Whilst writing %20” >the book the latest version of R changed several times. Although I started on an earlier version, the bulk of the book was written with 2.11 and it was finished under R 2.12. The final version of the R scripts were therefore run and checked using R 2.12 and, in the main, the most recent packages versions for R 2.12.
When it came to proof read R 2.13 was already out and therefore most of the examples were also checked with version, but I stuck with R 2.12 on my home and work machines until last week.
In general I don’t see the point of updating to a new version number if everything is working fine. One advantage of this approach is that the version I install will usually have bugs from the initial release already ironed out. That said, new versions of R have (in my experience) been very stable.
I tend to download the version only when I fall several versions behind or if it is a requirement for a new package or package version. On this occasion it turned out that the latest version of the ordinal package (for fitting ordered logistic regression and multilevel ordered logistic regression models). There are two main drawbacks with updating. The first is reinstalling all your favourite package libraries (and generally getting it set up how you like it). The second is dealing with changes in the way R behaves.
For re-installing all my packages I use a very crude system. For any given platform (Mac OS, Windows or Linux) there are cleverer solutions (that you can find via google). My solution works across cross-platform and is fairly robust, if inelegant. I simply keep an R script with a number of install.packages() commands such as:
install.packages(‘lme4′, ‘exactci’, ‘pwr’, ‘arm’)
I run these in batches after installing the new R version. I find this useful because I’m forever installing R on different machines (so far Mac OS or Windows) at work (e.g., for teaching or if working away from the office or on a borrowed machine). I can also comment the file (e.g., to note if there are issues with any of the packages under a particular version of R). This usually suffices for me as I usually run a ‘vanilla’ set-up without customization. It would be more efficient for me to customize my set-up, but for teaching purposes I find it helps not to do that. Likewise, I tend to work with a clean workspace (and use a script file to save R code that creates my workspaces). I should stress that this isn’t advice – and I would work differently myself if I didn’t use R so much for teaching.
One of the first things that happened after installing R 2.15 was that some of my own functions started producing warnings. R warnings can be pretty scary for new users but are generally benign. Some of them are there to detect behaviour associated with common R errors or common statistical errors (and thus give you a chance to check your work). Others alert you to non-standard behaviour from a function in R (e.g., changing the procedure it uses when sample sizes are small). Yet others offer tips on writing better R code. Only very rarely are they an indication that something has gone badly wrong.
Thus most R warnings are slightly annoying but potentially useful. In my case R 2.15 disliked a number of my functions of the form:
The precise warning was:
mean() is deprecated.
Use colMeans() or sapply(*, mean) instead.
All the functions worked just fine, but (after my initial irritation had receded) I realize that colMeans() is a much better function. It is more efficient but, even better, it is obvious that it calculates the means of the columns of a data frame or matrix. With the more general mean() function it is not immediately obvious what will happen when called with a data frame as an argument. It is also trivial to infer that rowMeans() calculates the row means.
I have now re-written a number of functions to deal with this problem and to make a few other minor changes. The latest version of my functions can be loaded with the call:
I will try and keep this file up-to-date with recent versions of R and correct any bugs as they are detected.
The functions can be downloaded as a text file from:
Filed under: news, R code, serious stats Tagged: R, software