This function converts a variable into a factor, but preserves variable and value label attributes.

as_factor(x, ...)

to_factor(x, ...)

# S3 method for data.frame
as_factor(x, ..., add.non.labelled = FALSE)

## Arguments

x A vector or data frame. Optional, unquoted names of variables that should be selected for further processing. Required, if x is a data frame (and no vector) and only selected variables from x should be processed. You may also use functions like : or tidyselect's select-helpers. See 'Examples'. Logical, if TRUE, non-labelled values also get value labels.

## Value

A factor, including variable and value labels. If x is a data frame, the complete data frame x will be returned, where variables specified in ... are coerced to factors (including variable and value labels); if ... is not specified, applies to all variables in the data frame.

## Details

as_factor converts numeric values into a factor with numeric levels. as_label, however, converts a vector into a factor and uses value labels as factor levels.

## Note

This function is intended for use with vectors that have value and variable label attributes. Unlike as.factor, as_factor converts a variable into a factor and preserves the value and variable label attributes.

Adding label attributes is automatically done by importing data sets with one of the read_*-functions, like read_spss. Else, value and variable labels can be manually added to vectors with set_labels and set_label.

## Examples

if (require("sjmisc") && require("magrittr")) {
data(efc)
# normal factor conversion, loses value attributes
x <- as.factor(efc$e42dep) frq(x) # factor conversion, which keeps value attributes x <- as_factor(efc$e42dep)
frq(x)

# create partially labelled vector
x <- set_labels(
efc\$e42dep,
labels = c(
1 = "independent",
4 = "severe dependency",
9 = "missing value"
))

# only copy existing value labels
get_labels(as_factor(x), values = "p")

# also add labels to non-labelled values
get_labels(as_factor(x, add.non.labelled = TRUE), values = "p")

# easily coerce specific variables in a data frame to factor
# and keep other variables, with their class preserved
as_factor(efc, e42dep, e16sex, c172code) %>% head()

# use select-helpers from dplyr-package
if (require("dplyr")) {
#> Error: 1 components of ... were not used.
#> * add.non.labelled