recode_values: Recode old values of variables into new values
In datawizard: Easy Data Wrangling and Statistical Transformations

recode_values

R Documentation

Recode old values of variables into new values

Description

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"`), 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.
`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.

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().

Value

x, where old values are replaced by new values.

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.

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.

Examples

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

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

# 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
table(out, useNA = "always")

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

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


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

out <- recode_values(x, list(x = "a", y = c("b", "c")))
out
table(out)

out <- recode_values(x, list(x = "a", y = "b", z = "c"))
out
table(out)

out <- recode_values(x, list(y = "b,c"), default = 77)
# same as
# recode_values(x, list(y = c("b", "c")), default = 77)
out
table(out)


# 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
)


# 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")

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

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

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

# reset options
options(data_recode_pattern = NULL)

datawizard documentation built on Oct. 6, 2024, 1:08 a.m.