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Janitor_presentation.html
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<!DOCTYPE html>
<html lang="" xml:lang="">
<head>
<title>Cleaning dirty data with {janitor}</title>
<meta charset="utf-8" />
<meta name="author" content="Elena Dreyer, Srhuti Kakade & Luis Fernando Ramirez" />
<script src="presentation_files/header-attrs/header-attrs.js"></script>
<link href="presentation_files/remark-css/default.css" rel="stylesheet" />
<link href="presentation_files/remark-css/metropolis.css" rel="stylesheet" />
<link href="presentation_files/remark-css/metropolis-fonts.css" rel="stylesheet" />
</head>
<body>
<textarea id="source">
class: center, middle, inverse, title-slide
.title[
# Cleaning dirty data with {janitor}
]
.subtitle[
## Workshop: I2DS Tools for Data Science
]
.author[
### Elena Dreyer, Srhuti Kakade & Luis Fernando Ramirez
]
.institute[
### Hertie School
]
.date[
### October 27th 2023
]
---
<style type="text/css">
@media print { # print out incremental slides; see https://stackoverflow.com/questions/56373198/get-xaringan-incremental-animations-to-print-to-pdf/56374619#56374619
.has-continuation {
display: block !important;
}
}
</style>
# Agenda
<br>
.pull-left[
<br>
1. Overview
2. Data cleaning
3. Data exploring
4. Resources
5. Summary
]
.pull-right-center[
<div align="left">
<br>
<img src="images/nyt_data.png" width=400>
</div>
[Forbes](https://www.forbes.com/sites/gilpress/2016/03/23/data-preparation-most-time-consuming-least-enjoyable-data-science-task-survey-says/?sh=53b7a7586f63)
]
.pull-right-center[
<div align="left">
<br>
<img src="images/janitor.jpeg" width=450>
</div>
]
---
# Overview
.pull-left[
<br><br>
{**Janitor**} is a package developed by [**Sam Firke**](https://samfirke.com/about/).
Main Uses:
- Simple functions for **cleaning** and **examining** data
- Optimized for **users**; easy to use
- Aimed at beginning and intermediate R users
- For advanced R users: <br>
- Do data wrangling faster
Let's get started <br>
--> `install.packages("janitor")` <br>
--> `library(janitor)`
]
.pull-right[
<div align="left">
<br>
<img src="images/janitor_firke.png" width=500>
</div>
<div align="left">
<br>
<img src="images/github_janitor.png" width=500>
</div>
[GitHub janitor](https://github.com/sfirke/janitor)
]
---
# Cleaning data
<br>
**Clean data frame names with `clean_names()`**
.pull-left[
##tidyverse grammar
Consistent variable names are important
**snake_case** <br>
*“Variable and function names should use only lowercase letters, numbers, and underscores. Use underscores (so called snake_case) to separate words within a name.”* <br>
[The tidyverse style guide](https://style.tidyverse.org/syntax.html)
]
.pull-right[
<div align="left">
<br>
<img src="images/tidydata.jpeg" width=500>
</div>
[Julie Lowndes and Allison Horst](https://www.openscapes.org/blog/2020/10/12/tidy-data/)
]
---
# Cleaning data
<br>
**Clean data frame names with `clean_names()`**
.pull-left[
```r
> # Create a test data frame with dirty variable names
> dirty_df <- as.data.frame(matrix(ncol = 7))
> names(dirty_df) <- c("firstName", "a@b!c'ß??",
+ "success.rate.in.%.(2022)",
+ "IDENTICAL", "IDENTICAL",
+ "", "#")
> clean_df <- dirty_df %>%
+ clean_names()
>
> colnames(clean_df)
```
```
## [1] "first_name" "a_b_css"
## [3] "success_rate_in_percent_2022" "identical"
## [5] "identical_2" "x"
## [7] "number"
```
]
---
# Cleaning data
<br>
**Clean data frame names with `clean_names()`**
.pull-left[
```r
> # Create a test data frame with dirty variable names
> dirty_df <- as.data.frame(matrix(ncol = 7))
> names(dirty_df) <- c("firstName", "a@b!c'ß??",
+ "success.rate.in.%.(2022)",
+ "IDENTICAL", "IDENTICAL",
+ "", "#")
> clean_df <- dirty_df %>%
+ clean_names()
>
> colnames(clean_df)
```
```
