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Overview of janitor functions

2024-12-22

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().

# Create a data.frame with dirty names
test_df <- as.data.frame(matrix(ncol = 6))
names(test_df) <- c("firstName", "ábc@!*", "% successful (2009)",
                    "REPEAT VALUE", "REPEAT VALUE", "")

Clean the variable names, returning a data.frame:

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

Compare to what base R produces:

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

This function is powered by the underlying exported function make_clean_names(), which accepts and returns a character vector of names (see below). This allows for cleaning the names of any object, not just a data.frame. clean_names() is retained for its convenience in piped workflows, and can be called on an sf simple features object or a tbl_graph tidygraph object in addition to a data.frame.

Do those data.frames actually contain the same columns?

Check with compare_df_cols()

For cases when you are given a set of data files that should be identical, and you wish to read and combine them for analysis. But then dplyr::bind_rows() or rbind() fails, because of different columns or because the column classes don’t match across data.frames.

compare_df_cols() takes unquoted names of data.frames / tibbles, or a list of data.frames, and returns a summary of how they compare. See what the column types are, which are missing or present in the different inputs, and how column types differ.

df1 <- data.frame(a = 1:2, b = c("big", "small"))
df2 <- data.frame(a = 10:12, b = c("medium", "small", "big"), c = 0, stringsAsFactors = TRUE) # here, column b is a factor
df3 <- df1 %>%
  dplyr::mutate(b = as.character(b))

compare_df_cols(df1, df2, df3)
#>   column_name       df1     df2       df3
#> 1           a   integer integer   integer
#> 2           b character  factor character
#> 3           c      <NA> numeric      <NA>

compare_df_cols(df1, df2, df3, return = "mismatch")
#>   column_name       df1    df2       df3
#> 1           b character factor character
compare_df_cols(df1, df2, df3, return = "mismatch", bind_method = "rbind") # default is dplyr::bind_rows
#>   column_name       df1     df2       df3
#> 1           b character  factor character
#> 2           c      <NA> numeric      <NA>

compare_df_cols_same() returns TRUE or FALSE indicating if the data.frames can be successfully row-bound with the given binding method:

compare_df_cols_same(df1, df3)
#> [1] TRUE
compare_df_cols_same(df2, df3)
#>   column_name    ..1       ..2
#> 1           b factor character
#> [1] FALSE

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() %>%
  adorn_title()
#>              cyl                                    
#>  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, but no duplicated pairs of unique ID & year. Say you want to check for and study any such duplicated records.

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

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

Explore relationships between columns with get_one_to_one()

This function shows which, if any, columns in a data.frame have one-to-one relationships with each other.

Here is a toy example looking at the first four rows of the starwars data.frame from the dplyr package. The variables are grouped into three sets of one-to-one clusters:

library(dplyr)
starwars[1:4,] %>%
  get_one_to_one()
#> [[1]]
#> [1] "name"       "height"     "mass"       "skin_color" "birth_year"
#> [6] "films"     
#> 
#> [[2]]
#> [1] "hair_color" "starships" 
#> 
#> [[3]]
#> [1] "sex"     "species"

Minor functions

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

Cleaning

Manipulate vectors of names with make_clean_names()

Like base R’s make.names(), but with the stylings and case choice of the long-time janitor function clean_names(). While clean_names() is still offered for use in data.frame pipeline with %>%, make_clean_names() allows for more general usage, e.g., on a vector.

It can also be used as an argument to .name_repair in the newest version of tibble::as_tibble:

tibble::as_tibble(iris, .name_repair = janitor::make_clean_names)
#> # A tibble: 150 × 5
#>    sepal_length sepal_width petal_length petal_width species
#>           <dbl>       <dbl>        <dbl>       <dbl> <fct>  
#>  1          5.1         3.5          1.4         0.2 setosa 
#>  2          4.9         3            1.4         0.2 setosa 
#>  3          4.7         3.2          1.3         0.2 setosa 
#>  4          4.6         3.1          1.5         0.2 setosa 
#>  5          5           3.6          1.4         0.2 setosa 
#>  6          5.4         3.9          1.7         0.4 setosa 
#>  7          4.6         3.4          1.4         0.3 setosa 
#>  8          5           3.4          1.5         0.2 setosa 
#>  9          4.4         2.9          1.4         0.2 setosa 
#> 10          4.9         3.1          1.5         0.1 setosa 
#> # ℹ 140 more rows

Validate that a column has a single_value() per group

This function returns the single value in a column, often used in combination with dplyr::group_by() to validate that every value of X has only one associated value of Y. It will complete that value of Y into missing values in that column. And if there’s more than a single value of Y, its info argument can help pinpoint where that occurs.

