R-bloggers.com is now two years young. The site is an (unofficial) online R journal written by bloggers who agreed to contribute their R articles to the site.
In this post I wish to celebrate R-bloggers’ second birthmounth by sharing with you:

1. Links to the top 20 posts of 2011
2. Statistics on “how well” R-bloggers did this year
3. An invitation for sponsors/supporters to help keep the site alive

1. Top 24 R posts of 2011

R-bloggers’ success is largely owed to the content submitted by the R bloggers themselves.  The R community currently has almost 300 active R bloggers (links to the blogs are clearly visible in the right navigation bar on the R-bloggers homepage).  In the past year, these bloggers wrote over 2800 posts about R.

Here is a list of the top visited posts on the site in 2011:

1. How much of r is written in r
2. Cpu and gpu trends over time
3. Select operations on r data frames
4. Getting started with sweave r latex eclipse statet texlipse
5. Delete rows from r data frame
6. Amanda cox on how the new york times graphics department uses r
7. Hipster programming languages
8. Opendata r google easy maps
9. New r generated video has stackoverflow posting behavior changed over time
10. SNA visualising an email box with r
11. 100 prisoners 100 lines of code
12. Google ai challenge languages used by the best programmers
13. Basics on markov chain for parents
14. Top 10 algorithms in data mining
15. A million random digits review of reviews
16. Character occurrence in passwords
17. Setting graph margins in r using the par function and lots of cow milk
18. The new r compiler package in r 2 13 0 some first experiments
19. Tutorial principal components analysis pca in r
20. Making guis using c and r with the help of r net

2. Statistics – how well did R-bloggers do this year

There are several matrices one can consider when evaluating the success of a website.  I’ll present a few of them here and will begin by talking about the visitors to the site.

This year, the site was visited by over 665,000 “Unique Visitors.”  There was a total of over 1.4 million visits and over 2.8 million page-views.  People have surfed the site from over 200 countries, with the greatest number of visitors coming from the United States (~40%) and then followed by the United Kingdom (6.9%), Germany (6.6%), Canada (4.7%), France (3.3%), and other countries.

The site has received between 15,000 to 45,000 visits a week in the past few months, and I suspect this number will remain stable in the next few months (unless something very interesting will happen).

I believe this number will stay constant thanks to visitors’ loyalty: 55% of the site’s visits came from returning users.

Another indicator of reader loyalty is the number of subscribers to R-bloggers as counted by feedburner, which includes both RSS readers and e-mail subscribers.  The range of subscribers is estimated to be between 5600 to 5900.

Thus, I am very happy to see that R-bloggers continues to succeed in offering a real service to the global R users community.

3. Invitation to sponsor/advertise on R-bloggers

This year I was sadly accused by google adsense of click fraud (which I did not do, but have no way of proving my innocence).  Therefor, I am no longer able to use google adsense to sustain R-bloggers high monthly bills, and I turned to rely on direct  sponsoring of R-bloggers.

If you are interested in sponsoring/placing-ads/supporting R-bloggers, then you are welcome to contact me.

Happy new year!
Yours,
Tal Galili

I was recently reminded that the wonderful team at warwick University made sure to put online many of the slides (and some videos) of talks from the recent useR 2011 conference.  You can browse through the talks by going between the timetables (where it will be the most updated, if more slides will be added later), but I thought it might be more convenient for some of you to have the links to all the talks (with slides/videos) in one place.

I am grateful for all of the wonderful people who put their time in making such an amazing event (organizers, speakers, attendees), and also for the many speakers who made sure to share their talk/slides online for all of us to reference.  I hope to see this open-slides trend will continue in the upcoming useR conferences…

Bellow are all the links:

Tuesday 16th August

Lightning Talks

• Community and Communication, MS.02, Chair: Ashley Ford
• George Zhang: China R user conference [Slides]
• Tal Galili: Blogging and R – present and future [Link]
• Markus Schmidberger: Get your R application onto a powerful and fully-configured Cloud Computing environment in less than 5 minutes. [Slides]
• Eirini Koutoumanou: Teaching R to Non Package Literate Users [Slides]
• Randall Pruim: Teaching Statistics using the mosaic Package [Slides]
• Statistics and Programming, MS.01, Chair: Elke Thönnes
• Toby Dylan Hocking: Fast, named capture regular expressions in R2.14 [Slides]
• John C. Nash: Developments in optimization tools for R [Slides]
• Christophe Dutang: A Unified Approach to fit probability distributions [Slides]
• Package Showcase, MS.03, Chair: Jennifer Rogers
• James Foadi: cRy: statistical applications in macromolecular crystallography [Slides]
• Emilio López: Six Sigma is possible with R [Slides]
• Jonathan Clayden: Medical image processing with TractoR [Slides]
• Richard A. Bilonick: Using merror 2.0 to Analyze Measurement Error and Determine Calibration Curves [Slides]

