - summarize(mean_flipper_length_mm = mean(flipper_length_mm, na.rm = TRUE)) -``` - -These functions are powerful. We can group by multiple columns at once AND -summarize can create several new summary variables within the same summarize -function, just by separating arguments by a comma in both cases. For instance, -we can group by species and sex, and generate two summary variables of mean and -standard deviation flipper length: - -```r -penguins %>% - group_by(species,sex) %>% - summarize(mean_flipper_length_mm = mean(flipper_length_mm, na.rm = TRUE), - sd_flipper_length_mm = sd(flipper_length_mm, na.rm = TRUE)) -``` - - -### Exercise - -Use summarize to calculate the mean and standard deviation for each year-sex -group combination. Before calculating those summaries, filter out the -observations where there is no data for penguin sex. Name this new summary table -'summary_challenge'. What is the mean and standard deviation of the body mass -for male penguins in 2008? - -
    -Check your answer -```r -summary_challenge <- penguins %>% - filter(!is.na(sex)) %>% - group_by(year,sex) %>% - summarize(mean_body_mass_g = mean(body_mass_g, na.rm = T), - sd_body_mass_g = sd(body_mass_g, na.rm = T)) -summary_challenge -# mean: 4632.456 sd: 722.0687 -``` -
    diff --git a/_episodes/05_further_wrangling.md b/_episodes/05_further_wrangling.md deleted file mode 100644 index 06903b4..0000000 --- a/_episodes/05_further_wrangling.md +++ /dev/null @@ -1,447 +0,0 @@ ---- -layout: page -title: "More Advanced Data Wrangling" -order: 5 -session: TODO -length: TODO -toc: true -adapted: true -attrib_name: "Tidyverse: Data wrangling & visualization - Data wrangling: beyond basics" -attrib_link: https://liza-wood.github.io/tidyverse_intro/data-wrangling-beyond-basics.html -attrib_license: Creative Commons BY-NC-SA 4.0 -attrib_license_link: https://creativecommons.org/licenses/by-nc-sa/4.0/ ---- - - -## Outline / cheat sheet - -- Manipulating date variables: - - `lubridate` package: Use date format functions (e.g. `ymd`) to specify date - data formats and extract particular date-related data. -- Joining multiple dataframes: - - `*_join` series: Combine two dataframes by one or more shared columns. - Specify with kind of join (`full_join`, `left_join`, etc.) to determine - which observations are kept/dropped. -- Reshaping (lengthening/widening) data: - - `pivot_longer`: Take values that are spread across multiple columns and - 'lengthen' the data by stacking those columns on top of one another - - `pivot_wider`: Take 'long' values stacked into one column and 'widen' the - data by spreading them out -- Working with text: - - `separate`: Divide the contents of one column into multiple based on - particular symbols. - - `stringr` package: Identify patterns using 'regular expressions' and apply a - series of commands (e.g. remove, extract, detect) to manipulate text data. - - `paste`: combine contents from multiple columns (or external text). - - -## Manipulating date variables - -Remember the weather data we loaded in at the start. We have weather data from -the Palmer Station in Antarctica from 1989 - 2019, where data are available for -each day. - -```r -library(tidyverse) -library(palmerpenguins) -weather <- read.csv("https://pasta.lternet.edu/package/data/eml/knb-lter-pal/28/8/375b34051b162d84516ec2d02f864675") -``` - -```r -str(weather) -``` - -However, the structure of the data above indicate that the first variable, Date, -is a 'character'. This means that R is not reading these dates as numbers, and -so it may be hard to manipulate them any further (e.g. extracting certain -years). - -```r -head(weather$Date) -class(weather$Date) -``` - -We want to use the `lubridate` package (loaded in when we load in `tidyverse`) -to convert these variables into dates and create a column for the year. This -package is handy because it has very intuitive function names and argument -structures. For instance, you can convert a character type into a date type by -using a function with letters representing the date format. You can see all of -these formats by specifically calling upon the `lubridate` package and then using -semicolons to call upon the functions, such as those starting with 'y'. - -```r -lubridate::y -``` - -In `lubridate`, y = year, m = month, d = day, h = hour, m = minute, s = second, etc. - -We can see that our data is formatted in the year-month-day format, and so we -can use the `ymd` function to indicate to R what format we want it to detect as -it converts characters to dates. If we use this function and assign the output -to a date vector, we can then evaluate the class to see that it is a date. - -```r -date_vector <- ymd(weather$Date) -class(date_vector) -``` - -It is important to remember that manipulating a column does not mean it -automatically saves into the dataframe. Instead, we need to remember to write -(or overwrite) that column into our dataframe. Remember that we can use the -mutate function to do this, where we can overwrite out date column. Note two -different ways of doing this. First, we could use the `ymd` function directly in -our mutate function: - -```r -weather <- mutate(weather, Date = ymd(Date)) -``` - -Or because we have saved the new date data as its own vector, we can feed that -vector into the dataframe as is. - -```r -weather <- mutate(weather, Date = date_vector) -``` - -Now we have our date data saved in our dataframe. - -```r -class(weather$Date) -``` - -Now that R recognizes these as dates, we can extract certain date-related -features, such as the year, month, and day. The lubridate package has functions -conveniently named `year`, `month`, `day` that can be used with a date argument, -and that element of the date will be extracted. For example: - -```r -head(year(weather$Date), n = 75) -``` - -Again, let's use mutate to assign this as a new column - -```r -weather <- mutate(weather, year = year(Date)) -``` - -### Exercise - -We are going to review our basic wrangling skills to prepare the Palmer Station -weather data to merge with the penguins data. - -We would like to merge a _summary_ of the weather data with the penguins. For -this summary, we want the weather data summarized by year, rather than by day -(as it currently is). Then per year, we want to know the maximum, minimum, and -average temperatures. Create this summary table called `weather_summary`. - -
    -Check your answer - -```r -weather_summary <- weather %>% - group_by(year) %>% - summarize(average_temp = mean(Temperature.Average..C., na.rm = T), - max_temp = max(Temperature.High..C., na.rm = T), - min_temp = min(Temperature.Low..C., na.rm = T)) - -``` - -
    - -## Joining multiple dataframes - -In your data wrangling journey, you will often find yourself wanting to combine -one dataframe with some kind of supplementary or partner dataframe. In our -case, we have the penguins and weather data stored separately, but if we ever -wanted to explore any relationships between them, we'd likely want all of that -data stored in one place so that we can line up data points. This lining up of -data points is called **joining** in the tidyverse language. - -The key to joining is to identify the variables by which you want to join the -data. In other words, which columns in each data are the ones that link them -together? In some cases these may be one-to-one matches (e.g. ID numbers to IDs -numbers), or in other cases there is data at different levels that need to be -lined up. - -By default, `*_join` functions will link the data together based on all of the -shared column names, but usually you'll want to double check to make sure that -this will get you the desired output. Typically you can do this by just checking -out the column names of each dataframe (for this exercise we will use the -`weather_summary` object created in the last exercise). - -```r -colnames(penguins) -``` - -```r -colnames(weather_summary) -``` - -We have one column overlapping, year, which makes it the likely candidate for -joining by. But it is also important to think: what is it that actually links -penguins to weather? Penguins experience weather at any given time, and so we -will need to choose a shared time scale at which penguins are experiencing -weather and the scale of data we collected for weather. In the penguins data set -we only know the year that the observations were taken, so we're interested in -the annual weather summaries. This is why we are looking at the weather summary -object, so that we can align the data relatively well. - -Now, let's join. The join functions take three arguments: the two dataframes -you'd like to join, and the name of the column by which to join. There are also -several types of joins, which we can explore in the exercise, but for now let's -use a `left_join`: - -```r -penguins_leftjoined <- left_join(penguins, weather_summary, by = "year") -dim(penguins_leftjoined) -``` - -We go from 8 columns in the penguins data to 11 now that we've appended the -three weather-related columns to the data. And we stick with 344 rows because -that is the number of rows in our 'left' dataframe, penguins. These dimensions -might change depending on the kind of join you use. - - -### Exercise - -Go ahead and check out the documentation for join by using the help function, a -question mark: - -```r -?join -``` - -You'll see the variety of joins that exist in `dplyr`. Try to experiment with -them: What happens when you use another kind of join? Check how the dimensions -of the data change depending on the join you choose. - -
    -Check your answer - -```r -penguins_fulljoined <- full_join(penguins, weather_summary, by = "year") -dim(penguins_fulljoined) -# We now have more rows. Why? We had weather data for more years than we have penguin data, so we have a lot of weather data that is unmatched to any pengion observation -``` - -
    - - -## Reshaping (lengthening/widening) data - -There are often cases where you'd like to change the shape of your data. For -instance, you might want to turn several columns into two: one with the -measurements or values from the previous columns, and one with a categorical -indicator of what is represented in the new column. This is a case for pivoting -longer. - -Let's say we wanted to take the key measures of our penguins and stack them on -top of one another (this is not actually useful given our data, but it is the -practice that counts). When we stack all of these columns on top of one another -we are going to be creating two new columns: one with the names of the columns -and another with the values of the columns. In the `pivot_longer` arguments, it -looks like this: - -```r -# Pivot the data to make it longer -penguins_longer <- pivot_longer(penguins, - cols = c(bill_length_mm, bill_depth_mm, - flipper_length_mm, body_mass_g), - names_to = "measure", values_to = "value") -``` - -```r -head(penguins_longer) -``` - -The reverse scenario is having data within one column that you'd like to widen -out into several columns. Here we have to identify the column we'd like to -un-stack (`values_from`), how we'd like to name the new columns filled with the -widened values (`names_from`). Note that we don't need to put these column names -in quotes because they already exist in the dataframe! - -```r -penguins_wider <- pivot_wider(penguins_longer, - names_from = measure, - values_from = value) -``` - -BUT, we get a bit of an issue here. Our result has condensed many of the -measures down into list forms, leaving us with only 35 observations and several -repeated measured for each observation. Why? When `dplyr` is widening out the data -it wants to match the new columns to _unique observations_. So the function -created one row for each species-island-sex-year combination, thinking those are -unique. We can fix this by creating an ID column (id_col). - -Here want to specify what it is that actually identifies unique observations in -the data. Our issue is that we don't have unique identifiers in the data. -Whoops. Let's fix that and re-run these functions to see why this matters. - -First, let's assign a unique ID column to the original penguins data and then -re-run our `pivot_longer`: - -```r -penguins$ID <- 1:nrow(penguins) -penguins_longer <- pivot_longer(penguins, - cols = c(bill_length_mm, bill_depth_mm, - flipper_length_mm, body_mass_g), - names_to = "measure", values_to = "value") -``` - -```r -head(penguins_longer) -``` - -Now this will play an important part in the `pivot_wider`. Let's set ID as the -the `id_cols` argument in the function: - -```r -penguins_widerID <- pivot_wider(penguins_longer, - id_cols = ID, - names_from = measure, - values_from = value) -head(penguins_widerID) -``` - -Uh, close, but still some issues. What happened to all of our other columns? -Well, since we said ID was our only id column, it dropped everything else. So we -can actually pile everything into our ID column that we wan to keep. - -```{r} -penguins_widerID_allcols <- pivot_wider(penguins_longer, - id_cols = species:ID, - names_from = "measure", - values_from = "value") -head(penguins_widerID_allcols) -``` - -## Working with text - -Let's back up a bit to the cleaning process. We've been working on getting our -data in summary form, bringing in new data, and changing data shape, all of -which is pretty far down the data wrangling pipeline. But we are going to take -this last moment to talk about an earlier stage task: cleaning text data. - -When we first create and collect data, it may not be in its tidiest form. One -common messy data practice is to have multiple types of data in one column. -For instance, we can look at the 'raw' version of the penguins data, also -available through the package. Go ahead and read that in: - -```r -penguins_raw <- penguins_raw -``` - -Let's look at the third and sixth columns, for instance. - -```r -head(select(penguins_raw, 3,6)) -``` - -The Stage column is a good example of where multiple data points might be -combined into one cell as text. Here we have 'Adult, 1 Egg Stage' reported. But, -we might want to separate out the title of the penguin's stage, Adult, from -their egg stage count. For this we can use the `separate` function to divide the -contents of one column into multiple based on particular symbols. In this case, -there is a convention to separate the word Adult (or other stage) from the egg -count using a comma (,). - -For the `separate` function we need to enter a few arguments: the data, the -column to separate, the names of the new columns, and the separator symbol. Note -that the help file says that the separator symbol is "interpreted as a regular -expression" -- we'll get to to that in a second. - -```r -penguins_raw_sep <- separate(penguins_raw, Stage, c("age_cat", "egg_stage"), ',') -``` - -Now we can see that these two have replaced the previous Stage column which adds -one more column to the dataframe. - -```r -head(select(penguins_raw_sep, 4:7)) -``` - -Now, we could even go one step further. The data here are well organized and -uniform, meaning that separate works quite well. Every Stage column has a comma, -and even now in the egg_stage column there are three words, so we could separate -by a space `" "` and get three new columns. However, when data is messy we might -need some more powerful text manipulation tools. - -Enter: the `stringr` package and regular expressions. `stringr` is a package -providing a set of functions for manipulating "strings", also called text or -character data. Check out the great -stringr cheat sheet. -Part of what makes `stringr` so powerful is that it relies on patterns -interpreted as 'regular expressions' (or regex). Regex is its own pattern -language that allows you to generalize string patterns for great adaptability to -string cleaning. - -We will practice just one example, which is to extract the number from the -egg_stage column. The power of regex is that instead of specifying every -possible number that could exist in the column (1,2,3,4,5,6,7,8,9), there is a -symbol for numbers in the regex language, which is `\\d`. There is also a symbol -to quantify the number of symbols, where `?` is zero or one, `*` is zero or -more, `+` is one or more, etc. By combining `\\d+` we've created a regular -expression for the pattern: one or more digits. We can then use this in one of -the string functions, `str_extract`, with the two arguments it needs: the -character vector and the pattern. - -```r -str_extract(penguins_raw_sep$egg_stage, '\\d+') -``` - -Excellent. Our pattern worked and we've successfully extracted the number -pattern from the column. One tiny tweak is that the numbers are in quotation -marks, meaning that they are still character data. We can coerce them to numeric -once they are all numbers. - -```r -penguins_raw_sep$egg_no <- as.numeric(str_extract(penguins_raw_sep$egg_stage, - '\\d+')) -head(penguins_raw_sep$egg_no) -``` - -Finally, maybe we want to put things back together? The `paste` function is a -straightforward way to paste columns or values back together. The arguments for -paste are as many value you'd like to paste together, separated by a comma. For -instance: - -```r -head(paste(penguins_raw_sep$age_cat, penguins_raw_sep$egg_stage)) -``` - -And you can also mix and match data types and lengths, etc: - -```r -paste("Paste together items of different lengths, for example:", - 1:10, "different items") -``` - -### Exercise - -Try to separate the common name from the scientific name from the 'Species' -column in penguins_raw. E.g. separate 'Adelie Penguin (Pygoscelis adeliae)' into -two columns: 'Adelie Penguin' and 'Pygoscelis adeliae'. You may need to combine -`separate` with a `stringr` function (e.g. `str_remove`) depending on how you'd -like to deal with the parentheses. Note that parentheses in regular expressions -are special characters and will need to be treated as such both in the -`separate` and `stringr` functions -- see the cheat sheet! - -
