Using the {plyr} (1.2) package parallel processing backend with windows

Hadley Wickham has just announced the release of a new R package “reshape2” which is (as Hadley wrote) “a reboot of the reshape package”. Alongside, Hadley announced the release of plyr 1.2.1 (now faster and with support to parallel computation!).
Both releases are exciting due to a significant speed increase they have now gained.

Yet in case of the new plyr package, an even more interesting new feature added is the introduction of the parallel processing backend.

    Reminder what is the `plyr` package all about

    (as written in Hadley’s announcement)

    plyr is a set of tools for a common set of problems: you need to __split__ up a big data structure into homogeneous pieces, __apply__ a function to each piece and then __combine__ all the results back together. For example, you might want to:

    • fit the same model each patient subsets of a data frame
    • quickly calculate summary statistics for each group
    • perform group-wise transformations like scaling or standardising

    It’s already possible to do this with base R functions (like split and the apply family of functions), but plyr makes it all a bit easier with:

    • totally consistent names, arguments and outputs
    • convenient parallelisation through the foreach package
    • input from and output to data.frames, matrices and lists
    • progress bars to keep track of long running operations
    • built-in error recovery, and informative error messages
    • labels that are maintained across all transformations

    Considerable effort has been put into making plyr fast and memory efficient, and in many cases plyr is as fast as, or faster than, the built-in functions.

    You can find out more at http://had.co.nz/plyr/, including a 20 page introductory guide, http://had.co.nz/plyr/plyr-intro.pdf.  You can ask questions about plyr (and data-manipulation in general) on the plyr mailing list. Sign up at http://groups.google.com/group/manipulatr

    What’s new in `plyr` (1.2.1)

    The exiting news about the release of the new plyr version is the added support for parallel processing.

    l*ply, d*ply, a*ply and m*ply all gain a .parallel argument that when TRUE, applies functions in parallel using a parallel backend registered with the
    foreach package.

    The new package also has some minor changes and bug fixes, all can be read here.

    In the original announcement by Hadley, he gave an example of using the new parallel backend with the doMC package for unix/linux.  For windows (the OS I’m using) you should use the doSMP package (as David mentioned in his post earlier today). However, this package is currently only released for “REvolution R” and not released yet for R 2.11 (see more about it here).  But due to the kind help of Tao Shi there is a solution for windows users wanting to have parallel processing backend to plyr in windows OS.

    All you need is to install the doSMP package, according to the instructions in the post “Parallel Multicore Processing with R (on Windows)“, and then use it like this:


    require(plyr) # make sure you have 1.2 or later installed
    x <- seq_len(20)
    wait <- function(i) Sys.sleep(0.1)
    system.time(llply(x, wait))
    # user system elapsed
    # 0 0 2
    require(doSMP)
    workers <- startWorkers(2) # My computer has 2 cores
    registerDoSMP(workers)
    system.time(llply(x, wait, .parallel = TRUE))
    # user system elapsed
    # 0.09 0.00 1.11

    Update (03.09.2012): the above code will no longer work with updated versions of R (R 2.15 etc.)

    Trying to run it will result in the error massage:

    Loading required package: doSMP
    Warning message:
    In library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE,  :
      there is no package called ‘doSMP’

    Because trying to install the package will give the error massage:

    > install.packages("doSMP")
    Installing package(s) into ‘D:/R/library(as ‘lib’ is unspecified)
    Warning message:
    package ‘doSMP’ is not available (for R version 2.15.0)

    You can fix this be replacing the use of {doSMP} package with the {doParallel}+{foreach} packages. Here is how:

    if(!require(foreach)) install.packages("foreach")
    if(!require(doParallel)) install.packages("doParallel")
    # require(doSMP) # will no longer work...
    library(foreach)
    library(doParallel)
    workers <- makeCluster(2) # My computer has 2 cores
    registerDoParallel(workers)
     
    x <- seq_len(20)
    wait <- function(i) Sys.sleep(0.3)
    system.time(llply(x, wait)) # 6 sec
    system.time(llply(x, wait, .parallel = TRUE)) # 3.53 sec

    Tips for the R beginner (a 5 page overview)

    In this post I publish a PDF document titled “A collection of tips for R in Finance”.
    It is a basic 5 page introduction to R in finances by Arnaud Amsellem (linked in profile).

    The article offers tips related to the following points:

    • Code Editor
    • Organizing R code
    • Update packages
    • Getting external data into R
    • Communicating with external applications
    • Optimizing R code

    This article is well articulated, and offers a perspective of someone who is experienced in the field and touches points that I can imagine beginners might otherwise overlook. I hope publishing it here will be of use to some readers out there.

