Convert value labels into factor levels
Usage
labels_to_levels(x, ...)
# S3 method for class 'factor'
labels_to_levels(x, verbose = TRUE, ...)
# S3 method for class 'data.frame'
labels_to_levels(
x,
select = NULL,
exclude = NULL,
ignore_case = FALSE,
append = FALSE,
regex = FALSE,
verbose = TRUE,
...
)
Arguments
- x
A data frame or factor. Other variable types (e.g. numerics) are not allowed.
- ...
Currently not used.
- verbose
Toggle warnings.
- 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")
),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
orc(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:
, orregex()
.starts_with()
,ends_with()
, andcontains()
accept several patterns, e.gstarts_with("Sep", "Petal")
.regex()
can be used to define regular expression patterns.a function testing for logical conditions, e.g.
is.numeric()
(oris.numeric
), or any user-defined function that selects the variables for which the function returnsTRUE
(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, theexclude
argument can be used alternatively. For instance,select=-ends_with("Length")
(with-
) is equivalent toexclude=ends_with("Length")
(no-
). In case negation should not work as expected, use theexclude
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 fromexclude
will be excluded instead of selected. IfNULL
(the default), excludes no columns.- ignore_case
Logical, if
TRUE
and when one of the select-helpers or a regular expression is used inselect
, ignores lower/upper case in the search pattern when matching against variable names.- append
Logical or string. If
TRUE
, recoded or converted variables get new column names and are appended (column bind) tox
, thus returning both the original and the recoded variables. The new columns get a suffix, based on the calling function:"_r"
for recode functions,"_n"
forto_numeric()
,"_f"
forto_factor()
, or"_s"
forslide()
. Ifappend=FALSE
, original variables inx
will be overwritten by their recoded versions. If a character value, recoded variables are appended with new column names (using the defined suffix) to the original data frame.- regex
Logical, if
TRUE
, the search pattern fromselect
will be treated as regular expression. Whenregex = 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()
orselect = regex()
, however, since the select-helpers may not work when called from inside other functions (see 'Details'), this argument may be used as workaround.
Examples
data(efc)
# create factor
x <- as.factor(efc$c172code)
# add value labels - these are not factor levels yet
x <- assign_labels(x, values = c(`1` = "low", `2` = "mid", `3` = "high"))
levels(x)
#> [1] "1" "2" "3"
data_tabulate(x)
#> x <categorical>
#> # total N=100 valid N=90
#>
#> Value | N | Raw % | Valid % | Cumulative %
#> ------+----+-------+---------+-------------
#> 1 | 8 | 8.00 | 8.89 | 8.89
#> 2 | 66 | 66.00 | 73.33 | 82.22
#> 3 | 16 | 16.00 | 17.78 | 100.00
#> <NA> | 10 | 10.00 | <NA> | <NA>
x <- labels_to_levels(x)
levels(x)
#> [1] "low" "mid" "high"
data_tabulate(x)
#> x <categorical>
#> # total N=100 valid N=90
#>
#> Value | N | Raw % | Valid % | Cumulative %
#> ------+----+-------+---------+-------------
#> low | 8 | 8.00 | 8.89 | 8.89
#> mid | 66 | 66.00 | 73.33 | 82.22
#> high | 16 | 16.00 | 17.78 | 100.00
#> <NA> | 10 | 10.00 | <NA> | <NA>