GitHub - sfirke/janitor: simple tools for data cleaning in R (original) (raw)

janitor

Data scientists, according to interviews and expert estimates, spend from 50 percent to 80 percent of their time mired in this more mundane labor of collecting and preparing unruly digital data, before it can be explored for useful nuggets.

– “For Big-Data Scientists, ‘Janitor Work’ Is Key Hurdle to Insight” (New York Times, 2014)

R-CMD-check Coverage Status lifecycle CRAN_Status_Badge !Monthly Downloads !Downloads

janitor has simple functions for examining and cleaning dirty data. It was built with beginning and intermediate R users in mind and is optimized for user-friendliness. Advanced R users can perform many of these tasks already, but with janitor they can do it faster and save their thinking for the fun stuff.

The main janitor functions:

The tabulate-and-report functions approximate popular features of SPSS and Microsoft Excel.

janitor is a#tidyverse-oriented package. Specifically, it plays nicely with the %>% pipe and is optimized for cleaning data brought in with thereadr andreadxl packages.

Installation

You can install:

install.packages("janitor")

install.packages("remotes")

remotes::install_github("sfirke/janitor")

or from r-universe

install.packages("janitor", repos = c("https://sfirke.r-universe.dev", "https://cloud.r-project.org"))

Using janitor

A full description of each function, organized by topic, can be found in janitor’s catalog of functions vignette. There you will find functions not mentioned in this README, likecompare_df_cols() which provides a summary of differences in column names and types when given a set of data.frames.

Below are quick examples of how janitor tools are commonly used.

Cleaning dirty data

Take this roster of teachers at a fictional American high school, stored in the Microsoft Excel filedirty_data.xlsx:All kinds of dirty.

Dirtiness includes:

Here’s that data after being read in to R:

library(readxl) library(janitor) library(dplyr) library(here)

roster_raw <- read_excel(here("dirty_data.xlsx")) # available at https://github.com/sfirke/janitor glimpse(roster_raw) #> Rows: 14 #> Columns: 11 #> $ Data most recently refreshed on: "First Name", "Jason", "Jason", "Alicia", "Ada", "Desus", "Chien-… #> $ ...2 "Last Name", "Bourne", "Bourne", "Keys", "Lovelace", "Nice", "Wu"… #> $ ...3 "Employee Status", "Teacher", "Teacher", "Teacher", "Teacher", "A… #> $ Dec-27 2020 "Subject", "PE", "Drafting", "Music", NA, "Dean", "Physics", "Che… #> $ ...5 "Hire Date", "39690", "43479", "37118", "38572", "42791", "11037"… #> $ ...6 "% Allocated", "0.75", "0.25", "1", "1", "1", "0.5", "0.5", NA, "… #> $ ...7 "Full time?", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", NA… #> $ ...8 "do not edit! --->", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, … #> $ ...9 "Certification", "Physical ed", "Physical ed", "Instr. music", "P… #> $ ...10 "Certification", "Theater", "Theater", "Vocal music", "Computers"… #> $ ...11 "Active?", "YES", "YES", "YES", "YES", "YES", "YES", "YES", NA, "…

Now, to clean it up, starting with the column names.

Name cleaning comes in two flavors. make_clean_names() operates on character vectors and can be used during data import:

roster_raw_cleaner <- read_excel(here("dirty_data.xlsx"), skip = 1, .name_repair = make_clean_names ) glimpse(roster_raw_cleaner) #> Rows: 13 #> Columns: 11 #> $ first_name "Jason", "Jason", "Alicia", "Ada", "Desus", "Chien-Shiung", "Chien-Shiung", NA, "J… #> $ last_name "Bourne", "Bourne", "Keys", "Lovelace", "Nice", "Wu", "Wu", NA, "Joyce", "Lamarr",… #> $ employee_status "Teacher", "Teacher", "Teacher", "Teacher", "Administration", "Teacher", "Teacher"… #> $ subject "PE", "Drafting", "Music", NA, "Dean", "Physics", "Chemistry", NA, "English", "Sci… #> $ hire_date 39690, 43479, 37118, 38572, 42791, 11037, 11037, NA, 36423, 27919, 42221, 34700, 4… #> $ percent_allocated 0.75, 0.25, 1.00, 1.00, 1.00, 0.50, 0.50, NA, 0.50, 0.50, NA, NA, 0.80 #> $ full_time "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", NA, "No", "No", "No", "No", "No" #> $ do_not_edit NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA #> $ certification "Physical ed", "Physical ed", "Instr. music", "PENDING", "PENDING", "Science 6-12"… #> $ certification_2 "Theater", "Theater", "Vocal music", "Computers", NA, "Physics", "Physics", NA, "E… #> $ active "YES", "YES", "YES", "YES", "YES", "YES", "YES", NA, "YES", "YES", "YES", "YES", "…

clean_names() is a convenience version of make_clean_names() that can be used for piped data.frame workflows. The equivalent steps withclean_names() would be:

roster_raw <- roster_raw %>% row_to_names(row_number = 1) %>% clean_names()

