Why?

April 22, 2016

R Courses at Newcastle

Filed under: Computing, R, Teaching — Tags: , — csgillespie @ 7:09 pm

Over the next two months I’m running a number of R courses at Newcastle University.

  • May 2016
    • May 10th, 11th: Predictive Analytics
    • May 16th – 20th: Bioconductor
    • May 23rd, 24th: Advanced programming
  • June 2016
    • June 8th: R for Big Data
    • June 9th: Interactive graphics with Shiny

Since these courses are on  advanced topics, numbers are limited (there’s only a couple of places left on Predictive Analytics). If you are interested in attending, sign up as soon as possible.

Getting to Newcastle is easy. The airport is 10 minutes from the city centre and has direct flights to the main airport hubs: Schiphol, Heathrow, and Paris.  The courses at Newcastle attract participants from around the world; at the April course, we had representatives from North America, Sweden, Germany,  Romania and Geneva.

Cost: The courses cost around £130 per day (more than half the price of certain London courses!)

 

Onsite courses available on request.

April 1, 2016

RStudio addins manager

Filed under: Computing, R — Tags: — csgillespie @ 12:36 am

RStudio addins let you execute a bit of R code or a Shiny app through the RStudio IDE, either via the Addins dropdown menu or with a keyboard shortcut. This package is an RStudio addin for managing other addins. To run these addins, you need the latest version of RStudio.

Installation

The package can be installed via devtools

## Need the latest version of DT as well
devtools::install_github('rstudio/DT')
devtools::install_github("csgillespie/addinmanager")

Running addins

After installing the package, the Addins menu toolbar will be populated with a new addin called Addin Manager. When you launch this addin, a DT table will be launched:

screenshot

In the screenshot above, the highlighted addins, shinyjs and ggThemeAssit, indicate that this addins have already installed.

When you click Done

  • Highlighted addins will be installed.
  • Un-highlighted addins will be removed.

Simple!

Including your addin

Just fork and alter the addin file which is located in the inst/extdata directory of the package. This file is a csv file with three columns:

  • addin Name/title
  • Brief Description
  • Package. If the package is only on github, use name/repo.

The initial list of addins was obtain from daattali’s repo.

February 16, 2016

RANDU: The case of the bad RNG

Filed under: Computing, R — Tags: , , — csgillespie @ 12:15 pm

The German Federal Office for Information Security (BSI) has established
criteria for quality random number generator (rng):

  • A sequence of random numbers has a high probability of containing no identical consecutive elements.
  • A sequence of numbers which is indistinguishable from true random’ numbers (tested using statistical tests.
  • It should be impossible to calculate, or guess, from any given sub-sequence, any previous or future values in the sequence.
  • It should be impossible, for all practical purposes, for an attacker to calculate, or guess the values used in the random number algorithm.

Points 3 and 4 are crucial for many applications. Everytime you make a
phone call, contact to a wireless point, pay using your credit card random
numbers are used.

Designing a good random number generator is hard and as a general rule you should never try to. R comes with many good quality random generators. The default generator is the Mersenne-Twister. This rng has a huge period of 2^{19937}-1 (how many random numbers are generated before we have a repeat).

Linear congruential generators

A linear congruential generator (lcg) is a relatively simple rng (popular in the 60’s and 70’s). It has a simple form of

r_{i}=(ar_{i-1}+b) \textrm{ mod }m, \quad i=1, 2, \ldots, m

where $latexr_0$ is the initial number, known as the seed, and \(a,b,m\) are the multiplier, additive constant and modulo respectively. The parameters are all integers.

The modulo operation means that at most m different numbers can be generated
before the sequence must repeat – namely the integers 0,1,2, \ldots, m-1. The
actual number of generated numbers is h \leq m, called the period of
the generator.

The key to random number generators is in setting the parameters.

RANDU

RANDU was a lcg with parameters m=2^{31}, a=65539 and b=0. Unfortunately this is a spectacularly bad choice of
parameters. On noting that a=65,539=2^{16}+3, then

r_{i+1} = a r_i = 65539 \times r_i = (2^{16}+3)r_i \;.