Take this data.frame. One pesky value of X has multiple values of Y where it should not:

not_one_to_one <- data.frame(
  X = rep(1:3, each = 2),
  Y = c(rep(1:2, each = 2), 1:2))

not_one_to_one
#>   X Y
#> 1 1 1
#> 2 1 1
#> 3 2 2
#> 4 2 2
#> 5 3 1
#> 6 3 2

# throws informative error:
try(not_one_to_one %>%
      dplyr::group_by(X) %>%
      dplyr::mutate(
        Z = single_value(Y, info = paste("Calculating Z for group X =", X)))
      )
#> Error in dplyr::mutate(., Z = single_value(Y, info = paste("Calculating Z for group X =",  : 
#>   ℹ In argument: `Z = single_value(Y, info = paste("Calculating Z for
#>   group X =", X))`.
#> ℹ In group 3: `X = 3`.
#> Caused by error in `single_value()`:
#> ! More than one (2) value found (1, 2): Calculating Z for group X = 3: Calculating Z for group X = 3

remove_empty() rows and columns

Does what it says. For cases like cleaning Excel files that contain empty rows and columns after being read into R.

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

Just a simple wrapper for one-line functions, but it saves a little thinking for both the code writer and the reader.

remove_constant() columns

Drops columns from a data.frame that contain only a single constant value (with an na.rm option to control whether NAs should be considered as different values from the constant).

remove_constant and remove_empty work on matrices as well as data.frames.

a <- data.frame(good = 1:3, boring = "the same")
a %>% remove_constant()
#>   good
#> 1    1
#> 2    2
#> 3    3

Directionally-consistent rounding behavior with round_half_up()

R uses “banker’s rounding”, i.e., halves are rounded to the nearest even number. This function, an exact implementation of https://stackoverflow.com/questions/12688717/round-up-from-5/12688836#12688836, will round all halves up. Compare:

nums <- c(2.5, 3.5)
round(nums)
#> [1] 2 4
round_half_up(nums)
#> [1] 3 4

Round decimals to precise fractions of a given denominator with round_to_fraction()

Say your data should only have values of quarters: 0, 0.25, 0.5, 0.75, 1, etc. But there are either user-entered bad values like 0.2 or floating-point precision problems like 0.25000000001. round_to_fraction() will enforce the desired fractional distribution by rounding the values to the nearest value given the specified denominator.

There’s also a digits argument for optional subsequent rounding.

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, with options for different Excel date encoding systems, preserving fractions of a date as time (in which case the returned value is of class POSIXlt), and specifying a time zone.

excel_numeric_to_date(41103)
#> [1] "2012-07-13"
excel_numeric_to_date(41103.01) # ignores decimal places, returns Date object
#> [1] "2012-07-13"
excel_numeric_to_date(41103.01, include_time = TRUE) # returns POSIXlt object
#> [1] "2012-07-13 00:14:24 EDT"
excel_numeric_to_date(41103.01, date_system = "mac pre-2011")
#> [1] "2016-07-14"

Convert a mix of date and datetime formats to date

Building on excel_numeric_to_date(), the new functions convert_to_date() and convert_to_datetime() are more robust to a mix of inputs. Handy when reading many spreadsheets that should have the same column formats, but don’t.

For instance, here a vector with a date and an Excel datetime sees both values successfully converted to Date class:

convert_to_date(c("2020-02-29", "40000.1"))
#> [1] "2020-02-29" "2009-07-06"

Elevate column names stored in a data.frame row

If a data.frame has the intended variable names stored in one of its rows, row_to_names() will elevate the specified row to become the names of the data.frame and optionally (by default) remove the row in which names were stored and/or the rows above it.

dirt <- data.frame(X_1 = c(NA, "ID", 1:3),
           X_2 = c(NA, "Value", 4:6))

row_to_names(dirt, 2)
#>   ID Value
#> 3  1     4
#> 4  2     5
#> 5  3     6

Find the header row buried within a messy data.frame

The function find_header() is a companion function to row_to_names(). By default it will search a data.frame for the first row with no missing values and return that row number.

It can also be used to return the row number where a given string is present in the first column, or in any specific column. Then this result can be supplied to row_to_names().

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

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They may not be fully stable and should be used with caution. We make no claims about them.
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