Archive for RStudio

R wins COPSS Award!

Posted in Statistics with tags , , , , , , , , on August 4, 2019 by xi'an

Hadley Wickham from RStudio has won the 2019 COPSS Award, which expresses a rather radical switch from the traditional recipient of this award in that this recognises his many contributions to the R language and in particular to RStudio. The full quote for the nomination is his  “influential work in statistical computing, visualisation, graphics, and data analysis” including “making statistical thinking and computing accessible to a large audience”. With the last part possibly a recognition of the appeal of Open Source… (I was not in Denver for the awards ceremony, having left after the ABC session on Monday morning. Unfortunately, this session only attracted a few souls, due to the competition of twentysome other sessions, including, excusez du peu!, David Dunson’s Medallion Lecture and Michael Lavine’s IOL on the likelihood principle. And Marco Ferreira’s short-course on Bayesian time series. This is the way the joint meeting goes, but it is disappointing to reach so few people.)

Astrostatistics school

Posted in Mountains, pictures, R, Statistics, Travel, University life with tags , , , , , , , , , , , , , , , , , , , , , on October 17, 2017 by xi'an

What a wonderful week at the Astrostat [Indian] summer school in Autrans! The setting was superb, on the high Vercors plateau overlooking both Grenoble [north] and Valence [west], with the colours of the Fall at their brightest on the foliage of the forests rising on both sides of the valley and a perfect green on the fields at the centre, with sun all along, sharp mornings and warm afternoons worthy of a late Indian summer, too many running trails [turning into X country ski trails in the Winter] to contemplate for a single week [even with three hours of running over two days], many climbing sites on the numerous chalk cliffs all around [but a single afternoon for that, more later in another post!]. And of course a group of participants eager to learn about Bayesian methodology and computational algorithms, from diverse [astronomy, cosmology and more] backgrounds, trainings and countries. I was surprised at the dedication of the participants travelling all the way from Chile, Péru, and Hong Kong for the sole purpose of attending the school. David van Dyk gave the first part of the school on Bayesian concepts and MCMC methods, Roberto Trotta the second part on Bayesian model choice and hierarchical models, and myself a third part on, surprise, surprise!, approximate Bayesian computation. Plus practicals on R.

As it happens Roberto had to cancel his participation and I turned for a session into Christian Roberto, presenting his slides in the most objective possible fashion!, as a significant part covered nested sampling and Savage-Dickey ratios, not exactly my favourites for estimating constants. David joked that he was considering postponing his flight to see me talk about these, but I hope I refrained from engaging into controversy and criticisms… If anything because this was not of interest for the participants. Indeed when I started presenting ABC through what I thought was a pedestrian example, namely Rasmus Baath’s socks, I found that the main concern was not running an MCMC sampler or a substitute ABC algorithm but rather an healthy questioning of the construction of the informative prior in that artificial setting, which made me quite glad I had planned to cover this example rather than an advanced model [as, e.g., one of those covered in the packages abc, abctools, or abcrf]. Because it generated those questions about the prior [why a Negative Binomial? why these hyperparameters? &tc.] and showed how programming ABC turned into a difficult exercise even in this toy setting. And while I wanted to give my usual warning about ABC model choice and argue for random forests as a summary selection tool, I feel I should have focussed instead on another example, as this exercise brings out so clearly the conceptual difficulties with what is taught. Making me quite sorry I had to leave one day earlier. [As did missing an extra run!] Coming back by train through the sunny and grape-covered slopes of Burgundy hills was an extra reward [and no one in the train commented about the local cheese travelling in my bag!]

 

[summer Astrostat school] room with a view [jatp]

Posted in Mountains, pictures, R, Running, Statistics, Travel, University life with tags , , , , , , , , , , on October 9, 2017 by xi'an

I just arrived in Autrans, on the Plateau du Vercors overlooking Grenoble and the view is fabulistic! Trees have started to turn red and yellow, the weather is very mild, and my duties are restricted to teaching ABC to a group of enthusiastic astronomers and cosmologists..! Second advanced course on ABC in the mountains this year, hard to beat (except by a third course). The surroundings are so serene and peaceful that I even conceded to install RStudio for my course! Instead of sticking to my favourite vim editor and line commands.

testing R code [book review]

Posted in R, Statistics, Travel with tags , , , , , , on March 1, 2017 by xi'an

When I saw this title among the CRC Press novelties, I immediately ordered it as I though it fairly exciting. Now that I have gone through the book, the excitement has died. Maybe faster than need be as I read it while being stuck in a soulless Schipol airport and missing the only ice-climbing opportunity of the year!

Testing R Code was written by Richard Cotton and is quite short: once you take out the appendices and the answers to the exercises, it is about 130 pages long, with a significant proportion of code and output. And it is about some functions developed by Hadley Wickham from RStudio, for testing the coherence of R code in terms of inputs more than outputs. The functions are assertive and testthat. Intended for run-time versus development-time testing. Meaning that the output versus the input are what the author of the code intends them to be. The other chapters contain advices and heuristics about writing maintainable testable code, and incorporating a testing feature in an R package.

While I am definitely a poorly qualified reader for this type of R books, my disappointment stems from my expectation of a book about debugging R code, which is possibly due to a misunderstanding of the term testing. This is an unrealistic expectation, for sure, as testing for a code to produce what it is supposed to do requires some advanced knowledge of what the output should be, at least in some representative situations. Which means using interface like RStudio is capital in spotting unsavoury behaviours of some variables, if not foolproof in any case.