| convert_to_na | R Documentation |
Convert non-missing values in a variable into missing values.
Description
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
# values 4 and 5 to NA
convert_to_na(x, na = 4:5)
# 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)
# for numerics, 5 to NA, for character/factor, "f" to NA
convert_to_na(x, na = list(6, "f"))
# select specific variables
convert_to_na(x, select = c("a", "b"), na = list(6, "f"))
