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.
to_long(data, keys, values, ..., labels = NULL, recode.key = FALSE)A data.frame that should be tansformed from wide to
long format.
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'.
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'.
Character vector of same length as values with variable
labels for the new variables created from gathered columns.
See 'Examples' and 'Details'.
Logical, if TRUE, the values of the key
column will be recoded to numeric values, in sequential ascending
order.
This function reshapes data from wide to long format, however,
you can gather multiple column groups at once. Value and variable labels
for non-gathered variables are preserved. Attributes from gathered variables,
such as information about the variable labels, are lost during reshaping.
Hence, the new created variables from gathered columns don't have any
variable label attributes. In such cases, use labels argument to set
back variable label attributes.
# 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))
# 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")
)
#> age sex time score speed
#> 1 20 Female score_t1 30 2
#> 2 30 Male score_t1 35 3
#> 3 40 Male score_t1 32 1
#> 4 20 Female score_t2 33 3
#> 5 30 Male score_t2 34 4
#> 6 40 Male score_t2 37 5
#> 7 20 Female score_t3 36 1
#> 8 30 Male score_t3 35 8
#> 9 40 Male score_t3 38 6
# alternative syntax, using "reshape_longer()"
reshape_longer(
mydat,
columns = list(
c("score_t1", "score_t2", "score_t3"),
c("speed_t1", "speed_t2", "speed_t3")
),
names.to = "time",
values.to = c("score", "speed")
)
#> age sex time score speed .id
#> 1 20 Female score_t1 30 2 1
#> 2 30 Male score_t1 35 3 2
#> 3 40 Male score_t1 32 1 3
#> 4 20 Female score_t2 33 3 1
#> 5 30 Male score_t2 34 4 2
#> 6 40 Male score_t2 37 5 3
#> 7 20 Female score_t3 36 1 1
#> 8 30 Male score_t3 35 8 2
#> 9 40 Male score_t3 38 6 3
# or ...
reshape_longer(
mydat,
list(3:5, 6:8),
names.to = "time",
values.to = c("score", "speed")
)
#> age sex time score speed .id
#> 1 20 Female score_t1 30 2 1
#> 2 30 Male score_t1 35 3 2
#> 3 40 Male score_t1 32 1 3
#> 4 20 Female score_t2 33 3 1
#> 5 30 Male score_t2 34 4 2
#> 6 40 Male score_t2 37 5 3
#> 7 20 Female score_t3 36 1 1
#> 8 30 Male score_t3 35 8 2
#> 9 40 Male score_t3 38 6 3
# 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
)
#> age sex time score speed
#> 1 20 Female 1 30 2
#> 2 30 Male 1 35 3
#> 3 40 Male 1 32 1
#> 4 20 Female 2 33 3
#> 5 30 Male 2 34 4
#> 6 40 Male 2 37 5
#> 7 20 Female 3 36 1
#> 8 30 Male 3 35 8
#> 9 40 Male 3 38 6
# 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
)
#> age sex time score speed
#> 1 20 Female 1 30 2
#> 2 30 Male 1 35 3
#> 3 40 Male 1 32 1
#> 4 20 Female 2 33 3
#> 5 30 Male 2 34 4
#> 6 40 Male 2 37 5
#> 7 20 Female 3 36 1
#> 8 30 Male 3 35 8
#> 9 40 Male 3 38 6
# 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")
)
#> age sex time_score score time_speed speed
#> 1 20 Female score_t1 30 speed_t1 2
#> 2 30 Male score_t1 35 speed_t1 3
#> 3 40 Male score_t1 32 speed_t1 1
#> 4 20 Female score_t2 33 speed_t2 3
#> 5 30 Male score_t2 34 speed_t2 4
#> 6 40 Male score_t2 37 speed_t2 5
#> 7 20 Female score_t3 36 speed_t3 1
#> 8 30 Male score_t3 35 speed_t3 8
#> 9 40 Male score_t3 38 speed_t3 6
# 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$score)
#> num [1:9] 30 35 32 33 34 37 36 35 38
#> - attr(*, "label")= chr "Test Score"
get_label(mydat$speed)
#> [1] "Time needed to finish"