    Update: as some readers have noted to me (by e-mail, and by commenting), this document touches very lightly on the topic of “finances” in R. I therefore decided to update the title from “R in finance – some tips for beginners”, to it’s current form.

    Lastly: if you (a reader of this blog) feel you have an article (“post”) to contribute, but don’t feel like starting your own blog, feel welcome to contact me, and I’ll be glad to post what you have to say on my blog (and subsequently, also on R bloggers).

    Here is the article:
    Continue reading Tips for the R beginner (a 5 page overview)

    Rose plot using Deducers ggplot2 plot builder

    The (excellent!) LearnR blog had a post today about making a rose plot in
    ggplot2.

    Following today’s announcement, by Ian Fellows, regarding the release of the new version of Deducer (0.4) offering a strong support for ggplot2 using a GUI plot builder, Ian also sent an e-mail where he shows how to create a rose plot using the new ggplot2 GUI included in the latest version of Deducer. After the template is made, the plot can be generated with 4 clicks of the mouse.

    Here is a video tutorial (Ian published) to show how this can be used:

    The generated template file is available at:
    http://neolab.stat.ucla.edu/cranstats/rose.ggtmpl

    I am excited about the work Ian is doing, and hope to see more people publish use cases with Deducer.

    ggplot2 plot builder is now on CRAN! (through Deducer 0.4 GUI for R)

    Ian fellows, a hard working contributer to the R community (and a cool guy), has announced today the release of Deducer (0.4) to CRAN (scheduled to update in the next day or so).
    This major update also includes the release of a new plug-in package (DeducerExtras), containing additional dialogs and functionality.

    Following is the e-mail he sent out with all the details and demo videos.

    Continue reading ggplot2 plot builder is now on CRAN! (through Deducer 0.4 GUI for R)

    ggplot2 gui: Major feature set complete

    (Written by Ian Fellows)

    There has been quite a bit of progress on the ggplot2 graphical user interface since the last post. All of the major features have been implemented, and are outlined in the vlog links below. What remains is to fix bugs, improve interface elements, and listen to feedback from users (that’s you). Please give it a try by installing the development version of Deducer
    install.packages(“Deducer”,,”http://www.rforge.net“,type=”source”) . It is best used with the R console JGR which you can find at http://rforge.net/JGR/ .

    Feature tour:
    http://neolab.stat.ucla.edu/cranstats/vlog4.mov

    Development and extension:
    http://neolab.stat.ucla.edu/cranstats/vlog5.mov

    Ian

    Blogging about R – presentation and audio

    At the useR!2010 conference I had the honor of giving a (~15 minute) talk titled “Blogging about R”. The following is the abstract I submited, followed by the slides of the talk and the audio file of a recording I made of the talk (I am sad it got a bit of “hall echo”, but it’s still listenable…)

    P.S: this post does not absolve me from writing up something (with many thanks and links to people) about the useR2010 conference, but I can see it taking a bit longer till I do that.

    —————–

    Abstract of the talk

    This talk is a basic introduction to blogs: why to blog, how to blog, and the importance of the R blogosphere to the R community.

    Because R is an open-source project, the R community members rely (mostly) on each other’s help for statistical guidance, generating useful code, and general moral support.

    Current online tools available for us to help each other include the R mailing lists, the community R-wiki, and the R blogosphere. The emerging R blogosphere is the only source, besides the R journal, that provides our community with articles about R. While these articles are not peer reviewed, they do come in higher volume (and often are of very high quality).

    According to the meta-blog R-bloggers.com, the (English) R blogosphere has produced, in January 2010, about 115 “articles” about R. There are (currently) a bit over 50 bloggers (now about 100) who write about R, with about 1000 (now ~2200) subscribers who read them daily (through e-mails or RSS). These numbers allow me to believe that there is a genuine interest in our community for more people – perhaps you? – to start (and continue) blogging about R.

    In this talk I intend to share knowledge about blogging so that more people are able to participate (freely) in the R blogosphere – both as readers and as writers. The talk will have three main parts:

    • What is a blog
    • How to blog – using the (free) blogging service WordPress.com (with specific emphasis on R)
    • How to develop readership – integration with other social media/networks platforms, SEO, and other best practices

    * * *
    Tal Galili founded www.R-bloggers.com and blogs on www.R-statistics.com
    * * *

    Audio recording of the talk

    Continue reading Blogging about R – presentation and audio

    Richard Stallman talk+Q&A at the useR! 2010 conference (audio files attached)

    The audio files of the full talk by Richard Stallman are attached to the end of this post.

