plyr (version 1.8.9)

mdply: Call function with arguments in array or data frame, returning a data frame.


Call a multi-argument function with values taken from columns of an data frame or array, and combine results into a data frame


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


A data frame, as described in the output section.



matrix or data frame to use as source of arguments


function to apply to each piece


other arguments passed on to .fun


should output be 1d (expand = FALSE), with an element for each row; or nd (expand = TRUE), with a dimension for each variable.


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


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.


Call a multi-argument function with values taken from columns of an data frame or array


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()).


The m*ply functions are the plyr version of mapply, specialised according to the type of output they produce. These functions are just a convenient wrapper around a*ply with margins = 1 and .fun wrapped in splat.


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

See Also

Other multiple arguments input: m_ply(), maply(), mlply()

Other data frame output: adply(), ddply(), ldply()


Run this code
mdply(data.frame(mean = 1:5, sd = 1:5), rnorm, n = 2)
mdply(expand.grid(mean = 1:5, sd = 1:5), rnorm, n = 2)
mdply(cbind(mean = 1:5, sd = 1:5), rnorm, n = 5)
mdply(cbind(mean = 1:5, sd = 1:5),, n = 5)

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