## [1] "first_name" "a_b_css"
## [3] "success_rate_in_percent_2022" "identical"
## [5] "identical_2" "x"
## [7] "number"
```
]
.pull-right[
+ **Consistent format** for letter cases and separators
+ **snake_case** is default, but other cases like camelCase are available
+ Handles **special characters** and **spaces**, including transliterating characters like `ß` to `ss`
+ Converts "%" to "**percent**" and "#" to "**number**"
+ Adds numbers to **duplicated names**
+ **Spacing** (or lack thereof) around numbers is preserved
- Works with the **%>% operator**
- Can be used for data frames and objects
- `make_names_clean` also works for character vectors
]
---
# Cleaning data
```r
> # install.packages("palmerpenguins")
> library(palmerpenguins)
> dirty_penguins_df <- penguins_raw
> colnames(dirty_penguins_df)
```
```
## [1] "studyName" "Sample Number" "Species"
## [4] "Region" "Island" "Stage"
## [7] "Individual ID" "Clutch Completion" "Date Egg"
## [10] "Culmen Length (mm)" "Culmen Depth (mm)" "Flipper Length (mm)"
## [13] "Body Mass (g)" "Sex" "Delta 15 N (o/oo)"
## [16] "Delta 13 C (o/oo)" "Comments"
```
---
# Cleaning data
```r
> # install.packages("palmerpenguins")
> library(palmerpenguins)
> dirty_penguins_df <- penguins_raw
> colnames(dirty_penguins_df)
```
```
## [1] "studyName" "Sample Number" "Species"
## [4] "Region" "Island" "Stage"
## [7] "Individual ID" "Clutch Completion" "Date Egg"
## [10] "Culmen Length (mm)" "Culmen Depth (mm)" "Flipper Length (mm)"
## [13] "Body Mass (g)" "Sex" "Delta 15 N (o/oo)"
## [16] "Delta 13 C (o/oo)" "Comments"
```
```r
> clean_penguins_df <- penguins_raw %>%
+ clean_names()
> colnames(clean_penguins_df)
```
```
## [1] "study_name" "sample_number" "species"
## [4] "region" "island" "stage"
## [7] "individual_id" "clutch_completion" "date_egg"
## [10] "culmen_length_mm" "culmen_depth_mm" "flipper_length_mm"
## [13] "body_mass_g" "sex" "delta_15_n_o_oo"
## [16] "delta_13_c_o_oo" "comments"
```
---
# Cleaning data
<br>
**Remove content with `remove_empty()`**
- `remove_empty()` rows and columns <br>
+ Cleans Excel files that contain empty rows and columns after being read into R <br>
+ Adding `quiet = FALSE` let's you know how many rows or columns were removed.
```r
> empty_df <- data.frame(v1 = c(1, NA, 3),
+ v2 = c(NA, NA, NA),
+ v3 = c("a", NA, "b"))
>
> empty_df %>%
+ remove_empty(c("rows", "cols"), quiet = FALSE) %>%
+ glimpse
```
```
## Removing 1 empty rows of 3 rows total (33.3%).
```
```
## Removing 1 empty columns of 3 columns total (Removed: v2).
```
```
## Rows: 2
## Columns: 2
## $ v1 <dbl> 1, 3
## $ v3 <chr> "a", "b"
```
---
# Cleaning data
<br>
**Drop constant columns with `remove_constant()`**
.pull-left[
`remove_constant()` columns <br>
Drops columns from a data frame that contain only a single constant value
```r
> adelie_df <- clean_penguins_df %>%
+ filter(species == "Adelie Penguin (Pygoscelis adeliae)")
>
> adelie_df %>%
+ names()
```
```
## [1] "study_name" "sample_number" "species"
## [4] "region" "island" "stage"
## [7] "individual_id" "clutch_completion" "date_egg"
## [10] "culmen_length_mm" "culmen_depth_mm" "flipper_length_mm"
## [13] "body_mass_g" "sex" "delta_15_n_o_oo"
## [16] "delta_13_c_o_oo" "comments"
```
]
.pull-right[
```r
> adelie_clean_df <- adelie_df %>%
+ remove_constant()
>
> adelie_clean_df %>%
+ names()
```
```
## [1] "study_name" "sample_number" "island"
## [4] "individual_id" "clutch_completion" "date_egg"
## [7] "culmen_length_mm" "culmen_depth_mm" "flipper_length_mm"
## [10] "body_mass_g" "sex" "delta_15_n_o_oo"
## [13] "delta_13_c_o_oo" "comments"
```
]
---
# Cleaning data
<br>
**Check content of columns with `compare_df_cols()`**
.pull-left[
Imagine you have a set of data frames that you want to combine by binding the rows together but `rbind()` fails. You can check if the column classes match and see if they are **matching** by running `compare_df_cols()`.
