reshape
Reshape Grouped Data
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.
- Keywords
- manip
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
idvarvariable in long format.- times
the values to use for a newly created
timevarvariable 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.namesandtimesarguments based on the names invarying. Ifsep == "", 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’.
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$fixed.
Value
The reshaped data frame with added attributes to simplify reshaping back to the original form.
See Also
Examples
library(stats)
# NOT RUN {
summary(Indometh)
wide <- reshape(Indometh, v.names = "conc", idvar = "Subject",
timevar = "time", direction = "wide")
wide
reshape(wide, direction = "long")
reshape(wide, idvar = "Subject", varying = list(2:12),
v.names = "conc", direction = "long")
## times need not be numeric
df <- data.frame(id = rep(1:4, rep(2,4)),
visit = I(rep(c("Before","After"), 4)),
x = rnorm(4), y = runif(4))
df
reshape(df, timevar = "visit", idvar = "id", direction = "wide")
## warns that y is really varying
reshape(df, timevar = "visit", idvar = "id", direction = "wide", v.names = "x")
## unbalanced 'long' data leads to NA fill in 'wide' form
df2 <- df[1:7, ]
df2
reshape(df2, timevar = "visit", idvar = "id", direction = "wide")
## Alternative regular expressions for guessing names
df3 <- data.frame(id = 1:4, age = c(40,50,60,50), dose1 = c(1,2,1,2),
dose2 = c(2,1,2,1), dose4 = c(3,3,3,3))
reshape(df3, direction = "long", varying = 3:5, sep = "")
## an example that isn't longitudinal data
state.x77 <- as.data.frame(state.x77)
long <- reshape(state.x77, idvar = "state", ids = row.names(state.x77),
times = names(state.x77), timevar = "Characteristic",
varying = list(names(state.x77)), direction = "long")
reshape(long, direction = "wide")
reshape(long, direction = "wide", new.row.names = unique(long$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)
# }