So

r_{i+2} = a\;r_{i+1} = (2^{16}+3) \times r_{i+1} = (2^{16}+3)^2 r_i \;.

On expanding the square, we get

r_{i+2} = (2^{32}+6\times 2^{16} + 9)r_i = [6 (2^{16}+3)-9]r_i = 6 r_{i+1} - 9 r_i \;.

Note: all these calculations should be to the mod 2^{31}. So there is a large
correlation between the three points!

If compare randu to a standard rng (code in a gist)

Rplot1

It’s obvious that randu doesn’t produce good random numbers. Plotting  x_i, x_{i-1} and x_{i-2} in 3d

Rplot2

Generating the graphics

The code is all in a gist and can be run via


devtools::source_gist("https://gist.github.com/csgillespie/0ba4bbd8da0d1264b124")

You can then get the 3d plot via


scatterplot3d::scatterplot3d(randu[,1], randu[,2], randu[,3],
angle=154)
## Interactive version
threejs::scatterplot3js(randu[,1], randu[,2], randu[,3])

February 15, 2016

Shiny benchmarks

Filed under: Computing, R — Tags: , — csgillespie @ 5:49 pm

A couple of months ago, the first version of benchmarkme was released. Around 140 machines have now been benchmarked.

From the fastest (an Apple i7) which ran the tests in around 10 seconds, to the slowest (an Atom(TM) CPU N450 @ 1.66GHz) which took 420 seconds! Other interesting statistics:

  • Around 6% of people ran BLAS optimised versions of R;
  • No-one (except for machines that I used) ran a byte compiled version of the package.

I intend to write to a blog post or two on BLAS and byte compiling, but for the meantime you can investigate the results via the new shiny interface. The package is still only available on github and can be installed via:


## Update the package
install.packages(c("drat", "httr", "Matrix", "shiny"))
drat::addRepo("csgillespie")
install.packages("benchmarkme", type="source")

You then load the package in the usual way


library("benchmarkme")
## View past results
plot_past()
## shine() # Needs shiny
## get_datatable_past() # Needs DT

To benchmark your system, use


## This will take somewhere between 0.5 and 5 minutes
## Increase runs if you have a higher spec machine
res = benchmark_std(runs=3)

You can then compare your results other users


plot(res)
## shine(res)
## get_datatable(res)

and upload your results


## You can control exactly what is uploaded. See details below.
upload_results(res)

This function returns a unique identifier that will allow you to identify your results from the public data sets.

December 1, 2015

Crowd sourced benchmarks

Filed under: Computing, R — Tags: , — csgillespie @ 10:25 am

When discussing how to speed up slow R code, my first question is what is your computer spec? It always surprises me when complex biological experiments, costing a significant amount of money, are analysed using a six year old laptop. A new desktop machine costs around £1000 and that money would be saved within a month in user time. Typically the more the RAM you have, the larger the dataset you can handle. However it’s not so obvious of the benefit of upgrading the processor.

To quantify the impact of the CPU on an analysis, I’ve create a simple benchmarking package. The aim of this package is to provide a set of benchmarks routines and data from past runs. You can then compare your machine, with other CPUs. The package currently isn’t on CRAN, but you can install it via my drat repository

install.packages(c("drat", "httr", "Matrix"))
drat::addRepo("csgillespie")
install.packages("benchmarkme", type="source")

You can load the package in the usual way, and view past results via


library("benchmarkme")
plot_past()

to get

Timings1

Currently around forty machines have been benchmarked. To benchmark and compare your own system just run


## On slower machines, reduce runs.
res = benchmark_std(runs=3)
plot(res)

gives

my_benchmark

The final step is to upload your benchmarks


## You can control exactly what is uploaded. See the help page
upload_results(res)

The current record is held by a Intel(R) Core(TM) i7-4712MQ CPU.

July 1, 2015

useR 2015: Romain Francois: My R adventures

Filed under: R, useR 2015 — Tags: , — csgillespie @ 8:05 am

Using R since 2002 and has been working on Rcpp, Rcpp11, Rcpp14 and dplyr
internals. Worked on a number of big projects.

  • 2005 he set up the R Graph Gallery
  • 2009 worked on rJava
  • 2010 Rcpp
  • 2013 dplyr

Key themes are Performance and usabililty

rJava 0.7-*

Creating objects was messy

d <-jnew("java/lang/Double", 42
.jcal(d, "D", "doubleValue)

rJava 0.8-*

d <- new(J("java/lang/Double"), 42)
d$doubleview

Also much easier to import java packages.

Rcpp

Suppose you have

double add(double a, double b){ return a+b;}

and you want to use it in R. This used to be a lot of work. Before Rcpp, you
used the R/C Api, i.e. use SEXP. A lot of work and boilerplate. With Rcpp the
number of characters needed to translate the simple function above went from 250
to 50. Around 66% of CRAN packages depend (on some way) on Rcpp.

RcppParallel (tbb: thread building blocks)

The package makes it much easier to run things in parallel. Amazingly, a simple
parallel version of sqrt is faster than sqrt

dplyr (everyone knows what it does)

Uses hybrid evaluation. Looking to bring RcppParallel in the (near?) future.

Please note that the notes/talks section of this post is merely my notes on the
presentation. I may have made mistakes: these notes are not guaranteed to be
correct. Unless explicitly stated, they represent neither my opinions nor the
opinions of my employers. Any errors you can assume to be mine and not the
speaker’s. I’m happy to correct any errors you may spot – just let me know!

July 13, 2012

Analysing time course microarray data using Bioconductor: a case study using yeast2 Affymetrix arrays

Filed under: latex, Microarray, Publications, R — Tags: , , — csgillespie @ 2:32 pm

A few years ago I was involved in analysing some time-course microarray data. Our biological collaborators were interested in how we analysed their data, so this lead to a creation of tutorial, which in turn lead to a paper. When we submitted the paper, one the referees “suggested” that we write the paper using Sweave; I had never used Sweave. At the time this was a massive pain and I regularly cursed the name of the anonymous referee.  A few years later, I’ve just updated code (due to a change in Bioconductor) and it was a breeze. A belated thanks to the referee.

In this latest update to the paper I’ve

  • moved the paper to github;
  • changed from Sweave to knitr;
  • used RStudio instead of emacs.

You can find details full details about analysis on the associated github page.

September 17, 2011

UK R Courses – 2012

Filed under: Conferences, R, Teaching — Tags: , , — csgillespie @ 1:01 pm

The School of Mathematics & Statistics at Newcastle University (UK), are again running some R courses. In January, 2012, we will run:

The courses aren’t aimed at teaching statistics, rather they aim to go through the fundemental concepts of R programming. Further information is available at the course website. If you have any questions, feel free to contact me: colin.gillespie@newcastle.ac.uk

 

Bespoke courses are also on request.

August 19, 2011

Development of R (useR! 2011)

Filed under: Conferences, R, useR! 2011 — Tags: , , — csgillespie @ 8:44 am

Michael Rutter – R for Ubuntu

Ubuntu 10.10 uses 2.10.1. Backports are newer versions of software for old releases. R backports are available CRAN (link).

Lauchpad is a website for users to develop and maintain software (Canonical). One of Launchpad’s services is the personal package archive (PPA). This allows users to upload .deb source files, allowing easy creation of multiple Ubuntu releases and arch’s.

Workflow:

Dirk creates source file -> Michael gets source file -> packages built on launchpad -> Post on CRAN using apt-mirror.

