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Replace missing values in a variable or a data frame.

Source: R/convert_na_to.R
convert_na_to.Rd

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"), 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 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
)
#> [1] 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"
)
#> [1] "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>