plyr

I have a data frame with 3 groups and 3 days: set.seed(10) dat <- data.frame(group=rep(c("g1","g2","g3"),each=3), day=rep(c(0,2,4),3), value=runif(9)) # group day value # 1 g1 0 0.507478 # 2 g1 2 0.306769 # 3 g1 4 0.426908 # 4 g2 0 0.693102 # 5 g2 2 0.085136 # 6 g2 4 0.225437 # 7 g3 0 0.274531 # 8 g3 2 0.272305 # 9 g3 4 0.615829 I want to take the log2 and divide each value with the day 0 value within each group. The way I'm doing it now is by calculating each day group in an intermediate step: day_0 <- dat[dat$day==0, "value"] day_2 <- dat[dat$day==2, "value"] day_4 <- dat[dat$day==4, "value"

I have a large data.frame (20000+ entries) in this format: id D1 D2 1 0.40 0.21 1 0.00 0.00 1 0.53 0.20 2 0.17 0.17 2 0.25 0.25 2 0.55 0.43 Where each id may be duplicated 3-20 times. I would like to merge the duplicated rows into new columns, so my new data.frame looks like: id D1 D2 D3 D4 D5 D6 1 0.40 0.21 0.00 0.00 0.53 0.20 2 0.17 0.17 0.25 0.25 0.55 0.43 I've manipulated data.frames before with plyr, but I'm not sure how to approach this problem. Any help would be appreciated.Thanks. The best option would be to just use melt and dcast from "reshape2". But before we jump to that option,

ok, I'm trying to wrap my head around dplyr, using it instead of plyr. In my short time with R I've grown somewhat accustomed to ddply. I'm using a "simple" example for how to use dplyr as opposed to ddply in plyr. Here goes: in the following: t1.table <- ddply(diamonds, c("clarity", "cut"), "nrow") I receive a summary table of counts of diamonds by clarity and cut. In dplyr, the simplest example I can come up with is: diamonds %>% select(clarity, cut) %>% group_by(clarity, cut) %>% summarise(count=n()) -> t2.table which seems a bit more involved. Is there a better way to simplify this? ~

I came across a table of freq. counts today I had to expand into a data frame of raw values. I was able to do it but was wondering if there's a faster way using the reshape package or data.table? The original table looked like this: i1 i2 i3 i4 m f 1 0 0 0 0 22 29 2 1 0 0 0 30 50 3 0 1 0 0 13 15 4 0 0 1 0 1 6 5 1 1 0 0 24 67 6 1 0 1 0 5 12 7 0 1 1 0 1 2 8 1 1 1 0 10 22 9 0 0 0 1 10 7 10 1 0 0 1 27 30 11 0 1 0 1 14 4 12 0 0 1 1 1 0 13 1 1 0 1 54 63 14 1 0 1 1 8 10 15 0 1 1 1 8 6 16 1 1 1 1 57 51 Here's an easy grab of the data using dput: dat <- structure(list(i1 = c(0L, 1L, 0L, 0L, 1L, 1L, 0L,

I want to use the parallel functionality of the plyr package within functions. I would have thought that the proper way to export objects that have been created within the body of the function (in this example, the object is df_2 ) is as follows # rm(list=ls()) library(plyr) library(doParallel) workers=makeCluster(2) registerDoParallel(workers,core=2) plyr_test=function() { df_1=data.frame(type=c("a","b"),x=1:2) df_2=data.frame(type=c("a","b"),x=3:4) #export df_2 via .paropts ddply(df_1,"type",.parallel=TRUE,.paropts=list(.export="df_2"),.fun=function(y) { merge(y,df_2,all=FALSE,by="type") })

I'm using dlply() with a custom function that averages slopes of lm() fits on data that contain some NA values, and I get the error "Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : 0 (non-NA) cases" This error only happens when I call dlply with two key variables - separating by one variable works fine. Annoyingly I can't reproduce the error with a simple dataset, so I've posted the problem dataset in my dropbox. Here's the code, as minimized as possible while still producing an error: masterData <- read.csv("http://dl.dropbox.com/u/48901983/SOquestionData.csv", na