\HeaderA{reshape}{Reshape Grouped Data}{reshape}
\keyword{manip}{reshape}
\begin{Description}\relax
This function reshapes a data frame between \sQuote{wide} format with
repeated measurements in separate columns of the same record and
\sQuote{long} format with the repeated measurements in separate
records.
\end{Description}
\begin{Usage}
\begin{verbatim}
reshape(data, varying = NULL, v.names = NULL, timevar = "time",
        idvar = "id", ids = 1:NROW(data),
        times = seq(length = length(varying[[1]])),
        drop = NULL, direction, new.row.names = NULL,
        split = list(regexp="\\.", include=FALSE))
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{data}] a data frame
\item[\code{varying}] names of sets of variables in the wide format that
correspond to single variables in long format
(\sQuote{time-varying}).  A list of vectors (or optionally a matrix
for \code{direction="wide"}).  See below for more details and
options.
\item[\code{v.names}] names of variables in the long format that correspond
to multiple variables in the wide format.
\item[\code{timevar}] the variable in long format that differentiates multiple
records from the same group or individual.
\item[\code{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
\item[\code{ids}] the values to use for a newly created \code{idvar}
variable in long format.
\item[\code{times}] the values to use for a newly created \code{timevar}
variable in long format.
\item[\code{drop}] a vector of names of variables to drop before reshaping
\item[\code{direction}] character string, either \code{"wide"} to reshape to
wide format, or \code{"long"} to reshape to long format.
\item[\code{new.row.names}] logical; if \code{TRUE} and \code{direction="wide"},
create new row names in long format from the values of the id and
time variables.
\item[\code{split}] information for guessing the \code{varying},
\code{v.names}, and \code{times} arguments.  See below for details.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
The arguments to this function are described in terms of longitudinal
data, as that is the application motivating the functions.  A \sQuote{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
\sQuote{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 \sQuote{long} format dataset also needs a \sQuote{time}
variable identifying which time point each record comes from and an
\sQuote{id} variable showing which records refer to the same person.

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

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

If \code{direction="wide"} the \code{varying} argument can be a vector
of column names or column numbers (converted to column names). The
function will attempt to guess the \code{v.names} and \code{times} from
these names.  The default is variable names like \code{x.1},
\code{x.2},where \code{split=list(regexp="\bsl{}.",include=FALSE)} to
specifies to split at the dot and drop it from the name. To have alphabetic
followed by numeric times use
\code{split=list(regexp="[A-Za-z][0-9]",include=TRUE)}. This splits
between the alphabetic and numeric parts of the name and does not drop
the regular expression.
\end{Details}
\begin{Value}
The reshaped data frame with added attributes to simplify reshaping
back to the original form.
\end{Value}
\begin{SeeAlso}\relax
\code{\LinkA{stack}{stack}}, \code{\LinkA{aperm}{aperm}}
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
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(names(wide)[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,
        split=list(regexp="[a-z][0-9]", include=TRUE))

## 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)

\end{ExampleCode}
\end{Examples}

