sjmisc (version 2.5.0)

set_labels: Add value labels to variables

Description

This function adds labels as attribute (named "labels") to a variable or vector x, resp. to a set of variables in a data frame or a list-object. A use-case is, for instance, the sjPlot-package, which supports labelled data and automatically assigns labels to axes or legends in plots or to be used in tables.

Usage

set_labels(x, ..., labels, force.labels = FALSE, force.values = TRUE,
  drop.na = TRUE)

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.

labels

(Named) character vector of labels that will be added to x as "labels" or "value.labels" attribute.

• if labels is not a named vector, its length must equal the value range of x, i.e. if x has values from 1 to 3, labels should have a length of 3;

• if length of labels is intended to differ from length of unique values of x, a warning is given. You can still add missing labels with the force.labels or force.values arguments; see 'Note'.

• if labels is a named vector, value labels will be set accordingly, even if x has a different length of unique values. See 'Note' and 'Examples'.

• if x is a data frame, labels may also be a list of (named) character vectors;

• if labels is a list, it must have the same length as number of columns of x;

• if labels is a vector and x is a data frame, labels will be applied to each column of x.

Use labels = "" to remove labels-attribute from x.

force.labels

Logical; if TRUE, all labels are added as value label attribute, even if x has less unique values then length of labels or if x has a smaller range then length of labels. See 'Examples'. This parameter will be ignored, if labels is a named vector.

force.values

Logical, if TRUE (default) and labels has less elements than unique values of x, additional values not covered by labels will be added as label as well. See 'Examples'. This parameter will be ignored, if labels is a named vector.

drop.na

Logical, whether existing value labels of tagged NA values (see tagged_na) should be removed (drop.na = TRUE, the default) or preserved (drop.na = FALSE). See get_na for more details on tagged NA values.

Value

x with value label attributes; or with removed label-attributes if labels = "". If x is a data frame, the complete data frame x will be returned, with removed or added to variables specified in ...; if ... is not specified, applies to all variables in the data frame.

See Also

See vignette Labelled Data and the sjmisc-Package for more details; set_label to manually set variable labels or get_label to get variable labels; add_labels to add additional value labels without replacing the existing ones.

Examples

# NOT RUN {
dummy <- sample(1:4, 40, replace = TRUE)
frq(dummy)

dummy <- set_labels(dummy, labels = c("very low", "low", "mid", "hi"))
frq(dummy)

# assign labels with named vector
dummy <- sample(1:4, 40, replace = TRUE)
dummy <- set_labels(dummy, labels = c("very low" = 1, "very high" = 4))
frq(dummy)

# force using all labels, even if not all labels
# have associated values in vector
x <- c(2, 2, 3, 3, 2)
# only two value labels
x <- set_labels(x, labels = c("1", "2", "3"))
x
frq(x)

# all three value labels
x <- set_labels(x, labels = c("1", "2", "3"), force.labels = TRUE)
x
frq(x)

# 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.values = FALSE)
x
# add all necessary labels
x <- set_labels(x, labels = c("yes", "maybe", "no"), force.values = TRUE)
x

# set labels and missings
x <- c(1, 1, 1, 2, 2, -2, 3, 3, 3, 3, 3, 9)
x <- set_labels(x, labels = c("Refused", "One", "Two", "Three", "Missing"))
x
set_na(x, na = c(-2, 9))


library(haven)
x <- labelled(c(1:3, tagged_na("a", "c", "z"), 4:1),
              c("Agreement" = 1, "Disagreement" = 4, "First" = tagged_na("c"),
                "Refused" = tagged_na("a"), "Not home" = tagged_na("z")))
# get current NA values
x
get_na(x)
# lose value labels from tagged NA by default, if not specified
set_labels(x, labels = c("New Three" = 3))
# do not drop na
set_labels(x, labels = c("New Three" = 3), drop.na = FALSE)


# set labels via named vector,
# not using all possible values
data(efc)
get_labels(efc$e42dep)

x <- set_labels(
  efc$e42dep,
  labels = c(`independent` = 1,
             `severe dependency` = 2,
             `missing value` = 9)
  )
get_labels(x, include.values = "p")
get_labels(x, include.values = "p", include.non.labelled = TRUE)

# labels can also be set for tagged NA value
# create numeric vector
x <- c(1, 2, 3, 4)
# set 2 and 3 as missing, which will automatically set as
# tagged NA by 'set_na()'
x <- set_na(x, na = c(2, 3))
x
# set label via named vector just for tagged NA(3)
set_labels(x, labels = c(`New Value` = tagged_na("3")))

# setting same value labels to multiple vectors
dummies <- data.frame(
  dummy1 = sample(1:4, 40, replace = TRUE),
  dummy2 = sample(1:4, 40, replace = TRUE),
  dummy3 = sample(1:4, 40, replace = TRUE)
)

# and set same value labels for two of three variables
dummies <- set_labels(
  dummies, dummy1, dummy2,
  labels = c("very low", "low", "mid", "hi")
)
# see result...
get_labels(dummies)

# }