Recode old values of variables into new values

This functions recodes old values into new values and can be used to to recode numeric or character vectors, or factors.

Usage

recode_values(x, ...)

# S3 method for class 'numeric'
recode_values(
  x,
  recode = NULL,
  default = NULL,
  preserve_na = TRUE,
  verbose = TRUE,
  ...
)

# S3 method for class 'data.frame'
recode_values(
  x,
  select = NULL,
  exclude = NULL,
  recode = NULL,
  default = NULL,
  preserve_na = TRUE,
  append = FALSE,
  ignore_case = FALSE,
  regex = FALSE,
  verbose = TRUE,
  ...
)

Arguments

x

A data frame, numeric or character vector, or factor.

...

not used.

recode

A list of named vectors, which indicate the recode pairs. The names of the list-elements (i.e. the left-hand side) represent the new values, while the values of the list-elements indicate the original (old) values that should be replaced. When recoding numeric vectors, element names have to be surrounded in backticks. For example, recode=list(`0`=1) would recode all 1 into 0 in a numeric vector. See also 'Examples' and 'Details'.

default

Defines the default value for all values that have no match in the recode-pairs. Note that, if preserve_na=FALSE, missing values (NA) are also captured by the default argument, and thus will also be recoded into the specified value. See 'Examples' and 'Details'.

preserve_na

Logical, if TRUE, NA (missing values) are preserved. This overrides any other arguments, including default. Hence, if preserve_na=TRUE, default will no longer convert NA into the specified default value.

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")),

• 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.

append

Logical or string. If TRUE, recoded or converted variables get new column names and are appended (column bind) to x, thus returning both the original and the recoded variables. The new columns get a suffix, based on the calling function: "_r" for recode functions, "_n" for to_numeric(), "_f" for to_factor(), or "_s" for slide(). If append=FALSE, original variables in x will be overwritten by their recoded versions. If a character value, recoded variables are appended with new column names (using the defined suffix) to the original data frame.

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 old values are replaced by new values.

Details

This section describes the pattern of the recode arguments, which also provides some shortcuts, in particular when recoding numeric values.

• Single values

  Single values either need to be wrapped in backticks (in case of numeric values) or "as is" (for character or factor levels). Example: recode=list(`0`=1,`1`=2) would recode 1 into 0, and 2 into 1. For factors or character vectors, an example is: recode=list(x="a",y="b") (recode "a" into "x" and "b" into "y").

• Multiple values

  Multiple values that should be recoded into a new value can be separated with comma. Example: recode=list(`1`=c(1,4),`2`=c(2,3)) would recode the values 1 and 4 into 1, and 2 and 3 into 2. It is also possible to define the old values as a character string, like: recode=list(`1`="1,4",`2`="2,3") For factors or character vectors, an example is: recode=list(x=c("a","b"),y=c("c","d")).

• Value range

  Numeric value ranges can be defined using the :. Example: recode=list(`1`=1:3,`2`=4:6) would recode all values from 1 to 3 into 1, and 4 to 6 into 2.

• min and max

  placeholder to use the minimum or maximum value of the (numeric) variable. Useful, e.g., when recoding ranges of values. Example: recode=list(`1`="min:10",`2`="11:max").

• default values

  The default argument defines the default value for all values that have no match in the recode-pairs. For example, recode=list(`1`=c(1,2),`2`=c(3,4)), default=9 would recode values 1 and 2 into 1, 3 and 4 into 2, and all other values into 9. If preserve_na is set to FALSE, NA (missing values) will also be recoded into the specified default value.

• Reversing and rescaling

  See reverse() and rescale().

Note

You can use options(data_recode_pattern = "old=new") to switch the behaviour of the recode-argument, i.e. recode-pairs are now following the pattern old values = new values, e.g. if getOption("data_recode_pattern") is set to "old=new", then recode(`1`=0) would recode all 1 into 0. The default for recode(`1`=0) is to recode all 0 into 1.

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.