Wednesday 17th August

Thursday 18th August

This post is a call for both R community members and R-bloggers, to come and help make The R Programming wikibook be amazing.

The R Programming wikibook is not just another one of the many free books about statistics/R, it is a community project which aims to create a cross-disciplinary practical guide to the R programming language.  Here is how you can join:

Dear R community member – please consider giving a visit to The R Programming wikibook.  If you wish to contribute your knowledge and editing skills to the project, then you could learn how to write in wiki-markup here, and how to edit a wikibook here (you can even use R syntax highlighting in the wikibook).  You could take information into the site from the (soon to be) growing list of available R resources for harvesting.

Dear R blogger, you can help The R Programming wikibook by doing the following:

• Write to your readers about the project and invite them to join.
• Add your blog’s R content as an available resourcefor other editors to use for the wikibook.  Here is how to do that:
  • First, make a clear indication on your blog that your content is licensed under cc-by-sa copyrights (*see what it means at the end of the post). You can do this by adding it to the footer of your blog, or by writing a post that clearly states that this is the case (what a great opportunity to write to your readers about the project…).
  • Next, go and add a link, to where all of your R content is located on your site, to the resource page (also with a link to the license post, if you wrote one).  For example, since I write about other things besides R, I would give a link to my R category page, and will also give a link to this post.  If you do not know how to add it to the wiki, just e-mail me about it (tal.galili@gmail.com).

If you are an R blogger, besides living up to the spirit of the R community, you will benefit from joining this project in that every time someone will use your content on the wikibook, they will add your post as a resource.  In the long run, this is likely to help visitors of the site get to know about you and strengthen your site’s SEO ranking.  Which reminds me, if you write about this, I always appreciate a link back to my blog

* Having a cc-by-sa copyrights means that you will agree that anyone may copy, distribute, display, and make derivative works based on your content, only if they give the author (you) the credits in the manner specified by you. And also that the user may distribute derivative works only under a license identical to the license that governs the original work.

———-

Three more points:

1) This post is a result of being contacted by Paul (a.k.a: PAC2), asking if I could help promote “The R Programming wikibook” among R-bloggers and their readers.   Paul has made many contributions to the book so far.  So thank you Paul for both reaching out and helping all of us with your work on this free open source project.

2) I should also mention that the R wiki exists and is open for contribution.  And naturally, every thing that will help the R wikibook will help the R wiki as well.

3) Copyright notice: I hereby release all of the writing material content that is categoriesed in the R category page, under the cc-by-sa copyrights (date: 20.06.2011), as long as the copied content comes with proper attribution which also  includes a link to the source of the article .  Now it’s your turn!

———-

List of R bloggers who have joined: (This list will get updated as this “group writing” project will progress)

• R-statistics blog (that’s me…)
• GETTING GENETICS DONE
• Struggling Through Problems
• Back side smack
• al3xandr3
• Cloudnumbers.com
• The R Tutorial Series blog
• …

For the most updated list, go to the resource page on the The R Programming wikibook.

It appears that just days ago, Google Tech Talk released a new, one hour long, video of a presentation (from June 6, 2011) made by one of R’s community more influential contributors, Hadley Wickham.

This seems to be one of the better talks to send a programmer friend who is interested in getting into R.

Talk abstract

Data analysis, the process of converting data into knowledge, insight and understanding, is a critical part of statistics, but there’s surprisingly little research on it. In this talk I’ll introduce some of my recent work, including a model of data analysis. I’m a passionate advocate of programming that data analysis should be carried out using a programming language, and I’ll justify this by discussing some of the requirement of good data analysis (reproducibility, automation and communication). With these in mind, I’ll introduce you to a powerful set of tools for better understanding data: the statistical programming language R, and the ggplot2 domain specific language (DSL) for visualisation.

