How to efficiently use Rprof in R?

by

Alert readers of yesterdays breaking news (R 3.0.0 is finally out) may have noticed something interesting that is directly relevant to this question:

  • Profiling via Rprof() now optionally records information at the statement level, not just the function level.

And indeed, this new feature answers my question and I will show how.

Let’s say, we want to compare whether vectorizing and pre-allocating are really better than good old for-loops and incremental building of data in calculating a summary statistic such as the mean. The, relatively stupid, code is the following:

# create big data frame:
n <- 1000
x <- data.frame(group = sample(letters[1:4], n, replace=TRUE), condition = sample(LETTERS[1:10], n, replace = TRUE), data = rnorm(n))

# reasonable operations:
marginal.means.1 <- aggregate(data ~ group + condition, data = x, FUN=mean)

# unreasonable operations:
marginal.means.2 <- marginal.means.1[NULL,]

row.counter <- 1
for (condition in levels(x$condition)) {
  for (group in levels(x$group)) {  
    tmp.value <- 0
    tmp.length <- 0
    for (c in 1:nrow(x)) {
      if ((x[c,"group"] == group) & (x[c,"condition"] == condition)) {
        tmp.value <- tmp.value + x[c,"data"]
        tmp.length <- tmp.length + 1
      }
    }
    marginal.means.2[row.counter,"group"] <- group 
    marginal.means.2[row.counter,"condition"] <- condition
    marginal.means.2[row.counter,"data"] <- tmp.value / tmp.length
    row.counter <- row.counter + 1
  }
}

# does it produce the same results?
all.equal(marginal.means.1, marginal.means.2)

To use this code with Rprof, we need to parse it. That is, it needs to be saved in a file and then called from there. Hence, I uploaded it to pastebin, but it works exactly the same with local files.

Now, we

  • simply create a profile file and indicate that we want to save the line number,
  • source the code with the incredible combination eval(parse(..., keep.source = TRUE)) (seemingly the infamous fortune(106) does not apply here, as I haven’t found another way)
  • stop the profiling and indicate that we want the output based on the line numbers.

The code is:

Rprof("profile1.out", line.profiling=TRUE)
eval(parse(file = "http://pastebin.com/download.php?i=KjdkSVZq", keep.source=TRUE))
Rprof(NULL)

summaryRprof("profile1.out", lines = "show")

Which gives:

$by.self
                           self.time self.pct total.time total.pct
download.php?i=KjdkSVZq#17      8.04    64.11       8.04     64.11
<no location>                   4.38    34.93       4.38     34.93
download.php?i=KjdkSVZq#16      0.06     0.48       0.06      0.48
download.php?i=KjdkSVZq#18      0.02     0.16       0.02      0.16
download.php?i=KjdkSVZq#23      0.02     0.16       0.02      0.16
download.php?i=KjdkSVZq#6       0.02     0.16       0.02      0.16

$by.total
                           total.time total.pct self.time self.pct
download.php?i=KjdkSVZq#17       8.04     64.11      8.04    64.11
<no location>                    4.38     34.93      4.38    34.93
download.php?i=KjdkSVZq#16       0.06      0.48      0.06     0.48
download.php?i=KjdkSVZq#18       0.02      0.16      0.02     0.16
download.php?i=KjdkSVZq#23       0.02      0.16      0.02     0.16
download.php?i=KjdkSVZq#6        0.02      0.16      0.02     0.16

$by.line
                           self.time self.pct total.time total.pct
<no location>                   4.38    34.93       4.38     34.93
download.php?i=KjdkSVZq#6       0.02     0.16       0.02      0.16
download.php?i=KjdkSVZq#16      0.06     0.48       0.06      0.48
download.php?i=KjdkSVZq#17      8.04    64.11       8.04     64.11
download.php?i=KjdkSVZq#18      0.02     0.16       0.02      0.16
download.php?i=KjdkSVZq#23      0.02     0.16       0.02      0.16

$sample.interval
[1] 0.02

$sampling.time
[1] 12.54

Checking the source code tells us that the problematic line (#17) is indeed the stupid if-statement in the for-loop. Compared with basically no time for calculating the same using vectorized code (line #6).

I haven’t tried it with any graphical output, but I am already very impressed by what I got so far.

More Related Contents:

  1. Measuring function execution time in R
  2. How to reproduce a table of student? [closed]
  3. Why is it not advisable to use attach() in R, and what should I use instead?
  4. Case Statement Equivalent in R
  5. Why does X[Y] join of data.tables not allow a full outer join, or a left join?
  6. Controlling ggplot2 legend display order
  7. UTF-8 file output in R
  8. How to sort a character vector where elements contain letters and numbers?
  9. Using Rcpp within parallel code via snow to make a cluster
  10. How do I import a CSV file in R? [closed]
  11. How to get a reversed, log10 scale in ggplot2?
  12. Is there a R function that applies a function to each pair of columns?
  13. knitr/rmarkdown/Latex: How to cross-reference figures and tables?
  14. The cause of “bad magic number” error when loading a workspace and how to avoid it?
  15. read.csv doesn’t seem to detect factors in R 4.0.0
  16. Getting the last n elements of a vector. Is there a better way than using the length() function?
  17. ggplot2: Adding secondary transformed x-axis on top of plot
  18. Rscript: There is no package called …?
  19. Split the title onto multiple lines?
  20. Find K nearest neighbors, starting from a distance matrix
  21. How to collapse many records into one while removing NA values
  22. Trying to merge multiple csv files in R
  23. Reshape a dataframe to long format with multiple sets of measure columns [duplicate]
  24. display a matrix, including the values, as a heatmap
  25. How to automatically adjust the width of each facet for facet_wrap?
  26. R specify function environment
  27. Draw lines between different elements in a stacked bar plot
  28. Quantmod Error ‘cannot open URL’
  29. R Warning ‘length(x) = 2 > 1’ in coercion to ‘logical(1)’
  30. R datetime series missing values

Leave a Comment