    -Check your answer - -```r -# Separate by the first opening parentheses -penguins_raw <- separate(penguins_raw, Species, - c("common_name", "sci_name"), "\\(") -# Then remove the second closing parentheses with stringr -penguins_raw$sci_name <- str_remove(penguins_raw$sci_name, "\\)") -# I also like to use the base R function 'trimws' to trim white space -penguins_raw$common_name <- trimws(penguins_raw$common_name) -penguins_raw$sci_name <- trimws(penguins_raw$sci_name) -# Let's take a look -head(select(penguins_raw, common_name, sci_name)) -``` - -
    - From 24926fe8719fc135e798ddb6c237c073b8271dc0 Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Sat, 2 Mar 2024 12:30:27 +0000 Subject: [PATCH 36/50] Add installation instructions --- setup.md | 30 +++++++++++++++++++++++------- 1 file changed, 23 insertions(+), 7 deletions(-) diff --git a/setup.md b/setup.md index 770580a..a38204c 100644 --- a/setup.md +++ b/setup.md @@ -6,12 +6,28 @@ title: Installation Guide In order to fully participate in this course, you should make sure you have the following in place **before** the first workshop. -Install: +- Install R, version 4.1 or later. You can download the latest version for + a variety of operating systems from + https://cran.rstudio.com, + or adopt whatever method you usually use. + +- Install RStudio desktop. This can be downloaded from + https://posit.co/download/rstudio-desktop/. + We will use RStudio to work with R notebooks throughout the course. If you have + another way of working with R notebooks that you prefer, then you are welcome + to use this, but we recommend using RStudio if you're unsure. + +- Ensure you have following R packages installed: + - `tidyverse` + - `palmerpenguins` (This contains a dataset we'll use in examples during the + course.) + + Note: if you use a package management system, such as + renv, + then you may want to create a project-specific environment for the course + materials. + +- Download the course materials. These will be made available from this page + by Monday 11 March 2024 at the latest. -- R -- RStudio -- tidyverse package -- rmarkdown package -- The course notebooks -Download datasets from TODO. From fc9ca015e16ea5b80723baf3d672cd945d4efeaf Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Sat, 2 Mar 2024 13:11:49 +0000 Subject: [PATCH 37/50] Add acknowledgements --- R_notebooks/01_Introducing_Tidyverse.Rmd | 2 +- R_notebooks/06_Reshaping_data.Rmd | 2 +- R_notebooks/data/README | 6 +++++- _episodes/01_intro.md | 18 +++++++++++++++- acknowledgements.md | 26 ++++++++++++++++++++++-- index.md | 25 +++++++++++++++++++++++ 6 files changed, 73 insertions(+), 6 deletions(-) diff --git a/R_notebooks/01_Introducing_Tidyverse.Rmd b/R_notebooks/01_Introducing_Tidyverse.Rmd index b3b7d65..fb89117 100644 --- a/R_notebooks/01_Introducing_Tidyverse.Rmd +++ b/R_notebooks/01_Introducing_Tidyverse.Rmd @@ -151,7 +151,7 @@ course](https://gge-ucd.github.io/R-DAVIS/index.html), which draws heavily on ## Footnotes [^1]: Horst AM, Hill AP, Gorman KB (2020). palmerpenguins: Palmer Archipelago - (Antarctica) penguin data. R package version 0.1.0. + (Antarctica) penguin data. R package version 0.1.1. . doi: 10.5281/zenodo.3960218. [^2]: Wickham, H. (2014). Tidy Data. *Journal of Statistical Software*, diff --git a/R_notebooks/06_Reshaping_data.Rmd b/R_notebooks/06_Reshaping_data.Rmd index fa388ef..ff33c63 100644 --- a/R_notebooks/06_Reshaping_data.Rmd +++ b/R_notebooks/06_Reshaping_data.Rmd @@ -181,6 +181,6 @@ producing and making available their model output. ## Footnotes -[^1]: Taken from Global Carbon Budget 2023 (Friedlingstein et al., 2023b, ESSD). +[^1]: Taken from Global Carbon Budget 2023 (Friedlingstein et al., 2023, ESSD). Excel workbook obtained from https://globalcarbonbudgetdata.org/downloads/latest-data/National_Fossil_Carbon_Emissions_2023v1.0.xlsx (accessed 2024-02-28). \ No newline at end of file diff --git a/R_notebooks/data/README b/R_notebooks/data/README index c3410eb..298fd7b 100644 --- a/R_notebooks/data/README +++ b/R_notebooks/data/README @@ -48,6 +48,10 @@ National_Fossil_Carbon_Emissions_2023v1.0.xlsx Friedlingstein, P., O'Sullivan, M., Jones, M. W., Andrew, R. M., Bakker, D. C. E., Hauck, J., Landschützer, P., Le Quéré, C., Luijkx, I. T., Peters, G. P., Peters, W., Pongratz, J., Schwingshackl, C., Sitch, S., Canadell, J. G., Ciais, P., Jackson, R. B., Alin, S. R., Anthoni, P., Barbero, L., Bates, N. R., Becker, M., Bellouin, N., Decharme, B., Bopp, L., Brasika, I. B. M., Cadule, P., Chamberlain, M. A., Chandra, N., Chau, T.-T.-T., Chevallier, F., Chini, L. P., Cronin, M., Dou, X., Enyo, K., Evans, W., Falk, S., Feely, R. A., Feng, L., Ford, D. J., Gasser, T., Ghattas, J., Gkritzalis, T., Grassi, G., Gregor, L., Gruber, N., Gürses, Ö., Harris, I., Hefner, M., Heinke, J., Houghton, R. A., Hurtt, G. C., Iida, Y., Ilyina, T., Jacobson, A. R., Jain, A., Jarníková, T., Jersild, A., Jiang, F., Jin, Z., Joos, F., Kato, E., Keeling, R. F., Kennedy, D., Klein Goldewijk, K., Knauer, J., Korsbakken, J. I., Körtzinger, A., Lan, X., Lefèvre, N., Li, H., Liu, J., Liu, Z., Ma, L., Marland, G., Mayot, N., McGuire, P. C., McKinley, G. A., Meyer, G., Morgan, E. J., Munro, D. R., Nakaoka, S.-I., Niwa, Y., O'Brien, K. M., Olsen, A., Omar, A. M., Ono, T., Paulsen, M., Pierrot, D., Pocock, K., Poulter, B., Powis, C. M., Rehder, G., Resplandy, L., Robertson, E., Rödenbeck, C., Rosan, T. M., Schwinger, J., Séférian, R., Smallman, T. L., Smith, S. M., Sospedra-Alfonso, R., Sun, Q., Sutton, A. J., Sweeney, C., Takao, S., Tans, P. P., Tian, H., Tilbrook, B., Tsujino, H., Tubiello, F., van der Werf, G. R., van Ooijen, E., Wanninkhof, R., Watanabe, M., Wimart-Rousseau, C., Yang, D., Yang, X., Yuan, W., Yue, X., Zaehle, S., Zeng, J., and Zheng, B.: Global Carbon Budget 2023, Earth Syst. Sci. Data, 15, 5301–5369, https://doi.org/10.5194/essd-15-5301-2023, 2023 -Obtained from https://globalcarbonbudgetdata.org/downloads/latest-data/National_Fossil_Carbon_Emissions_2023v1.0.xlsx +Fossil CO2 emissions by country (territorial) data obtained from obtained +from https://globalcarbonbudgetdata.org/downloads/latest-data/National_Fossil_Carbon_Emissions_2023v1.0.xlsx (accessed 2024-02-28). +We acknowledge the Global Carbon Project, which is responsible for the Global +Carbon Budget and we thank the fossil carbon emissions modelling groups for +producing and making available their model output. diff --git a/_episodes/01_intro.md b/_episodes/01_intro.md index 11db745..aa6cff5 100644 --- a/_episodes/01_intro.md +++ b/_episodes/01_intro.md @@ -47,4 +47,20 @@ experiences and viewpoints. ## Acknowledgement -TODO \ No newline at end of file +This course was developed by +[members]({{ site.url }}/acknowledgements.html) of the +University of Exeter's Research Software Engineering Group +who are enthusiastic about sharing their skills with the wider research +community. + +If you have benefited in any way from this course and want to support its long term +sustainability then please take the time to complete our feedback survey, +recommend it to your colleagues, and enthuse about it to your senior leadership +team. + +This workshop is brought to you by: + +- The University of Exeter Research Software Engineering Group +- The Institute of Data Science and Artificial Intelligence +- The University of Exeter Researcher Development Programme +- The University of Exeter Doctoral College diff --git a/acknowledgements.md b/acknowledgements.md index 86a50d5..ceb25a5 100644 --- a/acknowledgements.md +++ b/acknowledgements.md @@ -4,8 +4,7 @@ title: Acknowledgements --- This workshop was developed by -Dr Thomas Hawes -with the help of TODO. +Dr Thomas Hawes. The course material is adapted from Eliza Wood's Tidyverse: Data wrangling & visualization @@ -14,3 +13,26 @@ course, which is licensed under This in itself is based on material from UC Davis's R-DAVIS course, which draws heavily on Carpentries R lessons. The penguin artwork is from Allison Horst @allison_horst. + +The following datasets are used throughout the course: + +- Horst AM, Hill AP, Gorman KB (2020). palmerpenguins: Palmer Archipelago + (Antarctica) penguin data. R package version 0.1.1. + https://allisonhorst.github.io/palmerpenguins/. + doi: 10.5281/zenodo.3960218. + +- Palmer Station Antarctica LTER. 2023. Daily weather averages for Palmer Station, + Antarctica (1989-2023) ver 9. Environmental Data Initiative. + https://doi.org/10.6073/pasta/3eefb45dbfb784c3cabe3690ea46fe9e + (accessed 2024-01-08). (Further details available from the data README in the + course materials and relevant course notebooks.) + +- Friedlingstein, P., et al. (2023). Global Carbon Budget 2023, Earth Syst. Sci. + Data, 15, 5301–5369, https://doi.org/10.5194/essd-15-5301-2023. + Fossil CO2 emissions by country (territorial) data obtained from + https://globalcarbonbudgetdata.org/downloads/latest-data/National_Fossil_Carbon_Emissions_2023v1.0.xlsx + (accessed 2024-02-28). We acknowledge the Global Carbon Project, which is + responsible for the Global Carbon Budget and we thank the fossil carbon + emissions modelling groups for producing and making available their model + output. (Further details available from the data README in the course + materials and relevant course notebooks.) diff --git a/index.md b/index.md index 5e8b2ee..7d66e17 100644 --- a/index.md +++ b/index.md @@ -108,6 +108,8 @@ This workshop is brought to you by: - The University of Exeter Researcher Development Programme - The University of Exeter Doctoral College +### Course material and datasets + The course material is adapted from Eliza Wood's Tidyverse: Data wrangling & visualization course, which is licensed under @@ -115,3 +117,26 @@ course, which is licensed under This in itself is based on material from UC Davis's R-DAVIS course, which draws heavily on Carpentries R lessons. The penguin artwork is from Allison Horst @allison_horst. + +The following datasets are used throughout the course: + +- Horst AM, Hill AP, Gorman KB (2020). palmerpenguins: Palmer Archipelago + (Antarctica) penguin data. R package version 0.1.1. + https://allisonhorst.github.io/palmerpenguins/. + doi: 10.5281/zenodo.3960218. + +- Palmer Station Antarctica LTER. 2023. Daily weather averages for Palmer Station, + Antarctica (1989-2023) ver 9. Environmental Data Initiative. + https://doi.org/10.6073/pasta/3eefb45dbfb784c3cabe3690ea46fe9e + (accessed 2024-01-08). (Further details available from the data README in the + course materials and relevant course notebooks.) + +- Friedlingstein, P., et al. (2023). Global Carbon Budget 2023, Earth Syst. Sci. + Data, 15, 5301–5369, https://doi.org/10.5194/essd-15-5301-2023. + Fossil CO2 emissions by country (territorial) data obtained from + https://globalcarbonbudgetdata.org/downloads/latest-data/National_Fossil_Carbon_Emissions_2023v1.0.xlsx + (accessed 2024-02-28). We acknowledge the Global Carbon Project, which is + responsible for the Global Carbon Budget and we thank the fossil carbon + emissions modelling groups for producing and making available their model + output. (Further details available from the data README in the course + materials and relevant course notebooks.) From d2a13c649945b79a199a735af270deea73931abf Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Sat, 2 Mar 2024 13:26:04 +0000 Subject: [PATCH 38/50] Update installation instructions Include mention of Company Portal. --- setup.md | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/setup.md b/setup.md index a38204c..e8c0bf7 100644 --- a/setup.md +++ b/setup.md @@ -6,12 +6,13 @@ title: Installation Guide In order to fully participate in this course, you should make sure you have the following in place **before** the first workshop. -- Install R, version 4.1 or later. You can download the latest version for - a variety of operating systems from +- Install R, version 4.1 or later. You can download this from the University's + Company Portal software repository. Alternatively, visit https://cran.rstudio.com, or adopt whatever method you usually use. -- Install RStudio desktop. This can be downloaded from +- Install RStudio desktop. You can download this from the University's + Company Portal software repository. Alternatively, visit https://posit.co/download/rstudio-desktop/. We will use RStudio to work with R notebooks throughout the course. If you have another way of working with R notebooks that you prefer, then you are welcome From 0d1e385dc09d3e9c9aee825c9ca763cc4e88927d Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Sat, 2 Mar 2024 13:30:30 +0000 Subject: [PATCH 39/50] Update course homepage --- index.md | 33 +++++++++++++++++++++++++++------ 1 file changed, 27 insertions(+), 6 deletions(-) diff --git a/index.md b/index.md index 7d66e17..8b23c78 100644 --- a/index.md +++ b/index.md @@ -6,19 +6,40 @@ title: Course Information ## Overview -TODO +Over two sessions, learn how to use the Tidyverse to work with data in R. The +Tidyverse is a collection of R packages designed to help you manipulate, clean +and analyse data. This course will introduce you to the functionality of several +key packages, while also discussing some of the conventions the Tidyverse uses +when working with data. By the end of the course, participants should feel +confident to continue using the Tidyverse to work with datasets in R. ## Course objectives -On completion of this series of workshops, participants will be able to: +On completion of this series of workshops, participants will: -TODO +- Be able to explain what is meant by the 'tidy' format (a widely recognised + convention for structuring data). + +- Know how to read tabular data from a file to a dataframe. + +- Be able to manipulate datasets, including creating new variables, filtering + data, summarising and sorting data. + +- Know how to join multiple datasets together and reshape data. + +- Learn how to work with strings and dates (as time permits). + +- Gain hands on experience working with R notebooks for analysis. ## Pre-requisite knowledge -TODO +This course is for participants who already have some programming experience +with R. It assumes familiarity with things like: creating and setting variables, +the basic data structures (dataframes, vectors, lists), using functions, running +R code and viewing console output, installing R packages and using R packages in +your own code. Experience with R notebooks is _not_ required. ## Workshop format @@ -34,8 +55,8 @@ The sessions will be delivered according to the following timetable. | Session | Dates | Time | Venue | | --- |--- |--- |--- | --- | -| Session 1 | TODO DATE | TODO TIME | TODO LOCATION | -| Session 2 | TODO DATE | TODO TIME | TODO LOCATION | +| Session 1 | 12 March 2024 | 1pm – 4pm | Training Room 4, Old Library, Streatham Campus | +| Session 2 | 19 March 2024 | 1pm – 4pm | Training Room 4, Old Library, Streatham Campus | Registration for this course is handled by From a4d6045b9a2c41e89b1d56422951aabb528d7884 Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Sat, 2 Mar 2024 13:30:44 +0000 Subject: [PATCH 40/50] Tweak section on tidyverse packages in notebook 1 --- R_notebooks/01_Introducing_Tidyverse.Rmd | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/R_notebooks/01_Introducing_Tidyverse.Rmd b/R_notebooks/01_Introducing_Tidyverse.Rmd index fb89117..b4d5db3 100644 --- a/R_notebooks/01_Introducing_Tidyverse.Rmd +++ b/R_notebooks/01_Introducing_Tidyverse.Rmd @@ -20,14 +20,15 @@ The Tidyverse is self-described as: > > — -When you install the `tidyverse` package, you install a suite of 9 packages (at -the time of writing). These are as follows; we've marked the ones this course +When you install the `tidyverse` package, you install a suite of packages. These +include the following packages; we've marked the ones this course will introduce with a ☺: - ☺ `readr`: Load 'rectangular' data into an R session (e.g. CSV files). - ☺ `dplyr`: Manipulate data (filtering, computing summaries, etc.) - ☺ `tidyr`: Reshape data (e.g. into 'tidy' format). - ☺ `stringr`: Working with strings. +- ☺ `lubridate`: Working with dates - `ggplot2`: Visualise data. - `tibble`: A modern refresh of the core R dataframe, used throughout Tidyverse packages. @@ -48,9 +49,6 @@ plotting and modelling. The benefits of the Tidyverse include: - Built around a consistent philosophy of how to structure data for analysis (the 'tidy' data format). -**Note**: We'll also be using another very popular package for working with -timestamps, called `lubridate`, that is part of the 'non-core' Tidyverse. - ## Dataframes / tibbles From 87298d1472a1b589c54dd1fdc4c5c9e2950f6711 Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Sat, 2 Mar 2024 13:33:06 +0000 Subject: [PATCH 41/50] Rename R project --- R_notebooks/{R_notebooks.Rproj => Working_with_data_in_R.Rproj} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename R_notebooks/{R_notebooks.Rproj => Working_with_data_in_R.Rproj} (100%) diff --git a/R_notebooks/R_notebooks.Rproj b/R_notebooks/Working_with_data_in_R.Rproj similarity index 100% rename from R_notebooks/R_notebooks.Rproj rename to R_notebooks/Working_with_data_in_R.Rproj From d568f58b4cd8b1f06af89fa9dab1afec9848b6c4 Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Sat, 2 Mar 2024 13:52:26 +0000 Subject: [PATCH 42/50] Tweak notebook titles To include relevant tidyverse packages. --- R_notebooks/02_Loading_data.Rmd | 4 ++-- R_notebooks/05_Combining_datasets.Rmd | 2 +- R_notebooks/06_Reshaping_data.Rmd | 2 +- R_notebooks/07_Strings_and_dates.Rmd | 2 +- 4 files changed, 5 insertions(+), 5 deletions(-) diff --git a/R_notebooks/02_Loading_data.Rmd b/R_notebooks/02_Loading_data.Rmd index cfb17fe..ff45f64 100644 --- a/R_notebooks/02_Loading_data.Rmd +++ b/R_notebooks/02_Loading_data.Rmd @@ -1,12 +1,12 @@ --- -title: "Loading data into R" +title: "Loading data into R with `readr`" output: html_notebook --- ## Outline - Loading data from a csv file -- Other sources (Excel workbook, URL) +- Other file formats (Excel workbook, Google Sheets) ## Introduction diff --git a/R_notebooks/05_Combining_datasets.Rmd b/R_notebooks/05_Combining_datasets.Rmd index 1f07785..8df4e63 100644 --- a/R_notebooks/05_Combining_datasets.Rmd +++ b/R_notebooks/05_Combining_datasets.Rmd @@ -1,5 +1,5 @@ --- -title: "Combining datasets" +title: "Combining datasets with `dplyr`" output: html_notebook --- diff --git a/R_notebooks/06_Reshaping_data.Rmd b/R_notebooks/06_Reshaping_data.Rmd index ff33c63..399bf95 100644 --- a/R_notebooks/06_Reshaping_data.Rmd +++ b/R_notebooks/06_Reshaping_data.Rmd @@ -1,5 +1,5 @@ --- -title: "Reshaping data" +title: "Reshaping data with `tidyr`" output: html_notebook --- diff --git a/R_notebooks/07_Strings_and_dates.Rmd b/R_notebooks/07_Strings_and_dates.Rmd index 179ae44..a82a6ea 100644 --- a/R_notebooks/07_Strings_and_dates.Rmd +++ b/R_notebooks/07_Strings_and_dates.Rmd @@ -1,5 +1,5 @@ --- -title: "Working with strings and dates" +title: "Working with strings and dates with `stringr` and `lubridate`" output: html_notebook --- From 408b0baf4d24d717f9cebdf965657188299e0e37 Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Sat, 2 Mar 2024 13:52:49 +0000 Subject: [PATCH 43/50] Add course note placeholders. --- _episodes/02_notebooks.md | 4 ++-- _episodes/03_tidyverse_intro.md | 18 ++++++++++++++++++ _episodes/04_loading_data.md | 18 ++++++++++++++++++ _episodes/05_manipulating_dataframe.md | 18 ++++++++++++++++++ _episodes/06_piping.md | 18 ++++++++++++++++++ _episodes/07_combining_datasets.md | 18 ++++++++++++++++++ _episodes/08_reshaping_data.md | 18 ++++++++++++++++++ _episodes/09_strings_and_dates.md | 18 ++++++++++++++++++ _episodes/10_summary.md | 18 ++++++++++++++++++ _episodes/{99_wrapup.md => 11_wrapup.md} | 2 +- 10 files changed, 147 insertions(+), 3 deletions(-) create mode 100644 _episodes/03_tidyverse_intro.md create mode 100644 _episodes/04_loading_data.md create mode 100644 _episodes/05_manipulating_dataframe.md create mode 100644 _episodes/06_piping.md create mode 100644 _episodes/07_combining_datasets.md create mode 100644 _episodes/08_reshaping_data.md create mode 100644 _episodes/09_strings_and_dates.md create mode 100644 _episodes/10_summary.md rename _episodes/{99_wrapup.md => 11_wrapup.md} (99%) diff --git a/_episodes/02_notebooks.md b/_episodes/02_notebooks.md index dac9101..c318a07 100644 --- a/_episodes/02_notebooks.md +++ b/_episodes/02_notebooks.md @@ -3,12 +3,12 @@ layout: page title: "Notebooks" order: 2 session: 1 -length: TODO +length: 5 toc: true adapted: false --- -## Learning Objectives +## Notebooks This course will be delivered by working through a series of R notebooks within the workshop sessions alongside the course instructor. These notebooks diff --git a/_episodes/03_tidyverse_intro.md b/_episodes/03_tidyverse_intro.md new file mode 100644 index 0000000..ed4745b --- /dev/null +++ b/_episodes/03_tidyverse_intro.md @@ -0,0 +1,18 @@ +--- +layout: page +title: "Introducing the Tidyverse" +order: 3 +session: 1 +length: TBC +toc: true +adapted: false +--- + +## Outline + +TODO + +## Notebook + +An R notebook with the material covered in this episode will be available after +the episode has been delivered. diff --git a/_episodes/04_loading_data.md b/_episodes/04_loading_data.md new file mode 100644 index 0000000..f8e75da --- /dev/null +++ b/_episodes/04_loading_data.md @@ -0,0 +1,18 @@ +--- +layout: page +title: "Loading data into R" +order: 4 +session: 1 +length: TBC +toc: true +adapted: false +--- + +## Outline + +TODO + +## Notebook + +An R notebook with the material covered in this episode will be available after +the episode has been delivered. diff --git a/_episodes/05_manipulating_dataframe.md b/_episodes/05_manipulating_dataframe.md new file mode 100644 index 0000000..1729912 --- /dev/null +++ b/_episodes/05_manipulating_dataframe.md @@ -0,0 +1,18 @@ +--- +layout: page +title: "Manipulating a dataframe with dplyr" +order: 5 +session: 1 +length: TBC +toc: true +adapted: false +--- + +## Outline + +TODO + +## Notebook + +An R notebook with the material covered in this episode will be available after +the episode has been delivered. diff --git a/_episodes/06_piping.md b/_episodes/06_piping.md new file mode 100644 index 0000000..478fd5a --- /dev/null +++ b/_episodes/06_piping.md @@ -0,0 +1,18 @@ +--- +layout: page +title: "Piping and functional style" +order: 6 +session: 1 +length: TBC +toc: true +adapted: false +--- + +## Outline + +TODO + +## Notebook + +An R notebook with the material covered in this episode will be available after +the episode has been delivered. diff --git a/_episodes/07_combining_datasets.md b/_episodes/07_combining_datasets.md new file mode 100644 index 0000000..e5e63ff --- /dev/null +++ b/_episodes/07_combining_datasets.md @@ -0,0 +1,18 @@ +--- +layout: page +title: "Combining datasets with dplyr" +order: 7 +session: 2 +length: TBC +toc: true +adapted: false +--- + +## Outline + +TODO + +## Notebook + +An R notebook with the material covered in this episode will be available after +the episode has been delivered. diff --git a/_episodes/08_reshaping_data.md b/_episodes/08_reshaping_data.md new file mode 100644 index 0000000..f3266f4 --- /dev/null +++ b/_episodes/08_reshaping_data.md @@ -0,0 +1,18 @@ +--- +layout: page +title: "Reshaping data with tidyr" +order: 8 +session: 2 +length: TBC +toc: true +adapted: false +--- + +## Outline + +TODO + +## Notebook + +An R notebook with the material covered in this episode will be available after +the episode has been delivered. diff --git a/_episodes/09_strings_and_dates.md b/_episodes/09_strings_and_dates.md new file mode 100644 index 0000000..db682e1 --- /dev/null +++ b/_episodes/09_strings_and_dates.md @@ -0,0 +1,18 @@ +--- +layout: page +title: "Working with strings and dates with stringr and lubridate" +order: 9 +session: 2 +length: TBC +toc: true +adapted: false +--- + +## Outline + +TODO + +## Notebook + +An R notebook with the material covered in this episode will be available after +the episode has been delivered. diff --git a/_episodes/10_summary.md b/_episodes/10_summary.md new file mode 100644 index 0000000..01d627e --- /dev/null +++ b/_episodes/10_summary.md @@ -0,0 +1,18 @@ +--- +layout: page +title: "Summary and next steps" +order: 10 +session: 2 +length: 10 +toc: true +adapted: false +--- + +## Outline + +TODO + +## Notebook + +An R notebook with the material covered in this episode will be available after +the episode has been delivered. diff --git a/_episodes/99_wrapup.md b/_episodes/11_wrapup.md similarity index 99% rename from _episodes/99_wrapup.md rename to _episodes/11_wrapup.md index 752ed3c..2040f81 100644 --- a/_episodes/99_wrapup.md +++ b/_episodes/11_wrapup.md @@ -1,7 +1,7 @@ --- layout: page title: Wrap up -order: 99 +order: 11 session: 2 length: 5 toc: true From e15f6d3e9f5bcc910c65d1aa15dc7cb467dbd742 Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Sat, 2 Mar 2024 13:57:55 +0000 Subject: [PATCH 44/50] Turn course notes off --- _config.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_config.yml b/_config.yml index 08f7b5a..0089ff9 100644 --- a/_config.yml +++ b/_config.yml @@ -6,7 +6,7 @@ description: A course on working with data in R using the Tidyverse. n_sessions: 2 prereq: NA url: https://uniexeterrse.github.io/r-data-course -notes_on: true +notes_on: false # Specify that things in the episodes collection should be output. collections: From e862b50bf22c35d4c106d232c4a10a977e39acf6 Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Sat, 2 Mar 2024 14:15:22 +0000 Subject: [PATCH 45/50] Add acknowledgements to README.md --- README.md | 40 +++++++++++++++++++++++++++++++++------- 1 file changed, 33 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index d9cf54c..ca34e5a 100644 --- a/README.md +++ b/README.md @@ -45,6 +45,17 @@ This will serve the site on . ## Acknowledgements +The source code in this repository for generating the +[Working with data in R](https://uniexeterrse.github.io/r-data-course/) +website is based on the University of Exeter `UoE-workshop-template` template +repository. This template was developed with help from this +[tutorial](https://github.com/evanwill/go-go-ghpages). It was inspired and +influenced by [The Carpentries' template](https://github.com/carpentries/workshop-template) +for creating websites for workshops. + + +### Course material and datasets + The course material is adapted from Eliza Wood's Tidyverse: Data wrangling & visualization course, which is licensed under @@ -53,10 +64,25 @@ This in itself is based on material from Carpentries R lessons. The penguin artwork is from Allison Horst @allison_horst. -The source code in this repository for generating the -[Working with data in R](https://uniexeterrse.github.io/r-data-course/) -website is based on the University of Exeter `UoE-workshop-template` template -repository. This template was developed with help from this -[tutorial](https://github.com/evanwill/go-go-ghpages). It was inspired and -influenced by [The Carpentries' template](https://github.com/carpentries/workshop-template) -for creating websites for workshops. +The following datasets are used throughout the course: + +- Horst AM, Hill AP, Gorman KB (2020). palmerpenguins: Palmer Archipelago + (Antarctica) penguin data. R package version 0.1.1. + https://allisonhorst.github.io/palmerpenguins/. + doi: 10.5281/zenodo.3960218. + +- Palmer Station Antarctica LTER. 2023. Daily weather averages for Palmer Station, + Antarctica (1989-2023) ver 9. Environmental Data Initiative. + https://doi.org/10.6073/pasta/3eefb45dbfb784c3cabe3690ea46fe9e + (accessed 2024-01-08). (Further details available from the data README in the + course materials and relevant course notebooks.) + +- Friedlingstein, P., et al. (2023). Global Carbon Budget 2023, Earth Syst. Sci. + Data, 15, 5301–5369, https://doi.org/10.5194/essd-15-5301-2023. + Fossil CO2 emissions by country (territorial) data obtained from + https://globalcarbonbudgetdata.org/downloads/latest-data/National_Fossil_Carbon_Emissions_2023v1.0.xlsx + (accessed 2024-02-28). We acknowledge the Global Carbon Project, which is + responsible for the Global Carbon Budget and we thank the fossil carbon + emissions modelling groups for producing and making available their model + output. (Further details available from the data README in the course + materials and relevant course notebooks.) From fa45e17a6f201b7d92e21ac4d3adb432a8c7a9e5 Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Mon, 4 Mar 2024 09:53:03 +0000 Subject: [PATCH 46/50] Add summary notebook --- R_notebooks/08_Summary.Rmd | 57 ++++++++++++++++++++++++++++++-------- 1 file changed, 46 insertions(+), 11 deletions(-) diff --git a/R_notebooks/08_Summary.Rmd b/R_notebooks/08_Summary.Rmd index 4e6a67d..1689bc6 100644 --- a/R_notebooks/08_Summary.Rmd +++ b/R_notebooks/08_Summary.Rmd @@ -3,14 +3,49 @@ title: "Summary and next steps" output: html_notebook --- -## Outline - -- Review of Tidyverse philosophy - - Tidy data - - Common interfaces - - Functional style -- Next steps / further resources - - Cheat sheets - - Package help and vignettes - - Visualsation with `ggplot2` - - R for Data Science book \ No newline at end of file + +## Review + +This course has provided an introduction to working with data using packages +in the Tidyverse. We looked at + +- Using `readr` to reading csv data with explicit column specifications, and + touched on other packages for reading other kinds of files e.g. Excel + workbooks. +- Using `dplyr` to manipulate data (selecting columns, filtering, summarising, + sorting, etc.) and for joining multiple dataframes together. +- Using `tidyr` to reshape data e.g. from 'matrix' format to tidy format. +- Working with strings and dates using the `stringr` and `lubridate` packages. + +We also discussed some key philosophical underpinnings of the Tidyverse: + +- Adopting the convention of putting data into _tidy_ format. +- The idea that the functions in Tidyverse packages are designed with very + consistent 'interfaces' e.g. taking in a dataframe and returning a new + dataframe. +- The 'functional' style of programming, where we can set up pipelines for + performing successive transformations of data. + + +## Next steps / further resources + +This course has given you a grounding in using the Tidyverse and you +now feel confident using it in your own work. To take your knowledge further, +the following extra resources are available: + +- Cheatsheets for several Tidyverse packages, including the ones we've covered + in this course, are available at . + These are a great way to quickly look up function(s) that help you perform + some concrete task. Often it's a good idea to look at the cheatsheet to know + what functions(s) you need, then use the help system in R to read the + documentation, or search the web for further advice. + +- The [R for Data Science book (2nd ed.)](https://r4ds.hadley.nz/) by Hadley + Wickham, Mine Çetinkaya-Rundel and Garrett Grolemund is a freely available, + online book which covers lots of aspects of working with and visualising data + using the Tidyverse in a way that's approachable for non-expert R programmers. + +- A natural follow up to this course would be to learn about plotting and + visualising data in R with the `ggplot2` package, which is part of the + Tidyverse. The R for Data Science book above is a great resource to start + with for learning how to plot with `ggplot2`. From b28891f314f9549ddfaac39edc48f2e1e69ede97 Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Mon, 4 Mar 2024 09:59:30 +0000 Subject: [PATCH 47/50] Modify final exercise in workbook 8 --- R_notebooks/07_Strings_and_dates.Rmd | 22 +++++----------------- 1 file changed, 5 insertions(+), 17 deletions(-) diff --git a/R_notebooks/07_Strings_and_dates.Rmd b/R_notebooks/07_Strings_and_dates.Rmd index a82a6ea..6c1f4c3 100644 --- a/R_notebooks/07_Strings_and_dates.Rmd +++ b/R_notebooks/07_Strings_and_dates.Rmd @@ -398,31 +398,19 @@ head(weather) |> #### Exercise: working with dates -We are going to review our basic wrangling skills to prepare the Palmer Station -weather data to merge with the penguins data. - -We would like to merge a _summary_ of the weather data with the penguins. For -this summary, we want the weather data summarized by year, rather than by day -(as it currently is). Specifically, we want to know the maximum, minimum, and -average temperatures for each year. Create this summary table called -`weather_summary`. +Create a summary of the weather data that gives the maximum, minimum, and +mean of the average temperatures for each **month** of the year. ```{r} -weather_summary <- weather |> - dplyr::group_by(Year) |> +weather |> + dplyr::mutate(Month = lubridate::month(Date)) |> + dplyr::group_by(Month) |> dplyr::summarize(Average_temp = mean(Temperature.Average..C., na.rm = TRUE), Max_temp = max(Temperature.High..C., na.rm = TRUE), Min_temp = min(Temperature.Low..C., na.rm = TRUE)) ``` -Finally, we'll write the summary weather data to a new CSV file -`./data/weather_annual.csv`. - -```{r} -readr::write_csv(weather_summary, "./data/weather_annual.csv", na = "") -``` - ## Acknowledgement From a1dc0d0632681809d4d6f98546075fa2fa47d79b Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Mon, 4 Mar 2024 11:37:13 +0000 Subject: [PATCH 48/50] Add resources page --- resources.md | 52 ++++++++++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 50 insertions(+), 2 deletions(-) diff --git a/resources.md b/resources.md index 5d1509c..ab84d46 100644 --- a/resources.md +++ b/resources.md @@ -3,6 +3,54 @@ layout: info_page title: Resources --- -## Resources for learning R and the Tidyverse +## Resources for learning R -TODO +This course assumes participants already have some programming experience +with R, assuming familiarity with things like: creating and setting variables, +the basic data structures (dataframes, vectors, lists), using functions and running +R code and viewing console output. You can refresh your memory of these concepts +by consulting +Introduction to R. + + +### Installing and using packages + +Participants will need to install some extra packages for this course. In +general, this can be done by using the `install.packages` function in R. For +example, to install the `dplyr` package, run the following within an R session: + +```r +install.packages("dplyr") +``` + +To then use this package in your own code (or in the R console), you have two options: + +1. Include the line `library(dplyr)` in your code to import all the functions (and + any other objects e.g. example dataframes) from the package for you to use. + For example, this would allow you to use the `mutate` function from `dplyr`. + +2. Use 'namespace' notation to access a specific function / object from within + the package. For example, to use the `mutate` function from `dplyr` without + importing the whole package, we can write `dplyr::mutate`. Note that you + don't need to run `library(dplyr)` for this to work. + + +## Further resources for learning the Tidyverse + +This course will give you a grounding in using the Tidyverse collection of +packages in R to work with data. You may wish to look at the following +supplementary materials after the course: + +- Cheatsheets for several Tidyverse packages, including the ones we've covered + in this course, are available at + https://posit.co/resources/cheatsheets/. + These are a great way to quickly look up function(s) that help you perform + some concrete task. Often it's a good idea to look at the cheatsheet to know + what functions(s) you need, then use the help system in R to read the + documentation, or search the web