sjmisc (version 2.6.3)

to_long: Convert wide data to long format

Description

This function converts wide data into long format. It allows to transform multiple key-value pairs to be transformed from wide to long format in one single step.

Usage

to_long(data, keys, values, ..., labels = NULL, recode.key = FALSE)

Arguments

data

A data.frame that should be tansformed from wide to long format.

keys

Character vector with name(s) of key column(s) to create in output. Either one key value per column group that should be gathered, or a single string. In the latter case, this name will be used as key column, and only one key column is created. See 'Examples'.

values

Character vector with names of value columns (variable names) to create in output. Must be of same length as number of column groups that should be gathered. See 'Examples'.

...

Specification of columns that should be gathered. Must be one character vector with variable names per column group, or a numeric vector with column indices indicating those columns that should be gathered. See 'Examples'.

labels

Character vector of same length as values with variable labels for the new variables created from gathered columns. See 'Examples' and 'Details'.

recode.key

Logical, if TRUE, the values of the key column will be recoded to numeric values, in sequential ascending order.

Details

This function enhances tidyr's gather function that you can gather multiple column groups at once. Value and variable labels for non-gathered variables are preserved. However, gathered variables may have different variable label attributes. In this case, gather will drop these attributes. Hence, the new created variables from gathered columns don't have any variable label attributes. In such cases, use labels argument to set variable label attributes.

Examples

# NOT RUN {
# create sample
mydat <- data.frame(age = c(20, 30, 40),
                    sex = c("Female", "Male", "Male"),
                    score_t1 = c(30, 35, 32),
                    score_t2 = c(33, 34, 37),
                    score_t3 = c(36, 35, 38),
                    speed_t1 = c(2, 3, 1),
                    speed_t2 = c(3, 4, 5),
                    speed_t3 = c(1, 8, 6))

# check tidyr. score is gathered, however, speed is not
tidyr::gather(mydat, "time", "score", score_t1, score_t2, score_t3)

# gather multiple columns. both time and speed are gathered.
to_long(
  data = mydat,
  keys = "time",
  values = c("score", "speed"),
  c("score_t1", "score_t2", "score_t3"),
  c("speed_t1", "speed_t2", "speed_t3")
)

# gather multiple columns, use numeric key-value
to_long(
  data = mydat,
  keys = "time",
  values = c("score", "speed"),
  c("score_t1", "score_t2", "score_t3"),
  c("speed_t1", "speed_t2", "speed_t3"),
  recode.key = TRUE
)

# gather multiple columns by colum names and colum indices
to_long(
  data = mydat,
  keys = "time",
  values = c("score", "speed"),
  c("score_t1", "score_t2", "score_t3"),
  6:8,
  recode.key = TRUE
)

# gather multiple columns, use separate key-columns
# for each value-vector
to_long(
  data = mydat,
  keys = c("time_score", "time_speed"),
  values = c("score", "speed"),
  c("score_t1", "score_t2", "score_t3"),
  c("speed_t1", "speed_t2", "speed_t3")
)

# gather multiple columns, label columns
mydat <- to_long(
  data = mydat,
  keys = "time",
  values = c("score", "speed"),
  c("score_t1", "score_t2", "score_t3"),
  c("speed_t1", "speed_t2", "speed_t3"),
  labels = c("Test Score", "Time needed to finish")
)

library(sjlabelled)
str(mydat$score)
get_label(mydat$speed)

# }