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ddply {plyr}

Split data frame, apply function, and return results in a data frame.
Package: 
plyr
Version: 
1.8

Description

For each subset of a data frame, apply function then combine results into a data frame.

Usage

ddply(.data, .variables, .fun = NULL, ...,
    .progress = "none", .inform = FALSE, .drop = TRUE,
    .parallel = FALSE, .paropts = NULL)

Arguments

.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
.parallel
if TRUE, apply function in parallel, using parallel backend provided by foreach
.paropts
a list of additional options passed into the foreach function when parallel computation is enabled. This is important if (for example) your code relies on external data or packages: use the .export and .packages arguments to supply them so that all cluster nodes have the correct environment set up for computing.
.inform
produce informative error messages? This is turned off by by default because it substantially slows processing speed, but is very useful for debugging
.data
data frame to be processed
.variables
variables to split data frame by, as as.quoted variables, a formula or character vector
.drop
should combinations of variables that do not appear in the input data be preserved (FALSE) or dropped (TRUE, default)

Values

A data frame, as described in the output section.

Input

This function splits data frames by variables.

Output

The most unambiguous behaviour is achieved when .fun returns a data frame - in that case pieces will be combined with rbind.fill. If .fun returns an atomic vector of fixed length, it will be rbinded together and converted to a data frame. Any other values will result in an error.

If there are no results, then this function will return a data frame with zero rows and columns (data.frame()).

References

Hadley Wickham (2011). The Split-Apply-Combine Strategy for Data Analysis. Journal of Statistical Software, 40(1), 1-29. http://www.jstatsoft.org/v40/i01/.

See Also

tapply for similar functionality in the base package

Other data frame input: d_ply, daply, dlply

Other data frame output: adply, ldply, mdply

Examples

# Summarize a dataset by two variables
require(plyr)
dfx <- data.frame(
  group = c(rep('A', 8), rep('B', 15), rep('C', 6)),
  sex = sample(c("M", "F"), size = 29, replace = TRUE),
  age = runif(n = 29, min = 18, max = 54)
)
 
# Note the use of the '.' function to allow
# group and sex to be used without quoting
ddply(dfx, .(group, sex), summarize,
 mean = round(mean(age), 2),
 sd = round(sd(age), 2))
 
# An example using a formula for .variables
ddply(baseball[1:100,], ~ year, nrow)
# Applying two functions; nrow and ncol
ddply(baseball, .(lg), c("nrow", "ncol"))
 
# Calculate mean runs batted in for each year
rbi <- ddply(baseball, .(year), summarise,
  mean_rbi = mean(rbi, na.rm = TRUE))
# Plot a line chart of the result
plot(mean_rbi ~ year, type = "l", data = rbi)
 
# make new variable career_year based on the
# start year for each player (id)
base2 <- ddply(baseball, .(id), mutate,
 career_year = year - min(year) + 1
)

Documentation reproduced from package plyr, version 1.8. License: MIT