for further advice. + +- The R for Data Science book (2nd ed.) + by Hadley Wickham, Mine Çetinkaya-Rundel and Garrett Grolemund is a freely + available, online book which covers lots of aspects of working with and + visualising data using the Tidyverse in a way that's approachable for + non-expert R programmers. From f57ed26a71a3f7d7ec790813c858c11d844a767a Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Mon, 4 Mar 2024 11:51:45 +0000 Subject: [PATCH 49/50] Tweak installation instructions --- setup.md | 20 +++++++++++--------- 1 file changed, 11 insertions(+), 9 deletions(-) diff --git a/setup.md b/setup.md index e8c0bf7..50b1cff 100644 --- a/setup.md +++ b/setup.md @@ -3,15 +3,16 @@ layout: info_page title: Installation Guide --- -In order to fully participate in this course, you should make sure you -have the following in place **before** the first workshop. +In order to fully participate in this course, you should bring a laptop to work +on during the sessions and make sure you have the following installed on it +**before** the first session. -- Install R, version 4.1 or later. You can download this from the University's +- R, version 4.1 or later. You can install this from the University's Company Portal software repository. Alternatively, visit https://cran.rstudio.com, or adopt whatever method you usually use. -- Install RStudio desktop. You can download this from the University's +- RStudio desktop. You can download this from the University's Company Portal software repository. Alternatively, visit https://posit.co/download/rstudio-desktop/. We will use RStudio to work with R notebooks throughout the course. If you have @@ -23,12 +24,13 @@ have the following in place **before** the first workshop. - `palmerpenguins` (This contains a dataset we'll use in examples during the course.) - Note: if you use a package management system, such as + If you need a refresher on installing R packages, consult the + [resources page]({{ site.url }}/resources.html). (Note: if you use a package + management system for managing your R packages, such as renv, - then you may want to create a project-specific environment for the course - materials. + then you are of course free to use this.) -- Download the course materials. These will be made available from this page - by Monday 11 March 2024 at the latest. +Finally, you will also need to **download the course materials**. These will be +made available from this page by Monday 11 March 2024 at the latest. From 88baa631a27a3be84b115728288c9151cd54a10b Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Mon, 4 Mar 2024 11:55:57 +0000 Subject: [PATCH 50/50] Remove notebooks for first website release --- R_notebooks/01_Introducing_Tidyverse.Rmd | 156 - R_notebooks/02_Loading_data.Rmd | 130 - R_notebooks/03_Manipulating_a_dataframe.Rmd | 431 - R_notebooks/04_Piping.Rmd | 132 - R_notebooks/05_Combining_datasets.Rmd | 214 - R_notebooks/06_Reshaping_data.Rmd | 186 - R_notebooks/07_Strings_and_dates.Rmd | 431 - R_notebooks/08_Summary.Rmd | 51 - R_notebooks/Working_with_data_in_R.Rproj | 18 - ...onal_Fossil_Carbon_Emissions_2023v1.0.xlsx | Bin 737973 -> 0 bytes .../data/PalmerStation_Daily_Weather.csv | 12478 ---------------- R_notebooks/data/README | 57 - R_notebooks/data/heads_and_tales.csv | 14 - R_notebooks/data/timeseries_data.csv | 11 - R_notebooks/data/weather_annual.csv | 36 - R_notebooks/renv.lock | 756 - 16 files changed, 15101 deletions(-) delete mode 100644 R_notebooks/01_Introducing_Tidyverse.Rmd delete mode 100644 R_notebooks/02_Loading_data.Rmd delete mode 100644 R_notebooks/03_Manipulating_a_dataframe.Rmd delete mode 100644 R_notebooks/04_Piping.Rmd delete mode 100644 R_notebooks/05_Combining_datasets.Rmd delete mode 100644 R_notebooks/06_Reshaping_data.Rmd delete mode 100644 R_notebooks/07_Strings_and_dates.Rmd delete mode 100644 R_notebooks/08_Summary.Rmd delete mode 100644 R_notebooks/Working_with_data_in_R.Rproj delete mode 100644 R_notebooks/data/National_Fossil_Carbon_Emissions_2023v1.0.xlsx delete mode 100644 R_notebooks/data/PalmerStation_Daily_Weather.csv delete mode 100644 R_notebooks/data/README delete mode 100644 R_notebooks/data/heads_and_tales.csv delete mode 100644 R_notebooks/data/timeseries_data.csv delete mode 100644 R_notebooks/data/weather_annual.csv delete mode 100644 R_notebooks/renv.lock diff --git a/R_notebooks/01_Introducing_Tidyverse.Rmd b/R_notebooks/01_Introducing_Tidyverse.Rmd deleted file mode 100644 index b4d5db3..0000000 --- a/R_notebooks/01_Introducing_Tidyverse.Rmd +++ /dev/null @@ -1,156 +0,0 @@ ---- -title: "Introducing the Tidyverse" -output: html_notebook ---- - -## Outline - -- What is the Tidyverse? -- Dataframes / tibbles -- Tidy data format - - -## What is the Tidyverse? - -The Tidyverse is self-described as: - -> "...an opinionated collection of R packages designed for data science. All -> packages share an underlying design philosophy, grammar, and data -> structures'." -> -> — - -When you install the `tidyverse` package, you install a suite of packages. These -include the following packages; we've marked the ones this course -will introduce with a ☺: - -- ☺ `readr`: Load 'rectangular' data into an R session (e.g. CSV files). -- ☺ `dplyr`: Manipulate data (filtering, computing summaries, etc.) -- ☺ `tidyr`: Reshape data (e.g. into 'tidy' format). -- ☺ `stringr`: Working with strings. -- ☺ `lubridate`: Working with dates -- `ggplot2`: Visualise data. -- `tibble`: A modern refresh of the core R dataframe, used throughout - Tidyverse packages. -- `forcats`: Tools for working with R 'factors', often used for categorical - data. -- `purrr`: Tools for working with R objects in a functional way. - -These are designed to help you work with data, from cleaning and manipulation to -plotting and modelling. The benefits of the Tidyverse include: - -- They are increasingly popular with large user bases (good for support / - advice). -- The packages are generally very well-documented. -- The packages are designed to work together seamlessly and operate well with - many other modern R packages. -- Provide features to help you write expressive code and avoid common pitfalls - when working with data. -- Built around a consistent philosophy of how to structure data for analysis - (the 'tidy' data format). - - -## Dataframes / tibbles - -The key data structure that Tidyverse packages are designed to work with is the -*dataframe*. - -Actually, this is not quite the whole story. As you read the documentation for -Tidyverse packages, you'll inevitably come across the term *tibble*. This is -essentially the same thing as a dataframe, although there are some minor -differences. For the purposes of this course, you can consider dataframes and -tibbles to be interchangeable and mentally think of them as 'the same thing'. - -To demonstrate this, let's consider the Palmer Station penguins data related to -three species of Antarctic penguins from Horst, Hill, and Gorman [^1]. We'll -work with this dataset throughout this course. The data contains size -measurements for male and female adult foraging Adélie, Chinstrap, and Gentoo -penguins observed on islands in the Palmer Archipelago near Palmer Station, -Antarctica between 2007-2009. Data were collected and made available by -Dr Kristen Gorman and the Palmer Station Long Term Ecological Research (LTER) -Program. You can read more about the package on the -[`palmerpenguins` documentation website](https://allisonhorst.github.io/palmerpenguins/index.html). - -To load the dataset, we just import the `palmerpenguins` package and look at -the `penguins` object. - -```{r} -library(palmerpenguins) # loads `penguins` object -penguins -``` - -If we look closely, we see that the class of `penguins` (i.e. the kind of object -it is) is a tibble, indicated by the `"tbl_df"` in the output below: - -```{r} -class(penguins) -``` - - -## Tidy data - -*Tidy data* is a convention that specifies how to arrange our data into a table. -It states that - -- The columns of the table should correspond to the variables in the data. -- The rows of the table should correspond to observations (or samples) of the - variables in the data. - -Packages in the Tidyverse are designed to work with tidy data. - -You can read more about the tidy data format in [Hadley Wickham's Tidy -Data](https://doi.org/10.18637/jss.v059.i10) paper[^2]. - - -### Tidy data: Example - -Suppose we have a pet dog and a pet cat, and we want to study the average weight -of our pets by year, to see if there is some kind of association between the -species and the trend in weight variation over time. We could represent this in -a 'matrix-like' format, with the years corresponding to rows and species -corresponding to columns: - -| Year | Dog_weight_kg | Cat_weight_kg | -|:-----|:--------------|:--------------| -| 2021 | 21.4 | 8.7 | -| 2022 | 20.9 | 8.4 | -| 2023 | 21.8 | 8.1 | - -: Weights of pets by year (non-tidy format) - -However, this is not in a tidy format. The variables in our data we're -interested in studying are the year, animal species and weight, but there the -columns correspond to the weights for each kind of species. Instead, we should -have a separate column for the species: - -| Year | Species | Weight_kg | -|:-----|:--------|:----------| -| 2021 | dog | 21.4 | -| 2021 | cat | 8.7 | -| 2022 | dog | 20.9 | -| 2022 | cat | 8.4 | -| 2023 | dog | 21.8 | -| 2023 | cat | 8.1 | - -: Weights of pets by year (tidy format) - - -## Acknowledgement - -The material in this notebook is adapted from Eliza Wood's [Tidyverse: Data -wrangling & visualization](https://liza-wood.github.io/tidyverse_intro/) course, -which is licensed under [Creative Commons BY-NC-SA -4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/). This in itself is -based on material from [UC Davis's R-DAVIS -course](https://gge-ucd.github.io/R-DAVIS/index.html), which draws heavily on -[Carpentries](https://datacarpentry.org/R-ecology-lesson/) R lessons. - - -## Footnotes - -[^1]: Horst AM, Hill AP, Gorman KB (2020). palmerpenguins: Palmer Archipelago - (Antarctica) penguin data. R package version 0.1.1. - . doi: 10.5281/zenodo.3960218. - -[^2]: Wickham, H. (2014). Tidy Data. *Journal of Statistical Software*, - *59*(10), 1–23. diff --git a/R_notebooks/02_Loading_data.Rmd b/R_notebooks/02_Loading_data.Rmd deleted file mode 100644 index ff45f64..0000000 --- a/R_notebooks/02_Loading_data.Rmd +++ /dev/null @@ -1,130 +0,0 @@ ---- -title: "Loading data into R with `readr`" -output: html_notebook ---- - -## Outline - -- Loading data from a csv file -- Other file formats (Excel workbook, Google Sheets) - - -## Introduction - -The package `readr` provides functions for reading data from 'flat' files, -such as csv or other delimited files. The functionality provided by `readr` -offers some advantages over the base R functions available for performing the -same task. - - - -## Loading data from a csv file - -You may have used the R function `read.csv` to load data from a csv file into -a dataframe: - -```{r} -timeseries <- read.csv("./data/timeseries_data.csv") -print(timeseries) -``` - -This is fine in a lot of cases, however, note some assumptions have been made. -For example, in the above, the `Id` column is read as an integer and the `Date` -column is read in as a string. In fact, the original data file records the Id -with padded zeros, like so: - -``` -Id,Date,X_variable,Y_variable -001,2024-02-01,1.1967837194583961,7.900464364159182 -002,2024-02-02,-0.7507563344053951,12.690405000912477 -003,2024-02-03,-0.7850492689583279,10.280088829160382 -004,2024-02-04,-0.6185457481359723,10.073199260115578 -005,2024-02-05,0.6188251316696602,9.183892504016947 -006,2024-02-06,-1.295559729374875,4.830591718912964 -007,2024-02-07,-1.0478610080617479,11.950495760617637 -008,2024-02-08,1.2772821688688454,7.008456322119265 -009,2024-02-09,-0.06582643175389392,3.4034822391265145 -010,2024-02-10,-0.07455295463910701,11.6370557133845 -``` - -What we really want to is to read the Id as a string and the Date as a date -object. - -The `read_csv` from the `readr` package is more 'shouty' about assumptions made -when parsing the data, and can have better default interpretations. For example: - -```{r} -timeseries <- readr::read_csv("./data/timeseries_data.csv") -``` - -Notice how it has explicitly told us that - -- `Id` is parsed as a string (which is what we wanted!) -- `Date` is parsed as a date (more about dates later in the course) -- `X_variable` and `Y_variable` are parsed as doubles, as expected. - -In general, it's good practice to be explicit about how we parse data from -files, rather than assuming the defaults will work. For example, we've no way of -knowing whether in the future the `readr` package maintainers will decide in the -future to interpret padded numbers as in the `Id` column as integers. - -We can be explicit about how columns are parsed by using the `col_types` -optional argument. The general way to do this is to supply a list with names -being the column names and values being a _column specifier_, -`readr::col_()`: - -``` -readr::read_csv("path/to/data.csv", - col_types = list(colA = readr::col_logical(), # column colA is a boolean - colB = readr::col_integer(), # column colB is an integer - colC = readr::col_double(), # column colC is a floating point number - colD = readr::col_character(), # column colD is a string - colE = readr::col_date(), # column colE is a date - .default = readr::col_double() # columns are floating point numbers - # unless specified otherwise - )) -``` - -**Note:** For a full list of the available column specifiers, run -`vignette("readr")` in the R console. - -So, to specify that we want to read the `Id` column as a string, the `Date` -column as a date, and the remaining columns as floating point numbers: - -```{r} -timeseries <- readr::read_csv("./data/timeseries_data.csv", - col_types = list(Id = readr::col_character(), - Date = readr::col_date(), - .default = readr::col_double())) -print(timeseries) -``` - - -Notice how the informational message about parsing has gone away, because we -explicitly gave the types. - - -### Exercise: parsing data from files - -Change the above so that the `Id` column is parsed as an integer and the -`Date` column is parsed as a string. - -```{r} -timeseries <- readr::read_csv("./data/timeseries_data.csv", - col_types = list(Id = readr::col_integer(), - Date = readr::col_character(), - .default = readr::col_double())) -print(timeseries) -``` - - -## Other file formats - -There are related packages in the Tidyverse ecosystem that provide support for -reading data from file types other than csv: - -- `readxl`: package for reading data from Excel workbooks. -- `googlesheets4`: package for reading data from Google Sheets spreadsheets. - -We also mention here the `ncdf4` package for reading NetCDF files (not part of -the Tidyverse). \ No newline at end of file diff --git a/R_notebooks/03_Manipulating_a_dataframe.Rmd b/R_notebooks/03_Manipulating_a_dataframe.Rmd deleted file mode 100644 index 4445873..0000000 --- a/R_notebooks/03_Manipulating_a_dataframe.Rmd +++ /dev/null @@ -1,431 +0,0 @@ ---- -title: "Manipulating a dataframe with `dplyr`" -output: html_notebook ---- - -## Outline - -- Sub-setting data -- Creating new variables -- Sorting -- Summarising data - - -## Introduction - -The package `dplyr` from the Tidyverse contains functions for manipulating -dataframes. We will use the `penguins` dataset contained in the `palmerpenguins` -package. Note that this data is already in tidy format. - -```{r} -library(palmerpenguins) # loads `penguins` data -head(penguins) -``` - - -## Sub-setting data - -For taking subsets of data, we can use the functions `select` and -`filter` from `dplyr`: - -- `dplyr::select` is used to keep only specified columns of the dataframe, i.e. to - 'select' certain variables from the data. -- `dplyr::filter` is used to keep rows that meet some specified condition, i.e. to - 'filter' the observations in the data. - - -### Selecting columns - -To use `select`, name the columns to keep after supplying the dataframe -(or tibble), like so: - -``` -dplyr::select(data, column1, column2, ...) -``` - -**Note**: We _don't_ use strings to specify the columns! Instead, we write them -as if they were variables e.g. `dplyr::select(data, column1)` instead of -`dplyr::select(data, "column1")`. - -Let's first check the column names in our tibble: - -```{r} -colnames(penguins) -``` - -Now let's select the species, year, sex, and body mass columns: - -```{r} -# Select species, year, sex, and body mass -penguins_selected <- dplyr::select(penguins, species, year, sex, body_mass_g) - -head(penguins_selected) -``` - -The `select` function can also be used to _remove_ columns by using the `-` -operator. For example, we may want to remove only two columns, island and year: - -```{r} -# Remove the island and year columns by using `-`. -penguins_deselected <- dplyr::select(penguins, -island, -year) - -# Take a look at the columns -colnames(penguins_deselected) -``` - -### Filtering rows - -The `filter` function from `dplyr` is used to keep rows from a dataframe/tibble -that meet a _predicate condition_ in terms of the column values, i.e. a -statement that is either `TRUE` or `FALSE`. - - -#### Comparitive operators - -One way to create predicate conditions is to use the -[comparative operators](https://www.w3schools.com/r/r_operators.asp), which -should be familiar: - -``` -x == y : equal -x != y : not equal -x > y : greater than -x < y : less than -x >= y : greater than or equal -x <= y : less than or equal -``` - -For example, suppose we want to keep all penguin data only from the year -2008. In base R, we could accomplish this by indexing on a boolean vector -created using the `==` operator: - -```{r} -# Create boolean vector indicating where year = 2008 -is_year_2008 <- penguins$year == 2008 -head(is_year_2008, n = 75) -``` - -```{r} -# Index on this vector -head(penguins[is_year_2008,]) -``` - -The `filter` function works by specifying the predicate condition for filtering -in terms of the column names, like so: - -``` -dplyr::filter(data, condition) # keep rows satisfying condition -``` - -So to filter the penguins data to keep only the 2008 observations: - -```{r} -penguins_2008 <- dplyr::filter(penguins, year == 2008) -head(penguins_2008) -``` - - -#### Logical operators - -We can build more complicated predicate conditions by using the -[logical operators](https://www.w3schools.com/r/r_operators.asp) on columns: - -``` -A & B : A AND B e.g. col1 == 2 & col2 == 10 -A | B : A OR B e.g. col1 > 2 | col2 != 10 -!A : NOT A e.g. !