Recode (or "cut" / "bin") data into groups of values.

This functions divides the range of variables into intervals and recodes the values inside these intervals according to their related interval. It is basically a wrapper around base R's cut(), providing a simplified and more accessible way to define the interval breaks (cut-off values).

Usage

categorize(x, ...)

# S3 method for class 'numeric'
categorize(
  x,
  split = "median",
  n_groups = NULL,
  range = NULL,
  lowest = 1,
  breaks = "exclusive",
  labels = NULL,
  verbose = TRUE,
  ...
)

# S3 method for class 'data.frame'
categorize(
  x,
  select = NULL,
  exclude = NULL,
  split = "median",
  n_groups = NULL,
  range = NULL,
  lowest = 1,
  breaks = "exclusive",
  labels = NULL,
  append = FALSE,
  ignore_case = FALSE,
  regex = FALSE,
  verbose = TRUE,
  ...
)

Arguments

x

A (grouped) data frame, numeric vector or factor.

...

not used.

split

Character vector, indicating at which breaks to split variables, or numeric values with values indicating breaks. If character, may be one of "median", "mean", "quantile", "equal_length", or "equal_range". "median" or "mean" will return dichotomous variables, split at their mean or median, respectively. "quantile" and "equal_length" will split the variable into n_groups groups, where each group refers to an interval of a specific range of values. Thus, the length of each interval will be based on the number of groups. "equal_range" also splits the variable into multiple groups, however, the length of the interval is given, and the number of resulting groups (and hence, the number of breaks) will be determined by how many intervals can be generated, based on the full range of the variable.

n_groups

If split is "quantile" or "equal_length", this defines the number of requested groups (i.e. resulting number of levels or values) for the recoded variable(s). "quantile" will define intervals based on the distribution of the variable, while "equal_length" tries to divide the range of the variable into pieces of equal length.

range

If split = "equal_range", this defines the range of values that are recoded into a new value.

lowest

Minimum value of the recoded variable(s). If NULL (the default), for numeric variables, the minimum of the original input is preserved. For factors, the default minimum is 1. For split = "equal_range", the default minimum is always 1, unless specified otherwise in lowest.

breaks

Character, indicating whether breaks for categorizing data are "inclusive" (values indicate the upper bound of the previous group or interval) or "exclusive" (values indicate the lower bound of the next group or interval to begin). Use labels = "range" to make this behaviour easier to see.

labels

Character vector of value labels. If not NULL, categorize() will returns factors instead of numeric variables, with labels used for labelling the factor levels. Can also be "mean", "median", "range" or "observed" for a factor with labels as the mean/median, the requested range (even if not all values of that range are present in the data) or observed range (range of the actual recoded values) of each group. See 'Examples'.

verbose

Toggle warnings.

select

Variables that will be included when performing the required tasks. Can be either

• a variable specified as a literal variable name (e.g., column_name),

• a string with the variable name (e.g., "column_name"), a character vector of variable names (e.g., c("col1", "col2", "col3")), or a character vector of variable names including ranges specified via : (e.g., c("col1:col3", "col5")),

• for some functions, like data_select() or data_rename(), select can be a named character vector. In this case, the names are used to rename the columns in the output data frame. See 'Details' in the related functions to see where this option applies.

• a formula with variable names (e.g., ~column_1 + column_2),

• a vector of positive integers, giving the positions counting from the left (e.g. 1 or c(1, 3, 5)),

• a vector of negative integers, giving the positions counting from the right (e.g., -1 or -1:-3),

• one of the following select-helpers: starts_with(), ends_with(), contains(), a range using :, or regex(). starts_with(), ends_with(), and contains() accept several patterns, e.g starts_with("Sep", "Petal"). regex() can be used to define regular expression patterns.

• a function testing for logical conditions, e.g. is.numeric() (or is.numeric), or any user-defined function that selects the variables for which the function returns TRUE (like: foo <- function(x) mean(x) > 3),

• ranges specified via literal variable names, select-helpers (except regex()) and (user-defined) functions can be negated, i.e. return non-matching elements, when prefixed with a -, e.g. -ends_with(), -is.numeric or -(Sepal.Width:Petal.Length). Note: Negation means that matches are excluded, and thus, the exclude argument can be used alternatively. For instance, select=-ends_with("Length") (with -) is equivalent to exclude=ends_with("Length") (no -). In case negation should not work as expected, use the exclude argument instead.