The video

More resources

• Hadley’s homepage
• More talks/presentations by Hadley
• The ggplot2 book (sample chapters)
• GGplot2 on CRAN
• Hat (link) tip goes to my good, social media, internet and productivity researcher, friend Eyal Sela – for informing me about this talk.

A year ago (on December 9th 2009), I wrote about founding R-bloggers.com, an (unofficial) online R journal written by bloggers who agreed to contribute their R articles to the site.

In this post I wish to celebrate R-bloggers’ first birthday by sharing with you:

1. Links to the top 14 posts of 2010
2. Reflections about the origin of R-bloggers
3. Statistics on “how well” R-bloggers did this year
4. Links to other related projects
5. An invitation for sponsors/supporters to help keep the site alive

1. Top 14 R posts of 2010

R-bloggers’ success is largely owed to the content submitted by the R bloggers themselves.  The R community currently has 143 active R bloggers (links to the blogs are clearly visible in the right navigation bar on the R-bloggers homepage).  In a little over one year, these bloggers wrote almost 3,000 posts about R.  That is amazing!

Here is a list of the top visited posts on the site in 2010:

• 100 Prisoners, 100 lines of code
• Google AI Challenge: Languages Used by the Best Programmers
• Getting Started with Sweave: R, LaTeX, Eclipse, StatET, & TeXlipse
• Why Use R?
• “simply start over and build something better”
• R Tutorial Series: R Beginner’s Guide and R Bloggers Updates
• Select operations on R data frames
• Hacker News User Base Changed?
• Top 10 Algorithms in Data Mining
• Fantasy football (oops, soccer)
• Delete rows from R data frame
• Visualizing Facebook Friends: Eye Candy in R
• Advanced graphics in R
• How to build a world-beating predictive model using R

2. Motivation for creating R-bloggers

Originally, the idea for R-bloggers came when, over a year ago, I spent several hours trying to find bloggers who wrote about R.  I was successful in finding roughly 14 R bloggers (and too many blog posts of people talking about pirates).  This experience led me to wonder how many other people like myself were also struggling to find these bloggers.  The second thought I had was the answer to the following question: “What can I offer an R blogger so that he will tell me that he exists?”  The answer I came up with is “audience and traffic” – and that’s what R-bloggers tries to achieve: the site offers the blogger audience and traffic in exchange for the blogger giving permission to republish their “R content” on the site.

Part of my motivation was that, as one of these bloggers who wrote about R (here, on R-statistics.com), I too hoped to gain more audience and traffic for my content.   R-bloggers helped me achieve this goal.

3. Statistics – how well did R-bloggers do this year

There are several matrices one can consider when evaluating the success of a website.  I’ll present a few of them here and will begin by talking about the visitors to the site.

This year, the site was visited by about 286,000 “Absolute Unique Visitors.”  There was a total of nearly 600,000 visits and over 1 million page-views.  People have surfed the site from over 200 countries, with the greatest number of visitors coming from the United States (40%) and then followed by the United Kingdom (6.8%), Germany (6.6%), Canada (4.9%), France (3.3%), and other countries.

The site has received between 15,000 to 25,000 visits a week in the past few months, and I suspect this number will remain stable in the next few months (unless something very interesting will happen).

I believe this number will stay constant thanks to visitors’ loyalty: 60% of the site’s visits came from returning users.

Another indicator of reader loyalty is the number of subscribers to R-bloggers as counted by feedburner, which includes both RSS readers and e-mail subscribers.  The range of subscribers is estimated to be between 2600 to 2900.

Thus it seems that R-bloggers succeeds in offering a real service to the R users community.

4. Projects related to R-bloggers

During the last year I have created several R-bloggers clone sites in the hopes of serving other audiences.  I started a multilingual version of R-bloggers at www.r-bloggers.com/lang/ (currently hosting 7 bloggers, written in Dutch, Indonesian, Italian, Korean, Russian, Spanish).  I hope more will join the site in the future.

I’ve also started 3 other promising such sites: SAS-x.com, DanceBloggers.com and forex-bloggers.com.  How will they do?  Only time will tell.

5. Invitation to sponsor/advertise on R-bloggers

Due to the increase in the site’s popularity, 4 months ago I had to upgrade R-bloggers’ hosting account to a VPS hosting, which had directed me to decide on attempting to add ads to R-bloggers so to keep the site self sufficient financially.

