sjmisc (version 2.7.7)

to_factor: Convert variable into factor and keep value labels

Description

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

Usage

to_factor(x, ..., add.non.labelled = FALSE, ref.lvl = NULL)

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' or package-vignette.

add.non.labelled

Logical, if TRUE, non-labelled values also get value labels.

ref.lvl

Numeric, specifies the reference level for the new factor. Use this parameter if a different factor level than the lowest value should be used as reference level. If NULL, lowest value will become the reference level. See ref_lvl for details.

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

to_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.

See Also

as_numeric to convert a factor into a numeric vector and as_label to convert a vector into a factor with labelled factor levels.

Examples

# NOT RUN {
library(sjlabelled)
data(efc)
# normal factor conversion, loses value attributes
x <- as.factor(efc$e42dep)
frq(x)

# factor conversion, which keeps value attributes
x <- to_factor(efc$e42dep)
frq(x)

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

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

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


# Convert to factor, using different reference level
x <- to_factor(efc$e42dep)
str(x)
table(x)

x <- to_factor(efc$e42dep, ref.lvl = 3)
str(x)
table(x)


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

# use select-helpers from dplyr-package
library(dplyr)
to_factor(efc, contains("cop"), c161sex:c175empl)


# }