Short review of the R book

David Scott wrote a review of Introducing Monte Carlo Methods with R in the International Statistical Review that is rather negative, since the main bulk reads as follows:

I found some aspects of the book very disappointing. The first chapter (“Basic R Programming”) has some unfortunate mistakes and some statements, which are contentious at least (“An older assignment operator is <- . . . but it should be ignored for cleaner programming”). The graphs throughout the book appear to be bitmaps—they are quite blurred. Either postscript or pdf graphs should have been produced which would have immeasurably improved the presentation. The book is not an easy read, requiring considerable background to be understood. I found the chapters on MCMC easier than the chapters on general Monte Carlo methods because I had a reasonable understanding of the theory beforehand. There are exercises within and at the end  of all chapters, which appear to be fairly challenging, with the possible exception of the first two chapters.

As undoubtedly obvious to regular readers of the ‘Og, there are certainly many typos found throughout of the book and Chapter 1 is no exception. (The point about using “<-” versus “=” assignment operator is subject to many discussions and I should not have used “older”, even though I strongly dislike the “<-” operator!) David Scott is completely right in complaining about the quality of the graphs, in that the jpeg versions we used to speed up compilation were taken at the production stage instead of the postscript or pdf files we had prepared. We only saw the outcome once the book had been produced, i.e. too late… The French version avoids this editorial mistake altogether and we should see a corrected printing of the original version any time now. On the other hand, the criticism on the difficulty of the book feels a wee hard, given that we started from Monte Carlo Statistical Methods to build Introducing Monte Carlo Methods with R and that we use the book from the third university year onwards. The third-year students in my R class do manage to solve problems from the first four chapters on a regular basis and one can check through the student solution manual that the difficulty is altogether relative.

8 Responses to “Short review of the R book”

  1. […] of JASA contains a review of Introducing Monte Carlo Methods with R by Hedibert Lopes. As in the previous review, the poor quality of the figures is (rightly) pointed out by Hedie. However, the main message of […]

  2. […] the publication for January 20. I am very pleased with the quality of the output, in contrast with the first printing of the English […]

  3. I’m currently in the middle of chapter 4 and the book has, so far, been very useful. The one thing that has been frustrating is the lack of solutions for all of the in-text exercises (not the chapter end ones). So far I’ve been able to complete all the exercises that I’ve tried, and certainly they are useful. However, the problem for me is the time required — my goal is to read the book as quickly as possible and move on to the next thing I need to do (which is many). If there is an exercise that I can’t immediately figure out that is also important to the development of the material then I have to invest the time to solve it which introduces some unknown time commitment, which adds more uncertainty than I would like to my timeline. I’d much prefer if the solution to the in-text exercises were, say, at the back of the book.

    • Thanks Paul: the odd-numbered exercises are corrected in the manual, including those in-text. Having all of them in-text ones solved in the manual is certainly an idea worth considering (even though it involves extra-work from us, most exercises are already corrected for the instructors).

  4. I see! Thanks for the reply.

    I am rather new to R, but I relate to most of your concerns (though they seem more minor than the “strongly dislike” first led me to expect!).

    A follow-up concern regarding the “=” operator: wouldn’t it sometimes (though admittedly rarely) lead to ambiguity, given that the “=” sign is used to specify options within functions?

    • I do not see this “function(…,a=0,…)” as an ambiguity in that the variable is set to a default value that can be set to a new value by the user.

  5. Can you give us a quick explanation why you “strongly dislike” the <- operator? I am curious!

    • Tim: do no expect deep reasons there! First, I do not see the point in using two symbols when one is enough. Second, other computer languages work perfectly well with “=” and R distinguishes between “=” and “==” as those other computer languages, hence why complicate things. A very minor quibble is that x<-3 [versus x<(-3)] is unnecessarily ambiguous. And on a most personal basis, I had to rewrite a whole Splus program at the time the allocation was done by the underscore "_" operator because my then-C-programmer wife had decided to change names of underscored variables… As a low-end R user, I am actually curious to hear about operational reasons as to why we should use "<-".

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