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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”

  • The 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
  • isolate partially-duplicate records.

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")

janitor 2.0.0 is out!

This marks a major release for janitor, with many new functions and some breaking changes that may affect existing code. Please see the NEWS page to learn more about new capabilities.

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:

  • Dreadful column names
  • Rows and columns containing Excel formatting but no data
  • Dates stored as 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 http://github.com/sfirke/janitor
glimpse(roster_raw)
#> Rows: 13
#> Columns: 11
#> $ `First Name`        <chr> "Jason", "Jason", "Alicia", "Ada", "Desus", "Chien-Shiung", "Chien-Shiung", N...
#> $ `Last Name`         <chr> "Bourne", "Bourne", "Keys", "Lovelace", "Nice", "Wu", "Wu", NA, "Joyce", "Lam...
#> $ `Employee Status`   <chr> "Teacher", "Teacher", "Teacher", "Teacher", "Administration", "Teacher", "Tea...
#> $ Subject             <chr> "PE", "Drafting", "Music", NA, "Dean", "Physics", "Chemistry", NA, "English",...
#> $ `Hire Date`         <dbl> 39690, 39690, 37118, 27515, 41431, 11037, 11037, NA, 32994, 27919, 42221, 347...
#> $ `% 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", ...
#> $ `do not edit! --->` <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
#> $ Certification...9   <chr> "Physical ed", "Physical ed", "Instr. music", "PENDING", "PENDING", "Science ...
#> $ Certification...10  <chr> "Theater", "Theater", "Vocal music", "Computers", NA, "Physics", "Physics", N...
#> $ Certification...11  <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA

Excel formatting led to an untitled empty column and 5 empty rows at the bottom of the table (only 12 records have any actual data). Bad column names are preserved.

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"), 
                                 .name_repair = make_clean_names) 
# Tells read_excel() how to repair repetitive column names, overriding the
# default repair setting
glimpse(roster_raw_cleaner)
#> Rows: 13
#> Columns: 11
#> $ first_name        <chr> "Jason", "Jason", "Alicia", "Ada", "Desus", "Chien-Shiung", "Chien-Shiung", NA,...
#> $ last_name         <chr> "Bourne", "Bourne", "Keys", "Lovelace", "Nice", "Wu", "Wu", NA, "Joyce", "Lamar...
#> $ employee_status   <chr> "Teacher", "Teacher", "Teacher", "Teacher", "Administration", "Teacher", "Teach...
#> $ subject           <chr> "PE", "Drafting", "Music", NA, "Dean", "Physics", "Chemistry", NA, "English", "...
#> $ hire_date         <dbl> 39690, 39690, 37118, 27515, 41431, 11037, 11037, NA, 32994, 27919, 42221, 34700...
#> $ 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-...
#> $ certification_2   <chr> "Theater", "Theater", "Vocal music", "Computers", NA, "Physics", "Physics", NA,...
#> $ certification_3   <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA

This can be further cleaned:

roster <- roster_raw_cleaner %>%
  remove_empty(c("rows", "cols")) %>%
  mutate(hire_date = excel_numeric_to_date(hire_date),
         cert = coalesce(certification, certification_2)) %>% # from dplyr
  select(-certification, -certification_2) # drop unwanted columns

roster
#> # A tibble: 12 x 8
#>    first_name   last_name employee_status subject    hire_date  percent_allocated full_time cert          
#>    <chr>        <chr>     <chr>           <chr>      <date>                 <dbl> <chr>     <chr>         
#>  1 Jason        Bourne    Teacher         PE         2008-08-30              0.75 Yes       Physical ed   
#>  2 Jason        Bourne    Teacher         Drafting   2008-08-30              0.25 Yes       Physical ed   
#>  3 Alicia       Keys      Teacher         Music      2001-08-15              1    Yes       Instr. music  
#>  4 Ada          Lovelace  Teacher         <NA>       1975-05-01              1    Yes       PENDING       
#>  5 Desus        Nice      Administration  Dean       2013-06-06              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    1990-05-01              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

clean_names() is a convenience version that can be used for piped data.frame workflows:

data("iris")
names(iris) # before cleaning:
#> [1] "Sepal.Length" "Sepal.Width"  "Petal.Length" "Petal.Width"  "Species"

iris %>% 
  clean_names() %>% 
  names() # after cleaning:
#> [1] "sepal_length" "sepal_width"  "petal_length" "petal_width"  "species"

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 x 9
#>   first_name   last_name dupe_count employee_status subject   hire_date  percent_allocat~ full_time cert      
#>   <chr>        <chr>          <int> <chr>           <chr>     <date>                <dbl> <chr>     <chr>     
#> 1 Chien-Shiung Wu                 2 Teacher         Physics   1930-03-20             0.5  Yes       Science 6~
#> 2 Chien-Shiung Wu                 2 Teacher         Chemistry 1930-03-20             0.5  Yes       Science 6~
#> 3 Jason        Bourne             2 Teacher         PE        2008-08-30             0.75 Yes       Physical ~
#> 4 Jason        Bourne             2 Teacher         Drafting  2008-08-30             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 can be called two ways:

  • On a vector, when tabulating a single variable - e.g., 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

tabyl()

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

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

227,503

Version

2.0.1

License

MIT + file LICENSE

Issues

33

Pull Requests

4

Stars

1,399

Forks

130

Maintainer

Sam Firke

Last Published

April 12th, 2020

Functions in janitor (2.0.1)

adorn_rounding

Round the numeric columns in a data.frame.
adorn_title

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

Convert dates encoded as serial numbers to Date class.
compare_df_cols_same

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

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

Add tabyl attributes to a data.frame.
round_half_up

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

Removes empty rows from a data.frame.
use_first_valid_of

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

Remove tabyl attributes from a data.frame.
make_clean_names

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

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

Deprecated Functions in Package janitor
top_levels

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

Format a data.frame of decimals as percentages.
convert_to_NA

Convert string values to true NA values.
adorn_percentages

Convert a data.frame of counts to percentages.
convert_to_date

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

Append a totals column to a data.frame.
add_totals_row

Append a totals row to a data.frame.
crosstab

Generate a crosstabulation of two vectors.
chisq.test

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

Describe the class(es) of an object
clean_names

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

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

Pipe operator
remove_constant

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

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

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

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

Removes empty columns from a data.frame.
round_to_fraction

Round to the nearest fraction of a specified denominator.
janitor

janitor
row_to_names

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