    —————–

    Videos of all the invited talks of the useR! 2010 conference can be viewed on the R User Group blog

    —————–

    Last week I had the honor of attending the talk given by Richard Stallman, the last keynote speaker on the useR 2010 conference.  In this post I will give a brief context for the talk, and then give the audio files of the talk, with some description of what was said in the talk.

    Context for the talk

    Richard Stallman can be viewed as (one of) the fathers of free software (free as in speech, not as in beer).

    He is the man who led the GNU project for the creation of a free (as in speech, not as in beer) operation systems on the basis of which GNU-Linux, with its numerous distributions, was created.
    Richard also developed a number of pieces of widely used software, including the original Emacs,[4] the GNU Compiler Collection,[5], the GNU Debugger[6], and many tools in the GNU Coreutils

    Richard also initiated the free software movement and in October 1985 he also founded it’s formal foundation and co-founded the League for Programming Freedom in 1989.

    Stallman pioneered the concept of “copyleft” and he is the main author of several copyleft licenses including the GNU General Public License, the most widely used free software license.

    You can read about him in the wiki article titles “Richard Stallman

    The useR 2010 conference is an annual 4 days conference of the community of people using R.  R is a free open source software for data analysis and statistical computing (Here is a bit more about what is R).

    The conference this year was truly a wonderful experience for me.  I  had the pleasure of giving two talks (about which I will blog later this month), listened to numerous talks on the use of R, and had a chance to meet many (many) kind and interesting people.

    Richard Stallmans talk

    The talk took place on July 23rd 2010 at NIST U.S.  and was the concluding talk for the useR2010 conference.  The talk consisted of a two hour lecture followed by a half-hour question and answer session.

    On a personal note, I was very impressed by Richards talk.  Richard is not a shy computer geek, but rather a serious leader and thinker trying to stir people to action.  His speech was a sermon on free software, the history of GNU-Linux, the various versions of GPL, and his own history involving them.

    I believe this talk would be of interest to anyone who cares about social solidarity, free software, programming and the hope of a better world for all of us.

    I am eager for your thoughts in the comments (but please keep a kind tone).

    Here is Richard Stallmans  (2 hours) talk:

    Continue reading Richard Stallman talk+Q&A at the useR! 2010 conference (audio files attached)

    Want to join the closed BETA of a new Statistical Analysis Q&A site – NOW is the time!

    The bottom line of this post is for you to go to:
    Stack Exchange Q&A site proposal: Statistical Analysis
    And commit yourself to using the website for asking and answering questions.

    (And also consider giving the contender, MetaOptimize a visit)

    * * * *

    Statistical analysis Q&A website is about to go into BETA

    A month ago I invited readers of this blog to commit to using a new Q&A website for Data-Analysis (based on StackOverFlow engine), once it will open (the site was originally proposed by Rob Hyndman).
    And now, a month later, I am happy to write that over 500 people have shown interest in the website, and choose to commit themselves. This means we we have reached 100% completion of the website proposal process, and in the next few days we will move to the next step.

    The next step is that the website will go into closed BETA for about a week. If you want to be part of this – now is the time to join (<--- call for action people).
    From being part in some other closed BETA of similar projects, I can attest that the enthusiasm of the people trying to answer questions in the BETA is very impressive, so I strongly recommend the experience.

    If you won't make it by the time you see this post, then no worries - about a week or so after the website will go online, it will be open to the wide public.

    (p.s: thanks Romunov for pointing out to me that the BETA is about to open)

    p.s: MetaOptimize

    I would like to finish this post with mentioning MetaOptimize. This is a Q&A website which is of a more “machine learning” then a “statistical” community. It also started out some short while ago, and already it has around 700 users who have submitted ~160 questions with ~520 answers given. From my experience on the site so far, I have enjoyed the high quality of the questions and answers.
    When I first came by the website, I feared that supporting this website will split the R community of users between this website and the area 51 StackExchange website.
    But after a lengthy discussion (published recently as a post) with MetaOptimize founder, Joseph Turian, I came to have a more optimistic view of the competition of the two websites. Where at first I was afraid, I am now hopeful that each of the two website will manage to draw a tiny bit of different communities of people (that would otherwise wouldn’t be present in the other website) – thus offering all of us a wider variety of knowledge to tap into.

    See you there…