(col1 < 2) -``` - -In addition, `filter` allows us to specify multiple AND operations by listing -out multiple predicate conditions: - -``` -dplyr::filter(data, condition1, condition2, ...) # keep rows satisfying - # condition1 AND condition2 AND ... -``` - -Some examples on the penguin data: - -```{r} -# Observations of male penguins from the year 2008: -print(dplyr::filter(penguins, sex == "male" & year == 2008)) # using & -print(dplyr::filter(penguins, sex == "male", year == 2008)) # listing multiple conditions in filter - -# Observations of penguins with bill length > 40mm or bill depth < 20mm -print(dplyr::filter(penguins, bill_length_mm > 40 | bill_depth_mm < 20)) - -# Observations except those of male penguins on the island of Biscoe -print(dplyr::filter(penguins, !(sex == "male" & island == "Biscoe"))) -``` - - -#### Filtering missing values - -There are other functions we can use to evaluate columns and get a true/false -output. An important one is `is.na()`. This function evaluates a column and -reports back a `TRUE` value when there is an `NA` in that column's row. For -example, we can use the `head()` function to look at the top 6 values of the -`sex` column, and see that there is an NA in the fourth row. - -```{r} -head(penguins$sex) -``` - -The `is.na` function evaluates the whole column and gives us `TRUE`s whenever it -sees an NA. Not surprisingly, we see a `TRUE` in the fourth observation. - -```{r} -head(is.na(penguins$sex)) -``` - -Since `is.na()` gives us a `TRUE`/`FALSE` vector, we can use it with `filter`. -The following gives us all rows where the sex column has missing data (i.e. is -set to `NA`): - -```{r} -dplyr::filter(penguins, is.na(sex)) -``` - -To do the reverse, i.e. keep only the observations where the sex value is not -missing, combine `is.na` with the NOT operator: - -```{r} -dplyr::filter(penguins, !is.na(sex)) -``` - -### Exercise: subsetting data - -Write code to extract data for Gentoo penguins with weight between 4.8kg and -5.2kg (inclusive). Only keep the species, sex and body mass columns in the -output. Give the numbers of resulting observations for male penguins, female -penguins and penguins with unknown sex. - -```{r} -penguins_subset <- dplyr::filter(penguins, - species == "Gentoo", - 4800 <= body_mass_g & body_mass_g <= 5200) -penguins_subset <- dplyr::select(penguins_subset, species, sex, body_mass_g) -penguins_subset -``` - -```{r} -cat("Number of male penguins:", nrow(dplyr::filter(penguins_subset, sex == "male")), "\n") -cat("Number of female penguins:", nrow(dplyr::filter(penguins_subset, sex == "female")), "\n") -cat("Number of penguins of unknown sex:", nrow(dplyr::filter(penguins_subset, is.na(sex))), "\n") -``` - - -## Creating new variables - -We may often want to make a new column with some updated or transformed value. -We can use the `mutate` function in `dplyr` for this: - -``` -# value1, value2,... are expressions, possibly involving column names - -dplyr::mutate(data, new_column1 = value1, new_column2 = value2, ...) -``` - -For example, to add a column of IDs for identifying each row in the data: - -```{r} -penguins_with_ids <- dplyr::mutate(penguins, id = 1:nrow(penguins)) - -# Optional: Put the ID column at the beginning of the dataframe -penguins_with_ids <- dplyr::relocate(penguins_with_ids, id, .before = species) - -head(penguins_with_ids) -``` - -Another example: if we wanted to calculate a new value, the ratio of bill length -to bill depth, we could do the following - -```{r} -dplyr::mutate(penguins, bill_ratio = bill_length_mm / bill_depth_mm) -``` - -**Note**: the output of mutate is not just a new column on its own, -but the whole dataframe with the new column appended. To update `penguins` to -have the new column, we need to overwrite it: - -```{r} -penguins <- dplyr::mutate(penguins, bill_ratio = bill_length_mm / bill_depth_mm) -colnames(penguins) -``` - - -### Exercise: creating variables - -Update the `penguins` dataframe so that it contains an ID column -_with IDs represented as strings_. - -```{r} -# Solution 1 -penguins <- dplyr::mutate(penguins, id = as.character(1:nrow(penguins))) - -# Solution 2 -penguins <- dplyr::mutate(penguins, id = 1:nrow(penguins)) # int IDs -penguins <- dplyr::mutate(penguins, id = as.character(id)) # update column to strings -``` - - -## Sorting observations - -We can sort the rows of data by the value of a particular column using the -`arrange` function in `dplyr`. By default, the sorting is performed in increasing -order, although we can use the `desc()` function on the column to sort in -descending order: - -``` -dplyr::arrange(data, col) # sort rows by ascending order of values from column - # `col` - -dplyr::arrange(data, dplyr::desc(col)) # sort rows by descending order of - # values from column `col` -``` - -For example, to sort the `penguins` data by descending value of flipper length: - -```{r} -penguins_sorted_flipper <- dplyr::arrange(penguins, dplyr::desc(flipper_length_mm)) -penguins_sorted_flipper -``` - - -### Exercise: sorting - -Sort the `penguins` data by the id column we created in the previous exercise. -Explain the resulting order on the id column. - -```{r} -penguins_sorted_id <- dplyr::arrange(penguins, id) -head(dplyr::select(penguins_sorted_id, id, species, year, bill_length_mm)) -``` - - -## Summarising / aggregating data - -Often we want to aggregate data at certain levels to better understand -differences across groups. For instance, does flipper length differ by species? -Does body mass change between years? - -First, we can use `summarise` on its own, without any grouping, to get a single -summary about the dataframe. Here are some examples using the body mass variable: - -```{r} -# Count number of observations -print(dplyr::summarise(penguins, num_rows = dplyr::n())) - -# Count number of different years in the data -print(dplyr::summarise(penguins, num_years = dplyr::n_distinct(year))) - -# Get the min and max year -print(dplyr::summarise(penguins, min_year = min(year), max_year = max(year))) - -# calculate the sum of body masses for the whole data frame -print(dplyr::summarise(penguins, sum_flipper_length_mm = sum(flipper_length_mm))) -``` - -If the result is `NA`, it's likely because the column contained missing values. -We include the `na.rm = TRUE` optional argument to tell `summarise` to remove -`NA`s before calculating: - -```{r} -# remove missing values -dplyr::summarise(penguins, sum_flipper_length_mm = sum(flipper_length_mm, na.rm = TRUE)) -``` - -More generally, we can compute summaries for subgroups of the data by combining -the `group_by` and `summarise` (or `summarize`) functions in `dplyr`. The steps -to do this are: - -1. First use `group_by` to declare the column (or columns) that you want to - group the data by. -2. Then use `summarise` to actually do the summarising on each of the subgroups - from step 1. The resulting dataframe will have one summary per subgroup. - -For example, to get the average flipper length of each sex: - -```{r} -# First group the data by sex -penguins_grouped_by_sex <- dplyr::group_by(penguins, sex) - -# Then find the mean flipper length for each sex -dplyr::summarise(penguins_grouped_by_sex, mean_flipper_length_mm = mean(flipper_length_mm, na.rm = TRUE)) -``` - - -These functions are powerful. We can group by multiple columns at once to create -pairwise groups. As we saw above, we can also create several summary variables -at the same time: - -``` -dplyr::group_by(data, col1, col2, ...) # group by combinations of values in - # col1, col2, ... - -# Compute multiple summaries on the same data -dplyr::summarise(grouped_data, summary1 = <...>, summary2 = <...>, ...) -``` - - -### Exercise: grouping and summarising - -Create a summary dataframe that gives the mean, range (i.e. `max - min`) -and standard deviation of the body mass for each species / sex combination, as -well as the number of observations in each group. - -```{r} -penguins_grouped <- dplyr::group_by(penguins, species, sex) -dplyr::summarise(penguins_grouped, - mean_body_mass_g = mean(body_mass_g, na.rm = TRUE), - sd_body_mass_g = sd(body_mass_g, na.rm = TRUE), - range_body_mass_g = max(body_mass_g, na.rm = TRUE) - min(body_mass_g, na.rm = TRUE)) -``` - - -### An unimportant detail: grouped dataframes - -`dplyr::group_by` returns a 'grouped dataframe' (`dplyr::grouped_df`). These -behave just like tibbles/dataframes except they have some extra information -about the grouping attached to them. - -When you group by multiple columns, the result of `summarise` will typically -also be a grouped dataframe. Usually, the groups are given by all columns except -the last one in the grouped dataframe supplied to `summarise`. By default, you -get a console message telling you on which columns the output is grouped by (see -the result of the exercise above). - -The upshot of this is that it allows us to do successive summaries without -having to keep regrouping the data. For example, the code below will produce a -'max of averages': - -```{r} -penguins_grouped <- dplyr::group_by(penguins, sex, species) # grouped by sex and species - -# Average mass for each species / sex combination -# Result is grouped by sex (see console output) -penguins_grouped_mass <- dplyr::summarise(penguins_grouped, - mean_body_mass_g = mean(body_mass_g, na.rm = TRUE)) -print(penguins_grouped_mass) - -# Max of the average mass for each species -penguins_max_species_avg_mass <- dplyr::summarise( - penguins_grouped_mass, - max_species_avg_body_mass_g = max(mean_body_mass_g, na.rm = TRUE) - ) -print(penguins_max_species_avg_mass) -``` - -In practice, you don't have to worry about this detail. If you want to get back -to a plain old tibble/dataframe, use the `dplyr::ungroup` function. - - -## Acknowledgement - -The material in this notebook is adapted from Eliza Wood's [Tidyverse: Data -wrangling & visualization](https://liza-wood.github.io/tidyverse_intro/) course, -which is licensed under [Creative Commons BY-NC-SA -4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/). This in itself is -based on material from [UC Davis's R-DAVIS -course](https://gge-ucd.github.io/R-DAVIS/index.html), which draws heavily on -[Carpentries](https://datacarpentry.org/R-ecology-lesson/) R lessons. \ No newline at end of file diff --git a/R_notebooks/04_Piping.Rmd b/R_notebooks/04_Piping.Rmd deleted file mode 100644 index 7ada745..0000000 --- a/R_notebooks/04_Piping.Rmd +++ /dev/null @@ -1,132 +0,0 @@ ---- -title: "Piping and functional style" -output: html_notebook ---- - -## Outline - -- The functional philosophy of Tidyverse -- Piping operator - - -## The Functional philosophy of Tidyverse - -The Tidyverse packages are built around a philosophy of having functions that -work well together. Let's look at some of the functions we've seen so far: - -| Package | Function | Data input type | Output type | -|---------|-------------|-----------------|-------------------| -| `tidyr` | `read_csv` | string | dataframe | -| `dplyr` | `filter` | dataframe | dataframe | -| `dplyr` | `select` | dataframe | dataframe | -| `dplyr` | `mutate` | dataframe | dataframe | -| `dplyr` | `relocate` | dataframe | dataframe | -| `dplyr` | `arrange` | dataframe | dataframe | -| `dplyr` | `summarise` | dataframe | dataframe | -| `dplyr` | `group_by` | dataframe | grouped dataframe | - -Notice how consistent these are in accepting a dataframe as input and returning -a dataframe (or something like a dataframe). It is this consistency that makes -it easy to compose functions. - -In addition, it's important to note that none of the functions we've seen so -far modify the input dataframe 'in place', they always return a new dataframe. -(This is even the case for the `mutate` function, despite the name!) This -encourages a 'pipeline' approach, where we successively take the output -of one function and feed it in as the input to another function. - - -## Piping operator - -Since version 4.1, R has had a pipe operator `|>` which facilitates chaining -function calls. - -**Note:** Before a pipe operator was introduced into the base R language, people -used another pipe operator `%>%` from the `magrittr` package. It is still very -common to see people using the `magrittr` pipe and it behaves in a similar way -to base R's `|>` (though there are some minor differences). You can use -whichever you like, though for this course we'll use the base R pipe. - -The pipe operator takes the value in the left hand side and 'feeds it into' the -_first_ argument of a function call on the right hand side. So, using -`dplyr::select` as an example, the following two expressions are equivalent ways -to select two columns `colA` and `colB` from a dataframe `data`: - -``` -dplyr::select(data, colA, colB) data |> dplyr::select(colA, colB) -``` - -Notice how it looks like the dataframe argument to `dplyr::select` is missing -in the second expression. The pipe takes care of putting `data` into the first -argument slot, and the remaining slots are available for all other arguments -(in this case, the column names `colA` and `colB`). - -We can now chain multiple pipe calls together, like so: - -``` -data |> dplyr::select(colA, colB) |> dplyr::filter(colA > 10) -``` - -The result of the whole of the expression to the right of the assignment `<-` -gets stored in the `transformed_data`. In words, the above - -1. Takes a variable `data` -2. Selects columns `colA` and `colB` -3. Takes the result of 2. and filters it to keep rows where `colA > 10` - - -Usually, to make it easier to read, people will put successive pipe calls -on new lines, like so: - -``` -data |> - dplyr::select(colA, colB) |> - dplyr::filter(colA > 10) -``` - -If we want to store the overall result in a new variable `data_transformed`, -we can do so by putting the assignment `data_transformed <- ` at the beginning -of the chain, like so: - -``` -data_transformed <- data |> - dplyr::select(colA, colB) |> - dplyr::filter(colA > 10) -``` - - -### Exercise: piping - -The following code calculates the average bill ratio for penguins observed in -the year 2009. - -```{r} -library(palmerpenguins) # loads `penguins` data - -# Select species, year, bill_length_mm, bill_depth_mm -penguins1 <- dplyr::select(penguins, year, species, bill_length_mm, bill_depth_mm) - -# Filter to keep year 2009 data only -penguins2 <- dplyr::filter(penguins1, year == 2009) - -# Compute new bill_ratio column -penguins3 <- dplyr::mutate(penguins2, bill_ratio = bill_length_mm / bill_depth_mm) - -# Compute average bill ratio -penguins4 <- dplyr::summarise(penguins3, mean_bill_ratio = mean(bill_ratio, na.rm = TRUE)) - -print(penguins4) -``` - -Rewrite this code to use the pipe operator instead of storing each intermediate -dataframe, storing the overall result in a new variable called `summarised`. - -```{r} -summarised <- penguins |> - dplyr::select(year, species, bill_length_mm, bill_depth_mm) |> - dplyr::filter(year == 2009) |> - dplyr::mutate(bill_ratio = bill_length_mm / bill_depth_mm) |> - dplyr::summarise(mean_bill_ratio = mean(bill_ratio, na.rm = TRUE)) - -print(summarised) -``` \ No newline at end of file diff --git a/R_notebooks/05_Combining_datasets.Rmd b/R_notebooks/05_Combining_datasets.Rmd deleted file mode 100644 index 8df4e63..0000000 --- a/R_notebooks/05_Combining_datasets.Rmd +++ /dev/null @@ -1,214 +0,0 @@ ---- -title: "Combining datasets with `dplyr`" -output: html_notebook ---- - -## Outline - -- Joining datasets - - -## Joining datasets - -In your data wrangling journey, you will often find yourself wanting to combine -one dataframe with some kind of supplementary or partner dataframe. In our -case, we have the penguins and weather data stored separately, but if we ever -wanted to explore any relationships between them, we'd ideally want it in a -single dataframe / table. This requires lining up the observations and variables -in the datasets appropriately, something which is accomplished by performing -appropriate **joins**. - -The key to joining is to identify the variables by which you want to join the -data. In other words, which columns in each data are the ones that link them -together? In some cases these may be one-to-one matches (e.g. ID numbers to IDs -numbers), or in other cases there is data at different levels that need to be -lined up. - -### Left joins - -There are several kinds of join function in `dplyr`, but we'll just focus on -`left_join` and leave you to explore the others for yourself. - -Like all the join functions, `left_join` takes three arguments: the two dataframes -you'd like to join, and the name of the column (or columns) by which to join. - -``` -dplyr::left_join(data_left, data_right, by = ) -``` - -The way `left_join` works is to match up the columns given in the `by` column -and create a new dataframe by pasting new columns from `data_right` alongside -the columns from `data_left`. We bring in as many rows from `data_right` as we -can. The 'left' in `left_join` indicates that we're keeping everything from the -'left' dataframe (i.e. the first one) and joining the other dataframe onto the -'left' one. - -It's helpful to