| reshape {stats} | R Documentation |
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.
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)}
)
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
indexes which are interpreted as referring to |
v.names |
names of variables in the long format that correspond to multiple variables in the wide format. See below for details. |
timevar |
the variable in long format that differentiates multiple records from the same group or individual. |
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 |
times |
the values to use for a newly created |
drop |
a vector of names of variables to drop before reshaping |
direction |
character string, either |
new.row.names |
logical; if |
sep |
A character vector of length 1, indicating a separating
character in the variable names in the wide format. This is used for
guessing |
split |
A list with three components, |
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$regexp component is passed to either strsplit() or
regexp(), where the latter is used if split$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$fixed.
The reshaped data frame with added attributes to simplify reshaping back to the original form.
stack, aperm;
relist for reshaping the result of unlist.
summary(Indometh)
wide <- reshape(Indometh, v.names="conc", idvar="Subject",
timevar="time", direction="wide")
wide
reshape(wide, direction="long")
reshape(wide, idvar="Subject", varying=list(2:12),
v.names="conc", direction="long")
## times need not be numeric
df <- data.frame(id=rep(1:4,rep(2,4)),
visit=I(rep(c("Before","After"),4)),
x=rnorm(4), y=runif(4))
df
reshape(df, timevar="visit", idvar="id", direction="wide")
## warns that y is really varying
reshape(df, timevar="visit", idvar="id", direction="wide", v.names="x")
## unbalanced 'long' data leads to NA fill in 'wide' form
df2 <- df[1:7,]
df2
reshape(df2, timevar="visit", idvar="id", direction="wide")
## Alternative regular expressions for guessing names
df3 <- data.frame(id=1:4, age=c(40,50,60,50), dose1=c(1,2,1,2),
dose2=c(2,1,2,1), dose4=c(3,3,3,3))
reshape(df3, direction="long", varying=3:5, sep="")
## an example that isn't longitudinal data
state.x77 <- as.data.frame(state.x77)
long <- reshape(state.x77, idvar="state", ids=row.names(state.x77),
times=names(state.x77), timevar="Characteristic",
varying=list(names(state.x77)), direction="long")
reshape(long, direction="wide")
reshape(long, direction="wide", new.row.names=unique(long$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)