- takes unquoted names of data frames, tibbles, or a list of data frames <br>
--> Returns a summary of how they compare
- What column types are there?
- How do column types differ?
- Which are missing or present in the different inputs?
]
.pull-right[
```r
> chinstrap_df <- clean_penguins_df %>%
+ filter(species == "Chinstrap penguin (Pygoscelis antarctica)")
>
> # rbind(adelie, chinstrap)
>
> compare_df_cols(adelie_clean_df, chinstrap_df) %>%
+ tail()
```
```
## column_name adelie_clean_df chinstrap_df
## 12 region <NA> character
## 13 sample_number numeric numeric
## 14 sex character character
## 15 species <NA> character
## 16 stage <NA> character
## 17 study_name character character
```
]
---
# Exploring data
<br>
**`tabyl()` – a tidy, fully-featured approach to counting things**
.pull-left[
Why not use `table()`?
- It doesn't work with the `%>%` operator
- It doesn't output data frames
- Its results are hard to format
]
.pull-right[
**One variable**
```r
> table(clean_penguins_df$sex)
```
```
##
## FEMALE MALE
## 165 168
```
]
---
# Exploring data
<br>
**`tabyl()` – a tidy, fully-featured approach to counting things**
.pull-left[
Why not use `table()`?
- It doesn't work with the `%>%` operator
- It doesn't output data frames
- Its results are hard to format
Instead better use `tabyl()`
- Tidyverse-aligned - primarily built upon the `{dplyr}` and `{tidyr}` packages
- Compatible with the `{knitr}` package
- Useful for data exploration
- Generate frequencies along with the percent of total
- Counts combinations of one, two, or three variables
]
.pull-right[
**One variable**
```r
> table(clean_penguins_df$sex)
```
```
##
## FEMALE MALE
## 165 168
```
```r
> tabyl(clean_penguins_df$sex)
```
```
## clean_penguins_df$sex n percent valid_percent
## FEMALE 165 0.47965116 0.4954955
## MALE 168 0.48837209 0.5045045
## <NA> 11 0.03197674 NA
```
```r
> clean_penguins_no_na_df <- clean_penguins_df %>%
+ drop_na(sex)
```
]
---
# Exploring data
<br>
**`tabyl()` – a tidy, fully-featured approach to counting things**
**Two variables**
Two-way tabyl / "crosstab" or "contingency" table
```r
> clean_penguins_no_na_df %>%
+ tabyl(island, sex)
```
```
## island FEMALE MALE
## Biscoe 80 83
## Dream 61 62
## Torgersen 24 23
```
---
# Exploring data
<br>
**`tabyl()` – a tidy, fully-featured approach to counting things**
**Two variables**
Two-way tabyl / "crosstab" or "contingency" table
```r
> penguins_crosstab_table <- clean_penguins_no_na_df %>%
+ tabyl(island, sex) %>%
+ adorn_totals("col") %>% # total in each row
+ adorn_percentages("row") %>% # percentage value per row
+ adorn_pct_formatting(digits = 2) %>% # rounded percentage value
+ adorn_ns() %>% # adds the absolute numbers
+ adorn_title() # adds the variable name
>
> penguins_crosstab_table
```
```
## sex
## island FEMALE MALE Total
## Biscoe 49.08% (80) 50.92% (83) 100.00% (163)
## Dream 49.59% (61) 50.41% (62) 100.00% (123)
## Torgersen 51.06% (24) 48.94% (23) 100.00% (47)
```
---
# Exploring data
<br>
**`adorn_*()` options**
.pull-left[
- `tabyl()` can be formatted with a suite of `adorn_*` functions to add information and for pretty formatting: <br>
+ **`adorn_totals()`**: Add totals row, column, or both.