The data.frame now has clean names. Let’s tidy it up further:

roster <- roster_raw %>% remove_empty(c("rows", "cols")) %>% remove_constant(na.rm = TRUE, quiet = FALSE) %>% # remove the column of all "Yes" values mutate( hire_date = convert_to_date( hire_date, # handle the mixed-format dates character_fun = lubridate::mdy ), cert = dplyr::coalesce(certification, certification_2) ) %>% select(-certification, -certification_2) # drop unwanted columns #> Removing 1 constant columns of 10 columns total (Removed: active).

roster #> # A tibble: 12 × 8 #> first_name last_name employee_status subject hire_date percent_allocated full_time cert
#>
#> 1 Jason Bourne Teacher PE 2008-08-30 0.75 Yes Physical ed
#> 2 Jason Bourne Teacher Drafting 2019-01-14 0.25 Yes Physical ed
#> 3 Alicia Keys Teacher Music 2001-08-15 1 Yes Instr. music
#> 4 Ada Lovelace Teacher 2005-08-08 1 Yes PENDING
#> 5 Desus Nice Administration Dean 2017-02-25 1 Yes PENDING
#> 6 Chien-Shiung Wu Teacher Physics 1930-03-20 0.5 Yes Science 6-12
#> 7 Chien-Shiung Wu Teacher Chemistry 1930-03-20 0.5 Yes Science 6-12
#> 8 James Joyce Teacher English 1999-09-20 0.5 No English 6-12
#> 9 Hedy Lamarr Teacher Science 1976-06-08 0.5 No PENDING
#> 10 Carlos Boozer Coach Basketball 2015-08-05 No Physical ed
#> 11 Young Boozer Coach 1995-01-01 No Political sci. #> 12 Micheal Larsen Teacher English 2009-09-15 0.8 No Vocal music

Examining dirty data

Finding duplicates

Use get_dupes() to identify and examine duplicate records during data cleaning. Let’s see if any teachers are listed more than once:

roster %>% get_dupes(contains("name")) #> # A tibble: 4 × 9 #> first_name last_name dupe_count employee_status subject hire_date percent_allocated full_time cert
#>
#> 1 Chien-Shiung Wu 2 Teacher Physics 1930-03-20 0.5 Yes Science … #> 2 Chien-Shiung Wu 2 Teacher Chemistry 1930-03-20 0.5 Yes Science … #> 3 Jason Bourne 2 Teacher PE 2008-08-30 0.75 Yes Physical… #> 4 Jason Bourne 2 Teacher Drafting 2019-01-14 0.25 Yes Physical…

Yes, some teachers appear twice. We ought to address this before counting employees.

Tabulating tools

A variable (or combinations of two or three variables) can be tabulated with tabyl(). The resulting data.frame can be tweaked and formatted with the suite of adorn_ functions for quick analysis and printing of pretty results in a report. adorn_ functions can be helpful with non-tabyls, too.

tabyl()

Like table(), but pipe-able, data.frame-based, and fully featured.

tabyl() can be called two ways:

One variable:

roster %>% tabyl(subject) #> subject n percent valid_percent #> Basketball 1 0.08333333 0.1 #> Chemistry 1 0.08333333 0.1 #> Dean 1 0.08333333 0.1 #> Drafting 1 0.08333333 0.1 #> English 2 0.16666667 0.2 #> Music 1 0.08333333 0.1 #> PE 1 0.08333333 0.1 #> Physics 1 0.08333333 0.1 #> Science 1 0.08333333 0.1 #> 2 0.16666667 NA

Two variables:

roster %>% filter(hire_date > as.Date("1950-01-01")) %>% tabyl(employee_status, full_time) #> employee_status No Yes #> Administration 0 1 #> Coach 2 0 #> Teacher 3 4

Three variables:

roster %>% tabyl(full_time, subject, employee_status, show_missing_levels = FALSE) #> $Administration #> full_time Dean #> Yes 1 #> #> $Coach #> full_time Basketball NA_ #> No 1 1 #> #> $Teacher #> full_time Chemistry Drafting English Music PE Physics Science NA_ #> No 0 0 2 0 0 0 1 0 #> Yes 1 1 0 1 1 1 0 1

Adorning tabyls

The adorn_ functions dress up the results of these tabulation calls for fast, basic reporting. Here are some of the functions that augment a summary table for reporting:

roster %>% tabyl(employee_status, full_time) %>% adorn_totals("row") %>% adorn_percentages("row") %>% adorn_pct_formatting() %>% adorn_ns() %>% adorn_title("combined") #> employee_status/full_time No Yes #> Administration 0.0% (0) 100.0% (1) #> Coach 100.0% (2) 0.0% (0) #> Teacher 33.3% (3) 66.7% (6) #> Total 41.7% (5) 58.3% (7)

Pipe that right into knitr::kable() in your RMarkdown report.

These modular adornments can be layered to reduce R’s deficit against Excel and SPSS when it comes to quick, informative counts. Learn more about tabyl() and the adorn_ functions from the tabyls vignette.

Contact me

You are welcome to: