Generating a Call Graph in R

Question

I've been given a big chunk of poorly formatted monolithic R code with plenty of functions, and I'd like to work out what functions call what functions.

I thought I could use roxygen's @callGraph stuff, but a) the code needs to be in a package, which will be a headache with this code, and b) it doesn't even seem to work when I do run it on a simple package of mine. I see a posting from one of the Roxygen authors saying call graph generation is disabled because of the Rgraphviz dependency, but the code is there. Anyway.

Anyone got a better way of quickly working out that foo calls bar, baz, and qux, and that qux calls quux?

Edit: Solutions based on R's profiling system are great, assuming you can actually run the code... Half the stuff in the files doesn't get run, and I don't know what it does... Static analysis is too much to hope for, I guess.

Edit 2: Roxygen's call graph stuff seems to do a static analysis, based on recursive descent of the expression of the function and checking for is.callable. It would be lovely to be able to run this on any function... I may play with this tomorrow...

Answer 1

Would profr help you out? From the documentation:

> ?profr > glm_ex <- profr(example(glm)) Read 17 items >      head(glm_ex)              f level time start  end  leaf source 8      example     1 0.32  0.00 0.32 FALSE  utils 9  <Anonymous>     2 0.04  0.00 0.04 FALSE   <NA> 10      source     2 0.28  0.04 0.32 FALSE   base 11  prepare_Rd     3 0.02  0.00 0.02 FALSE   <NA> 12      render     3 0.02  0.02 0.04 FALSE   <NA> 13 getSrcLines     3 0.02  0.04 0.06 FALSE   base >      summary(glm_ex)                f          level             time          start         eval.with.vis  :10   Min.   : 1.000   Min.   :0.02   Min.   :0.0000    <Anonymous>    : 3   1st Qu.: 4.000   1st Qu.:0.02   1st Qu.:0.1200    lazyLoadDBfetch: 3   Median : 6.000   Median :0.02   Median :0.2000    %in%           : 3   Mean   : 7.211   Mean   :0.03   Mean   :0.1769    inherits       : 3   3rd Qu.: 9.000   3rd Qu.:0.02   3rd Qu.:0.2600    is.factor      : 3   Max.   :22.000   Max.   :0.32   Max.   :0.3000    (Other)        :65                                                          end            leaf            source           Min.   :0.0200   Mode :logical   Length:90           1st Qu.:0.1500   FALSE:75        Class :character    Median :0.2400   TRUE :15        Mode  :character    Mean   :0.2069   NA's :0                             3rd Qu.:0.2800                                       Max.   :0.3200                                       > plot(glm_ex) 

Not quite what you are after, but you may be able to adapt it to your needs.