datawizard (version 0.13.0)

assign_labels: Assign variable and value labels

Description

Assign variable and values labels to a variable or variables in a data frame. Labels are stored as attributes ("label" for variable labels and "labels") for value labels.

Usage

assign_labels(x, ...)
# S3 method for numeric
assign_labels(x, variable = NULL, values = NULL, ...)
# S3 method for data.frame
assign_labels(
  x,
  select = NULL,
  exclude = NULL,
  values = NULL,
  ignore_case = FALSE,
  regex = FALSE,
  verbose = TRUE,
  ...
)

Value

A labelled variable, or a data frame of labelled variables.

Arguments

x

A data frame, factor or vector.

...

Currently not used.

variable

The variable label as string.

values

The value labels as (named) character vector. If values is not a named vector, the length of labels must be equal to the length of unique values. For a named vector, the left-hand side (LHS) is the value in x, the right-hand side (RHS) the associated value label. Non-matching labels are omitted.

select

Variables that will be included when performing the required tasks. Can be either

• a variable specified as a literal variable name (e.g., column_name),

• a string with the variable name (e.g., "column_name"), or a character vector of variable names (e.g., c("col1", "col2", "col3")),

• a formula with variable names (e.g., ~column_1 + column_2),

• a vector of positive integers, giving the positions counting from the left (e.g. 1 or c(1, 3, 5)),

• a vector of negative integers, giving the positions counting from the right (e.g., -1 or -1:-3),

• one of the following select-helpers: starts_with(), ends_with(), contains(), a range using : or regex(""). starts_with(), ends_with(), and contains() accept several patterns, e.g starts_with("Sep", "Petal").

• or a function testing for logical conditions, e.g. is.numeric() (or is.numeric), or any user-defined function that selects the variables for which the function returns TRUE (like: foo <- function(x) mean(x) > 3),

• ranges specified via literal variable names, select-helpers (except regex()) and (user-defined) functions can be negated, i.e. return non-matching elements, when prefixed with a -, e.g. -ends_with(""), -is.numeric or -(Sepal.Width:Petal.Length). Note: Negation means that matches are excluded, and thus, the exclude argument can be used alternatively. For instance, select=-ends_with("Length") (with -) is equivalent to exclude=ends_with("Length") (no -). In case negation should not work as expected, use the exclude argument instead.

If NULL, selects all columns. Patterns that found no matches are silently ignored, e.g. extract_column_names(iris, select = c("Species", "Test")) will just return "Species".

exclude

See select, however, column names matched by the pattern from exclude will be excluded instead of selected. If NULL (the default), excludes no columns.

ignore_case

Logical, if TRUE and when one of the select-helpers or a regular expression is used in select, ignores lower/upper case in the search pattern when matching against variable names.

regex

Logical, if TRUE, the search pattern from select will be treated as regular expression. When regex = TRUE, select must be a character string (or a variable containing a character string) and is not allowed to be one of the supported select-helpers or a character vector of length > 1. regex = TRUE is comparable to using one of the two select-helpers, select = contains("") or select = regex(""), however, since the select-helpers may not work when called from inside other functions (see 'Details'), this argument may be used as workaround.

verbose

Toggle warnings.

Selection of variables - the <code>select</code> argument

For most functions that have a select argument (including this function), the complete input data frame is returned, even when select only selects a range of variables. That is, the function is only applied to those variables that have a match in select, while all other variables remain unchanged. In other words: for this function, select will not omit any non-included variables, so that the returned data frame will include all variables from the input data frame.

Examples

x <- 1:3
# labelling by providing required number of labels
assign_labels(
  x,
  variable = "My x",
  values = c("one", "two", "three")
)

# labelling using named vectors
data(iris)
out <- assign_labels(
  iris$Species,
  variable = "Labelled Species",
  values = c(`setosa` = "Spec1", `versicolor` = "Spec2", `virginica` = "Spec3")
)
str(out)

# data frame example
out <- assign_labels(
  iris,
  select = "Species",
  variable = "Labelled Species",
  values = c(`setosa` = "Spec1", `versicolor` = "Spec2", `virginica` = "Spec3")
)
str(out$Species)

# Partial labelling
x <- 1:5
assign_labels(
  x,
  variable = "My x",
  values = c(`1` = "lowest", `5` = "highest")
)