If NULL, selects all columns. Patterns that found no matches are silently ignored, e.g. extract_column_names(iris, select = c("Species", "Test")) will just return "Species".

exclude

See select, however, column names matched by the pattern from exclude will be excluded instead of selected. If NULL (the default), excludes no columns.

append

Logical or string. If TRUE, recoded or converted variables get new column names and are appended (column bind) to x, thus returning both the original and the recoded variables. The new columns get a suffix, based on the calling function: "_r" for recode functions, "_n" for to_numeric(), "_f" for to_factor(), or "_s" for slide(). If append=FALSE, original variables in x will be overwritten by their recoded versions. If a character value, recoded variables are appended with new column names (using the defined suffix) to the original data frame.

ignore_case

Logical, if TRUE and when one of the select-helpers or a regular expression is used in select, ignores lower/upper case in the search pattern when matching against variable names.

regex

Logical, if TRUE, the search pattern from select will be treated as regular expression. When regex = TRUE, select must be a character string (or a variable containing a character string) and is not allowed to be one of the supported select-helpers or a character vector of length > 1. regex = TRUE is comparable to using one of the two select-helpers, select = contains() or select = regex(), however, since the select-helpers may not work when called from inside other functions (see 'Details'), this argument may be used as workaround.

Value

x, recoded into groups. By default x is numeric, unless labels is specified. In this case, a factor is returned, where the factor levels (i.e. recoded groups are labelled accordingly.

Splits and breaks (cut-off values)

Breaks are by default exclusive, this means that these values indicate the lower bound of the next group or interval to begin. Take a simple example, a numeric variable with values from 1 to 9. The median would be 5, thus the first interval ranges from 1-4 and is recoded into 1, while 5-9 would turn into 2 (compare cbind(1:9, categorize(1:9))). The same variable, using split = "quantile" and n_groups = 3 would define breaks at 3.67 and 6.33 (see quantile(1:9, probs = c(1/3, 2/3))), which means that values from 1 to 3 belong to the first interval and are recoded into 1 (because the next interval starts at 3.67), 4 to 6 into 2 and 7 to 9 into 3.

The opposite behaviour can be achieved using breaks = "inclusive", in which case

Recoding into groups with equal size or range

split = "equal_length" and split = "equal_range" try to divide the range of x into intervals of similar (or same) length. The difference is that split = "equal_length" will divide the range of x into n_groups pieces and thereby defining the intervals used as breaks (hence, it is equivalent to cut(x, breaks = n_groups)), while split = "equal_range" will cut x into intervals that all have the length of range, where the first interval by defaults starts at 1. The lowest (or starting) value of that interval can be defined using the lowest argument.

Selection of variables - the select argument

For most functions that have a select argument (including this function), the complete input data frame is returned, even when select only selects a range of variables. That is, the function is only applied to those variables that have a match in select, while all other variables remain unchanged. In other words: for this function, select will not omit any non-included variables, so that the returned data frame will include all variables from the input data frame.

See also

• Add a prefix or suffix to column names: data_addprefix(), data_addsuffix()

• Functions to reorder or remove columns: data_reorder(), data_relocate(), data_remove()

• Functions to reshape, pivot or rotate data frames: data_to_long(), data_to_wide(), data_rotate()

• Functions to recode data: rescale(), reverse(), categorize(), recode_values(), slide()

• Functions to standardize, normalize, rank-transform: center(), standardize(), normalize(), ranktransform(), winsorize()

• Split and merge data frames: data_partition(), data_merge()

• Functions to find or select columns: data_select(), extract_column_names()

• Functions to filter rows: data_match(), data_filter()

Examples

set.seed(123)
x <- sample(1:10, size = 50, replace = TRUE)

table(x)
#> x
#>  1  2  3  4  5  6  7  8  9 10 
#>  2  3  5  3  7  5  5  2 11  7 

# by default, at median
table(categorize(x))
#> 
#>  1  2 
#> 25 25 

# into 3 groups, based on distribution (quantiles)
table(categorize(x, split = "quantile", n_groups = 3))
#> 
#>  1  2  3 
#> 13 19 18 

# into 3 groups, user-defined break
table(categorize(x, split = c(3, 5)))
#> 
#>  1  2  3 
#>  5  8 37 

set.seed(123)
x <- sample(1:100, size = 500, replace = TRUE)

