RcmdrMisc (version 2.9-1)

reshapeW2L: Reshape Repeated-Measures Data from Wide to Long Format

Description

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.

Usage

reshapeW2L(data, within, levels, varying, ignore, id = "id")

Value

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.

Arguments

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 within argument.

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 "id".

Author

John Fox

Details

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.

See Also

reshapeL2W, reshape, OBrienKaiser, OBrienKaiserLong.

Examples

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
}