Enable Profiling of R's Execution

Enable or disable profiling of the execution of Rexpressions.

Rprof(filename = "Rprof.out", append = FALSE, interval = 0.02,
       memory.profiling = FALSE, gc.profiling = FALSE, 
       line.profiling = FALSE, numfiles = 100L, bufsize = 10000L)
The file to be used for recording the profiling results. Set to NULL or "" to disable profiling.
logical: should the file be over-written or appended to?
real: time interval between samples.
logical: write memory use information to the file?
logical: record whether GC is running?
logical: write line locations to the file?
numfiles, bufsize
integers: line profiling memory allocation

Enabling profiling automatically disables any existing profiling to another or the same file.

Profiling works by writing out the call stack every interval seconds, to the file specified. Either the summaryRprof function or the wrapper script R CMD Rprof can be used to process the output file to produce a summary of the usage; use R CMD Rprof --help for usage information.

#ifdef windows Exactly what the time interval measures is subtle: it is time that the Rprocess is running and executing an Rcommand. It is not however just CPU time, for if readline() is waiting for input, that counts (on Windows, but not on a Unix-alike).

Note that the timing interval cannot be too small, for the time spent in each profiling step is added to the interval. What is feasible is machine-dependent, but 10ms seemed as small as advisable on a 1GHz machine. #endif #ifdef unix How time is measured varies by platform: on a Unix-alike it is the CPU time of the Rprocess, so for example excludes time when Ris waiting for input or for processes run by system to return.

Note that the timing interval cannot usefully be too small: once the timer goes off, the information is not recorded until the next timing click (probably in the range 1--10msecs). #endif

Functions will only be recorded in the profile log if they put a context on the call stack (see sys.calls). Some primitive functions do not do so: specifically those which are of type "special" (see the R Internals manual for more details). Individual statements will be recorded in the profile log if line.profiling is TRUE, and if the code being executed was parsed with source references. See parse for a discussion of source references. By default the statement locations are not shown in summaryRprof, but see that help page for options to enable the display.


  • Profiling is not available on all platforms. By default, support for profiling is compiled in if possible -- configure Rwith to change this.

    As Rprofiling uses the same mechanisms as C profiling, the two cannot be used together, so do not use Rprof in an executable built for C-level profiling.

  • filename can be a UTF-8-encoded filepath that cannot be translated to the current locale.
  • The profiler interrupts R asynchronously, and it cannot allocate memory to store results as it runs. This affects line profiling, which needs to store an unknown number of file pathnames. The numfiles and bufsize arguments control the size of pre-allocated buffers to hold these results: the former counts the maximum number of paths, the latter counts the numbers of bytes in them. If the profiler runs out of space it will skip recording the line information for new files, and issue a warning when Rprof(NULL) is called to finish profiling.

See Also

The chapter on Tidying and profiling R code in Writing RExtensions (see the doc/manual subdirectory of the Rsource tree).

summaryRprof to analyse the output file.

tracemem, Rprofmem for other ways to track memory use.

  • Rprof
library(utils) Rprof() ## some code to be profiled Rprof(NULL) ## some code NOT to be profiled Rprof(append = TRUE) ## some code to be profiled Rprof(NULL) \dots ## Now post-process the output as described in Details
Documentation reproduced from package utils, version 3.3, License: Part of R 3.3

Community examples

Looks like there are no examples yet.