Convert non-missing values in a variable into missing values.

Usage

convert_to_na(x, ...)

# S3 method for class 'numeric'
convert_to_na(x, na = NULL, verbose = TRUE, ...)

# S3 method for class 'factor'
convert_to_na(x, na = NULL, drop_levels = FALSE, verbose = TRUE, ...)

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

Arguments

x

A vector, factor or a data frame.

...

Not used.

na

Numeric, character vector or logical (or a list of numeric, character vectors or logicals) with values that should be converted to NA. Numeric values applied to numeric vectors, character values are used for factors, character vectors or date variables, and logical values for logical vectors.

verbose

Toggle warnings.

drop_levels

Logical, for factors, when specific levels are replaced by NA, should unused levels be dropped?

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.

Value

x, where all values in na are converted to NA.

Examples

x <- sample(1:6, size = 30, replace = TRUE)
x
#>  [1] 5 5 6 3 1 4 6 1 6 1 3 6 4 1 6 6 3 6 5 3 6 2 5 5 3 2 2 2 4 2
# values 4 and 5 to NA
convert_to_na(x, na = 4:5)
#>  [1] NA NA  6  3  1 NA  6  1  6  1  3  6 NA  1  6  6  3  6 NA  3  6  2 NA NA  3
#> [26]  2  2  2 NA  2

# data frames
set.seed(123)
x <- data.frame(
  a = sample(1:6, size = 20, replace = TRUE),
  b = sample(letters[1:6], size = 20, replace = TRUE),
  c = sample(c(30:33, 99), size = 20, replace = TRUE)
)
# for all numerics, convert 5 to NA. Character/factor will be ignored.
convert_to_na(x, na = 5)
#> Could not convert values into `NA` for a factor or character variable.
#>   To do this, `na` needs to be a character vector, or a list that contains
#>   character vector elements.
#>     a b  c
#> 1   3 a 33
#> 2   6 e 99
#> 3   3 c 99
#> 4   2 b 32
#> 5   2 b 30
#> 6   6 a 31
#> 7   3 f 99
#> 8  NA c 99
#> 9   4 d 33
#> 10  6 f 99
#> 11  6 a 31
#> 12  1 c 30
#> 13  2 e 30
#> 14  3 d 32
#> 15 NA b 30
#> 16  3 e 99
#> 17  3 a 30
#> 18  1 a 31
#> 19  4 b 33
#> 20  1 c 33

# for numerics, 5 to NA, for character/factor, "f" to NA
convert_to_na(x, na = list(6, "f"))
#>     a    b  c
#> 1   3    a 33
#> 2  NA    e 99
#> 3   3    c 99
#> 4   2    b 32
#> 5   2    b 30
#> 6  NA    a 31
#> 7   3 <NA> 99
#> 8   5    c 99
#> 9   4    d 33
#> 10 NA <NA> 99
#> 11 NA    a 31
#> 12  1    c 30
#> 13  2    e 30
#> 14  3    d 32
#> 15  5    b 30
#> 16  3    e 99
#> 17  3    a 30
#> 18  1    a 31
#> 19  4    b 33
#> 20  1    c 33

# select specific variables
convert_to_na(x, select = c("a", "b"), na = list(6, "f"))
#>     a    b  c
#> 1   3    a 33
#> 2  NA    e 99
#> 3   3    c 99
#> 4   2    b 32
#> 5   2    b 30
#> 6  NA    a 31
#> 7   3 <NA> 99
#> 8   5    c 99
#> 9   4    d 33
#> 10 NA <NA> 99
#> 11 NA    a 31
#> 12  1    c 30
#> 13  2    e 30
#> 14  3    d 32
#> 15  5    b 30
#> 16  3    e 99
#> 17  3    a 30
#> 18  1    a 31
#> 19  4    b 33
#> 20  1    c 33