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)
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