use small, toy dataframes to explore how joins work (or remind -yourself after a period of time away). - -```{r} -df1 <- data.frame(colA = c(1, 2, 3, 4), - colB = c(2021, 2022, 2023, 2024), - colC = c('a', 'b', 'c', 'd')) - -df2 <- data.frame(colA = c(1, 3), - colD = c('foo', 'bar'), - colE = c('dog', 'cat')) -``` - -```{r} -df1 -``` - -```{r} -df2 -``` - -Now let's join `df2` onto `df1` by the column `colA`: - -```{r} -df1 |> - dplyr::left_join(df2, by = "colA") -``` - -Notice: - -- All the data from the left-hand dataframe, `df1`, is kept (columns `colA` -- `colC`). - -- The data from the right-hand dataframe `df2` has been brought over - for the rows where the 'by' column `colA` has matching values in the - left-hand dataframe `df1`. - -- The rows where there aren't matching values in the 'by' column `colA` have - missing values for the other right-hand dataframe columns (`colD` and `colE` - in this case). - - -### Exercise: left join practice - -Suppose now the right hand dataframe is as follows: - -```{r} -df2 <- data.frame(colA = c(1, 3, 2, 4, 5), - colD = c('foo', 'bar', 'baz', 'qux', 'foo'), - colE = c('dog', 'cat', 'mouse', 'rabbit', 'horse')) -``` - -Can you guess what the output of `dplyr::left_join(df1, df2, by = "colA")` will -be? Check your guess with code below. - -```{r} -df1 |> - dplyr::left_join(df2, by = "colA") -``` - -Now what if you swap the order of `df1` and `df2`? Guess the output of -`dplyr::left_join(df2, df1, by = "colA")` and check your answer below. - -```{r} -df2 |> - dplyr::left_join(df1, by = "colA") -``` - - -### Optional exercise: repeated keys - -The values in the 'by' column(s) are sometimes called _keys_ for the join. The -rules we described in the above example are not the whole story and can be -complicated by the presence of repeated keys. - -What do you think happens if there are repeated keys? For example, try to -guess the output of the following code: - -```{r} -df1 <- data.frame(colA = c(1, 2, 3, 3), # repeated key 3 - colB = c(2021, 2022, 2023, 2024), - colC = c('a', 'b', 'c', 'd')) - -df2 <- data.frame(colA = c(1, 3), - colD = c('foo', 'bar'), - colE = c('dog', 'cat')) - -df1 |> - dplyr::left_join(df2, by = "colA") -``` - -Now what if we swap the roles of `df1` and `df2`? - -```{r} -df2 |> - dplyr::left_join(df1, by = "colA") -``` - -#### Warning about doubles - -It's generally not a good idea to join on a 'by' column (or columns) -that contain doubles. This is because matching in the join will be done -by an exact equality test on the doubles, which can create strange -results due to numerical imprecision and be difficult to reproduce. Example: in -the following, two doubles that look distinct are considered equal to -`1 / 3 = 0.333...`, so 'match' on the `1 / 3` entry in data frame `x`. - -```{r} -x <- data.frame(colA = c(1 / 3, 2 / 3), - colB = c(1000, 2000)) - -y <- data.frame(colA = c(0.33333333333333331, 0.33333333333333334), - colB = c("foo", "bar")) - -dplyr::left_join(x, y, by = "colA") -``` - - -### Exercise: joining penguin and weather data - -Recall the penguin data from the `palmerpenguins` package. We're going to join -this with some annual weather data, taken from the Palmer Station in Antarctica -from 1989 - 2019[^1]. - -```{r} -library(palmerpenguins) # loads `penguins` data -weather <- readr::read_csv("./data/weather_annual.csv") - -penguins -weather -``` - -Use a join to create a single dataframe that has all the penguin data and -weather data combined. Hint: there are some subtleties to be aware of: - -- First think about which column(s) to join on -- a call to `dplyr::rename` - might be in order! - -- We should make sure that the 'by' column(s) to join on are of the same type. - Examine the kind of data in each dataframe closely and coerce if necessary! - -```{r} -weather_cleaned <- weather |> - dplyr::rename(year = Year) |> - dplyr::mutate(year = as.integer(year)) - -penguins |> - dplyr::left_join(weather_cleaned, by = "year") -``` - - -## Acknowledgement - -The material in this notebook is adapted from Eliza Wood's [Tidyverse: Data -wrangling & visualization](https://liza-wood.github.io/tidyverse_intro/) course, -which is licensed under [Creative Commons BY-NC-SA -4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/). This in itself is -based on material from [UC Davis's R-DAVIS -course](https://gge-ucd.github.io/R-DAVIS/index.html), which draws heavily on -[Carpentries](https://datacarpentry.org/R-ecology-lesson/) R lessons. - - -## Footnotes - -[^1]: The dataset was derived from the following dataset: Palmer Station - Antarctica LTER. 2023. Daily weather averages for Palmer - Station, Antarctica (1989-2023) ver 9. Environmental Data Initiative. - https://doi.org/10.6073/pasta/3eefb45dbfb784c3cabe3690ea46fe9e (Accessed - 2024-01-08). See the README in the data folder accompanying these - notebooks for more details. \ No newline at end of file diff --git a/R_notebooks/06_Reshaping_data.Rmd b/R_notebooks/06_Reshaping_data.Rmd deleted file mode 100644 index 399bf95..0000000 --- a/R_notebooks/06_Reshaping_data.Rmd +++ /dev/null @@ -1,186 +0,0 @@ ---- -title: "Reshaping data with `tidyr`" -output: html_notebook ---- - -## Outline - -- Converting to/from tidy format - -## Introduction - -There are often cases where you'd like to change the shape of your data, by -which we mean to change the structure of the columns and/or rows. The `tidyr` -package contains functions to support this. - -As an example dataset for reshaping, we'll use data on territorial fossil CO2 -emissions by country (Friedlingstein et al. 2023)[^1]. - -**Note:** To read the data, we'll use a call to the `read_excel` function from -the `readxl` package. We only touched on this in notebook 2, so don't worry -about understanding exactly how the data is read in. Treat this as a glimpse of -seeing the Tidyverse in action 'in the wild'. You can read up about `read_excel` -as extra homework, if you're keen :-) - -```{r} -emissions <- readxl::read_excel("data/National_Fossil_Carbon_Emissions_2023v1.0.xlsx", - sheet = "Territorial Emissions", - skip = 11) |> - dplyr::rename(Year = `...1`) |> # missing column name in data got assigned name `...1` when reading - dplyr::select(Year, World, Africa, Asia, `Central America`, Europe, - `Middle East`, `North America`, Oceania, `South America`, - Bunkers) |> - dplyr::mutate(Year = as.integer(Year)) - -print(emissions) -``` - -## Converting to/from tidy format - -One scenario for reshaping data is when we receive data that is not in tidy -format and want to transform it so that it is tidy. - -In the emissions data above, notice how each column other than 'Year' -corresponds to a region of the world (or the category 'Bunkers'): - -```{r} -colnames(emissions) -``` - -This is not in tidy format. The observations correspond to year-region pairs, -while the variable being measured is territorial emissions. But the data is -structured as a matrix, where to get the emissions value for a region and year -you look up the entry for the appropriate row and column. - -What we'd like to do is create a dataframe with columns 'Year', 'Country' and -'Territorial_emissions' columns, with rows corresponding to year-region pairs. - -### Matrix format to tidy format with `pivot_longer` - -We can use the function `pivot_longer` from the `tidyr` package to help us make -the required transformation. Like most of the Tidyverse functions, there are a -lot of possible parameters to use in this function, but we only need to use it -as follows: - -``` -tidyr::pivot_longer(data, , names_to = "Name_for_gathered_columns", values_to = "Variable_name") -``` - -where - -- `data` is the dataframe to transform. -- `` is a specification of the columns that should be - gathered into a new column. -- `names_to` defines the name of the new column to create, whose values will - be the names in ``. -- `values_to` is the name of a new column that contains the values that were - in the columns `` (i.e. the variable that the original - data was giving). - -**Note**: More specifically, `` is a 'tidy-select'. This is a -way of specifying collections of columns within the Tidyverse. Unfortunately -details of this are outside the scope of this course, but you can learn more by -running `?tidyr::tidyr_tidy_select` in the R console. We will explain our use of -'tidy-select' in code comments below. - -The behaviour of `pivot_longer` is much easier to see with an example. The -following creates a new column called 'Region' that contains all the column -names in `emissions` from 'World' to 'Bunkers'. The values from these columns -are put into a new column called 'Territorial_emissions'. - -```{r} -emissions_tidy <- emissions |> - tidyr::pivot_longer(World:Bunkers, # All columns from 'World' to 'Bunkers' - names_to = "Region", - values_to = "Territorial_emissions") - -print(emissions_tidy) -``` - -Notice how each row corresponds to a year-region pair. The -'Territorial_emissions' values line up with the year and region they -corresponded to in the original data. - -Notice that the number of rows is equal to the number of regions multiplied by -the number of years, as we'd expect: - -```{r} -cat("Number of rows in emissions_tidy:", nrow(emissions_tidy), "\n") - -# Use ncol(emissions) - 1 so not to include the year column -cat("Number of year-region pairs:", length(emissions$Year) * (ncol(emissions) - 1), "\n") -``` - -In general, `pivot_longer` is called such because it make the table of data -'longer' i.e. have more rows. - -#### Exercise: pivoting - -What do you think the output of the following `pivot_longer` will be? - -``` -emissions |> - tidyr::pivot_longer(!Africa, # this means all columns except 'Africa' - names_to = "Names_column", - values_to = "Territorial_emissions?") -``` - -Run the code below to see if you're correct! - -```{r} -emissions |> - tidyr::pivot_longer(!Africa, # this means all columns except 'Africa' - names_to = "Names_column", - values_to = "Territorial_emissions?") -``` - -What do values in the 'Africa' and 'Territorial_emissions?' columns correspond -to in the following output? - -```{r} -emissions |> - tidyr::pivot_longer(!Africa, # this means all columns except 'Africa' - names_to = "Names_column", - values_to = "Territorial_emissions?") |> - dplyr::filter(Names_column == "Year") -``` - -### Tidy format to matrix format with `pivot_wider` - -What if we want to go the other way, i.e. turn a tidy dataframe into a matrix -format? In that case, we can use `pivot_wider` from the `tidyr` package. - -``` -tidyr::pivot_wider(data, names_from = , values_from = ) -``` - -where - -- `data` is the dataframe to transform. -- `names_from` is the column whose values will be the names of new columns in - the new dataframe. -- `values_from` is the name of a column whose values will go into the 'cells' - of the new dataframe. - -To see this in action, the following use of `pivot_wider` will take our tidied -emissions data and put it back into the original 'matrix' format, where regions -corresponded to columns and the entries gave the emissions for each year-region -combination. - -```{r} -emissions_tidy |> - tidyr::pivot_wider(names_from = Region, values_from = Territorial_emissions) -``` - -## Acnowledgements - -We acknowledge the Global Carbon Project, which is responsible for the Global -Carbon Budget and we thank the fossil carbon emissions modelling groups for -producing and making available their model output. - - -## Footnotes - -[^1]: Taken from Global Carbon Budget 2023 (Friedlingstein et al., 2023, ESSD). - Excel workbook obtained from https://globalcarbonbudgetdata.org/downloads/latest-data/National_Fossil_Carbon_Emissions_2023v1.0.xlsx - (accessed 2024-02-28). \ No newline at end of file diff --git a/R_notebooks/07_Strings_and_dates.Rmd b/R_notebooks/07_Strings_and_dates.Rmd deleted file mode 100644 index 6c1f4c3..0000000 --- a/R_notebooks/07_Strings_and_dates.Rmd +++ /dev/null @@ -1,431 +0,0 @@ ---- -title: "Working with strings and dates with `stringr` and `lubridate`" -output: html_notebook ---- - -## Outline - -- Working with strings - - Cleaning strings - - Splitting / pasting strings - - Extracting substrings -- Working with dates - - Converting from strings - - Accessing timestamp components - - -## Introduction - -In this notebook we'll be looking at ways to work with two common kinds of -data: text data and dates/timestamps. When we first create and collect data, it -may not be in its tidiest form, especially when it comes to working with -text data and timestamps. The following packages provide lots of useful -functions for working with strings / timestamps: - -- `stringr`: working with strings (included with `tidyverse`). -- `lubridate`: working with dates/timestamps (a non-core Tidyverse package). - -To demonstrate these packages, we'll be working with two datasets: - -- A toy dataset consisting of some coin flipping experiments (thrilling stuff). -- Weather data from the Palmer Station in Antarctica from 1989 - 2019, where - data are available for each day[^1]. - -**Note** We'll be making use of the pipe operator `|>` throughout; see -the `04_Piping.Rmd` notebook. - - -## Working with text - heads and tails - -We have a toy dataset for getting familiar with working with text. The -`heads_and_tails.csv` file contains data on some coin flipping experiments -performed by two people, Joe Bloggs and Jane Doe. The experiments were performed -on different days and are split up into different parts (coin flipping is -tiring work). - -The data is not in a clean state: -- Joe and Jane's names are recorded in multiple ways -- The results are inconsistently formatted. -- The experiments look pretty consistent, but note that in some rows the date is - separated from the `part n` bit by a colon `:` whereas in other rows it's - separated by a space. - -```{r} -experiments <- readr::read_csv("./data/heads_and_tales.csv") -experiments -``` - -We'll some of the functions in `stringr` to clean up this dataset. - - -### Cleaning whitespace - -Before tidying up the columns, let's first rename the current columns to indicate -they contain the 'raw' values. We can use the function `rename` from the `dplyr` -package to do this: - -``` -dplyr::rename(new_col_name1 = old_col_name1, new_col_name1 = old_col_name1, ...) -``` - -```{r} -experiments <- experiments |> - dplyr::rename(Experiment_raw = Experiment, - Technician_raw = Technician, - Result_raw = Result) -print(colnames(experiments)) -``` - -Let's first tackle cleaning up the technician's names. Look at the unique values -in the Technician column: - -```{r} -unique(experiments$Technician_raw) -``` - -Let's make the case consistent. There are several `stringr` functions we could -use for this, of the form `str_to_*` - -```{r} -print(stringr::str_to_lower("aLbErt EinStEin")) # albert einstein -print(stringr::str_to_upper("aLbErt EinStEin")) # ALBERT EINSTEIN -print(stringr::str_to_title("aLbErt EinStEin")) # Albert Einstein -print(stringr::str_to_sentence("aLbErt EinStEin")) # Albert einstein -``` -Let's convert the technicians' names to title case. Do we now have consistent -names for Joe and Jane? - -```{r} -experiments <- experiments |> - dplyr::mutate(Technician = stringr::str_to_title(Technician_raw)) - -unique(experiments$Technician) -``` - -Not quite. We can replace the two spaces in the middle of "Joe Bloggs" with a -single space by using `stringr::str_squish`: - -```{r} -experiments <- experiments |> - dplyr::mutate(Technician = stringr::str_squish(Technician)) - -unique(experiments$Technician) -``` - -Note that a closely related `stringr` function, `str_trim`, can be used for only -removing leading and trailing whitespace, if for some reason we wanted to keep -all the whitespace between the words: - -```{r} -# \t = tab character, \n = new line character -cat("\tAda Lovelace \n") -cat(stringr::str_trim("\tAda Lovelace \n")) # "Ada Lovelace" -``` - -### Replacing characters / substrings - -Next, let's make the Result entries consistently formatted. We'll remove -the commas from the first few experiments and instead just have a string of -`H`s and `T`s. We can view this as replacing each comma with the empty string, -`""`. - -In general, to perform text replacement, we can use one of the following -`stringr` functions: - -```{r} -# Replace the *first* instance of "--" with ", " -print(stringr::str_replace("a--e--i--o--u", pattern = "--", replacement = ", ")) # a, e--i--o--u - -# Replace *all* instances of "--" with ", " -print(stringr::str_replace_all("a--e--i--o--u", pattern = "--", replacement = ", ")) # a, e, i, o, u -``` - -You may wonder why the parameter name 'pattern' is used for the string we want -to replace. The reason is that we actually need to supply a _regular expression_ -to match on. We'll come back to this. - -Let's now clean up the Result column by removing the commas: - -```{r} -experiments <- experiments |> - dplyr::mutate(Result = stringr::str_replace_all(Result_raw, ",", "")) - -experiments |> - dplyr::select(Result_raw, Result) -``` - - -### Exercise: replacing strings - -Put the Experiment column into a consistent format, so that the experiment is -recorded as `:part ` where `` is a date string and `` is a -number `1`, `2`, etc. - -```{r} -experiments <- experiments |> - dplyr::mutate(Experiment = stringr::str_replace(Experiment_raw, " part", ":part")) - -experiments |> - dplyr::select(Experiment_raw, Experiment) -``` - - -### Splitting and concatenating columns - -With the columns cleaned up, we no longer need the 'raw' columns, so let's -remove them: - -```{r} -experiments <- experiments |> - dplyr::select(-Experiment_raw, -Technician_raw, -Result_raw) -``` - -It would be good to have the Experiment column consist of a unique identifier -for each experiment. We could just give it an integer label, but let's do -something more sophisticated. Each row is uniquely identifiable from the -current Experiment string and the name of the Technician. So let's combine -these together to make a new ID string, as follows: - -``` -Joe Bloggs + 2024-01-04:part 2 --> 2024-01-04:2:JoeBloggs -``` - -Note that we've removed the `part ` string and removed the inner space in the -name. How can we accomplish this transformation in smaller steps? - -- First split the experiment label `2024-01-04:part 2` into two pieces: - `2024-01-04` and `2`. - -- Remove the space from the name, `Joe Bloggs` to `JoeBloggs`. - -- Paste the results together using colons: - `2024-01-04` + `2` + `JoeBloggs` --> `2024-01-04:2:JoeBloggs`. - -We know how to do the second step (use `str_replace`), but steps 1 and 3 are new. - -The trick is to do this column-wise in the dataframe. Se we'll -**make new columns** containing the pieces we want to paste together, and then -create a new column by pasting these two columns together. - -To perform the splitting, we can use the `separate` function from the -`tidyr` package (not `stringr`!) to split a column on a separator. The result is -one column for each piece of the split; we thus need to also provide names for -the new columns: - -``` -tidyr::separate(data, - col, - into = c("NewCol1", "NewCol2", ...), - sep = ) -``` - -To paste columns back together with a custom separator, we can use the -`str_c` function from `stringr`: - -```{r} -stringr::str_c("foo", "bar", "baz", sep = " -- ") # foo -- bar -- baz -``` - - -### Exercise: splitting and concatenating columns - -Use `tidyr::separate` and `stringr:str_c` to create a unique experiment ID by -pasting together: - -- The experiment date -- The sub-experiment number -- The technician's name (without a space) - -E.g. -``` -Joe Bloggs + 2024-01-04:part 2 --> 2024-01-04:2:JoeBloggs -``` - -Hint: don't try to do this all in one go. Instead, build it up step-by-step and -use the pipe operator `|>` join the steps together. - -```{r} -experiments |> - tidyr::separate(col = Experiment, - into = c("Experiment_date", "Sub_experiment"), - sep = ":part ") |> # don't forget the space! - dplyr::mutate(Experiment_id = stringr::str_c(Experiment_date, - Sub_experiment, - stringr::str_remove(Technician, " "), - sep = ":")) -``` - - -### Finding substrings - -How many of the experiments features a run of 5 heads or 5 tails? - -We can answer this by: -- For each row, determine whether it contains a run of 5 heads or 5 tails -- Filter on the result - -To perform the first step, we can use `str_detect` from `stringr`. This simply -returns `TRUE` or `FALSE` depending on whether it finds a given pattern in -a string: - -``` -stringr::str_detect(a_string, pattern) -``` - -The `pattern` argument is interpreted as a _regular expression_. We mentioned -this earlier when looking at `str_replace` / `str_replace_all`. Regular expressions -are a mini-language for specifying matches on strings. We're not going to go into -this much here, but they are useful to know about, especially if you work with -text data a lot; check out the great -[stringr cheat sheet](https://github.com/rstudio/cheatsheets/blob/main/strings.pdf) -for a summary, or consult the -[chapter on regular expressions in R for Data Science (2e)](https://r4ds.hadley.nz/regexps) -for an introduction. Here are some example regular expressions in action, using -`str_detect` to indicate whether the given string matches or not: - -```{r} -# Match on a string as-is (case sensitive) -stringr::str_detect("Regex is cool.", pattern = "ex") |> print() # true -stringr::str_detect("Regex is cool.", pattern = "COOL") |> print() # false - -# Match on 1 or more instances -stringr::str_detect("Regex is cool.", pattern = "o+") |> print() # true -stringr::str_detect("Regex is cool.", pattern = "coolcool+") |> print() # false - -# Regex-special characters need escaping e.g. to match on a period, use \\. -stringr::str_detect("Regex is cool.", pattern = "\\.") |> print() # true - -# Match on either one pattern or another with pipe | -stringr::str_detect("Regex is cool.", pattern = "cool|meh") |> print() # true -stringr::str_detect("Regex is meh.", pattern = "cool|meh") |> print() # true -stringr::str_detect("Regex is cool.", pattern = "meh|lame") |> print() # false - -# Match at the beginning of the string only with ^ -stringr::str_detect("Regex is cool.", pattern = "^Regex") |> print() # true -stringr::str_detect("Regex is cool.", pattern = "^cool") |> print() # false -``` - - -### Exercise: finding substrings - -With the help of `str_detect` and a suitable regular expression, write code to -work out how many experiments feature a run of at least 5 straight heads or 5 -straight tails. - -```{r} -experiments |> - dplyr::mutate(Has_run_of_5 = stringr::str_detect(Result, pattern = "HHHHH|TTTTT")) |> - dplyr::filter(Has_run_of_5) |> - nrow() -``` - -## Working with dates - -We have weather data from the Palmer Station in Antarctica from 1989 - 2019[^1], -where data are available for each day. - -```{r} -weather <- read.csv("./data/PalmerStation_Daily_Weather.csv") -str(weather) -``` - -However, the structure of the data above indicate that the first variable, Date, -is a 'character'. This means that R is not reading these dates as numbers, and -so it may be hard to manipulate them any further (e.g. extracting certain -years). - -```{r} -head(weather$Date) -class(weather$Date) -``` - -(**Note**: if we'd used `read_csv` from the `readr` package then these would -automatically have been coerced into dates. We're avoiding this for now to -demonstrate how to convert raw strings into dates.) - -We'll use the `lubridate` package (loaded in when we load in `tidyverse`) -to convert these variables into dates and create a column for the year. - - -### Converting from strings - -The `lubridate` package has very intuitive function names and argument -structures. For instance, you can convert a character type into a date type by -using a function with letters representing the date format. In `lubridate`, -y = year, m = month, d = day, h = hour, m = minute, s = second, etc. - -(You can see all of these formats by using the auto-complete box in RStudio, -e.g. to see all functions starting with 'y', begin typing `lubridate::y`.) - -We can see that our data is formatted in the year-month-day format, and so we -can use the `ymd` function to indicate to R what format we want it to detect as -it converts characters to dates. If we use this function and assign the output -to a date vector, we can then evaluate the class to see that it is a date. - -```{r} -date_vector <- lubridate::ymd(weather$Date) -class(date_vector) -``` - -Remember that manipulating a column does not mean it automatically saves into -the dataframe. Instead, we need to remember to write (or overwrite) that column -into our dataframe. Remember that we can use the mutate function to do this, -where we can overwrite out date column: - -```{r} -weather <- weather |> - dplyr::mutate(Date = lubridate::ymd(Date)) - -class(weather$Date) -``` - - -### Accessing timestamp components - -Now that R recognizes these as dates, we can extract certain date-related -features, such as the year, month, and day. The `lubridate` package has functions -conveniently named `year`, `month`, `day` that can be used with a date argument, -and that element of the date will be extracted. For example: - -```{r} -weather <- weather |> - dplyr::mutate(Year = lubridate::year(Date)) - -head(weather) |> - dplyr::select(Date, Year) -``` - - -#### Exercise: working with dates - -Create a summary of the weather data that gives the maximum, minimum, and -mean of the average temperatures for each **month** of the year. - -```{r} -weather |> - dplyr::mutate(Month = lubridate::month(Date)) |> - dplyr::group_by(Month) |> - dplyr::summarize(Average_temp = mean(Temperature.Average..C., na.rm = TRUE), - Max_temp = max(Temperature.High..C., na.rm = TRUE), - Min_temp = min(Temperature.Low..C., na.rm = TRUE)) - -``` - - -## Acknowledgement - -The material in this notebook is adapted from Eliza Wood's [Tidyverse: Data -wrangling & visualization](https://liza-wood.github.io/tidyverse_intro/) course, -which is licensed under [Creative Commons BY-NC-SA -4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/). This in itself is -based on material from [UC Davis's R-DAVIS -course](https://gge-ucd.github.io/R-DAVIS/index.html), which draws heavily on -[Carpentries](https://datacarpentry.org/R-ecology-lesson/) R lessons. - - -## Footnotes - -[^1]: Palmer Station Antarctica LTER. 2023. Daily weather averages for Palmer - Station, Antarctica (1989-2023) ver 9. Environmental Data Initiative. - https://doi.org/10.6073/pasta/3eefb45dbfb784c3cabe3690ea46fe9e (Accessed - 2024-01-08). \ No newline at end of file diff --git a/R_notebooks/08_Summary.Rmd b/R_notebooks/08_Summary.Rmd deleted file mode 100644 index 1689bc6..0000000 --- a/R_notebooks/08_Summary.Rmd +++ /dev/null @@ -1,51 +0,0 @@ ---- -title: "Summary and next steps" -output: html_notebook ---- - - -## Review - -This course has provided an introduction to working with data using packages -in the Tidyverse. We looked at - -- Using `readr` to reading csv data with explicit column specifications, and - touched on other packages for reading other kinds of files e.g. Excel - workbooks. -- Using `dplyr` to manipulate data (selecting columns, filtering, summarising, - sorting, etc.) and for joining multiple dataframes together. -- Using `tidyr` to reshape data e.g. from 'matrix' format to tidy format. -- Working with strings and dates using the `stringr` and `lubridate` packages. - -We also discussed some key philosophical underpinnings of the Tidyverse: - -- Adopting the convention of putting data into _tidy_ format. -- The idea that the functions in Tidyverse packages are designed with very - consistent 'interfaces' e.g. taking in a dataframe and returning a new - dataframe. -- The 'functional' style of programming, where we can set up pipelines for - performing successive transformations of data. - - -## Next steps / further resources - -This course has given you a grounding in using the Tidyverse and you -now feel confident using it in your own work. To take your knowledge further, -the following extra resources are available: - -- Cheatsheets for several Tidyverse packages, including the ones we've covered - in this course, are available at . - These are a great way to quickly look up function(s) that help you perform - some concrete task. Often it's a good idea to look at the cheatsheet to know - what functions(s) you need, then use the help system in R to read the - documentation, or search the web for further advice. - -- The [R for Data Science book (2nd ed.)](https://r4ds.hadley.nz/) by Hadley - Wickham, Mine Çetinkaya-Rundel and Garrett Grolemund is a freely available, - online book which covers lots of aspects of working with and visualising data - using the Tidyverse in a way that's approachable for non-expert R programmers. - -- A natural follow up to this course would be to learn about plotting and - visualising data in R with the `ggplot2` package, which is part of the - Tidyverse. The R for Data Science book above is a great resource to start - with for learning how to plot with `ggplot2`. diff --git a/R_notebooks/Working_with_data_in_R.Rproj b/R_notebooks/Working_with_data_in_R.Rproj deleted file mode 100644 index 6d8b9a6..0000000 --- a/R_notebooks/Working_with_data_in_R.Rproj +++ /dev/null @@ -1,18 +0,0 @@ -Version: 1.0 - -RestoreWorkspace: Default -SaveWorkspace: Default -AlwaysSaveHistory: Default - -EnableCodeIndexing: Yes -UseSpacesForTab: Yes -NumSpacesForTab: 2 -Encoding: UTF-8 - -RnwWeave: Sweave -LaTeX: pdfLaTeX - -MarkdownWrap: Column -MarkdownWrapAtColumn: 80 - -SpellingDictionary: en_GB diff --git a/R_notebooks/data/National_Fossil_Carbon_Emissions_2023v1.0.xlsx b/R_notebooks/data/National_Fossil_Carbon_Emissions_2023v1.0.xlsx deleted file mode 100644 index 58c0753b67749df21d8616301a03cfed6ecda533..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 737973 zcmeFYby!`^vM0Q8cLXO@J9o}I 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This will allow us to move the string and dates notebook to the end of the course. --- R_notebooks/05_Strings_and_dates.Rmd | 9 ++++++--- R_notebooks/06_Combining_datasets.Rmd | 15 +++++++++++++-- 2 files changed, 19 insertions(+), 5 deletions(-) diff --git a/R_notebooks/05_Strings_and_dates.Rmd b/R_notebooks/05_Strings_and_dates.Rmd index 4b0f987..179ae44 100644 --- a/R_notebooks/05_Strings_and_dates.Rmd +++ b/R_notebooks/05_Strings_and_dates.Rmd @@ -225,6 +225,8 @@ To paste columns back together with a custom separator, we can use the ```{r} stringr::str_c("foo", "bar", "baz", sep = " -- ") # foo -- bar -- baz ``` + + ### Exercise: splitting and concatenating columns Use `tidyr::separate` and `stringr:str_c` to create a unique experiment ID by @@ -319,7 +321,7 @@ experiments |> ## Working with dates -We have weather data from the Palmer Station in Antarctica from 1989 - 2019, +We have weather data from the Palmer Station in Antarctica from 1989 - 2019[^1], where data are available for each day. ```{r} @@ -401,8 +403,9 @@ weather data to merge with the penguins data. We would like to merge a _summary_ of the weather data with the penguins. For this summary, we want the weather data summarized by year, rather than by day -(as it currently is). Then per year, we want to know the maximum, minimum, and -average temperatures. Create this summary table called `weather_summary`. +(as it currently is). Specifically, we want to know the maximum, minimum, and +average temperatures for each year. Create this summary table called +`weather_summary`. ```{r} weather_summary <- weather |> diff --git a/R_notebooks/06_Combining_datasets.Rmd b/R_notebooks/06_Combining_datasets.Rmd index 94d1d92..1f07785 100644 --- a/R_notebooks/06_Combining_datasets.Rmd +++ b/R_notebooks/06_Combining_datasets.Rmd @@ -162,8 +162,9 @@ dplyr::left_join(x, y, by = "colA") ### Exercise: joining penguin and weather data -Recall the penguin data from the `palmerpenguins` package and the annual weather -data we created at the end of workbook `05_Strings_and_dates.Rmd`: +Recall the penguin data from the `palmerpenguins` package. We're going to join +this with some annual weather data, taken from the Palmer Station in Antarctica +from 1989 - 2019[^1]. ```{r} library(palmerpenguins) # loads `penguins` data @@ -201,3 +202,13 @@ which is licensed under [Creative Commons BY-NC-SA based on material from [UC Davis's R-DAVIS course](https://gge-ucd.github.io/R-DAVIS/index.html), which draws heavily on [Carpentries](https://datacarpentry.org/R-ecology-lesson/) R lessons. + + +## Footnotes + +[^1]: The dataset was derived from the following dataset: Palmer Station + Antarctica LTER. 2023. Daily weather averages for Palmer + Station, Antarctica (1989-2023) ver 9. Environmental Data Initiative. + https://doi.org/10.6073/pasta/3eefb45dbfb784c3cabe3690ea46fe9e (Accessed + 2024-01-08). See the README in the data folder accompanying these + notebooks for more details. \ No newline at end of file From 5f5eee0f0e9e10fe87ef8cba991658ceb71fe8ec Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Thu, 29 Feb 2024 09:01:10 +0000 Subject: [PATCH 33/50] Reorder notebooks --- .../{06_Combining_datasets.Rmd => 05_Combining_datasets.Rmd} | 0 R_notebooks/{07_Reshaping_data.Rmd => 06_Reshaping_data.Rmd} | 0 .../{05_Strings_and_dates.Rmd => 07_Strings_and_dates.Rmd} | 0 3 files changed, 0 insertions(+), 0 deletions(-) rename R_notebooks/{06_Combining_datasets.Rmd => 05_Combining_datasets.Rmd} (100%) rename R_notebooks/{07_Reshaping_data.Rmd => 06_Reshaping_data.Rmd} (100%) rename R_notebooks/{05_Strings_and_dates.Rmd => 07_Strings_and_dates.Rmd} (100%) diff --git a/R_notebooks/06_Combining_datasets.Rmd b/R_notebooks/05_Combining_datasets.Rmd similarity index 100% rename from R_notebooks/06_Combining_datasets.Rmd rename to R_notebooks/05_Combining_datasets.Rmd diff --git a/R_notebooks/07_Reshaping_data.Rmd b/R_notebooks/06_Reshaping_data.Rmd similarity index 100% rename from R_notebooks/07_Reshaping_data.Rmd rename to R_notebooks/06_Reshaping_data.Rmd diff --git a/R_notebooks/05_Strings_and_dates.Rmd b/R_notebooks/07_Strings_and_dates.Rmd similarity index 100% rename from R_notebooks/05_Strings_and_dates.Rmd rename to R_notebooks/07_Strings_and_dates.Rmd From 376073e7cdeb4b84e882fe7b853c3157dc8aaaad Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Sat, 2 Mar 2024 11:29:26 +0000 Subject: [PATCH 34/50] Update Ruby gem dependencies --- Gemfile | 3 ++ Gemfile.lock | 93 +++++++++++++++++++++++++++------------------------- 2 files changed, 51 insertions(+), 45 deletions(-) diff --git a/Gemfile b/Gemfile index 78e58d2..bc05aac 100644 --- a/Gemfile +++ b/Gemfile @@ -12,3 +12,6 @@ gem 'github-pages', group: :jekyll_plugins if Gem::Version.new(RUBY_VERSION) >= Gem::Version.new('3.0.0') gem 'webrick', '>= 1.6.1' end + +gem 'rubyzip', '~> 2.3.0' + diff --git a/Gemfile.lock b/Gemfile.lock index 25aa4bf..371cf58 100644 --- a/Gemfile.lock +++ b/Gemfile.lock @@ -1,7 +1,7 @@ GEM remote: https://rubygems.org/ specs: - activesupport (7.1.2) + activesupport (7.1.3.2) base64 bigdecimal concurrent-ruby (~> 1.0, >= 1.0.2) @@ -14,19 +14,18 @@ GEM addressable (2.8.6) public_suffix (>= 2.0.2, < 6.0) base64 (0.2.0) - bigdecimal (3.1.5) + bigdecimal (3.1.6) coffee-script (2.4.1) coffee-script-source execjs - coffee-script-source (1.11.1) + coffee-script-source (1.12.2) colorator (1.1.0) commonmarker (0.23.10) - concurrent-ruby (1.2.2) + concurrent-ruby (1.2.3) connection_pool (2.4.1) dnsruby (1.70.0) simpleidn (~> 0.2.1) - drb (2.2.0) - ruby2_keywords + drb (2.2.1) em-websocket (0.5.3) eventmachine (>= 0.12.9) http_parser.rb (~> 0) @@ -34,24 +33,23 @@ GEM ffi (>= 1.15.0) eventmachine (1.2.7) execjs (2.9.1) - faraday (2.8.1) - base64 - faraday-net_http (>= 2.0, < 3.1) - ruby2_keywords (>= 0.0.4) - faraday-net_http (3.0.2) + faraday (2.9.0) + faraday-net_http (>= 2.0, < 3.2) + faraday-net_http (3.1.0) + net-http ffi (1.16.3) forwardable-extended (2.6.0) - gemoji (3.0.1) - github-pages (228) - github-pages-health-check (= 1.17.9) - jekyll (= 3.9.3) - jekyll-avatar (= 0.7.0) - jekyll-coffeescript (= 1.1.1) + gemoji (4.1.0) + github-pages (231) + github-pages-health-check (= 1.18.2) + jekyll (= 3.9.5) + jekyll-avatar (= 0.8.0) + jekyll-coffeescript (= 1.2.2) jekyll-commonmark-ghpages (= 0.4.0) - jekyll-default-layout (= 0.1.4) - jekyll-feed (= 0.15.1) + jekyll-default-layout (= 0.1.5) + jekyll-feed (= 0.17.0) jekyll-gist (= 1.5.0) - jekyll-github-metadata (= 2.13.0) + jekyll-github-metadata (= 2.16.1) jekyll-include-cache (= 0.2.1) jekyll-mentions (= 1.6.0) jekyll-optional-front-matter (= 0.3.2) @@ -78,20 +76,20 @@ GEM jekyll-theme-tactile (= 0.2.0) jekyll-theme-time-machine (= 0.2.0) jekyll-titles-from-headings (= 0.5.3) - jemoji (= 0.12.0) - kramdown (= 2.3.2) + jemoji (= 0.13.0) + kramdown (= 2.4.0) kramdown-parser-gfm (= 1.1.0) liquid (= 4.0.4) mercenary (~> 0.3) minima (= 2.5.1) nokogiri (>= 1.13.6, < 2.0) - rouge (= 3.26.0) + rouge (= 3.30.0) terminal-table (~> 1.4) - github-pages-health-check (1.17.9) + github-pages-health-check (1.18.2) addressable (~> 2.3) dnsruby (~> 1.60) - octokit (~> 4.0) - public_suffix (>= 3.0, < 5.0) + octokit (>= 4, < 8) + public_suffix (>= 3.0, < 6.0) typhoeus (~> 1.3) html-pipeline (2.14.3) activesupport (>= 2) @@ -99,7 +97,7 @@ GEM http_parser.rb (0.8.0) i18n (1.14.1) concurrent-ruby (~> 1.0) - jekyll (3.9.3) + jekyll (3.9.5) addressable (~> 2.4) colorator (~> 1.0) em-websocket (~> 0.5) @@ -112,11 +110,11 @@ GEM pathutil (~> 0.9) rouge (>= 1.7, < 4) safe_yaml (~> 1.0) - jekyll-avatar (0.7.0) + jekyll-avatar (0.8.0) jekyll (>= 3.0, < 5.0) - jekyll-coffeescript (1.1.1) + jekyll-coffeescript (1.2.2) coffee-script (~> 2.2) - coffee-script-source (~> 1.11.1) + coffee-script-source (~> 1.12) jekyll-commonmark (1.4.0) commonmarker (~> 0.22) jekyll-commonmark-ghpages (0.4.0) @@ -124,15 +122,15 @@ GEM jekyll (~> 3.9.0) jekyll-commonmark (~> 1.4.0) rouge (>= 2.0, < 5.0) - jekyll-default-layout (0.1.4) - jekyll (~> 3.0) - jekyll-feed (0.15.1) + jekyll-default-layout (0.1.5) + jekyll (>= 3.0, < 5.0) + jekyll-feed (0.17.0) jekyll (>= 3.7, < 5.0) jekyll-gist (1.5.0) octokit (~> 4.2) - jekyll-github-metadata (2.13.0) + jekyll-github-metadata (2.16.1) jekyll (>= 3.4, < 5.0) - octokit (~> 4.0, != 4.4.0) + octokit (>= 4, < 7, != 4.4.0) jekyll-include-cache (0.2.1) jekyll (>= 3.7, < 5.0) jekyll-mentions (1.6.0) @@ -203,16 +201,16 @@ GEM jekyll (>= 3.3, < 5.0) jekyll-watch (2.2.1) listen (~> 3.0) - jemoji (0.12.0) - gemoji (~> 3.0) + jemoji (0.13.0) + gemoji (>= 3, < 5) html-pipeline (~> 2.2) jekyll (>= 3.0, < 5.0) - kramdown (2.3.2) + kramdown (2.4.0) rexml kramdown-parser-gfm (1.1.0) kramdown (~> 2.0) liquid (4.0.4) - listen (3.8.0) + listen (3.9.0) rb-fsevent (~> 0.10, >= 0.10.3) rb-inotify (~> 0.9, >= 0.9.10) mercenary (0.3.6) @@ -220,23 +218,24 @@ GEM jekyll (>= 3.5, < 5.0) jekyll-feed (~> 0.9) jekyll-seo-tag (~> 2.1) - minitest (5.20.0) + minitest (5.22.2) mutex_m (0.2.0) - nokogiri (1.15.5-x86_64-linux) + net-http (0.4.1) + uri + nokogiri (1.16.2-x86_64-linux) racc (~> 1.4) octokit (4.25.1) faraday (>= 1, < 3) sawyer (~> 0.9) pathutil (0.16.2) forwardable-extended (~> 2.6) - public_suffix (4.0.7) + public_suffix (5.0.4) racc (1.7.3) rb-fsevent (0.11.2) rb-inotify (0.10.1) ffi (~> 1.0) rexml (3.2.6) - rouge (3.26.0) - ruby2_keywords (0.0.5) + rouge (3.30.0) rubyzip (2.3.2) safe_yaml (1.0.5) sass (3.7.4) @@ -259,15 +258,19 @@ GEM unf_ext unf_ext (0.0.9.1) unicode-display_width (1.8.0) + uri (0.13.0) + webrick (1.8.1) PLATFORMS x86_64-linux DEPENDENCIES github-pages + rubyzip (~> 2.3.0) + webrick (>= 1.6.1) RUBY VERSION ruby 2.7.0p0 BUNDLED WITH - 2.4.12 + 2.5.6 From a44f19924a06fa91707cbb1ff9931be278dc1dee Mon Sep 17 00:00:00 2001 From: Thomas Hawes <105276609+thawes-rse@users.noreply.github.com> Date: Sat, 2 Mar 2024 12:00:23 +0000 Subject: [PATCH 35/50] Make revised episode placeholders Since we are using R notebooks for the course notes, the episode webpages need revising. --- _episodes/02_notebooks.md | 15 + _episodes/02_tidyverse_intro.md | 37 --- _episodes/03_data.md | 102 ------- _episodes/04_basic_wrangling.md | 407 --------------------------- _episodes/05_further_wrangling.md | 447 ------------------------------ 5 files changed, 15 insertions(+), 993 deletions(-) create mode 100644 _episodes/02_notebooks.md delete mode 100644 _episodes/02_tidyverse_intro.md delete mode 100644 _episodes/03_data.md delete mode 100644 _episodes/04_basic_wrangling.md delete mode 100644 _episodes/05_further_wrangling.md diff --git a/_episodes/02_notebooks.md b/_episodes/02_notebooks.md new file mode 100644 index 0000000..dac9101 --- /dev/null +++ b/_episodes/02_notebooks.md @@ -0,0 +1,15 @@ +--- +layout: page +title: "Notebooks" +order: 2 +session: 1 +length: TODO +toc: true +adapted: false +--- + +## Learning Objectives + +This course will be delivered by working through a series of R notebooks +within the workshop sessions alongside the course instructor. These notebooks +will be made available shortly before the start of the first workshop. diff --git a/_episodes/02_tidyverse_intro.md b/_episodes/02_tidyverse_intro.md deleted file mode 100644 index 75d404c..0000000 --- a/_episodes/02_tidyverse_intro.md +++ /dev/null @@ -1,37 +0,0 @@ ---- -layout: page -title: "Introduction to the Tidyverse" -order: 2 -session: 1 -length: TODO -toc: true -adapted: true -attrib_name: "Tidyverse: Data wrangling & visualization - Introduction to the Tidyverse" -attrib_link: https://liza-wood.github.io/tidyverse_intro/introduction-to-the-tidyverse.html -attrib_license: Creative Commons BY-NC-SA 4.0 -attrib_license_link: https://creativecommons.org/licenses/by-nc-sa/4.0/ ---- - -## Learning Objectives - -By the end of this episode participants will be able to: - -TODO: objectives - -## What is the Tidyverse? - -The Tidyverse is self-described as: - -> "...an opinionated collection of R packages designed for data science. All -> packages share an underlying design philosophy, grammar, and data structures'." -> -> — https://www.tidyverse.org/ - -When you install the `tidyverse` package, you install a suite of 9 tidy-related -packages, all of which are designed to help you work with data, from cleaning -and manipulation to plotting and modelling. They are increasingly popular, -have large user bases, and are generally very well-documented. - -TODO: highlight common data structure - dataframe / tibble - -TODO: highlight functional 'pipeline' approach diff --git a/_episodes/03_data.md b/_episodes/03_data.md deleted file mode 100644 index 979d528..0000000 --- a/_episodes/03_data.md +++ /dev/null @@ -1,102 +0,0 @@ ---- -layout: page -title: "Example Data" -order: 3 -session: TODO -length: TODO -toc: true -adapted: true -attrib_name: "Tidyverse: Data wrangling & visualization - Data" -attrib_link: https://liza-wood.github.io/tidyverse_intro/data.html -attrib_license: Creative Commons BY-NC-SA 4.0 -attrib_license_link: https://creativecommons.org/licenses/by-nc-sa/4.0/ ---- - -## Learning Objectives - -By the end of this episode participants will be able to: - -TODO: objectives - - -## Palmer Station penguins - -TODO: add code outputs - -The Palmer Station penguins data is a tidy data set related to three species of -Antarctic penguins from Horst, Hill, and Gorman [[^1]]. - -The data contains size measurements for male and female adult foraging -Adélie, Chinstrap, and Gentoo penguins observed on islands in the Palmer -Archipelago near Palmer Station, Antarctica between 2007-2009. Data were -collected and made available by Dr. Kristen Gorman and the Palmer Station Long -Term Ecological Research (LTER) Program. You can read more about the package -on the -`palmerpenguins` documentation website. - -Let's start by installing the package: - -```r -# Install from CRAN -install.packages('palmerpenguins') -``` - -Now we can load in the package library, which stores the `penguins` dataset. - -```r -library(palmerpenguins) -head(penguins) -``` - -Learn more about each variable in the documentation - -```r -?penguins -``` - -Let's take a look at its structure: - -```r -str(penguins) -``` - - -## Palmer Station weather - -To practice data wrangling skills, this course will also use another data -set from the Antarctic LTER Program -- the 'Daily averaged weather timeseries -at Palmer Station, Antarctica'[[^2]], which includes various weather metrics -measured between 1989-2019. We'll read in these data directly from online and -name the data frame `weather`. - -TODO: replace with `readr` functionality? - -```r -weather <- read.csv("https://pasta.lternet.edu/package/data/eml/knb-lter-pal/28/9/a3b91017602b29acbf60e319d6a5e9bc") -``` - -These data have been made available through the Environmental Data Initiative -and more information can be found on the -EDI Data Portal. - -Let's take a look at the data's structure: - -```r -str(weather) -``` - -Together this tutorial uses data collected from penguins across three islands -in the Palmer archipelago and weather sensing technology at the US Palmer -Station. - - -## References - -[^1]: Horst AM, Hill AP, Gorman KB (2020). palmerpenguins: Palmer Archipelago - (Antarctica) penguin data. R package version 0.1.0. - . doi: 10.5281/zenodo.3960218. - -[^2]: Palmer Station Antarctica LTER. 2023. Daily weather averages for Palmer - Station, Antarctica (1989-2023) ver 9. Environmental Data Initiative. - - (Accessed 2024-01-04). diff --git a/_episodes/04_basic_wrangling.md b/_episodes/04_basic_wrangling.md deleted file mode 100644 index 4296b62..0000000 --- a/_episodes/04_basic_wrangling.md +++ /dev/null @@ -1,407 +0,0 @@ ---- -layout: page -title: "Data Wrangling Basics" -order: 4 -session: TODO -length: TODO -toc: true -adapted: true -attrib_name: "Tidyverse: Data wrangling & visualization - Data wrangling basics" -attrib_link: https://liza-wood.github.io/tidyverse_intro/data-wrangling-basics.html -attrib_license: Creative Commons BY-NC-SA 4.0 -attrib_license_link: https://creativecommons.org/licenses/by-nc-sa/4.0/ ---- - -## Learning Objectives - -By the end of this episode participants will be able to: - -TODO: objectives - - -## Outline / cheat sheet - -- Reducing data dimensionality: - - `select`: Name the columns to keep, separated by a comma. Using `select` - will reduce the number of columns in your dataframe. - - `filter`: Set conditions by which you want to filter (keep) rows of data. - Using the `filter` function will reduce the number of rows in your dataframe. - - `is.na()`: Specify a column name to get a logical vector indicated whether - each value in that row has an NA value. Useful when paired with `filter`. - -- Combining multiple steps - - `%>%`: Chain together multiple steps by 'piping' the output of one line of - code into the next. - -- Creating new variables - - New columns - - `mutate`: Create a new column by assigning a value (transformed or - otherwise). - - `case_when`: Pair with `mutate` to create a new variable that is based on - one or more conditions, typically for creating categorical variables. - - Summary tables - - `group_by`: Set a column (or columns) by which you want to perform a - summarizing function on. Using this function does apparently nothing on - its own, but is powerful when paired with `summarize` or `mutate`. - - `summarize`: Create a new variable by performing a variety of - summary/transformative functions on an existing column (or set of - columns). When paired with `group_by`, data is reduced to a summary table. - - -## Reducing data dimensionality - -The functions, `select` and `filter`, are what you can use to reduce -dimensionality of your dataframe. That means reducing the number of columns -(with `select`) and/or the number of rows (with `filter`). To use `select`, -name the columns to keep, separated by a comma. For example, let's select the -species, year, sex, and body mass columns. - -```r -library(tidyverse) -library(palmerpenguins) -``` - -```r -# Hint: check your column names first -colnames(penguins) -``` - -```r -# Select species, year, sex, and body mass -penguins_selected <- select(penguins, species, year, sex, body_mass_g) -# Take a look at the 'dimensions' with dim() -dim(penguins_selected) -``` - -The `select` function can also be used to remove columns by using the `-` -operator. For example, we may want to remove only two columns, island and year: - -```r -# Remove the island and year columns by using -. -penguins_unselected <- select(penguins, -island, -year) - -# Take a look at the number of columns -ncol(penguins_unselected) -``` - -Now, let's filter the data based on a certain condition. Creating 'conditions' -in R means writing logical statements with a response that will be either `TRUE` -or `FALSE`. To write conditions we can use -[comparative operators](https://www.w3schools.com/r/r_operators.asp), which give -a logical output: - -| Comparative Operator | Name | Example | -|----------------------|--------------------------|---------| -| == | Equal | x == y | -| != | Not equal | x != y | -| > | Greater than | x > y | -| < | Less than | x < y | -| >= | Greater than or equal to | x >= y | -| <= | Less than or equal to | x <= y | - -```r -head(penguins$year == 2007, n = 75) -``` - -The filter function evaluates the condition and keeps only the rows for which the value is TRUE. For example, let's filter observations so that we only keep those from the year 2007. - -```r -penguins_filtered <- filter(penguins, year == 2007) -dim(penguins_filtered) -``` - -TODO: compare with base R way of filtering on boolean vector - -There are other functions we can use to evaluate columns and get a true/false -output. An important one is `is.na()`. This function evaluates a column and -reports back a `TRUE` value when there is an NA in that column's row. For -example, we can use the `head()` function to look at the top 6 values of the -`sex` column, and see that there is an NA in the fourth row. - -```r -head(penguins$sex, n = 10) -``` - -The `is.na` function evaluates the whole column and gives us `TRUE`s whenever it -sees an NA. Not surprisingly, we see a `TRUE` in the fourth observation. - -```r -head(is.na(penguins$sex), n = 10) -``` - -Since `is.na()` gives us a `TRUE`/`FALSE` vector, it is already a good -candidate for using it as a condition in filter. Below we are asking the -filter function to keep only the observations that have an NA in the sex column. -In other others, evaluate the logical conditions -"are there NAs in the sex column?" then filter out the rows where the condition -is `TRUE`, meaning there are NAs. - -```r -penguins_filtered_nas <- filter(penguins, is.na(sex)) -nrow(penguins_filtered_nas) -nrow(penguins) -``` - -To do the reverse, i.e. keep only the observations that do NOT have an NA in -the sex column, we can use the `!` operator to denote 'not' and reverse the -true/false. - -```r -penguins_filtered_nonas <- filter(penguins, !is.na(sex)) -dim(penguins_filtered_nonas) -``` - -You can combine multiple conditions using -[logical operators](https://www.w3schools.com/r/r_operators.asp): - -| Logical Operator | Name | Example | -|------------------|---------|-------------------| -| & | AND | x == 2 & y == 10 | -| \| | OR | x == 2 \| y == 10 | - -For example, we can filter out the rows where there are NAs in the sex column -AND keep only the rows of the penguin species named Adelie. - -```r -penguins_filtered_2conditions <- filter(penguins, !is.na(sex) & species == "Adelie") -nrow(penguins_filtered_2conditions) -``` - - -## Combining multiple steps - -TODO: rearrange the discussion to first do nested calls, then successive updating, then piping - -There are several ways to combine steps in coding. You can perform one -function at a time and save intermediate objects as you work, you can 'nest' -functions, or you can use 'pipes' (`%>%`). So for example, if you wanted to -select the species, sex, bill length, and bill depth, and filter data so to -only keep data 'Adelie' penguins, with no NAs in the sex column, these first -two options would look as follows: - -```r -# Multiple steps with intermediate objects -penguins_step1 <- select(penguins, species, sex, bill_length_mm, bill_depth_mm) -penguins_step2 <- filter(penguins_step1, species == 'Adelie' & !is.na(sex)) -summary(penguins_step2) -``` - -```r -# Multiple steps with nested functions -penguins_nested <- filter(select(penguins, species, sex, bill_length_mm, bill_depth_mm), - species == 'Adelie' & !is.na(sex)) -summary(penguins_nested) -``` - -The third option is pipes. Pipes let you take the output of one function and -send it directly to the next, which is useful when you need to do many things -to the same data set. Pipes in R look like `%>%` and are made available via the -`magrittr` package, installed automatically with the `tidyverse`. (If you use -RStudio, you can type the pipe with Ctrl + Shift + M if you have a PC or -Cmd + Shift + M if you have a Mac.) - -```r -# Multiple steps with pipes -penguins_piped <- penguins %>% - select(species, sex, bill_length_mm, bill_depth_mm) %>% - filter(species == 'Adelie' & !is.na(sex)) -summary(penguins_piped) -``` - -We will use the piping approach, as it helps make a smoother workflow for other -wrangling functions we'll use. - -TODO: note about how result is entered as first arg and no need to specify. - - -### Exercise - -Use pipes to select only the columns for species, sex, bill_length_mm, and -body_mass_g, and filter out where sex is missing and body_mass_g is greater than -3500. Name this new data frame 'filter_challenge' and look at a summary of it to -check your work. - -

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