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 numeric
categorize(
x,
split = "median",
n_groups = NULL,
range = NULL,
lowest = 1,
labels = NULL,
verbose = TRUE,
...
)
# S3 method for data.frame
categorize(
x,
select = NULL,
exclude = NULL,
split = "median",
n_groups = NULL,
range = NULL,
lowest = 1,
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`

.- 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"`

or`"median"`

for a factor with labels as the mean/median of each groups.- 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"`

), or a character vector of variable names (e.g.,`c("col1", "col2", "col3")`

),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")`

.or 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.`find_columns(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 in general *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.

## 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

Functions to rename stuff:

`data_rename()`

,`data_rename_rows()`

,`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()`

,`data_find()`

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
```