Catalog of janitor functions

2017-11-03

The janitor functions expedite the initial data exploration and cleaning that comes with any new data set. This catalog describes the usage for each function.

Major functions

Functions for everyday use.

Cleaning

Clean data.frame names with clean_names()

Call this function every time you read data.

It works in a %>% pipeline, and handles problematic variable names, especially those that are so well preserved by readxl::read_excel() and readr::read_csv().

# Load dplyr for the %>% pipe
library(dplyr)
# Create a data.frame with dirty names
test_df <- as.data.frame(matrix(ncol = 6))
names(test_df) <- c("hIgHlo", "REPEAT VALUE", "REPEAT VALUE",
                    "% successful (2009)",  "abc@!*", "")

Clean the variable names, returning a data.frame:

test_df %>%
  clean_names()
#>   h_ig_hlo repeat_value repeat_value_2 percent_successful_2009 abc  x
#> 1       NA           NA             NA                      NA  NA NA

Compare to what base R produces:

make.names(names(test_df))
#> [1] "hIgHlo"               "REPEAT.VALUE"         "REPEAT.VALUE"        
#> [4] "X..successful..2009." "abc..."               "X"

Exploring

tabyl() - a better version of table()

tabyl() is a tidyverse-oriented replacement for table(). It counts combinations of one, two, or three variables, and then can be formatted with a suite of adorn_* functions to look just how you want. For instance:

mtcars %>%
  tabyl(gear, cyl) %>%
  adorn_totals("col") %>%
  adorn_percentages("row") %>%
  adorn_pct_formatting(digits = 2) %>%
  adorn_ns()
#>  gear          4          6           8        Total
#>     3  6.67% (1) 13.33% (2) 80.00% (12) 100.00% (15)
#>     4 66.67% (8) 33.33% (4)  0.00%  (0) 100.00% (12)
#>     5 40.00% (2) 20.00% (1) 40.00%  (2) 100.00%  (5)

Learn more in the tabyls vignette.

Explore records with duplicated values for specific combinations of variables with get_dupes()

This is for hunting down and examining duplicate records during data cleaning - usually when there shouldn't be any.

For example, in a tidy data frame you might expect to have a unique ID repeated for each year, and year repeated for each unique ID, but no duplicated pairs of unique ID & year. Say you want to check for their presence, and study any such duplicated records.

get_dupes() returns the records (and inserts a count of duplicates) so you can sleuth out the problematic cases:

get_dupes(mtcars, wt, cyl) # or mtcars %>% get_dupes(wt, cyl) if you prefer to pipe
#> # A tibble: 4 x 12
#>      wt   cyl dupe_count   mpg  disp    hp  drat  qsec    vs    am  gear
#>   <dbl> <dbl>      <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1  3.44     6          2  19.2 167.6   123  3.92 18.30     1     0     4
#> 2  3.44     6          2  17.8 167.6   123  3.92 18.90     1     0     4
#> 3  3.57     8          2  14.3 360.0   245  3.21 15.84     0     0     3
#> 4  3.57     8          2  15.0 301.0   335  3.54 14.60     0     1     5
#> # ... with 1 more variables: carb <dbl>

Minor functions

Smaller functions for use in particular situations. More human-readable than the equivalent code they replace.

Cleaning

Fix dates stored as serial numbers with excel_numeric_to_date()

Ever load data from Excel and see a value like 42223 where a date should be? This function converts those serial numbers to class Date, and contains an option for specifying the alternate date system for files created with Excel for Mac 2008 and earlier versions (which count from a different starting point).

excel_numeric_to_date(41103)
#> [1] "2012-07-13"
excel_numeric_to_date(41103, date_system = "mac pre-2011")
#> [1] "2016-07-14"

remove_empty_cols() and remove_empty_rows()

One-line wrapper functions that do what they say. For cases like cleaning Excel files containing empty rows and columns.

q <- data.frame(v1 = c(1, NA, 3),
                v2 = c(NA, NA, NA),
                v3 = c("a", NA, "b"))
q %>%
  remove_empty_cols() %>%
  remove_empty_rows()
#>   v1 v3
#> 1  1  a
#> 3  3  b

Exploring

Count factor levels in groups of high, medium, and low with top_levels()

Originally designed for use with Likert survey data stored as factors. Returns a tbl_df frequency table with appropriately-named rows, grouped into head/middle/tail groups.

f <- factor(c("strongly agree", "agree", "neutral", "neutral", "disagree", "strongly agree"),
            levels = c("strongly agree", "agree", "neutral", "disagree", "strongly disagree"))
top_levels(f)
#>                            f n   percent
#>        strongly agree, agree 3 0.5000000
#>                      neutral 2 0.3333333
#>  disagree, strongly disagree 1 0.1666667
top_levels(f, n = 1)
#>                         f n   percent
#>            strongly agree 2 0.3333333
#>  agree, neutral, disagree 4 0.6666667
#>         strongly disagree 0 0.0000000