Recode (or "cut") data into groups of values.Source:
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).
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, ... )
A (grouped) data frame, numeric vector or factor.
Character vector, indicating at which breaks to split variables, or numeric values with values indicating breaks. If character, may be one of
"mean"will return dichotomous variables, split at their mean or median, respectively.
"equal_length"will split the variable into
n_groupsgroups, 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.
"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.
split = "equal_range", this defines the range of values that are recoded into a new value.
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
split = "equal_range", the default minimum is always
1, unless specified otherwise in
Character vector of value labels. If not
categorize()will returns factors instead of numeric variables, with
labelsused for labelling the factor levels. Can also be
"median"for a factor with labels as the mean/median of each groups.
Variables that will be included when performing the required tasks. Can be either
a variable specified as a literal variable name (e.g.,
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.
c(1, 3, 5)),
a vector of negative integers, giving the positions counting from the right (e.g.,
one of the following select-helpers:
contains(), a range using
contains()accept several patterns, e.g
or a function testing for logical conditions, e.g.
is.numeric), or any user-defined function that selects the variables for which the function returns
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
-(Sepal.Width:Petal.Length). Note: Negation means that matches are excluded, and thus, the
excludeargument can be used alternatively. For instance,
-) is equivalent to
-). In case negation should not work as expected, use the
NULL, selects all columns. Patterns that found no matches are silently ignored, e.g.
find_columns(iris, select = c("Species", "Test"))will just return
select, however, column names matched by the pattern from
excludewill be excluded instead of selected. If
NULL(the default), excludes no columns.
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,
append=FALSE, original variables in
xwill 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.
TRUEand 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.
TRUE, the search pattern from
selectwill 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 = TRUEis 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.
x, recoded into groups. By default
x is numeric, unless
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,
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
x into intervals of similar (or same) length. The difference is
split = "equal_length" will divide the range of
pieces and thereby defining the intervals used as breaks (hence, it is
cut(x, breaks = n_groups)), while
split = "equal_range"
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
Selection of variables - the
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.
Functions to rename stuff:
Functions to reorder or remove columns:
Functions to reshape, pivot or rotate data frames:
Functions to recode data:
Functions to standardize, normalize, rank-transform:
Split and merge data frames:
Functions to find or select columns:
Functions to filter rows:
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 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")) #>  low low high low mid mid mid mid high high mid low high high high #>  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") #>  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 #>  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") #>  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 #>  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