Replace missing values in a variable or a data frame.Source:
Replace missing values in a variable or a data frame.
convert_na_to(x, ...) # S3 method for numeric convert_na_to(x, replacement = NULL, verbose = TRUE, ...) # S3 method for character convert_na_to(x, replacement = NULL, verbose = TRUE, ...) # S3 method for 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, ... )
A numeric, factor, or character vector, or a data frame.
Numeric or character value that will be used to replace
Variables that will be included when performing the required tasks. Can be either
a variable specified as a literal variable name (e.g.,
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.
c(1, 3, 5)),
a vector of negative integers, giving the positions counting from the right (e.g.,
one of the following select-helpers:
contains(), a range using
contains()accept several patterns, e.g
or a function testing for logical conditions, e.g.
is.numeric), or any user-defined function that selects the variables for which the function returns
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
-(Sepal.Width:Petal.Length). Note: Negation means that matches are excluded, and thus, the
excludeargument can be used alternatively. For instance,
-) is equivalent to
-). In case negation should not work as expected, use the
NULL, selects all columns. Patterns that found no matches are silently ignored, e.g.
find_columns(iris, select = c("Species", "Test"))will just return
select, however, column names matched by the pattern from
excludewill be excluded instead of selected. If
NULL(the default), excludes no columns.
Value to replace
NAwhen variable is of type numeric.
Value to replace
NAwhen variable is of type character.
Value to replace
NAwhen variable is of type factor.
TRUEand 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.
TRUE, the search pattern from
selectwill 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 = TRUEis 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.
Selection of variables - the
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.
# Convert NA to 0 in a numeric vector convert_na_to( c(9, 3, NA, 2, 3, 1, NA, 8), replacement = 0 ) #>  9 3 0 2 3 1 0 8 # Convert NA to "missing" in a character vector convert_na_to( c("a", NA, "d", "z", NA, "t"), replacement = "missing" ) #>  "a" "missing" "d" "z" "missing" "t" ### 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" ) #> x x2 y #> 1 1 4 a #> 2 2 5 b #> 3 0 0 missing # Convert a specific variable in the data frame convert_na_to( test_df, replace_num = 0, replace_char = "missing", select = "x" ) #> x x2 y #> 1 1 4 a #> 2 2 5 b #> 3 0 NA <NA> # Convert all variables starting with "x" convert_na_to( test_df, replace_num = 0, replace_char = "missing", select = starts_with("x") ) #> x x2 y #> 1 1 4 a #> 2 2 5 b #> 3 0 0 <NA> # 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) ) #> x x2 y #> 1 1 4 a #> 2 2 5 b #> 3 0 1 <NA>