See also

• Add a prefix or suffix to column names: data_addprefix(), data_addsuffix()

• Functions to reorder or remove columns: data_reorder(), data_relocate(), data_remove()

• Functions to reshape, pivot or rotate data frames: data_to_long(), data_to_wide(), data_rotate()

• Functions to recode data: rescale(), reverse(), categorize(), recode_values(), slide()

• Functions to standardize, normalize, rank-transform: center(), standardize(), normalize(), ranktransform(), winsorize()

• Split and merge data frames: data_partition(), data_merge()

• Functions to find or select columns: data_select(), extract_column_names()

• Functions to filter rows: data_match(), data_filter()

Examples

# numeric ----------
set.seed(123)
x <- sample(c(1:4, NA), 15, TRUE)
table(x, useNA = "always")
#> x
#>    1    2    3    4 <NA> 
#>    2    3    6    2    2 

out <- recode_values(x, list(`0` = 1, `1` = 2:3, `2` = 4))
out
#>  [1]  1  1  1  1  1 NA  2  0  1  1 NA  1  1  0  2
table(out, useNA = "always")
#> out
#>    0    1    2 <NA> 
#>    2    9    2    2 

# to recode NA values, set preserve_na to FALSE
out <- recode_values(
  x,
  list(`0` = 1, `1` = 2:3, `2` = 4, `9` = NA),
  preserve_na = FALSE
)
out
#>  [1] 1 1 1 1 1 9 2 0 1 1 9 1 1 0 2
table(out, useNA = "always")
#> out
#>    0    1    2    9 <NA> 
#>    2    9    2    2    0 

# preserve na ----------
out <- recode_values(x, list(`0` = 1, `1` = 2:3), default = 77)
out
#>  [1]  1  1  1  1  1 NA 77  0  1  1 NA  1  1  0 77
table(out, useNA = "always")
#> out
#>    0    1   77 <NA> 
#>    2    9    2    2 

# recode na into default ----------
out <- recode_values(
  x,
  list(`0` = 1, `1` = 2:3),
  default = 77,
  preserve_na = FALSE
)
out
#>  [1]  1  1  1  1  1 77 77  0  1  1 77  1  1  0 77
table(out, useNA = "always")
#> out
#>    0    1   77 <NA> 
#>    2    9    4    0 


# factors (character vectors are similar) ----------
set.seed(123)
x <- as.factor(sample(c("a", "b", "c"), 15, TRUE))
table(x)
#> x
#> a b c 
#> 2 7 6 

out <- recode_values(x, list(x = "a", y = c("b", "c")))
out
#>  [1] y y y y y y y y y x y y x y y
#> Levels: x y
table(out)
#> out
#>  x  y 
#>  2 13 

out <- recode_values(x, list(x = "a", y = "b", z = "c"))
out
#>  [1] z z z y z y y y z x y y x y z
#> Levels: x y z
table(out)
#> out
#> x y z 
#> 2 7 6 

out <- recode_values(x, list(y = "b,c"), default = 77)
# same as
# recode_values(x, list(y = c("b", "c")), default = 77)
out
#>  [1] y  y  y  y  y  y  y  y  y  77 y  y  77 y  y 
#> Levels: 77 y
table(out)
#> out
#> 77  y 
#>  2 13 


# data frames ----------
set.seed(123)
d <- data.frame(
  x = sample(c(1:4, NA), 12, TRUE),
  y = as.factor(sample(c("a", "b", "c"), 12, TRUE)),
  stringsAsFactors = FALSE
)

recode_values(
  d,
  recode = list(`0` = 1, `1` = 2:3, `2` = 4, x = "a", y = c("b", "c")),
  append = TRUE
)
#>     x y x_r y_r
#> 1   3 c   1   y
#> 2   3 a   1   x
#> 3   2 a   1   x
#> 4   2 a   1   x
#> 5   3 a   1   x
#> 6  NA c  NA   y
#> 7   4 b   2   y
#> 8   1 c   0   y
#> 9   2 b   1   y
#> 10  3 a   1   x
#> 11 NA b  NA   y
#> 12  3 c   1   y


# switch recode pattern to "old=new" ----------
options(data_recode_pattern = "old=new")

# numeric
set.seed(123)
x <- sample(c(1:4, NA), 15, TRUE)
table(x, useNA = "always")
#> x
#>    1    2    3    4 <NA> 
#>    2    3    6    2    2 

out <- recode_values(x, list(`1` = 0, `2:3` = 1, `4` = 2))
table(out, useNA = "always")
#> out
#>    0    1    2 <NA> 
#>    2    9    2    2 

# factors (character vectors are similar)
set.seed(123)
x <- as.factor(sample(c("a", "b", "c"), 15, TRUE))
table(x)
#> x
#> a b c 
#> 2 7 6 

out <- recode_values(x, list(a = "x", `b, c` = "y"))
table(out)
#> out
#>  x  y 
#>  2 13 

# reset options
options(data_recode_pattern = NULL)