+ **`adorn_percentages()`**: Calculate percentages along either axis or the entire tabyl
+ **`adorn_pct_formatting()`**: Format percentage columns, controlling the number of digits to display and whether to append the `%` symbol
+ **`adorn_rounding()`**: Round a data frame of numbers
]
.pull-right[
<br><br><br>
+ **`adorn_ns()`**: Add Ns to a tabyl - drawn from the tabyl's underlying counts or they can be supplied by the user
+ **`adorn_title()`**: Add a title to a tabyl - pptions include putting the column title in a new row on top of the data frame or combining the row and column titles in the data frame's first name slot.
]
---
# Exploring data
**`tabyl()`** **Three variables**
Three-way tabyl
```r
> clean_penguins_no_na_df %>% tabyl(island, species, sex) %>%
+ adorn_percentages("all") %>%
+ adorn_pct_formatting(digits = 1) %>%
+ adorn_title() %>%
+ kable()
```
<table class="kable_wrapper">
<tbody>
<tr>
<td>
<table>
<thead>
<tr>
<th style="text-align:left;"> </th>
<th style="text-align:left;"> species </th>
<th style="text-align:left;"> </th>
<th style="text-align:left;"> </th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;"> island </td>
<td style="text-align:left;"> Adelie Penguin (Pygoscelis adeliae) </td>
<td style="text-align:left;"> Chinstrap penguin (Pygoscelis antarctica) </td>
<td style="text-align:left;"> Gentoo penguin (Pygoscelis papua) </td>
</tr>
<tr>
<td style="text-align:left;"> Biscoe </td>
<td style="text-align:left;"> 13.3% </td>
<td style="text-align:left;"> 0.0% </td>
<td style="text-align:left;"> 35.2% </td>
</tr>
<tr>
<td style="text-align:left;"> Dream </td>
<td style="text-align:left;"> 16.4% </td>
<td style="text-align:left;"> 20.6% </td>
<td style="text-align:left;"> 0.0% </td>
</tr>
<tr>
<td style="text-align:left;"> Torgersen </td>
<td style="text-align:left;"> 14.5% </td>
<td style="text-align:left;"> 0.0% </td>
<td style="text-align:left;"> 0.0% </td>
</tr>
</tbody>
</table>
</td>
<td>
<table>
<thead>
<tr>
<th style="text-align:left;"> </th>
<th style="text-align:left;"> species </th>
<th style="text-align:left;"> </th>
<th style="text-align:left;"> </th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;"> island </td>
<td style="text-align:left;"> Adelie Penguin (Pygoscelis adeliae) </td>
<td style="text-align:left;"> Chinstrap penguin (Pygoscelis antarctica) </td>
<td style="text-align:left;"> Gentoo penguin (Pygoscelis papua) </td>
</tr>
<tr>
<td style="text-align:left;"> Biscoe </td>
<td style="text-align:left;"> 13.1% </td>
<td style="text-align:left;"> 0.0% </td>
<td style="text-align:left;"> 36.3% </td>
</tr>
<tr>
<td style="text-align:left;"> Dream </td>
<td style="text-align:left;"> 16.7% </td>
<td style="text-align:left;"> 20.2% </td>
<td style="text-align:left;"> 0.0% </td>
</tr>
<tr>
<td style="text-align:left;"> Torgersen </td>
<td style="text-align:left;"> 13.7% </td>
<td style="text-align:left;"> 0.0% </td>
<td style="text-align:left;"> 0.0% </td>
</tr>
</tbody>
</table>
</td>
</tr>
</tbody>
</table>
---
# Cleaning data
<br>
### Fixing dates
.pull-left[
- `excel_numeric_to_date()` <br>
Fix dates stored as serial numbers <br>
Converts serial date numbers from Excel to class `Date`
```r
> excel_numeric_to_date(44886)
```
```
## [1] "2022-11-21"
```
]
.pull-right[
- `convert_to_date()` <br>
Convert a mix of date and datetime formats to date
```r
> convert_to_date(c("2020-02-29", "40000.1"))
```
```
## [1] "2020-02-29" "2009-07-06"
```
```r
> convert_to_datetime(40000.1)
```
```
## [1] "2009-07-06 02:24:00 UTC"
```
]
---
# Cleaning data
<br>
### Rounding numbers
.pull-left[
Careful: In base R `round()` uses **"banker's rounding"**, <br>
i.e., halves are rounded to the nearest *even* number.
