janitor has simple little functions for examining and cleaning dirty data. An intermediate R user can already do all of this, but with janitor you can save your brainpower for the fun stuff.
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 is not yet on CRAN. Install the development version from GitHub:
# install.packages("devtools")
devtools::install_github("sfirke/janitor")Below are quick examples of how janitor functions are commonly used. A full description of each function can be found in janitor's catalog of functions.
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.
Take this roster of teachers at a fictional American high school, stored in the Microsoft Excel file dirty_data.xlsx: 
Dirtiness includes:
Here's that data after being read in to R:
library(pacman) # for loading packages
p_load(readxl, janitor, dplyr)
roster_raw <- read_excel("dirty_data.xlsx") # available at http://github.com/sfirke/janitor
glimpse(roster_raw)
#> Observations: 17
#> Variables: 12
#> $ 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...
#> $ % 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, NA, NA,...
#> $ Full time? <chr> "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", NA, "No", "No", "No", "No", "N...
#> $ do not edit! ---> <dbl> NA, NA, NA, NA, 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 <chr> "Theater", "Theater", "Vocal music", "Computers", NA, "Physics", "Physics", NA,...
#> $ Certification <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
#> $ <dttm> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NAExcel 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.
Clean it with janitor functions:
roster <- roster_raw %>%
clean_names() %>%
remove_empty_rows() %>%
remove_empty_cols() %>%
convert_to_NA(c("TBD", "PENDING")) %>%
mutate(hire_date = excel_numeric_to_date(hire_date),
main_cert = use_first_valid_of(certification, certification_2)) %>%
select(-certification, -certification_2) # drop unwanted columns
roster
#> # A tibble: 12 × 8
#> first_name last_name employee_status subject hire_date percent_allocated full_time main_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 1.00 Yes Instr. music
#> 4 Ada Lovelace Teacher <NA> 1975-05-01 1.00 Yes Computers
#> 5 Desus Nice Administration Dean 2013-06-06 1.00 Yes <NA>
#> 6 Chien-Shiung Wu Teacher Physics 1930-03-20 0.50 Yes Science 6-12
#> 7 Chien-Shiung Wu Teacher Chemistry 1930-03-20 0.50 Yes Science 6-12
#> 8 James Joyce Teacher English 1990-05-01 0.50 No English 6-12
#> 9 Hedy Lamarr Teacher Science 1976-06-08 0.50 No <NA>
#> 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 0.80 No Vocal musicThe core janitor cleaning function is clean_names() - call it whenever you load data into R.
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(first_name, last_name)
#> # A tibble: 4 × 9
#> first_name last_name dupe_count employee_status subject hire_date percent_allocated full_time
#> <chr> <chr> <int> <chr> <chr> <date> <dbl> <chr>
#> 1 Chien-Shiung Wu 2 Teacher Physics 1930-03-20 0.50 Yes
#> 2 Chien-Shiung Wu 2 Teacher Chemistry 1930-03-20 0.50 Yes
#> 3 Jason Bourne 2 Teacher PE 2008-08-30 0.75 Yes
#> 4 Jason Bourne 2 Teacher Drafting 2008-08-30 0.25 Yes
#> # ... with 1 more variables: main_cert <chr>Yes, some teachers appear twice. We ought to address this before counting employees.
janitor has several functions for quick counts. The big ones are tabyl() and crosstab().
Notably, they can be called two ways:
tabyl(roster$subject)roster %>% tabyl(subject).
Like table(), but pipe-able and more functional.
roster %>%
tabyl(subject)
#> subject n percent valid_percent
#> 1 Basketball 1 0.08333333 0.1
#> 2 Chemistry 1 0.08333333 0.1
#> 3 Dean 1 0.08333333 0.1
#> 4 Drafting 1 0.08333333 0.1
#> 5 English 2 0.16666667 0.2
#> 6 Music 1 0.08333333 0.1
#> 7 PE 1 0.08333333 0.1
#> 8 Physics 1 0.08333333 0.1
#> 9 Science 1 0.08333333 0.1
#> 10 <NA> 2 0.16666667 NAroster %>%
filter(hire_date > as.Date("1950-01-01")) %>%
crosstab(employee_status, full_time)
#> employee_status No Yes
#> 1 Administration 0 1
#> 2 Coach 2 0
#> 3 Teacher 3 4Other janitor 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, sort = TRUE) %>%
add_totals_row()
#> employee_status n percent
#> 1 Teacher 9 0.75000000
#> 2 Coach 2 0.16666667
#> 3 Administration 1 0.08333333
#> 4 Total 12 1.00000000
roster %>%
crosstab(full_time, employee_status) %>%
adorn_crosstab(denom = "col", show_totals = TRUE)
#> full_time Administration Coach Teacher Total
#> 1 No 0.0% (0) 100.0% (2) 33.3% (3) 41.7% (5)
#> 2 Yes 100.0% (1) 0.0% (0) 66.7% (6) 58.3% (7)Together, these tabulation functions reduce R's deficit against Excel and SPSS when it comes to quick, informative counts.
You are welcome to:
