Assign variable and value labels

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 class 'numeric'
assign_labels(x, variable = NULL, values = NULL, ...)

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

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"), a character vector of variable names (e.g., c("col1", "col2", "col3")), or a character vector of variable names including ranges specified via : (e.g., c("col1:col3", "col5")),
for some functions, like data_select() or data_rename(), select can be a named character vector. In this case, the names are used to rename the columns in the output data frame. See 'Details' in the related functions to see where this option applies.
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"). regex() can be used to define regular expression patterns.
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.

Value

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

Selection of variables - the `select` 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")
)
#> [1] 1 2 3
#> attr(,"label")
#> [1] "My x"
#> attr(,"labels")
#>   one   two three 
#>     1     2     3 

# labelling using named vectors
data(iris)
out <- assign_labels(
  iris$Species,
  variable = "Labelled Species",
  values = c(`setosa` = "Spec1", `versicolor` = "Spec2", `virginica` = "Spec3")
)
str(out)
#>  Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
#>  - attr(*, "label")= chr "Labelled Species"
#>  - attr(*, "labels")= Named chr [1:3] "setosa" "versicolor" "virginica"
#>   ..- attr(*, "names")= chr [1:3] "Spec1" "Spec2" "Spec3"

# data frame example
out <- assign_labels(
  iris,
  select = "Species",
  variable = "Labelled Species",
  values = c(`setosa` = "Spec1", `versicolor` = "Spec2", `virginica` = "Spec3")
)
str(out$Species)
#>  Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
#>  - attr(*, "label")= chr "Labelled Species"
#>  - attr(*, "labels")= Named chr [1:3] "setosa" "versicolor" "virginica"
#>   ..- attr(*, "names")= chr [1:3] "Spec1" "Spec2" "Spec3"

# Partial labelling
x <- 1:5
assign_labels(
  x,
  variable = "My x",
  values = c(`1` = "lowest", `5` = "highest")
)
#> [1] 1 2 3 4 5
#> attr(,"label")
#> [1] "My x"
#> attr(,"labels")
#>  lowest highest 
#>       1       5

Usage

Arguments

Value

Selection of variables - the select argument

Examples

Selection of variables - the `select` argument