zap_labels: Drop, add or convert (non-)labelled values

In sjlabelled: Labelled Data Utility Functions

Drop, add or convert (non-)labelled values

Description

For (partially) labelled vectors, zap_labels() will replace all values that have a value label attribute with NA; zap_unlabelled(), as counterpart, will replace all values that don't have a value label attribute with NA.

drop_labels() drops all value labels for unused values, i.e. values that are not present in a vector. fill_labels() is the counterpart to drop_labels() and adds value labels to a partially labelled vector, i.e. if not all values are labelled, non-labelled values get labels.

Usage

drop_labels(x, ..., drop.na = TRUE)

fill_labels(x, ...)

zap_labels(x, ...)

zap_unlabelled(x, ...)

Arguments

x	(partially) labelled() vector or a data frame with such vectors.
...	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'.
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

• For zap_labels(), x, where all labelled values are converted to NA.

• For zap_unlabelled(), x, where all non-labelled values are converted to NA.

• For drop_labels(), x, where value labels for non-existing values are removed.

• For fill_labels(), x, where labels for non-labelled values are added.

If x is a data frame, the complete data frame x will be returned, with variables specified in ... being converted; if ... is not specified, applies to all variables in the data frame.

Examples

if (require("sjmisc") && require("dplyr")) {

  # zap_labels() ----

  data(efc)
  str(efc$e42dep)

  x <- set_labels(
    efc$e42dep,
    labels = c("independent" = 1, "severe dependency" = 4)
  )
  table(x)
  get_values(x)
  str(x)

  # zap all labelled values
  table(zap_labels(x))
  get_values(zap_labels(x))
  str(zap_labels(x))

  # zap all unlabelled values
  table(zap_unlabelled(x))
  get_values(zap_unlabelled(x))
  str(zap_unlabelled(x))

  # in a pipe-workflow
  efc %>%
    select(c172code, e42dep) %>%
    set_labels(
      e42dep,
      labels = c("independent" = 1, "severe dependency" = 4)
    ) %>%
    zap_labels()


  # drop_labels() ----

  rp <- rec_pattern(1, 100)
  rp

  # sample data
  data(efc)
  # recode carers age into groups of width 5
  x <- rec(efc$c160age, rec = rp$pattern)
  # add value labels to new vector
  x <- set_labels(x, labels = rp$labels)

  # watch result. due to recode-pattern, we have age groups with
  # no observations (zero-counts)
  frq(x)
  # now, let's drop zero's
  frq(drop_labels(x))

  # drop labels, also drop NA value labels, then also zap tagged NA
  if (require("haven")) {
    x <- labelled(c(1:3, tagged_na("z"), 4:1),
                  c("Agreement" = 1, "Disagreement" = 4, "Unused" = 5,
                    "Not home" = tagged_na("z")))
    x
    drop_labels(x, drop.na = FALSE)
    drop_labels(x)
    zap_na_tags(drop_labels(x))

    # fill_labels() ----

    # create labelled integer, with tagged missings
    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 values and labels
    x
    get_labels(x)

    fill_labels(x)
    get_labels(fill_labels(x))
    # same as
    get_labels(x, non.labelled = TRUE)
  }
}

sjlabelled documentation built on April 10, 2022, 5:05 p.m.