This is (roughly) the lightning talk I gave in useR2011. If you are a reader of R-bloggers.com then this talk is not likely to tell you anything new. However, if you have a friend, college or student who is a new useRs of R, this talk will offer him a decent introduction to what the R blogosphere is all about.

The talk is a call for people of the R community to participate more in reading, writing and interacting with blogs.

I was encouraged to record this talk per the request of Chel Hee Lee, so it may be used in the recent useR conference in Korea (2011)

The talk (briefly) goes through:

1. The widespread influence of the R blogosphere
2. What R bloggers write about
3. How to encourage a blogger you enjoy reading to keep writing
4. How to start your own R blog (just go to wordpress.com)
5. Basic tips about writing a blog
6. One advice about marketing your R blog (add it to R-bloggers.com)
7. And two thoughts about the future of R blogging (more bloggers and readers, and more interactive online visualization)

My apologies for any of the glitches in my English. For more talks about R, you can visit the R user groups blog. I hope more speakers from useR 2011 will consider uploading their talks online.

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

Today I was informed of (what I believe is) a better the best WordPress plugin for R syntax highlighting called WP-CodeBox.  This plugin doesn’t require any hacks to make it work (as opposed to the WP-Syntax plugin, which I wrote about in the past).  WP-CodeBox can be downloaded and installed on a WordPress by searching for it in the “Add New” section in the plugins menu.

WP-CodeBox provides some nice features (some AJAX based) to the display of the code in the post:

1. The code box in the post can now be folded (top right of the code box) so the code can be hidden so to not clutter the post (if the code is too long)
2. The code box is added with another button  (top left of the code box) which allows the reader to see the code in a new window – so to easily enable a copy paste of the code.
3. The options of the plugin allows automatic row numbering of the code, control over “tab” length and some other features.

Usage

After installing (and activating) the plugin, in order to add R code to your post you will need to:
1) Only work in HTML mode (not the Visual mode). Or else, the code you will paste will be messed up.
2) Put your code between the <pre> tag, like this:

(Note: make sure that you rewrite the ” – so it will work.)

<pre lang=”rsplus” line=”1″>

…Your R code here…

</pre>

p.s: Lastly, my thanks goes to guangchuang yu who’s comment on my original post, and he’s post on wp-codebox and R, has introduced me to this better plugin.

p.p.s: in case you blog on WordPress.com, there is also a solution for R syntax highlighting for WordPress.com bloggers.

Good news for R bloggers who are using WordPress.com to host their blog.

This week, the good people running WordPress.com (special thanks goes to Yoav Farhi), have added the ability for all the users of the WordPress.com platform to be able to highlight their R code inside posts.

Basically you’ll need to wrap the code in your post like this:

[sourcecode language="r"]
test.function = function(r) {
    return(pi * r^2)
}
test.function(1)
[/sourcecode]

(Which will then look like this:

)

Further details (and other supported languages) can be read about on this WordPress.com support page.

This new feature was possible thanks to the work of Yihui Xie (who create the famous cool animation package for R), who created a R syntax brush for the syntaxhighlighter WordPress plugin (the plugin used by WordPress.com for sytnax highlighting) . And thanks should also go to Andrew Redd, the creator of NppToR (which connects between notepad++ to R). He both made some good suggestions, and was game to take on the brush creation in case there would be problems, which thankfully so far there aren’t any)

p.s: If you are a WordPress.org users (e.g: have a self hosted WordPress blog) and want to enable R syntax highlighting for your blog, I would recommend the use of the WP-Syntax plugin (enhanced with GeSHi version 1.0.8.6) which can be downloaded here.

Motivation

In the past few months I have been using DropBox for syncing my work files between my home and work computer. It has saved me from numerous mistakes and from sending the files to myself via e-mail.

Recently I found this service highly useful for sharing files with 4 other people with whom I am working on a data analysis project. Being so happy with it (and also by gaining more storage space by inviting friends to use it), I thought of sharing my experience here with other R users that might benefit from this cool (free) service.

What is Dropbox?

Dropbox is a Software/Web2.0 file hosting service which enable users to synchronize files and folders between computers across the internet.
This is done by installing a software and then picking a “shared folder” on your computer. From that moment on, that folder will be synced with any computer you choose to install the software on (for example, your home/work computer, your laptop – and so on)

DropBox also enables users to share some of their folders with other DropBox users. This seamless integration of the service with your OS file system (Windows, Mac or Linux) is what’s making this service so comfortable, by allowing me to work with co-workers and have the same “project tree” of folders, all of which are always synced.

You could also share a file “online”, by getting a link to it which you could share with others. So for example, you could write an R code, share it online, and call to it later with source(). This is the easiest way I know of how to do this.

Dropbox is a “cloud computing” Web2.0 file hosting service offering both free and paid services. The free version (which I use) offers 2GB of “shared storage” (unless you invite other users, in which case you get some extended storage space. Which is one of my motivations in writing this post).

