plyr (version 1.8.9)

ddply: 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.


  .fun = NULL,
  .progress = "none",
  .inform = FALSE,
  .drop = TRUE,
  .parallel = FALSE,
  .paropts = NULL


A data frame, as described in the output section.



data frame to be processed


variables to split data frame by, as as.quoted variables, a formula or character vector


function to apply to each piece


other arguments passed on to .fun


name of the progress bar to use, see create_progress_bar


produce informative error messages? This is turned off by default because it substantially slows processing speed, but is very useful for debugging


should combinations of variables that do not appear in the input data be preserved (FALSE) or dropped (TRUE, default)


if TRUE, apply function in parallel, using parallel backend provided by foreach


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.


This function splits data frames by variables.


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()).


Hadley Wickham (2011). The Split-Apply-Combine Strategy for Data Analysis. Journal of Statistical Software, 40(1), 1-29.

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()


Run this code
# 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

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