rec: Recode variables
In sjPlot/sjmisc: Data and Variable Transformation Functions

View source: R/rec.R

rec	R Documentation

Recode variables

Description

rec() recodes values of variables, where variable selection is based on variable names or column position, or on select helpers (see documentation on ...). rec_if() is a scoped variant of rec(), where recoding will be applied only to those variables that match the logical condition of predicate.

Usage

rec(
  x,
  ...,
  rec,
  as.num = TRUE,
  var.label = NULL,
  val.labels = NULL,
  append = TRUE,
  suffix = "_r",
  to.factor = !as.num
)

rec_if(
  x,
  predicate,
  rec,
  as.num = TRUE,
  var.label = NULL,
  val.labels = NULL,
  append = TRUE,
  suffix = "_r",
  to.factor = !as.num
)

Arguments

`x`	A vector or data frame.
`...`	Optional, unquoted names of variables that should be selected for further processing. Required, if `x` is a data frame (and no vector) and only selected variables from `x` should be processed. You may also use functions like `:` or tidyselect's select-helpers. See 'Examples' or package-vignette.
`rec`	String with recode pairs of old and new values. See 'Details' for examples. `rec_pattern` is a convenient function to create recode strings for grouping variables.
`as.num`	Logical, if `TRUE`, return value will be numeric, not a factor.
`var.label`	Optional string, to set variable label attribute for the returned variable (see vignette Labelled Data and the sjlabelled-Package). If `NULL` (default), variable label attribute of `x` will be used (if present). If empty, variable label attributes will be removed.
`val.labels`	Optional character vector, to set value label attributes of recoded variable (see vignette Labelled Data and the sjlabelled-Package). If `NULL` (default), no value labels will be set. Value labels can also be directly defined in the `rec`-syntax, see 'Details'.
`append`	Logical, if `TRUE` (the default) and `x` is a data frame, `x` including the new variables as additional columns is returned; if `FALSE`, only the new variables are returned.
`suffix`	String value, will be appended to variable (column) names of `x`, if `x` is a data frame. If `x` is not a data frame, this argument will be ignored. The default value to suffix column names in a data frame depends on the function call: recoded variables (`rec()`) will be suffixed with `"_r"` recoded variables (`recode_to()`) will be suffixed with `"_r0"` dichotomized variables (`dicho()`) will be suffixed with `"_d"` grouped variables (`split_var()`) will be suffixed with `"_g"` grouped variables (`group_var()`) will be suffixed with `"_gr"` standardized variables (`std()`) will be suffixed with `"_z"` centered variables (`center()`) will be suffixed with `"_c"` If `suffix = ""` and `append = TRUE`, existing variables that have been recoded/transformed will be overwritten.
`to.factor`	Logical, alias for `as.num`. If `TRUE`, return value will be a factor, not numeric.
`predicate`	A predicate function to be applied to the columns. The variables for which `predicate` returns `TRUE` are selected.

Details

The rec string has following syntax:

recode pairs: each recode pair has to be separated by a ;, e.g. rec = "1=1; 2=4; 3=2; 4=3"
multiple values: multiple old values that should be recoded into a new single value may be separated with comma, e.g. "1,2=1; 3,4=2"
value range: a value range is indicated by a colon, e.g. "1:4=1; 5:8=2" (recodes all values from 1 to 4 into 1, and from 5 to 8 into 2)
value range for doubles: for double vectors (with fractional part), all values within the specified range are recoded; e.g. 1:2.5=1;2.6:3=2 recodes 1 to 2.5 into 1 and 2.6 to 3 into 2, but 2.55 would not be recoded (since it's not included in any of the specified ranges)
"min" and "max": minimum and maximum values are indicates by min (or lo) and max (or hi), e.g. "min:4=1; 5:max=2" (recodes all values from minimum values of x to 4 into 1, and from 5 to maximum values of x into 2)
"else": all other values, which have not been specified yet, are indicated by else, e.g. "3=1; 1=2; else=3" (recodes 3 into 1, 1 into 2 and all other values into 3)
"copy": the "else"-token can be combined with copy, indicating that all remaining, not yet recoded values should stay the same (are copied from the original value), e.g. "3=1; 1=2; else=copy" (recodes 3 into 1, 1 into 2 and all other values like 2, 4 or 5 etc. will not be recoded, but copied, see 'Examples')
NA's: NA values are allowed both as old and new value, e.g. "NA=1; 3:5=NA" (recodes all NA into 1, and all values from 3 to 5 into NA in the new variable)
"rev": "rev" is a special token that reverses the value order (see 'Examples')
direct value labelling: value labels for new values can be assigned inside the recode pattern by writing the value label in square brackets after defining the new value in a recode pair, e.g. "15:30=1 [young aged]; 31:55=2 [middle aged]; 56:max=3 [old aged]". See 'Examples'.

