| I think this article nails exactly what's right and wrong with R. This in particular sums up the learning curve of R. > Thankfully, I’m long past the point where R syntax is perpetually confusing. I’m now well into the phase where it’s only frequently confusing, and I even have high hopes of one day making it to the point where it barely confuses me at all. Warning personal opinion ahead... R, the language can get you up and running alot faster than other languages for statistics like say python with Pandas or scipy but even people who use it on a daily basis will curse the languages "quirks". I find most of the confusion comes from R trying to be too friendly to the user via type conversions. The ease in which the R's type system will convert values has probably caused me more grief when first learning the language than any other issue I ran into. And this illustrates the down side of using R > library(Hmisc)
apply(ice.cream, 2, all.is.numeric) > …which had the desirable property of actually working. But it still wasn’t very satisfactory, because it requires loading a pretty large library (Hmisc) with a bunch of dependencies just to do something very simple that should really be doable in the base R distribution. Since R is rarely a programmer's most used language, I find there tends to be an above average use of google and paste type code that pulls in 50 different packages, each of which is used on 1-2 lines of a 1000 line script. Perhaps this is just a function of most programmers not really understanding the mathematical domain and hence they slowly google and iterate their way towards a solution. Often I'll see people pull in 5 different time series libraries just because each of them operate on a ts object, so they all can work on the same object, and each one provides one additional method the other's don't and the programmer needs to create their solution. You'll hear people talk about writing R in the Hadley universe or the basic R universe but there isn't much talk about what a canonical R solution looks like. R is a great language in the sense that Perl and C++. It allows you to do anything but there often isn't an agreed upon way of writing it and two different programmers can come up with wildly different but valid solutions to the same problem. |