Convert data to factors
to_factor(x, ...)# S3 method for numeric
to_factor(x, labels_to_levels = TRUE, verbose = TRUE, ...)
# S3 method for data.frame
to_factor(
x,
select = NULL,
exclude = NULL,
ignore_case = FALSE,
append = FALSE,
regex = FALSE,
verbose = TRUE,
...
)
A factor, or a data frame of factors.
A data frame or vector.
Arguments passed to or from other methods.
Logical, if TRUE
, value labels are used as factor
levels after x
was converted to factor. Else, factor levels are based on
the values of x
(i.e. as if using as.factor()
).
Toggle warnings.
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. extract_column_names(iris, select = c("Species", "Test"))
will just return "Species"
.
See select
, however, column names matched by the pattern
from exclude
will be excluded instead of selected. If NULL
(the default),
excludes no columns.
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.
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.
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.
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.
Convert variables or data into factors. If the data is labelled, value labels
will be used as factor levels. The counterpart to convert variables into
numeric is to_numeric()
.
str(to_factor(iris))
# use labels as levels
data(efc)
str(efc$c172code)
head(to_factor(efc$c172code))
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