convert_na_to {datawizard} | R Documentation |
Replace missing values in a variable or a data frame.
Description
Replace missing values in a variable or a data frame.
Usage
convert_na_to(x, ...)
## S3 method for class 'numeric'
convert_na_to(x, replacement = NULL, verbose = TRUE, ...)
## S3 method for class 'character'
convert_na_to(x, replacement = NULL, verbose = TRUE, ...)
## S3 method for class 'data.frame'
convert_na_to(
x,
select = NULL,
exclude = NULL,
replacement = NULL,
replace_num = replacement,
replace_char = replacement,
replace_fac = replacement,
ignore_case = FALSE,
regex = FALSE,
verbose = TRUE,
...
)
Arguments
x |
A numeric, factor, or character vector, or a data frame.
|
... |
Not used.
|
replacement |
Numeric or character value that will be used to
replace NA .
|
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" ), 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.
|
replace_num |
Value to replace NA when variable is of type numeric.
|
replace_char |
Value to replace NA when variable is of type character.
|
replace_fac |
Value to replace NA when variable is of type factor.
|
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 NA
values are replaced by replacement
.
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
# Convert NA to 0 in a numeric vector
convert_na_to(
c(9, 3, NA, 2, 3, 1, NA, 8),
replacement = 0
)
# Convert NA to "missing" in a character vector
convert_na_to(
c("a", NA, "d", "z", NA, "t"),
replacement = "missing"
)
### For data frames
test_df <- data.frame(
x = c(1, 2, NA),
x2 = c(4, 5, NA),
y = c("a", "b", NA)
)
# Convert all NA to 0 in numeric variables, and all NA to "missing" in
# character variables
convert_na_to(
test_df,
replace_num = 0,
replace_char = "missing"
)
# Convert a specific variable in the data frame
convert_na_to(
test_df,
replace_num = 0,
replace_char = "missing",
select = "x"
)
# Convert all variables starting with "x"
convert_na_to(
test_df,
replace_num = 0,
replace_char = "missing",
select = starts_with("x")
)
# Convert NA to 1 in variable 'x2' and to 0 in all other numeric
# variables
convert_na_to(
test_df,
replace_num = 0,
select = list(x2 = 1)
)
[Package
datawizard version 0.13.0
Index]