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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)

janitor

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 already do everything covered here, but with janitor they can do it faster and save their thinking for the fun stuff.

The main janitor functions:

  • perfectly format data.frame column names;
  • create and format frequency tables of one, two, or three variables - think an improved table(); and
  • provide other tools for cleaning and examining data.frames.

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 the readr and readxl packages.

Installation

You can install:

  • the most recent officially-released version from CRAN with

    install.packages("janitor")

  • the latest development version from GitHub with

    install.packages("devtools")
devtools::install_github("sfirke/janitor")

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, like compare_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 file dirty_data.xlsx:

Dirtiness includes:

  • A header at the top
  • Dreadful column names
  • Rows and columns containing Excel formatting but no data
  • Dates in two different formats in a single column (MM/DD/YYYY and numbers)
  • Values spread inconsistently over the “Certification” columns

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:` <chr> "First Name", "Jason", "Jason", "Alicia", "Ada", "Desus", "Chien-…
#> $ ...2                               <chr> "Last Name", "Bourne", "Bourne", "Keys", "Lovelace", "Nice", "Wu"…
#> $ ...3                               <chr> "Employee Status", "Teacher", "Teacher", "Teacher", "Teacher", "A…
#> $ `Dec-27 2020`                      <chr> "Subject", "PE", "Drafting", "Music", NA, "Dean", "Physics", "Che…
#> $ ...5                               <chr> "Hire Date", "39690", "43479", "37118", "38572", "42791", "11037"…
#> $ ...6                               <chr> "% Allocated", "0.75", "0.25", "1", "1", "1", "0.5", "0.5", NA, "…
#> $ ...7                               <chr> "Full time?", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", NA…
#> $ ...8                               <chr> "do not edit! --->", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
#> $ ...9                               <chr> "Certification", "Physical ed", "Physical ed", "Instr. music", "P…
#> $ ...10                              <chr> "Certification", "Theater", "Theater", "Vocal music", "Computers"…
#> $ ...11                              <chr> "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        <chr> "Jason", "Jason", "Alicia", "Ada", "Desus", "Chien-Shiung", "Chien-Shiung", NA, "J…
#> $ last_name         <chr> "Bourne", "Bourne", "Keys", "Lovelace", "Nice", "Wu", "Wu", NA, "Joyce", "Lamarr",…
#> $ employee_status   <chr> "Teacher", "Teacher", "Teacher", "Teacher", "Administration", "Teacher", "Teacher"…
#> $ subject           <chr> "PE", "Drafting", "Music", NA, "Dean", "Physics", "Chemistry", NA, "English", "Sci…
#> $ hire_date         <dbl> 39690, 43479, 37118, 38572, 42791, 11037, 11037, NA, 36423, 27919, 42221, 34700, 4…
#> $ percent_allocated <dbl> 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         <chr> "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", NA, "No", "No", "No", "No", "No"
#> $ do_not_edit       <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
#> $ certification     <chr> "Physical ed", "Physical ed", "Instr. music", "PENDING", "PENDING", "Science 6-12"…
#> $ certification_2   <chr> "Theater", "Theater", "Vocal music", "Computers", NA, "Physics", "Physics", NA, "E…
#> $ active            <chr> "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 with clean_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          
#>    <chr>        <chr>     <chr>           <chr>      <date>     <chr>             <chr>     <chr>         
#>  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         <NA>       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 <NA>              No        Physical ed   
#> 11 Young        Boozer    Coach           <NA>       1995-01-01 <NA>              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     
#>   <chr>        <chr>          <int> <chr>           <chr>     <date>     <chr>             <chr>     <chr>    
#> 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:

  • On a vector, when tabulating a single variable: tabyl(roster$subject)
  • On a data.frame, specifying 1, 2, or 3 variable names to tabulate: roster %>% tabyl(subject, employee_status).
    • Here the data.frame is passed in with the %>% pipe; this allows tabyl to be used in an analysis pipeline

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
#>        <NA> 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.

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Version

Install

install.packages('janitor')

Monthly Downloads

317,136

Version

2.2.0

License

MIT + file LICENSE

Issues

33

Pull Requests

3

Stars

1,398

Forks

130

Maintainer

Sam Firke

Last Published

February 2nd, 2023

Functions in janitor (2.2.0)

adorn_ns

Add underlying Ns to a tabyl displaying percentages.
adorn_crosstab

Add presentation formatting to a crosstabulation table.
adorn_totals

Append a totals row and/or column to a data.frame.
describe_class

Describe the class(es) of an object
crosstab

Generate a crosstabulation of two vectors.
%>%

Pipe operator
chisq.test

Apply stats::chisq.test to a two-way tabyl
clean_names

Cleans names of an object (usually a data.frame).
mu_to_u

Constant to help map from mu to u
adorn_title

Add column name to the top of a two-way tabyl.
remove_empty

Remove empty rows and/or columns from a data.frame or matrix.
remove_constant

Remove constant columns from a data.frame or matrix.
as_tabyl

Add tabyl attributes to a data.frame.
get_one_to_one

Find the list of columns that have a 1:1 mapping to each other
janitor

janitor
compare_df_cols

Generate a comparison of data.frames (or similar objects) that indicates if they will successfully bind together by rows.
round_to_fraction

Round to the nearest fraction of a specified denominator.
compare_df_cols_same

Do the the data.frames have the same columns & types?
round_half_up

Round a numeric vector; halves will be rounded up, ala Microsoft Excel.
excel_numeric_to_date

Convert dates encoded as serial numbers to Date class.
find_header

Find the header row in a data.frame
remove_empty_rows

Removes empty rows from a data.frame.
make_clean_names

Cleans a vector of text, typically containing the names of an object.
row_to_names

Elevate a row to be the column names of a data.frame.
untabyl

Remove tabyl attributes from a data.frame.
remove_empty_cols

Removes empty columns from a data.frame.
janitor_deprecated

Deprecated Functions in Package janitor
sas_numeric_to_date

Convert a SAS date, time or date/time to an R object
use_first_valid_of

Returns first non-NA value from a set of vectors.
signif_half_up

Round a numeric vector to the specified number of significant digits; halves will be rounded up.
single_value

Ensure that a vector has only a single value throughout.
fisher.test

Apply stats::fisher.test to a two-way tabyl
get_dupes

Get rows of a data.frame with identical values for the specified variables.
convert_to_date

Convert many date and datetime formats as may be received from Microsoft Excel
top_levels

Generate a frequency table of a factor grouped into top-n, bottom-n, and all other levels.
tabyl

Generate a frequency table (1-, 2-, or 3-way).
convert_to_NA

Convert string values to true NA values.
adorn_percentages

Convert a data.frame of counts to percentages.
add_totals_row

Append a totals row to a data.frame.
adorn_pct_formatting

Format a data.frame of decimals as percentages.
adorn_rounding

Round the numeric columns in a data.frame.
add_totals_col

Append a totals column to a data.frame.