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] 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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