ddply

From plyr v1.8.4 by Hadley Wickham

Split data frame, apply function, and return results in a data frame.

For each subset of a data frame, apply function then combine results into a data frame. To apply a function for each row, use adply with .margins set to 1.

Keywords: manip

Usage

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

Arguments

.data: data frame to be processed
.variables: variables to split data frame by, as 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
.inform: produce informative error messages? This is turned off by default because it substantially slows processing speed, but is very useful for debugging
.drop: should combinations of variables that do not appear in the input data be preserved (FALSE) or dropped (TRUE, default)
.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.

Value

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/.

Examples

library(plyr)

# Summarize a dataset by two variables
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 1.8.4, License: MIT + file LICENSE

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