daply {plyr} | R Documentation |
For each subset of data frame, apply function then combine results into an array
daply(.data, .variables, .fun = NULL, ..., .progress = "none", .drop = TRUE)
.data |
data frame to be processed |
.variables |
variables to split data frame by, as quoted variables, a formula or character vector |
.fun |
function to apply to each piece |
... |
other arguments passed on to .fun |
.progress |
name of the progress bar to use, see create_progress_bar |
.drop |
should extra dimensions of length 1 be dropped, simplifying the output. Defaults to TRUE |
All plyr functions use the same split-apply-combine strategy: they split the
input into simpler pieces, apply .fun
to each piece, and then combine
the pieces into a single data structure. This function splits data frames
by variable and combines the result into an array. If there are no results,
then this function will return a vector of length 0 (vector()
).
daply
with a function that operates column-wise is similar to
aggregate
.
@keyword manip
@arguments data frame to be processed
@arguments variables to split data frame by, as quoted variables, a formula or character vector
@arguments function to apply to each piece
@arguments other arguments passed on to .fun
@arguments name of the progress bar to use, see create_progress_bar
@arguments should extra dimensions of length 1 be dropped, simplifying the output. Defaults to TRUE
@value if results are atomic with same type and dimensionality, a vector, matrix or array; otherwise, a list-array (a list with dimensions)
if results are atomic with same type and dimensionality, a vector, matrix or array; otherwise, a list-array (a list with dimensions)
Hadley Wickham <h.wickham@gmail.com>
daply(baseball, .(year), nrow) # Several different ways of summarising by variables that should not be # included in the summary daply(baseball[, c(2, 6:9)], .(year), mean) daply(baseball[, 6:9], .(baseball$year), mean) daply(baseball, .(year), function(df) mean(df[, 6:9]))