Dropbox has other non-trivial uses allowing one to:

• Monitor your Computer Remotely,
• Selective Folder Syncing (BETA),
• Remote Launching of Applications with Dropbox and AutoHotkey,
• And some other uses.

The service’s major competitors are Box.net, Sugarsync and Mozy, non of which I have had the chance of trying.

How to start?

Simply go to: DropBox.com
Sign up, install the software, use the new shared folder, and let me know if it helped you

How to get Extra space?

You can:

• Earn another 750MB of space by connecting your dropbox to your twitter/facebook account and sending a status update about them. To get this bonus, head over to “Get extra space free!” page.
• Refer a friend to open a dropbox account (every friend joining earns you another 250MB of space). This bonus is bounded by a total of 8GB of added space (after that, you won’t be allowed any more extra space)
• Upgrade – pay 10$ a month and get extra 50GB

A friend just e-mailed me about a blog post by Dr. AnnMaria De Mars titled “The Next Big Thing”.

In it Dr. De Mars wrote (I allowed myself to emphasize some parts of the text):

Contrary to what some people seem to think, R is definitely not the next big thing, either. I am always surprised when people ask me why I think that, because to my mind it is obvious. [...]
for me personally and for most users, both individual and organizational, the much greater cost of software is the time it takes to install it, maintain it, learn it and document it. On that, R is an epic fail. It does NOT fit with the way the vast majority of people in the world use computers. The vast majority of people are NOT programmers. They are used to looking at things and clicking on things.

Here are my two cents on the subject:

First, I agree with Dr. De Mars that R (out of the box) is not very (non programmer) user friendly – there is (almost) no point and click capabilities.  And while there are several projects offering a GUI layer interface to R (a good list of them can be found here), still non of them is in the level of refinement of what softwares like SPSS, JMP or SAS offers to users today.

But is traditional “point and click” the next “big thing”?  My suspicion is that the answer is – no.
Neither does Dr. De Mars thinks so, since her predictions for the next big thing are “Data visualization” and “Analyzing enormous quantities of unstructured data”. Both of which R is offering quite powerful solutions to (assuming that you will go through the learning curve).

Dr. De Mars question is a fascinating one – what IS going to be the next big thing?

I think that the next BIG thing is (becoming to be) “Statistics in the Cloud“.  This intuition came from (among other things) my review of the “Future of Open Source” Survey (see “conclusion 3″).

In the near future, I believe, we will see more statisticians and data analysts tapping into the opportunities that cloud computing offers them.  Here are some examples of what I came a cross (or covered) lately in the topic of cloud computing and R:

1. Easy online collecting of data (via google forms)
2. High-performance computing - Running a statistical package software on the cloud for accessing a powerful computer or running stuff in parallel.  The former can be done through services like Amazon cloud, Elastic R, and lately R-Node combines the running of R and Protovis on a server.  The later I don’t have experience in, but understand there are various solutions in R (a known company in the field is, of course, REvolution computing)
3. Online statistical analysis/visualization of data - Having a web interface to a statistical analysis.  One wonderful example of that is Jeroen Oom’s (beautiful) web interface to ggplot2.  Such projects offer “point and click” capabilities through the internet (/cloud)
4. Online interactive visualization of data.  I came a cross three people offering to develop solutions for doing this with R in this year’s Google summer of code, I hope something will come out of it

All of these are well connected to the emerging trend of “web of data”/“linked data web” that some are talking about. For example, here is a good Ted talk by Tim Berners-Lee (the inventor of the World Wide Web). Talking about building a web for open, linked data that could do for numbers what the Web did for words, pictures, video: unlock our data and reframe the way we use it together.

The same plea is given by Hans rosling in his famous Ted talk showing GapMinder.
Although at he same time, some R users are saying – “You don’t have to bother linking the data. I’ll do with just the data, really, just release it…”

In conclusion, I don’t know what capabilities other projects/products offer for doing statistics in the cloud.  But it is clear to me that the R community is (not surprisingly) bringing very diverse and innovative solutions to the world.

Is R the next big thing?  I don’t think so.  But I do think that some of the next big things will be built with R.
* * *
I would love to know your thought about Dr. De Mars post, and also about what the “next big thing” is going to be (and what role will R have in it).

Good news.

Jeroen Ooms released a new version of his (amazing) online ggplot2 web interface:

yeroon.net/ggplot2 is a web interface for Hadley Wickham’s R package ggplot2. It is used as a tool for rapid prototyping, exploratory graphical analysis and education of statistics and R. The interface is written completely in javascript, therefore there is no need to install anything on the client side: a standard browser will do.

The new version has a lot of cool new features, like advanced data import, integration with Google docs, converting variables from numeric to factor to dates and vice versa, and a lot of new geom’s. Some of which you can watch in his new video demo of the application:

The application is on:
http://www.yeroon.net/ggplot2/

p.s: other posts about this (including videos explaining how some of this was done) can be views on the category page: R and the web

Update (April 6 – 2010) : R-Node now has it’s own a website, with a dedicated google group (you can join it here)

* * * *

The integration of R into online web services is (for me) one of the more exciting prospects in R’s future. That is way I was very excited coming across Jamie Love’s recent creation: R-Node.