# into 5 groups, try to recode into intervals of similar length,
# i.e. the range within groups is the same for all groups
table(categorize(x, split = "equal_length", n_groups = 5))
#> 
#>   1   2   3   4   5 
#>  89 116  96  94 105 

# into 5 groups, try to return same range within groups
# i.e. 1-20, 21-40, 41-60, etc. Since the range of "x" is
# 1-100, and we have a range of 20, this results into 5
# groups, and thus is for this particular case identical
# to the previous result.
table(categorize(x, split = "equal_range", range = 20))
#> 
#>   1   2   3   4   5 
#>  89 116  96  94 105 

# return factor with value labels instead of numeric value
set.seed(123)
x <- sample(1:10, size = 30, replace = TRUE)
categorize(x, "equal_length", n_groups = 3)
#>  [1] 1 1 3 1 2 2 2 2 3 3 2 1 3 3 3 1 3 3 3 3 3 1 2 1 3 2 3 3 3 3
categorize(x, "equal_length", n_groups = 3, labels = c("low", "mid", "high"))
#>  [1] low  low  high low  mid  mid  mid  mid  high high mid  low  high high high
#> [16] low  high high high high high low  mid  low  high mid  high high high high
#> Levels: low mid high

# cut numeric into groups with the mean or median as a label name
x <- sample(1:10, size = 30, replace = TRUE)
categorize(x, "equal_length", n_groups = 3, labels = "mean")
#>  [1] 8.45 8.45 5.33 8.45 5.33 5.33 8.45 1.57 5.33 8.45 1.57 1.57 8.45 8.45 5.33
#> [16] 5.33 8.45 8.45 5.33 5.33 8.45 5.33 5.33 8.45 1.57 5.33 1.57 1.57 1.57 5.33
#> Levels: 1.57 5.33 8.45
categorize(x, "equal_length", n_groups = 3, labels = "median")
#>  [1] 9.00 9.00 5.50 9.00 5.50 5.50 9.00 2.00 5.50 9.00 2.00 2.00 9.00 9.00 5.50
#> [16] 5.50 9.00 9.00 5.50 5.50 9.00 5.50 5.50 9.00 2.00 5.50 2.00 2.00 2.00 5.50
#> Levels: 2.00 5.50 9.00

# cut numeric into groups with the requested range as a label name
# each category has the same range, and labels indicate this range
categorize(mtcars$mpg, "equal_length", n_groups = 5, labels = "range")
#>  [1] [19.8,24.5) [19.8,24.5) [19.8,24.5) [19.8,24.5) [15.1,19.8) [15.1,19.8)
#>  [7] [10.4,15.1) [19.8,24.5) [19.8,24.5) [15.1,19.8) [15.1,19.8) [15.1,19.8)
#> [13] [15.1,19.8) [15.1,19.8) [10.4,15.1) [10.4,15.1) [10.4,15.1) [29.2,33.9]
#> [19] [29.2,33.9] [29.2,33.9] [19.8,24.5) [15.1,19.8) [15.1,19.8) [10.4,15.1)
#> [25] [15.1,19.8) [24.5,29.2) [24.5,29.2) [29.2,33.9] [15.1,19.8) [15.1,19.8)
#> [31] [10.4,15.1) [19.8,24.5)
#> Levels: [10.4,15.1) [15.1,19.8) [19.8,24.5) [24.5,29.2) [29.2,33.9]
# in this example, each category has the same range, but labels only refer
# to the ranges of the actual values (present in the data) inside each group
categorize(mtcars$mpg, "equal_length", n_groups = 5, labels = "observed")
#>  [1] (21-24.4)   (21-24.4)   (21-24.4)   (21-24.4)   (15.2-19.7) (15.2-19.7)
#>  [7] (10.4-15)   (21-24.4)   (21-24.4)   (15.2-19.7) (15.2-19.7) (15.2-19.7)
#> [13] (15.2-19.7) (15.2-19.7) (10.4-15)   (10.4-15)   (10.4-15)   (30.4-33.9)
#> [19] (30.4-33.9) (30.4-33.9) (21-24.4)   (15.2-19.7) (15.2-19.7) (10.4-15)  
#> [25] (15.2-19.7) (26-27.3)   (26-27.3)   (30.4-33.9) (15.2-19.7) (15.2-19.7)
#> [31] (10.4-15)   (21-24.4)  
#> Levels: (10.4-15) (15.2-19.7) (21-24.4) (26-27.3) (30.4-33.9)