There’s also a PPA available. PPAs are easier to add to the user’s system. Ubuntu has about 75 r-cran packages available in the main repository. A PPA could build the packages if the .deb packages were available. Could we use cran2deb?

cran2deb:  (no longer works), since maintaining the (virtual) machines to build the packages is time-consuming. Use launchpad.

cran2deb4ubuntu (PPA):  Contains most of the packages and dependencies from CRAN – 1107 in total. All packages can be installed with: sudo apt-get install r-cran-foo

  • Exceptions: non-free licences, windows/mac, dependencies not available to Launchpad (CUDA);
  • Problems(?): Can only install r-cran-foo outside of current R session. Can we get install.packages("foo") to look for r-cran-foo first?
  • Benefits: automatic updates to packages and creating R instances in the cloud.
  • Issues: c2d4u only available for 11.04. Naming and building issues for future versions. Space limitations on Launchpad may limit previous versions.

Andrew Runnalls – The CXXR project

The CXXR is progressively re-engineering the fundamental parts of the R interpreter from C to C++. Started in 2007, current release shadows 2.12.1. The aim of the project is to make the R interpreter more accessible to developers and researchers.

  • Improve documentation;
  • Encapsulation;
  • Move to an object-oriented structure;
  • Express internal algorithms.

RObjects

In CR, the C union is used to implement R object. This has a few disadvantages:

  • compiler doesn’t know which of the 23 types is at an address;
  • debugging at the C level is tricky
  • Adding a new type of R object means modifying a data definition at the heart of the interpreter

CXXR maps R objects to a particular C++ class.

Objectives:

  • Move program code relating to a datatype into one places
  • Use C++ public/protected/private mechanism
  • Allow developers to extend the class hierarchy.

Illustrative example: write a package to handle large integers

GNU MP library defines a C++ class mpz_class to represent an arbitrarily large integer, but not NA’s In CXXR, NA’s are added with a single line of C. Another line of code is used to create a vector of BigInts. It’s straightforward to add binary operations.

Subscripting in R

R is renowned for the power of its subscripting operations. In the CR interpreter, there are around 2000 C-language statements to implement these facilities. But this C code is locked up; no API and hard-wired around CR’s built-in data types. This is buried treasure.

CXXR makes an API available through its API. The API abstracts away from the type of elements and container. Result: adding subscripting operations is fairly simple.

Current problems: no serialization. No provision for BigIntVectors to be saved across sessions

Claudia Beleites: Google Summer of Code 2011

Open source software coding projects. Results can be used as part of thesis or article.

  • Student stipend: US$5000. Mentoring Organization: US $50;
  • Project topics: 7 GUI/images/visualisation, 4 optimization, 1 on High performance computing.
  • Aims: introduce students to the R developer community and push forward their project. roxygen and cran2deb were previous GSoC projects.
  • Communication channels: email, IM, skype, personal meetings.

Experiences:

  • Two mentors per student.  The two admins ping projects every now and again;
  • Time lines are based on US summer holidays;
  • Vanishing mentor and student.

Advice for Mentors:

  • Start to look early (January) for students. Look for a co-mentor;
  • Plan the time carefully;
  • Remember that coding time is also holiday time and students range from 1st year to PhD students.

August 18, 2011

Simon Urbanek – R Graphics: supercharged

Filed under: Conferences, R, useR! 2011 — Tags: , , — csgillespie @ 2:50 pm

New features:

  • rasterImage() (R2.11)
    • bitmap raster drawing;
    • have maps as data backdrops.
  • Polygons with holes: polypath() -(R2.12)
  • At present there is no way to tell when to actually show the plot. For example: plot(x); lines(x). Should we display the plot after plot or after lines
    • Solution dev.hold() and dev.flush()
    • Better performance and useful for animations – (currently in R-dev).

Challenges

Data size increases, but large RAM (>100GB) and CPU power is affordable. Visualization needs to keep up.

  • Currently rendering is slow. Solutions: OpenGL + GPUs.
  • Visualisation methods for large data
    • interactivity (divide and conquer, shift of focus): use iPlots eXtreme (very nice demo of iplots!)
    • sufficient statistics, aggregations, etc.

Links

Note: lots of very nice demos, hence the lack of notes.
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