What is R-Node

R-Node is a (open source) web front-end to R (the statistical analysis package).

Using this front-end, you can from any web browser connect to an R instance running on a remote (or local) server, and interact with it, sending commands and receiving the responses. In particular, graphing commands such as plot() and hist() will execute in the browser, drawing the graph as an SVG image.

You can see a live demonstration of this interface by visiting:
http://69.164.204.238:2904/
And using the following user/password login info:
User: pvdemouser
Password: svL35NmPwMnt
(This link was originally posted here)

Here are some screenshots:

In the second screenshot you see the results of the R command ‘plot(x, y)’ (with the reimplementation of plot doing the actual plotting), and in the fourth screenshot you see a similar plot command along with a subsequent best fit line (data points calculated with ‘lowess()’) drawn in.

Once in, you can try out R by typing something like:

1
2
3
4

x <- rnorm(100) 
plot(x, main="Random numbers") 
l <- lowess(x) 
lines (l$y)

The plot and lines commands will bring up a graph – you can escape out of it, download the graph as a SVG file, and change the graph type (e.g. do: plot (x, type=”o”) ).
Many R commands will work, though only the hist(), plot() and lines() work for graphing.
Please don’t type the R command q() – it will quit the server, stopping it working for everyone! Also, as everyone shares the same session for now, using more unique variable name than ‘x’ and ‘l’ will help you.

Currently there is only limited error checking but the code continues to be improved and developed. You can download it from:
http://gitorious.org/r-node

How do you may imagine yourself using something like this? Feel invited to share with me and everyone else in the comments.

Here are some of the more technical details of R-Node:

How does R-Node works

(Credit: The following text is based on this forum thread)

R-node, uses protovis for drawing graphs. Protovis is a visualization toolkit written in JavaScript using the canvas element. Using simple graphical marks, like boxes and dots, one can construct custom views to present or explore data.

Besides Protovis, R-node also uses jquery and ExtJS core on the front-end.

Most R commands are passed back to the server and their results returned to the client. Some, such as the graph commands, are parsed and the arguments used in javascript re-implementations of the R commands (e.g. the R command ‘plot’ has a protovis equivalent).

The server side is R+Rserve, and to connect the browser client to the R server Jamie used a nodejs based application server.

Projects utilised in this include:

• Protovis – http://vis.stanford.edu/protovis/
• Nodejs – http://nodejs.org/
• R – http://www.r-project.org/
• Rserve – http://www.rforge.net/Rserve/doc.html
• Shjs – http://shjs.sourceforge.net/

I would love to read your thoughts about this in the comments.

Someone on the R mailing list (link) asked: how can you easily (daily) collect data from many people into a spreadsheet and then analyse it using R.

The answer people gave to it where on various ways of using excel.  But excel files (at least for now),  are not “on the cloud”.  A better answer might be to create a google form that will update a google spreadsheet that will then be read by R.

If my last sentence wasn’t clear to you, then this post is for you.

First view the following video (2:50 min) which explains what google spreadsheets are

Then view the next video (2 min), which explains what using google forms with google spreadsheet is all about

The next step for you is to access the spreadsheet without having to make it public is to go thorough this tutorial on how to use the R package RGoogleDocs.

If you feel comfortable to publish the google spreadsheet, there is a simpler tutorial for you here.  And you could see examples of using this method here and here.

Update. Farrel Buchinsky wrote (in the mailing list) the following:

There is also a package called RGoogleData that can import data from Google
Spreadsheets (even when they are not public). It has one benefit over
RGoogleDocs; RGoogleData can download a spreadsheet as a csv file to your
harddrive. You can then read the csv file into R – read.csv(). Those steps can
easily be written into a script so that you do not have to manually futz with
them each time. The benefit of the csv download is that it is very fast. Both
the RGoogleDocs and the RGoogleData are slow in reading the spreadsheet into R.
I understand, but may be wrong, that Google Stores all the data as HTML and its
API spits it out as xml which these r packages then need to parse out line by
line. The csv treatment by comparison is instantaneous.

Only one big problem. As of a few months ago, RGoogleData has stopped working
for me. I think it is still working for its writer, Adrian Dragulescu. Adrian
has been very generous with his time in trying to help me. He predicts that he
will go back to exploring his package once Google updates their API.

Try it out. I would love to hear what happens.

http://r-forge.r-project.org/projects/rgoogledata/

Do you have any good example for using R with google spreadsheets? please share with us in the comments…

I just noticed a google buzz from Jeroen ooms, with a Youtube video titled “RApache Hello World + POST arguments + catching errors.”

In this ~7 min video tutorial, Jeroen shares with us:

1. How to write ”Hello World” in a website using RApache.
2. How to extract arguments from a form submited by the website visitor (and then inserting it into an “rnorm” function so to control the output). And finally,
3. How to catch an error in case of an invalid argument on an R Web Application.

Thank you Jeroen for a very simple, step by step, tutorial:

p.s: For more videos by Jeroen, have a look at