Rprof()

Rprof("profile.out")

Rprof(NULL)

> summaryRprof("method1.out")

$by.self

self.time self.pct total.time total.pct

"strsplit"          4.08     94.0       4.10      94.5

"+"                 0.12      2.8       0.12       2.8

"is.character"      0.12      2.8       0.12       2.8

"as.logical"        0.02      0.5       0.02       0.5

$by.total

total.time total.pct self.time self.pct

"strsplit"           4.10      94.5      4.08     94.0

"+"                  0.12       2.8      0.12      2.8

"is.character"       0.12       2.8      0.12      2.8

"as.logical"         0.02       0.5      0.02      0.5

proftools

plotProfileCallGraph("profile.out")

profr

out <- profr(code_to_profile)

> head(out)

f level time start  end  leaf source

8      example     1 0.44  0.00 0.44 FALSE  utils

9  <Anonymous>     2 0.04  0.00 0.04 FALSE   <NA>

10     library     2 0.02  0.04 0.06 FALSE   base

11      source     2 0.38  0.06 0.44 FALSE   base

12  prepare_Rd     3 0.04  0.00 0.04 FALSE   <NA>

13        %in%     3 0.02  0.04 0.06 FALSE   base

> summary(out)

f          level             time          start

match        : 7   Min.   : 1.000   Min.   :0.02   Min.   :0.0000

%in%         : 6   1st Qu.: 5.000   1st Qu.:0.02   1st Qu.:0.1200

is.factor    : 6   Median : 7.000   Median :0.02   Median :0.1600

eval.with.vis: 6   Mean   : 8.113   Mean   :0.04   Mean   :0.1685

inherits     : 5   3rd Qu.:10.500   3rd Qu.:0.02   3rd Qu.:0.2200

<Anonymous>  : 3   Max.   :19.000   Max.   :0.44   Max.   :0.4200

(Other)      :82

end            leaf            source

Min.   :0.0200   Mode :logical   Length:115

1st Qu.:0.1400   FALSE:99        Class :character

Median :0.2000   TRUE :16        Mode  :character

Mean   :0.2085   NA's :0

3rd Qu.:0.2500

Max.   :0.4400

Here I should say that `code_to_profile` was the standard `example(glm)`. One can also use `plot()` or `ggplot()` to plot the ‘call tree’. I’m not quite sure what a ‘call tree’ is, and to be honest the (adimittedly pretty) graph that `ggplot(out)` produces doesn’t enlighten me a great deal. That’s OK though – all I really wanted was the time spent.

A quick point about Hadley’s code, that I think highlights something wrong with R in general (and right with Python) is that in `profr`, from a user perspective I don’t really have to learn anything new to make it work. I apply a function (handily called `profr`) to some code and I get back a `data.frame` on which I can use very standard tools in order to investigate. I don’t have to write to a file (or generate a temp file to write to or anything), I don’t have to learn to stop and start hidden things, or that to stop profiling I pass `NULL` to a function that started profiling. I don’t have to use a whole new tool to parse what is basically a table. And to plot I can just use `plot` rather than having to dissapear down the (admittedly fun) hole that is graphviz. It feels like every new thing I come across in R means learning a whole API and set of concepts and object structure. (Admittedly ggplot is the total opposite of this, but hey I guess that was the point)