So in the hopes of keeping the site sustainable in the long run, I am now (somewhat forced) to make this call: if you are interested in sponsoring/placing ads/supporting R-bloggers, then you’re welcome to contact me.

Happy new year!
Yours,
Tal Galili

Repeated measures ANOVA is a common task for the data analyst.

There are (at least) two ways of performing “repeated measures ANOVA” using R but none is really trivial, and each way has it’s own complication/pitfalls (explanation/solution to which I was usually able to find through searching in the R-help mailing list).

So for future reference, I am starting this page to document links I find to tutorials, explanations (and troubleshooting) of “repeated measure ANOVA” done with R

Functions and packages

(I suggest using the tutorials supplied bellow for how to use these functions)

• aov {stats} – offers SS type I repeated measures anova, by a call to lm for each stratum. A short example is given in the ?aov help file
• Anova {car} – Calculates type-II or type-III analysis-of-variance tables for model objects produced by lm, and for various other object. The ?Anova help file offers an example for how to use this for repeated measures
• ezANOVA {ez} – This function provides easy analysis of data from factorial experiments, including purely within-Ss designs (a.k.a. “repeated measures”), purely between-Ss designs, and mixed within-and-between-Ss designs, yielding ANOVA results and assumption checks. It is a wrapper of the Anova {car} function, and is easier to use. The ez package also offers the functions ezPlot and ezStats to give plot and statistics of the ANOVA analysis. The ?ezANOVA help file gives a good demonstration for the functions use (My thanks goes to Matthew Finkbe for letting me know about this cool package)
• friedman.test {stats} – Performs a Friedman rank sum test with unreplicated blocked data. That is, a non-parametric one-way repeated measures anova. I also wrote a wrapper function to perform and plot a post-hoc analysis on the friedman test results
• Non parametric multi way repeated measures anova – I believe such a function could be developed based on the Proportional Odds Model, maybe using the {repolr} or the {ordinal} packages. But I still didn’t come across any function that implements these models (if you do – please let me know in the comments).
• Repeated measures, non-parametric, multivariate analysis of variance – as far as I know, such a method is not currently available in R.  There is, however, the Analysis of similarities (ANOSIM) analysis which provides a way to test statistically whether there is a significantdifference between two or more groups of sampling units.  Is is available in the {vegan} package through the “anosim” function.  There is also a tutorial and a relevant published paper.

Good Tutorials

• A basic tutorial about ANOVA with R (only the last bit holds some example of repeated measures) on personality-project
• A thorough tutorial on motor control lab
• A thorough tutorial on UCLA seminar page
• Another good tutorial by
  Jonathan Baron and Yuelin Li on “Notes on the use of R for psychology experiments and questionnaires”

Troubelshooting

Unbalanced design
Unbalanced design doesn’t work when doing repeated measures ANOVA with aov, it just doesn’t. This situation occurs if there are missing values in the data or that the data is not from a fully balanced design. The way this will show up in your output is that you will see the between subject section showing withing subject variables.

A solution for this might be to use the Anova function from library car with parameter type=”III”. But before doing that, first make sure you understand the difference between SS type I, II and III. Here is a good tutorial for helping you out with that.
By the way, these links are also useful in case you want to do a simple two way ANOVA for unbalanced design

I will “later” add R-help mailing list discussions that I found helpful on the subject.

If you come across good resources, please let me know about them in the comments.

Patrick Burns just posted to the mailing list the following massage:

There is now a document called “Some hints for the R beginner” whose purpose is to get people up and running with R as quickly as possible.

Direct access to it is:
http://www.burns-stat.com/pages/Tutor/hints_R_begin.html

JRR Tolkien wrote a story (sans hobbits) called ‘Leaf by Niggle’ that has always resonated with me. I offer you an imperfect, incomplete tree (but my roof is intact).

Suggestions for improvements are encouraged.

And here is the link tree for the document (for your easy reviewing of the offered content) :

This page has several sections, they can be put into the four categories: General, Objects, Actions, Help.

General
Introduction
Blank screen syndrome
Misconceptions because of a previous language
Helpful computer environments
R vocabulary
Epilogue

Objects
Key objects
Reading data into R
Seeing objects
Saving objects
Magic functions, magic objects
Some file types
Packages

Help
How to read a help file
Searching for functionality
Some other documents
R-help mailing list