Reshape a data frame from wide form to long form using the variable names
wideToLong(data, within = "within", sep = "_", split = TRUE)The data frame.
Name to give to the long-form within-subject factor(s)
Separator string used in wide-form variable names
Should multiple within-subject factors be split into multiple variables?
A data frame containing the reshaped data
The wideToLong function is the companion function to longToWide.
The data argument is a "wide form" data frame, in which each row corresponds to
a single experimental unit (e.g., a single subject). The output is a "long form" data
frame, in which each row corresponds to a single observation.
The wideToLong function relies on the variable names to determine how the data
should be reshaped. The naming scheme for these variables places the name of the
measured variable first, followed by the levels of the within-subjects variable(s),
separated by the separator string sep (default is _) The separator
string cannot appear anywhere else in the variable names: variables without
the separator string are assumed to be between-subject variables.
If the experiment measured the accuracy of participants at some task
at two different points in time, then the wide form data frame would contain
variables of the form accuracy_t1 and accuracy_t2. After
reshaping, the long form data frame would contain one measured variable
called accuracy, and a within-subjects factor with levels t1
and t2. The name of the within-subjects factor is the within
argument.
The function supports experimental designs with multiple within-subjects
factors and multi-variable observations. For example, suppose each
experimental subject is tested in two conditions (cond1 and
cond2), on each of two days (day1 and day2),
yielding an experimental design in which four observations are made for
each subject. For each such observation, we record the mean response time
MRT for and proportion of correct responses PC for the
participant. The variable names needed for a design such as this one would
be MRT_cond1_day1, MRT_cond1_day2, PC_cond1_day1, etc.
The within argument should be a vector of names for the
within-subject factors: in this case, within = c("condition","day").
By default, if there are multiple within-subject factors implied by the
existence of multiple separators, the output will keep these as distinct
variables in the long form data frame (split=FALSE). If
split=TRUE, the within-subject factors will be collapsed into a
single variable.
# Outcome measure is mean response time (MRT), measured in two conditions
# with 4 participants. All participants participate in both conditions.
wide <- data.frame( accuracy_t1 = c( .15,.50,.78,.55 ), # accuracy at time point 1
accuracy_t2 = c( .55,.32,.99,.60 ), # accuracy at time point 2
id = 1:4 ) # id variable
# convert to long form
wideToLong( wide, "time" )
#> id time accuracy
#> 1 1 t1 0.15
#> 2 2 t1 0.50
#> 3 3 t1 0.78
#> 4 4 t1 0.55
#> 5 1 t2 0.55
#> 6 2 t2 0.32
#> 7 3 t2 0.99
#> 8 4 t2 0.60
# A more complex design with multiple within-subject factors. Again, we have only
# four participants, but now we have two different outcome measures, mean response
# time (MRT) and the proportion of correct responses (PC). Additionally, we have two
# different repeated measures variables. As before, we have the experimental condition
# (cond1, cond2), but this time each participant does both conditions on two different
# days (day1, day2). Finally, we have multiple between-subject variables too, namely
# id and gender.
wide2 <- data.frame( id = 1:4,
gender = factor( c("male","male","female","female") ),
MRT_cond1_day1 = c( 415,500,478,550 ),
MRT_cond2_day1 = c( 455,532,499,602 ),
MRT_cond1_day2 = c( 400,490,468,502 ),
MRT_cond2_day2 = c( 450,518,474,588 ),
PC_cond1_day1 = c( 79,83,91,75 ),
PC_cond2_day1 = c( 82,86,90,78 ),
PC_cond1_day2 = c( 88,92,98,89 ),
PC_cond2_day2 = c( 93,97,100,95 ) )
# conversion to long form:
wideToLong( wide2 )
#> id gender MRT PC within1 within2
#> 1 1 male 415 79 cond1 day1
#> 2 2 male 500 83 cond1 day1
#> 3 3 female 478 91 cond1 day1
#> 4 4 female 550 75 cond1 day1
#> 5 1 male 455 82 cond2 day1
#> 6 2 male 532 86 cond2 day1
#> 7 3 female 499 90 cond2 day1
#> 8 4 female 602 78 cond2 day1
#> 9 1 male 400 88 cond1 day2
#> 10 2 male 490 92 cond1 day2
#> 11 3 female 468 98 cond1 day2
#> 12 4 female 502 89 cond1 day2
#> 13 1 male 450 93 cond2 day2
#> 14 2 male 518 97 cond2 day2
#> 15 3 female 474 100 cond2 day2
#> 16 4 female 588 95 cond2 day2
wideToLong( wide2, within = c("condition","day") )
#> id gender MRT PC condition day
#> 1 1 male 415 79 cond1 day1
#> 2 2 male 500 83 cond1 day1
#> 3 3 female 478 91 cond1 day1
#> 4 4 female 550 75 cond1 day1
#> 5 1 male 455 82 cond2 day1
#> 6 2 male 532 86 cond2 day1
#> 7 3 female 499 90 cond2 day1
#> 8 4 female 602 78 cond2 day1
#> 9 1 male 400 88 cond1 day2
#> 10 2 male 490 92 cond1 day2
#> 11 3 female 468 98 cond1 day2
#> 12 4 female 502 89 cond1 day2
#> 13 1 male 450 93 cond2 day2
#> 14 2 male 518 97 cond2 day2
#> 15 3 female 474 100 cond2 day2
#> 16 4 female 588 95 cond2 day2
# treat "condition x day" as a single repeated measures variable:
wideToLong( wide2, split = FALSE)
#> id gender within MRT PC
#> 1 1 male cond1_day1 415 79
#> 2 2 male cond1_day1 500 83
#> 3 3 female cond1_day1 478 91
#> 4 4 female cond1_day1 550 75
#> 5 1 male cond2_day1 455 82
#> 6 2 male cond2_day1 532 86
#> 7 3 female cond2_day1 499 90
#> 8 4 female cond2_day1 602 78
#> 9 1 male cond1_day2 400 88
#> 10 2 male cond1_day2 490 92
#> 11 3 female cond1_day2 468 98
#> 12 4 female cond1_day2 502 89
#> 13 1 male cond2_day2 450 93
#> 14 2 male cond2_day2 518 97
#> 15 3 female cond2_day2 474 100
#> 16 4 female cond2_day2 588 95