sjmisc (version 2.8.7)

dicho: Dichotomize variables

Description

Dichotomizes variables into dummy variables (0/1). Dichotomization is either done by median, mean or a specific value (see dich.by). dicho_if() is a scoped variant of dicho(), where recoding will be applied only to those variables that match the logical condition of predicate.

Usage

dicho(
  x,
  ...,
  dich.by = "median",
  as.num = FALSE,
  var.label = NULL,
  val.labels = NULL,
  append = TRUE,
  suffix = "_d"
)
dicho_if(
  x,
  predicate,
  dich.by = "median",
  as.num = FALSE,
  var.label = NULL,
  val.labels = NULL,
  append = TRUE,
  suffix = "_d"
)

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.

dich.by

Indicates the split criterion where a variable is dichotomized. Must be one of the following values (may be abbreviated):

"median" or "md"

by default, x is split into two groups at the median.

"mean" or "m"

splits x into two groups at the mean of x.

numeric value

splits x into two groups at the specific value. Note that the value is inclusive, i.e. dich.by = 10 will split x into one group with values from lowest to 10 and another group with values greater than 10.

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 (of length two), to set value label attributes of dichotomized variable (see set_labels). If NULL (default), no value labels will be set.

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

Indicates which suffix will be added to each dummy variable. Use "numeric" to number dummy variables, e.g. x_1, x_2, x_3 etc. Use "label" to add value label, e.g. x_low, x_mid, x_high. May be abbreviated.

predicate

A predicate function to be applied to the columns. The variables for which predicate returns TRUE are selected.

Value

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

Details

dicho() also works on grouped data frames (see group_by). In this case, dichotomization is applied to the subsets of variables in x. See 'Examples'.

Examples

# NOT RUN {
data(efc)
summary(efc$c12hour)
# split at median
table(dicho(efc$c12hour))
# split at mean
table(dicho(efc$c12hour, dich.by = "mean"))
# split between value lowest to 30, and above 30
table(dicho(efc$c12hour, dich.by = 30))

# sample data frame, values from 1-4
head(efc[, 6:10])

# dichtomized values (1 to 2 = 0, 3 to 4 = 1)
library(dplyr)
efc %>%
  select(6:10) %>%
  dicho(dich.by = 2) %>%
  head()

# dichtomize several variables in a data frame
dicho(efc, c12hour, e17age, c160age, append = FALSE)

# dichotomize and set labels
frq(dicho(
  efc, e42dep,
  var.label = "Dependency (dichotomized)",
  val.labels = c("lower", "higher"),
  append = FALSE
))

# works also with gouped data frames
mtcars %>%
  dicho(disp, append = FALSE) %>%
  table()

mtcars %>%
  group_by(cyl) %>%
  dicho(disp, append = FALSE) %>%
  table()

# dichotomizing grouped data frames leads to different
# results for a dichotomized variable, because the split
# value is different for each group.
# compare:
mtcars %>%
  group_by(cyl) %>%
  summarise(median = median(disp))

median(mtcars$disp)

# dichotomize only variables with more than 10 unique values
p <- function(x) dplyr::n_distinct(x) > 10
dicho_if(efc, predicate = p, append = FALSE)
# }