In my last post I mentioned that I had improved on R’s summaryRprof()
function with a custom function called proftable()
. I’ve updated proftable()
to take advantage of R 3.0.0’s ability to record line numbers while profiling. I’ve put it on github – you can get it there or below.
proftable
reads in a file generated by Rprof()
and creates an easy-to read table of the most time-consuming calls in your code, ordered from most time-consuming to least. Unlike summaryRprof()
, it prints the entire call stack, so you can trace the origin of the time hogs. To make this easier to read, I lop off the “parent stack” common to all of the function calls, and display it just once, below the table. Here’s some example output:
> Rprof("profile1.out", line.profiling=TRUE)
> source("http://pastebin.com/download.php?i=KjdkSVZq")
> Rprof(NULL)
> proftable("profile1.out", lines=10)
PctTime Call
20.47 1#17 > [ > 1#17 > [.data.frame
9.73 1#17 > [ > 1#17 > [.data.frame > [ > [.factor
8.72 1#17 > [ > 1#17 > [.data.frame > [ > [.factor > NextMethod
8.39 == > Ops.factor
5.37 ==
5.03 == > Ops.factor > noNA.levels > levels
4.70 == > Ops.factor > NextMethod
4.03 1#17 > [ > 1#17 > [.data.frame > [ > [.factor > levels
4.03 1#17 > [ > 1#17 > [.data.frame > dim
3.36 1#17 > [ > 1#17 > [.data.frame > length
#File 1: http://pastebin.com/download.php?i=KjdkSVZq
Parent Call: source > withVisible > eval > eval >
Total Time: 5.96 seconds
Percent of run time represented: 73.8 %
Note that the “Parent Call” at the bottom shows some functions which RStudio wrapped my code in. Also, I chose only to display the top 10 time-consuming calls, but proftable
told me that those 10 calls represent 73.8% of the run time. I find this display a lot more intuitive than summaryRprof()
Here’s the whole function. If you have improvements, fork it on github:
proftable <- function(file, lines=10) {
# require(plyr)
interval <- as.numeric(strsplit(readLines(file, 1), "=")[[1L]][2L])/1e+06
profdata <- read.table(file, header=FALSE, sep=" ", comment.char = "",
colClasses="character", skip=1, fill=TRUE,
na.strings="")
filelines <- grep("#File", profdata[,1])
files <- aaply(as.matrix(profdata[filelines,]), 1, function(x) {
paste(na.omit(x), collapse = " ") })
profdata <- profdata[-filelines,]
total.time <- interval*nrow(profdata)
profdata <- as.matrix(profdata[,ncol(profdata):1])
profdata <- aaply(profdata, 1, function(x) {
c(x[(sum(is.na(x))+1):length(x)],
x[seq(from=1,by=1,length=sum(is.na(x)))])
})
stringtable <- table(apply(profdata, 1, paste, collapse=" "))
uniquerows <- strsplit(names(stringtable), " ")
uniquerows <- llply(uniquerows, function(x) replace(x, which(x=="NA"), NA))
dimnames(stringtable) <- NULL
stacktable <- ldply(uniquerows, function(x) x)
stringtable <- stringtable/sum(stringtable)*100
stacktable <- data.frame(PctTime=stringtable[], stacktable)
stacktable <- stacktable[order(stringtable, decreasing=TRUE),]
rownames(stacktable) <- NULL
stacktable <- head(stacktable, lines)
na.cols <- which(sapply(stacktable, function(x) all(is.na(x))))
stacktable <- stacktable[-na.cols]
parent.cols <- which(sapply(stacktable, function(x) length(unique(x)))==1)
parent.call <- paste0(paste(stacktable[1,parent.cols], collapse = " > ")," >")
stacktable <- stacktable[,-parent.cols]
calls <- aaply(as.matrix(stacktable[2:ncol(stacktable)]), 1, function(x) {
paste(na.omit(x), collapse= " > ")
})
stacktable <- data.frame(PctTime=stacktable$PctTime, Call=calls)
frac <- sum(stacktable$PctTime)
attr(stacktable, "total.time") <- total.time
attr(stacktable, "parent.call") <- parent.call
attr(stacktable, "files") <- files
attr(stacktable, "total.pct.time") <- frac
cat("\n")
print(stacktable, row.names=FALSE, right=FALSE, digits=3)
cat("\n")
cat(paste(files, collapse="\n"))
cat("\n")
cat(paste("\nParent Call:", parent.call))
cat(paste("\n\nTotal Time:", total.time, "seconds\n"))
cat(paste0("Percent of run time represented: ", format(frac, digits=3)), "%")
invisible(stacktable)
}