plyr

I'm trying to apply a function to a dataframe using ddply from the plyr package, but I'm getting some results that I don't understand. I have 3 questions about the results Given: mydf<- data.frame(c(12,34,9,3,22,55),c(1,2,1,1,2,2) , c(0,1,2,1,1,2)) colnames(mydf)[1] <- 'n' colnames(mydf)[2] <- 'x' colnames(mydf)[3] <- 'x1' mydf looks like this: n x x1 1 12 1 0 2 34 2 1 3 9 1 2 4 3 1 1 5 22 2 1 6 55 2 2 Question #1 If I do: k <- function(x) { mydf$z <- ifelse(x == 1, 0, mydf$n) return (mydf) } mydf <- ddply(mydf, c("x") , .fun = k, .inform = TRUE) I get the following error: Error in `$<-.data

I have the following, somewhat large dataset: > dim(dset) [1] 422105 25 > class(dset) [1] "data.frame" > Without doing anything, the R process seems to take about 1GB of RAM. I am trying to run the following code: dset <- ddply(dset, .(tic), transform, date.min <- min(date), date.max <- max(date), daterange <- max(date) - min(date), .parallel = TRUE) Running that code, RAM usage skyrockets. It completely saturated 60GB's of RAM, running on a 32 core machine. What am I doing wrong? If performance is an issue, it might be a good idea to switch to using data.table s from the package of the same

I'm trying to use the parallel package in R for parallel operations rather than doSNOW since it's built-in and ostensibly the way the R Project wants things to go. I'm doing something wrong that I can't pin down though. Take for example this: a <- rnorm(50) b <- rnorm(50) arr <- matrix(cbind(a,b),nrow=50) aaply(arr,.margin=1,function(x){x[1]+x[2]},.parallel=F) This works just fine, producing the sums of my two columns. But if I try to bring in the parallel package: library(parallel) nodes <- detectCores() cl <- makeCluster(nodes) setDefaultCluster(cl) aaply(arr,.margin=1,function(x){x[1]+x[2]}

I am using ddply to aggregate and summarize data frame variables, and I am interested in looping through my data frame's list to create the new variables. new.data <- ddply(old.data, c("factor", "factor2"), function(df) c(a11_a10 = CustomFunction(df$a11_a10), a12_a11 = CustomFunction(df$a12_a11), a13_a12 = CustomFunction(df$a13_a12), ... ... ...)) Is there a way for me to insert a loop in ddply so that I can avoid writing each new summary variable out, e.g. for (i in 11:n) { paste("a", i, "_a", i - 1) = CustomFunction(..... ) } I know that this is not how it would actually be done, but I just

Apply function table() to each column of a data.frame using dplyr I often apply the table-function on each column of a data frame using plyr , like this: library(plyr) ldply( mtcars, function(x) data.frame( table(x), prop.table( table(x) ) ) ) Is it possible to do this in dplyr also? My attempts fail: mtcars %>% do( table %>% data.frame() ) melt( mtcars ) %>% do( table %>% data.frame() ) Caner You can try the following which does not rely on the tidyr package. mtcars %>% lapply(table) %>% lapply(as.data.frame) %>% Map(cbind,var = names(mtcars),.) %>% rbind_all() %>% group_by(var) %>% mutate

Consider the following data: d = data.frame( experiment = as.factor(c("foo", "foo", "foo", "bar", "bar")), si = runif(5), ti = runif(5) ) I would like to perform a correlation test for si and ti , for each experiment factor level. So I thought I'd run: ddply(d, .(experiment), cor.test) But how do I pass the values of si and ti to the cor.test call? I tried this: > ddply(d, .(experiment), cor.test, x = si, y = ti) Error in .fun(piece, ...) : object 'si' not found > ddply(d, .(experiment), cor.test, si, ti) Error in match.arg(alternative) : 'arg' must be NULL or a character vector Is there