Reshape Repeated-Measures Data from Wide to Long Format
The data are assumed to be in "wide" format, with a single row for each subject, and different columns for values of one or more repeated-measures variables classified by one or more within-subjects factors.
reshapeW2L(data, within, levels, varying, ignore, id = "id")
data |
wide version of data set. |
within |
a character vector of names for the crossed within-subjects factors to be created in the long form of the data. |
levels |
a named list of character vectors, each element giving the names of the levels for a within-subjects factor; the names of the list elements are the names of the within-subjects factor, given in the |
varying |
a named list of the names of variables in the wide data set specifying the occasion-varying variables to be created in the long data set; each element in the list is named for an occasion-varying variable and is a character vector of column names in the wide data for that occasion-varying variable. |
ignore |
a character vector of names of variables in the wide data to be dropped in the long form of the data. |
id |
the (character) name of the subject ID variable to be created in the long form of the data, default |
Between-subjects variables don't vary by occasions for each subject. Variables that aren't listed explicitly in the arguments to the function are assumed to be between-subjects variables. The values of these variables are duplicated in each row pertaining to a given subject.
Within-subjects factors vary by occasions for each subject, and it is assumed that the within-subjects design is regular, completely crossed, and balanced, so that the same combinations of within-subjects factors are observed for each subject. There are typically one or two within-subject factors.
Occasion-varying variables, as their name implies, (potentially) vary by occasions for each subject, and include one or more "response" variables, possibly along with occasion-varying covariates; these variables can be factors as well as numeric variables. Each occasion-varying variable is encoded in multiple columns of the wide form of the data and in a single column in the long form. There is typically one occasion-varying variable, a response variable.
There is one value of each occasion-varying variable for each combination of levels of the within-subjects factors. Thus, the number of variables in the wide data for each occasion-varying variable must be equal to the product of levels of the within-subjects factors, with the levels of the within-subjects factors varying most quickly from right to left in the
within argument.
a data frame in "long" format, with multiple rows for each subject (equal to the number of combinations of levels of the within-subject factors) and one column for each between-subjects and occasion-varying variable.
John Fox jfox@mcmaster.ca
if (require(carData)){
OBrienKaiserL <- reshapeW2L(OBrienKaiser, within=c("phase", "hour"),
levels=list(phase=c("pre", "post", "fup"), hour=1:5),
varying=list(score=c("pre.1", "pre.2", "pre.3", "pre.4", "pre.5",
"post.1", "post.2", "post.3", "post.4", "post.5",
"fup.1", "fup.2", "fup.3", "fup.4", "fup.5")))
brief(OBrienKaiserL, c(15, 15))
m1 <- Tapply(score ~ phase + hour + treatment + gender, mean, data=OBrienKaiserL)
m2 <- Tapply(score ~ phase + hour + treatment + gender, mean, data=OBrienKaiserLong)
all.equal(m1, m2) # should be equal
}
if (require(carData)){
OBrienKaiserL2 <- reshapeW2L(OBrienKaiser, within="phase",
levels=list(phase=c("pre", "post", "fup")),
ignore=c("pre.2", "pre.3", "pre.4", "pre.5",
"post.2", "post.3", "post.4", "post.5",
"fup.2", "fup.3", "fup.4", "fup.5"),
varying=list(score=c("pre.1", "post.1", "fup.1")))
brief(OBrienKaiserL2, c(6, 6))
m1 <- Tapply(score ~ phase + treatment + gender, mean, data=OBrienKaiserL2)
m2 <- Tapply(score ~ phase + treatment + gender, mean,
data=subset(OBrienKaiserLong, hour==1))
all.equal(m1, m2) # should be equal
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