Value

x with recoded categories. If x is a data frame, for append = TRUE, x including the recoded variables as new columns is returned; if append = FALSE, only the recoded variables will be returned. If append = TRUE and suffix = "", recoded variables will replace (overwrite) existing variables.

Note

Please note following behaviours of the function:

the "else"-token should always be the last argument in the rec-string.
Non-matching values will be set to NA, unless captured by the "else"-token.
Tagged NA values (see tagged_na) and their value labels will be preserved when copying NA values to the recoded vector with "else=copy".
Variable label attributes (see, for instance, get_label) are preserved (unless changed via var.label-argument), however, value label attributes are removed (except for "rev", where present value labels will be automatically reversed as well). Use val.labels-argument to add labels for recoded values.
If x is a data frame, all variables should have the same categories resp. value range (else, see second bullet, NAs are produced).

Examples

data(efc)
table(efc$e42dep, useNA = "always")

# replace NA with 5
table(rec(efc$e42dep, rec = "1=1;2=2;3=3;4=4;NA=5"), useNA = "always")

# recode 1 to 2 into 1 and 3 to 4 into 2
table(rec(efc$e42dep, rec = "1,2=1; 3,4=2"), useNA = "always")

# keep value labels. variable label is automatically preserved
library(dplyr)
efc %>%
  select(e42dep) %>%
  rec(rec = "1,2=1; 3,4=2",
      val.labels = c("low dependency", "high dependency")) %>%
  frq()

# works with mutate
efc %>%
  select(e42dep, e17age) %>%
  mutate(dependency_rev = rec(e42dep, rec = "rev")) %>%
  head()

# recode 1 to 3 into 1 and 4 into 2
table(rec(efc$e42dep, rec = "min:3=1; 4=2"), useNA = "always")

# recode 2 to 1 and all others into 2
table(rec(efc$e42dep, rec = "2=1; else=2"), useNA = "always")

# reverse value order
table(rec(efc$e42dep, rec = "rev"), useNA = "always")

# recode only selected values, copy remaining
table(efc$e15relat)
table(rec(efc$e15relat, rec = "1,2,4=1; else=copy"))

# recode variables with same category in a data frame
head(efc[, 6:9])
head(rec(efc[, 6:9], rec = "1=10;2=20;3=30;4=40"))

# recode multiple variables and set value labels via recode-syntax
dummy <- rec(
  efc, c160age, e17age,
  rec = "15:30=1 [young]; 31:55=2 [middle]; 56:max=3 [old]",
  append = FALSE
)
frq(dummy)

# recode variables with same value-range
lapply(
  rec(
    efc, c82cop1, c83cop2, c84cop3,
    rec = "1,2=1; NA=9; else=copy",
    append = FALSE
  ),
  table,
  useNA = "always"
)

# recode character vector
dummy <- c("M", "F", "F", "X")
rec(dummy, rec = "M=Male; F=Female; X=Refused")

# recode numeric to character
rec(efc$e42dep, rec = "1=first;2=2nd;3=third;else=hi") %>% head()

# recode non-numeric factors
data(iris)
table(rec(iris, Species, rec = "setosa=huhu; else=copy", append = FALSE))

# recode floating points
table(rec(
  iris, Sepal.Length, rec = "lo:5=1;5.01:6.5=2;6.501:max=3", append = FALSE
))

# preserve tagged NAs
if (require("haven")) {
  x <- labelled(c(1:3, tagged_na("a", "c", "z"), 4:1),
                c("Agreement" = 1, "Disagreement" = 4, "First" = tagged_na("c"),
                  "Refused" = tagged_na("a"), "Not home" = tagged_na("z")))
  # get current value labels
  x
  # recode 2 into 5; Values of tagged NAs are preserved
  rec(x, rec = "2=5;else=copy")
}

# use select-helpers from dplyr-package
out <- rec(
  efc, contains("cop"), c161sex:c175empl,
  rec = "0,1=0; else=1",
  append = FALSE
)
head(out)

# recode only variables that have a value range from 1-4
p <- function(x) min(x, na.rm = TRUE) > 0 && max(x, na.rm = TRUE) < 5
out <- rec_if(efc, predicate = p, rec = "1:3=1;4=2;else=copy")
head(out)

sjPlot/sjmisc documentation built on May 15, 2024, 6:43 p.m.