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R in a Nutshell
Joseph Adler
O’Reilly, 2010
ISBN: 978-0-596-80170-0

Graphics for Statistics and Data Analysis with R
Kevin J. Keen
Chapman and Hall/CRC, 2010
ISBN: 9781584880875

Only thing is that R cannot really fit a nutshell!

I wanted to give it a try as I am using R for some heavy duty matrix calculations.

I downloaded the MKL Intel Blas/Lapack libraries and installed it. This went very smooth.

I then tried to follow Michael’s suggestions to compile R 2.11.1 (well, it’s suggestions are about 2.10). It compiled fine, but I could not get R to use more than one core (no matter how hard I tried!). To get a multithreaded version of R I had to disable compilation of R as a shared library. It’s probably better not to compile R as a shared library anyway (see here for details).

This is my configuration (including compiler flags and commands)

$ gcc -v
gcc version 4.4.0 20090514 (Red Hat 4.4.0-6) (GCC)
export FFLAGS="-march=core2 -O3"
export CFLAGS="-march=core2 -O3"
export CXXFLAGS="-march=core2 -O3"
export FCFLAGS="-march=core2 -O3"
MKL_LIB_PATH=/opt/intel/mkl/10.2.5.035/lib/em64t
export LD_LIBRARY_PATH=$MKL_LIB_PATH
export LDFLAGS="-L${MKL_LIB_PATH},-Bdirect,--hash-style=both,-Wl,-O1"
MKL="-L${MKL_LIB_PATH} -lmkl_gf_lp64 -lmkl_gnu_thread -lmkl_lapack -lmkl_core -liomp5 -lpthread"
./configure --with-blas="$MKL" --with-lapack
make
make check


Benchmark

The simple R-benchmark-25.R test script is a quick-running survey of general R performance. The Community-developed test consists of three sets of small benchmarks, referred to in the script as Matrix Calculation, Matrix Functions, and Program Control.

See Simon Urbanek’s notes about benchmarking and a reference benchmark.

Below, the results of my new R with MKL:


R Benchmark 2.5
===============
Number of times each test is run__________________________: 3

Total time for all 15 tests_________________________ (sec): 9.85600000000015
Overall mean (sum of I, II and III trimmed means/3)_ (sec): 0.55016494784761
--- End of test ---


Speed is paramount here as the output of my routine is a likelihood function that is then passed to a Metropolis-Hasting mcmc.

With my surprise, the biggest improvement came from changing the normal random number  from “Inversion” (the default) to “Kinderman-Ramage”.

RNGkind(normal.kind = 'Kinderman-Ramage')

I am getting a speed up of about 25%: not bad at all. But then I am wondering why “Kinderman-Ramage” is not the default normal RNG. The “Kenderman-Ramage” algorithm used to be buggy in R, but it is fixed in recent implementations (see this article).

This is an example of the speed up on my machine:

> RNGkind(normal.kind = 'Inversion')
> system.time(rnorm(1000000))
user system elapsed
0.125 0.006 0.131

> RNGkind(normal.kind = 'Kinderman-Ramage')
> system.time(rnorm(1000000))
user system elapsed
0.06 0.00 0.06


I probably should try Eclipse one day.

on run
tell application "Finder"
if selection is {} then
quit
else
set finderSelection to selection as alias list
end if
end tell
deletepage(finderSelection)
end run



on open (theList)
deletepage(theList)
end open



on deletepage(listOfAliases)
repeat with eachitem in listOfAliases
tell application "Adobe Acrobat Professional"
activate
open eachitem
set Doc_Ref to the front document
delete first page
close Doc_Ref saving yes
end tell
end repeat
end deletepage


The biggest complain I have about TextMate is that it does not handle indentation very well. I write a lot of R code and I am used to the indentation provided by ess (Emacs Speak Statistics). Frustrated, I decided to do something about it. After looking for option I realized that I could combine the feature of Emacs with those of TextMate to improve indentation. In practice I was able to use emacs (with ess) as beckend for the indentation. If you want to try follow the following steps:

1. Install ess (Emacs Speak Statistics) from http://ess.r-project.or in /usr/share/emacs/site-lisp;

2. In TextMate go to Bundles|Bundle Editor|Show Bundle Editor…

3. Create a new command, say Tidy

4. Add the following script:

#!/usr/bin/perl
my $in;
my $now = "tidyRcode";
my $file = "/tmp/tmptx_${now}.R";
my $eb=$ENV{'TM_BUNDLE_SUPPORT'};
open FILE, ">$file" or die "unable to open $file $!";
while () {
print FILE $_;
}
close(FILE);
`emacs -batch --eval "(require 'ess)" ${file} --eval '(indent-region (point-min) (point-max) nil)' -f ess-fix-miscellaneous -f save-buffer &> /dev/null`;
my $in = `cat ${file}`;
print $in;

5. Select the following options:

• Input: Selected Text or Document;
• Output: Replace Selected Text;
• Key Equivalent: Shift+Cmd+H;
• Scope Selector: source.r

You can see the picture below with my setting

Now you are ready to go. While you are editing R code, simply select and press Shift+Cmd+H and you will see your code….the ess way.

UPDATE: The perl code does not work on Leopard (at least my Leopard with Emacs 22.1.1 + Textmate 1.5.9).

This version in Ruby works:

#!/usr/bin/ruby

require “ftools”

# Handle selection (less useful, probably) or full file.

tmp_file = “/tmp/textmate-tidy.#{$}.R”

selected = ENV['TM_SELECTED_TEXT'] || ”

if (selected.empty?)

raise(“No selected text or file.”) if (ENV['TM_FILEPATH'].empty?)

File.copy(ENV['TM_FILEPATH'], tmp_file)

else

File.open(tmp_file, ‘w’) { |fout| fout.print selected }

end

user = ENV['USER'] ||”

user_opt = “-u #{user}” unless (user.empty?)

cmd = “emacs -u \”#{user}\” -batch \”#{tmp_file}\” -eval \”(require ‘ess)\” -eval \”(indent-region (point-min) (point-max) nil)\” -f ess-fix-miscellaneous -f save-buffer &> /dev/null”

system(cmd) || raise(“Failed to indent with Emacs.”)

system(“cat #{tmp_file}”)

File.delete(tmp_file)