sjmisc (version 2.6.3)

to_label: Convert variable into factor with associated value labels

Description

This function converts (replaces) values of a variable (also of factors or character vectors) with their associated value labels. Might be helpful for factor variables. For instance, if you have a Gender variable with 0/1 value, and associated labels are male/female, this function would convert all 0 to male and all 1 to female and returns the new variable as factor.

Usage

to_label(x, ..., add.non.labelled = FALSE, prefix = FALSE,
  var.label = NULL, drop.na = TRUE, drop.levels = 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 dplyr's select_helpers. See 'Examples' or package-vignette.

add.non.labelled

Logical, if TRUE, values without associated value label will also be converted to labels (as is). See 'Examples'.

prefix

Logical, if TRUE, the value labels used as factor levels or character values will be prefixed with their associated values. See 'Examples'.

var.label

Optional string, to set variable label attribute for the returned variable (see vignette Labelled Data and the sjlabelled-Package). If NULL (default), variable label attribute of x will be used (if present). If empty, variable label attributes will be removed.

drop.na

Logical, if TRUE, tagged NA values with value labels will be converted to regular NA's. Else, tagged NA values will be replaced with their value labels. See 'Examples' and get_na.

drop.levels

Logical, if TRUE, unused factor levels will be dropped (i.e. droplevels will be applied before returning the result).

Value

A factor with the associated value labels as factor levels. If x is a data frame, the complete data frame x will be returned, where variables specified in ... are coerced to factors; if ... is not specified, applies to all variables in the data frame.

Examples

# NOT RUN {
library(sjlabelled)
data(efc)
print(get_labels(efc)['c161sex'])
head(efc$c161sex)
head(to_label(efc$c161sex))

print(get_labels(efc)['e42dep'])
table(efc$e42dep)
table(to_label(efc$e42dep))

head(efc$e42dep)
head(to_label(efc$e42dep))

# structure of numeric values won't be changed
# by this function, it only applies to labelled vectors
# (typically categorical or factor variables)
str(efc$e17age)
str(to_label(efc$e17age))


# factor with non-numeric levels
to_label(factor(c("a", "b", "c")))

# factor with non-numeric levels, prefixed
x <- factor(c("a", "b", "c"))
x <- set_labels(x, labels = c("ape", "bear", "cat"))
to_label(x, prefix = TRUE)


# create vector
x <- c(1, 2, 3, 2, 4, NA)
# add less labels than values
x <- set_labels(x,
                labels = c("yes", "maybe", "no"),
                force.labels = FALSE,
                force.values = FALSE)
# convert to label w/o non-labelled values
to_label(x)
# convert to label, including non-labelled values
to_label(x, add.non.labelled = TRUE)


# create labelled integer, with missing flag
library(haven)
x <- labelled(c(1:3, tagged_na("a", "c", "z"), 4:1, 2:3),
              c("Agreement" = 1, "Disagreement" = 4, "First" = tagged_na("c"),
                "Refused" = tagged_na("a"), "Not home" = tagged_na("z")))
# to labelled factor, with missing labels
to_label(x, drop.na = FALSE)
# to labelled factor, missings removed
to_label(x, drop.na = TRUE)
# keep missings, and use non-labelled values as well
to_label(x, add.non.labelled = TRUE, drop.na = FALSE)


# convert labelled character to factor
dummy <- c("M", "F", "F", "X")
dummy <- set_labels(
  dummy,
  labels = c(`M` = "Male", `F` = "Female", `X` = "Refused")
)
get_labels(dummy,, "p")
to_label(dummy)

# drop unused factor levels, but preserve variable label
x <- factor(c("a", "b", "c"), levels = c("a", "b", "c", "d"))
x <- set_labels(x, labels = c("ape", "bear", "cat"))
set_label(x) <- "A factor!"
x
to_label(x, drop.levels = TRUE)

# change variable label
to_label(x, var.label = "New variable label!", drop.levels = TRUE)


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

# }