plyr

I am using the mtcars dataset. I want to find the number of records for a particular combination of data. Something very similar to the count(*) group by clause in SQL. ddply() from plyr is working for me library(plyr) ddply(mtcars, .(cyl,gear),nrow) has output cyl gear V1 1 4 3 1 2 4 4 8 3 4 5 2 4 6 3 2 5 6 4 4 6 6 5 1 7 8 3 12 8 8 5 2 Using this code library(dplyr) g <- group_by(mtcars, cyl, gear) summarise(g, length(gear)) has output length(cyl) 1 32 I found various functions to pass in to summarise() but none seem to work for me. One function I found is sum(G) , which returned Error in

I have a simulation that has a huge aggregate and combine step right in the middle. I prototyped this process using plyr's ddply() function which works great for a huge percentage of my needs. But I need this aggregation step to be faster since I have to run 10K simulations. I'm already scaling the simulations in parallel but if this one step were faster I could greatly decrease the number of nodes I need. Here's a reasonable simplification of what I am trying to do: library(Hmisc) # Set up some example data year <- sample(1970:2008, 1e6, rep=T) state <- sample(1:50, 1e6, rep=T) group1 <-

This question already has an answer here: Calculate group mean (or other summary stats) and assign to original data 4 answers One of the design patterns I use over and over is performing a "group by" or "split, apply, combine (SAC)" on a data frame and then joining the aggregated data back to the original data. This is useful, for example, when calculating each county's deviation from the state mean in a data frame with many states and counties. Rarely is my aggregate calculation only a simple mean, but it makes a good example. I often solve this problem the following way: require(plyr) set

I think I am using plyr incorrectly. Could someone please tell me if this is 'efficient' plyr code? require(plyr) plyr <- function(dd) ddply(dd, .(price), summarise, ss=sum(volume)) A little context: I have a few large aggregation problems and I have noted that they were each taking some time. In trying to solve the issues, I became interested in the performance of various aggregation procedures in R. I tested a few aggregation methods - and found myself waiting around all day. When I finally got results back, I discovered a huge gap between the plyr method and the others - which makes me