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 news of the new release of R 2.13.0 is out, and the R blogosphere is buzzing. Bloggers posting excitedly about the new R compiler package that brings with it the hope to speed up our R code with up to 4 times improvement and even a JIT compiler for R. So it is time to upgrade, and bloggers are here to help. Some wrote how to upgrade R on Linux and mac OSX (based on posts by Paolo). And it is now my turn, with suggestions on how to upgrade R on windows 7.

There are basically two strategies for R upgrading on windows. The first is to install a new R version and copy paste all the packages to the new R installation folder. The second is to have a global R package folder, each time synced to the most current R installation (thus saving us the time of copying the package library each we upgrade R).

p.s: If this is the first time you are upgrading R using this method, then first run the following two lines on your old R installation (before running the above code in the new R intallation):

The above code should be enough. However, there are some common pitfalls you might encounter when upgrading R on windows 7, bellow I outline the ones I know about, and how they can be solved.

So it is no surprise that the new release of plyr 1.5 got me curious. While going through the news file with the new features and bug fixes, I noticed how (quietly) Hadley has also released (6 days ago) another version of plyr prior to 1.5 which was numbered 1.4.1. That version included only one more function, but a very important one – a new citation reference for when using the plyr package. Here is how to use it:

install.packages("plyr")# so to upgrade to the latest releasecitation("plyr")

The output gives both a simple text version as well as a BibTeX entry for LaTeX users. Here it is (notice the download link for yourself to read):

To cite plyr in publications use: Hadley Wickham (2011). The Split-Apply-Combine Strategy for Data Analysis. Journal of Statistical Software, 40(1), 1-29. URL http://www.jstatsoft.org/v40/i01/.

I hope to see more R contributers and users will make use of the ?citation() function in the future.

(The image above is called a “Beeswarm Boxplot” , the code for producing this image is provided at the end of this post)

The above plot is implemented under different names in different softwares. This “Scatter Dot Beeswarm Box Violin – plot” (in the lack of an agreed upon term) is a one-dimensional scatter plot which is like “stripchart”, but with closely-packed, non-overlapping points; the positions of the points are corresponding to the frequency in a similar way as the violin-plot. The plot can be superimposed with a boxplot to give a very rich description of the underlaying distribution.

This plot has been implemented in various statistical packages, in this post I will list the few I came by so far. And if you know of an implementation I’ve missed please tell me about it in the comments.

Recently I was asked by O’Reilly publishing to give a book review for Paul Teetor new introductory book to R. After giving the book some attention and appreciating it’s delivery of the material, I was happy to write and post this review. Also, I’m very happy to see how a major publishing house like O’Reilly is producing more and more R books, great news indeed.

And now for the book review:

Executive summary: a book that offers a well designed gentle introduction for people with some background in statistics wishing to learn how to get common (basic) tasks done with R.

Information

By: Paul Teetor Publisher:O’Reilly MediaReleased: January 2011 Pages: 58 (est.)

Format

The book “25 Recipes for Getting Started with R” offers an interesting take on how to bring R to the general (statistically oriented) public.

In this post I offer an alternative function for boxplot, which will enable you to label outlier observations while handling complex uses of boxplot.

In this post I present a function that helps to label outlier observations When plotting a boxplot using R.

An outlier is an observation that is numerically distant from the rest of the data. When reviewing a boxplot, an outlier is defined as a data point that is located outside the fences (“whiskers”) of the boxplot (e.g: outside 1.5 times the interquartile range above the upper quartile and bellow the lower quartile).

Identifying these points in R is very simply when dealing with only one boxplot and a few outliers. That can easily be done using the “identify” function in R. For example, running the code bellow will plot a boxplot of a hundred observation sampled from a normal distribution, and will then enable you to pick the outlier point and have it’s label (in this case, that number id) plotted beside the point:

set.seed(482)
y <-rnorm(100)boxplot(y)identify(rep(1, length(y)), y, labels=seq_along(y))

However, this solution is not scalable when dealing with:

Many outliers

Overlapping data-points, and

Multiple boxplots in the same graphic window

For such cases I recently wrote the function “boxplot.with.outlier.label” (which you can download from here). This function will plot operates in a similar way as “boxplot” (formula) does, with the added option of defining “label_name”. When outliers are presented, the function will then progress to mark all the outliers using the label_name variable. This function can handle interaction terms and will also try to space the labels so that they won’t overlap (my thanks goes to Greg Snow for his function “spread.labs” from the {TeachingDemos} package, and helpful comments in the R-help mailing list).

Here is some example code you can try out for yourself:

source("https://raw.githubusercontent.com/talgalili/R-code-snippets/master/boxplot.with.outlier.label.r")# Load the function# sample some points and labels for us:set.seed(492)
y <-rnorm(2000)
x1 <-sample(letters[1:2], 2000,T)
x2 <-sample(letters[1:2], 2000,T)
lab_y <-sample(letters[1:4], 2000,T)# plot a boxplot with interactions:
boxplot.with.outlier.label(y~x2*x1, lab_y)

Here is the resulting graph:

You can also have a try and run the following code to see how it handles simpler cases:

# plot a boxplot without interactions:
boxplot.with.outlier.label(y~x1, lab_y, ylim =c(-5,5))# plot a boxplot of y only
boxplot.with.outlier.label(y, lab_y, ylim =c(-5,5))
boxplot.with.outlier.label(y, lab_y, spread_text =F)# here the labels will overlap (because I turned spread_text off)

Here is the output of the last example, showing how the plot looks when we allow for the text to overlap (we would often prefer to NOT allow it).

Regarding package dependencies: notice that this function requires you to first install the packages {TeachingDemos} (by Greg Snow) and {plyr} (by Hadley Wickham)

Updates: 19.04.2011 – I’ve added support to the boxplot “names” and “at” parameters.

You are very much invited to leave your comments if you find a bug, think of ways to improve the function, or simply enjoyed it and would like to share it with me.

Rob Calver wrote an interesting invitation on the R mailing list today, inviting potential authors to submit their vision of the next great book about R. The announcement originated from the Chapman & Hall/CRC publishing houses, backed up by an impressive team of R celebrities, chosen as the editors of this new R books series, including: