stats (version 3.4.3)

reshape: Reshape Grouped Data

Description

This function reshapes a data frame between ‘wide’ format with repeated measurements in separate columns of the same record and ‘long’ format with the repeated measurements in separate records.

Usage

reshape(data, varying = NULL, v.names = NULL, timevar = "time",
        idvar = "id", ids = 1:NROW(data),
        times = seq_along(varying[[1]]),
        drop = NULL, direction, new.row.names = NULL,
        sep = ".",
        split = if (sep == "") {
            list(regexp = "[A-Za-z][0-9]", include = TRUE)
        } else {
            list(regexp = sep, include = FALSE, fixed = TRUE)}
        )

Arguments

data

a data frame

varying

names of sets of variables in the wide format that correspond to single variables in long format (‘time-varying’). This is canonically a list of vectors of variable names, but it can optionally be a matrix of names, or a single vector of names. In each case, the names can be replaced by indices which are interpreted as referring to names(data). See ‘Details’ for more details and options.

v.names

names of variables in the long format that correspond to multiple variables in the wide format. See ‘Details’.

timevar

the variable in long format that differentiates multiple records from the same group or individual. If more than one record matches, the first will be taken (with a warning).

idvar

Names of one or more variables in long format that identify multiple records from the same group/individual. These variables may also be present in wide format.

ids

the values to use for a newly created idvar variable in long format.

times

the values to use for a newly created timevar variable in long format. See ‘Details’.

drop

a vector of names of variables to drop before reshaping.

direction

character string, partially matched to either "wide" to reshape to wide format, or "long" to reshape to long format.

new.row.names

character or NULL: a non-null value will be used for the row names of the result.

sep

A character vector of length 1, indicating a separating character in the variable names in the wide format. This is used for guessing v.names and times arguments based on the names in varying. If sep == "", the split is just before the first numeral that follows an alphabetic character. This is also used to create variable names when reshaping to wide format.

split

A list with three components, regexp, include, and (optionally) fixed. This allows an extended interface to variable name splitting. See ‘Details’.

Value

The reshaped data frame with added attributes to simplify reshaping back to the original form.

Details

The arguments to this function are described in terms of longitudinal data, as that is the application motivating the functions. A ‘wide’ longitudinal dataset will have one record for each individual with some time-constant variables that occupy single columns and some time-varying variables that occupy a column for each time point. In ‘long’ format there will be multiple records for each individual, with some variables being constant across these records and others varying across the records. A ‘long’ format dataset also needs a ‘time’ variable identifying which time point each record comes from and an ‘id’ variable showing which records refer to the same person.

If the data frame resulted from a previous reshape then the operation can be reversed simply by reshape(a). The direction argument is optional and the other arguments are stored as attributes on the data frame.

If direction = "wide" and no varying or v.names arguments are supplied it is assumed that all variables except idvar and timevar are time-varying. They are all expanded into multiple variables in wide format.

If direction = "long" the varying argument can be a vector of column names (or a corresponding index). The function will attempt to guess the v.names and times from these names. The default is variable names like x.1, x.2, where sep = "." specifies to split at the dot and drop it from the name. To have alphabetic followed by numeric times use sep = "".

Variable name splitting as described above is only attempted in the case where varying is an atomic vector, if it is a list or a matrix, v.names and times will generally need to be specified, although they will default to, respectively, the first variable name in each set, and sequential times.

Also, guessing is not attempted if v.names is given explicitly. Notice that the order of variables in varying is like x.1,y.1,x.2,y.2.

The split argument should not usually be necessary. The split$regexp component is passed to either strsplit or regexpr, where the latter is used if split$include is TRUE, in which case the splitting occurs after the first character of the matched string. In the strsplit case, the separator is not included in the result, and it is possible to specify fixed-string matching using split$fixed.

See Also

stack, aperm; relist for reshaping the result of unlist.

Examples

# NOT RUN {
summary(Indometh)
wide <- reshape(Indometh, v.names = "conc", idvar = "Subject",
                timevar = "time", direction = "wide")
wide

reshape(wide, direction = "long")
reshape(wide, idvar = "Subject", varying = list(2:12),
        v.names = "conc", direction = "long")

## times need not be numeric
df <- data.frame(id = rep(1:4, rep(2,4)),
                 visit = I(rep(c("Before","After"), 4)),
                 x = rnorm(4), y = runif(4))
df
reshape(df, timevar = "visit", idvar = "id", direction = "wide")
## warns that y is really varying
reshape(df, timevar = "visit", idvar = "id", direction = "wide", v.names = "x")


##  unbalanced 'long' data leads to NA fill in 'wide' form
df2 <- df[1:7, ]
df2
reshape(df2, timevar = "visit", idvar = "id", direction = "wide")

## Alternative regular expressions for guessing names
df3 <- data.frame(id = 1:4, age = c(40,50,60,50), dose1 = c(1,2,1,2),
                  dose2 = c(2,1,2,1), dose4 = c(3,3,3,3))
reshape(df3, direction = "long", varying = 3:5, sep = "")


## an example that isn't longitudinal data
state.x77 <- as.data.frame(state.x77)
long <- reshape(state.x77, idvar = "state", ids = row.names(state.x77),
                times = names(state.x77), timevar = "Characteristic",
                varying = list(names(state.x77)), direction = "long")

reshape(long, direction = "wide")

reshape(long, direction = "wide", new.row.names = unique(long$state))

## multiple id variables
df3 <- data.frame(school = rep(1:3, each = 4), class = rep(9:10, 6),
                  time = rep(c(1,1,2,2), 3), score = rnorm(12))
wide <- reshape(df3, idvar = c("school","class"), direction = "wide")
wide
## transform back
reshape(wide)

# }