```r
> numbers <- c(3.5, 2.5)
> round(numbers)
```
```
## [1] 4 2
```
Instead use: `round_half_up()` <br>
directionally-consistent rounding behavior <br>
```r
> round_half_up(numbers)
```
```
## [1] 4 3
```
]
.pull-right[
`round_to_fraction()` <br>
round decimals to precise fractions of a given denominator
```r
> round_to_fraction(0.175, denominator = 4)
```
```
## [1] 0.25
```
```r
> round_to_fraction(0.2, denominator = 4)
```
```
## [1] 0.25
```
```r
> round_to_fraction(0.25000000001, denominator = 4)
```
```
## [1] 0.25
```
]
---
# Exploring data
<br>
**Detect duplicated records with `get_dupes()`**
.pull-left[
Checking for duplicated records is important because they can interfere with your tabulations in the analysis.
{`janitor`} makes this tedious task simple.
`get_dupes()` <br>
- Detects duplicate records during data cleaning
- Returns the records (and inserts a count of duplicates) so you can examine the potentially problematic cases
]
.pull-right[
```r
> clean_penguins_df %>%
+ get_dupes(individual_id) %>%
+ head(6)
```
```
## # A tibble: 6 × 18
## individual_id dupe_count study_name sample_number species region island stage
## <chr> <int> <chr> <dbl> <chr> <chr> <chr> <chr>
## 1 N13A1 3 PAL0708 25 Adelie … Anvers Biscoe Adul…
## 2 N13A1 3 PAL0809 51 Gentoo … Anvers Biscoe Adul…
## 3 N13A1 3 PAL0910 87 Gentoo … Anvers Biscoe Adul…
## 4 N13A2 3 PAL0708 26 Adelie … Anvers Biscoe Adul…
## 5 N13A2 3 PAL0809 52 Gentoo … Anvers Biscoe Adul…
## 6 N13A2 3 PAL0910 88 Gentoo … Anvers Biscoe Adul…
## # ℹ 10 more variables: clutch_completion <chr>, date_egg <date>,
## # culmen_length_mm <dbl>, culmen_depth_mm <dbl>, flipper_length_mm <dbl>,
## # body_mass_g <dbl>, sex <chr>, delta_15_n_o_oo <dbl>, delta_13_c_o_oo <dbl>,
## # comments <chr>
```
]
---
# Resources
<br>
.pull-left[
Original material:
+ [Package janitor documentation](https://cran.r-project.org/web/packages/janitor/janitor.pdf)
+ [GitHub Repository](https://github.com/sfirke/janitor)
Further resources:
+ [exploringdata.org - How to Clean Data: {janitor} Package](https://www.exploringdata.org/post/how-to-clean-data-janitor-package/)
+ [towardsdatascience.com - Cleaning and Exploring Data with the “janitor” Package ](https://towardsdatascience.com/cleaning-and-exploring-data-with-the-janitor-package-ee4a3edf085e)
+ [jenrichmond.rbind.io - Cleaning penguins with the janitor package ](http://jenrichmond.rbind.io/post/digging-into-the-janitor-package/)
]
.pull-right[
More on tidying data:<br>
[tidyr cheatsheet](https://github.com/rstudio/cheatsheets/blob/main/tidyr.pdf)
Unfortunately, there is no cheatsheet for {`janitor`}. <br>
Here are two alternative tips:
- `ls("package:janitor")` gives you a list of all functions
- or type `janitor::` in your console and you get to scroll up and down the list of all functions in the package.
BTW, this works for all packages ;)
]
---
# Summary
<br>
.pull-left[
+ Integrate `{janitor}` into your data wrangling and cleaning pipeline
+ It works faster than regular functions from the `{tidyverse}`
+ It can help you to:
+ **quickly** clean variable names
+ remove empty rows and columns
+ remove constant columns
+ **easily** create **better** tables that work in the tidyverse, can be stored as data frames and are almost worth publishing!
]
.pull-right[
<div align="left">
<img src="images/unsplash.jpg" width=500>
</div>
## **Happy data cleaning!**
]
</textarea>
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