I started using R in 1998 when I was a college undergraduate working on my senior thesis. The version was 0.63. I was an applied mathematics major with a statistics concentration and I was working with Dr. Nicolas Hengartner on an analysis of word frequencies in classic texts (Shakespeare, Milton, etc.). The idea was to see if we could identify the authorship of each of the texts based on how frequently they used certain words. We downloaded the data from Project Gutenberg and used some basic linear discriminant analysis for the modeling. The work was eventually published and was my first ever peer-reviewed publication. I guess you could argue it was my first real “data science” experience.
Back then, no one was using R. Most of my classes were taught with Minitab, SPSS, Stata, or Microsoft Excel. The cool people on the cutting edge of statistical methodology used S-PLUS. I was working on my thesis late one night and I had a problem. I didn’t have a copy of any of those software packages because they were expensive and I was a student. I didn’t feel like trekking over to the computer lab to use the software because it was late at night.
But I had the Internet! After a couple of Yahoo! searches I found a web page for something called R, which I figured was just a play on the name of the S-PLUS package. From what I could tell, R was a “clone” of S-PLUS that was free. I had already written some S-PLUS code for my thesis so I figured I would try to download R and see if I could just run the S-PLUS code.
It didn’t work. At least not at first. It turns out that R is not exactly a clone of S-PLUS and quite a few modifications needed to be made before the code would run in R. In particular, R was missing a lot of statistical functionality that had existed in S-PLUS for a long time already. Luckily, R’s programming language was pretty much there and I was able to more or less re-implement the features that were missing in R.
After college, I enrolled in a PhD program in statistics at the University of California, Los Angeles. At the time the department was brand new and they didn’t have a lot of policies or rules (or classes, for that matter!). So you could kind of do what you wanted, which was good for some students and not so good for others. The Chair of the department, Jan de Leeuw, was a big fan of XLisp-Stat and so all of the department’s classes were taught using XLisp-Stat. I diligently bought my copy of Luke Tierney’s book and learned to really love XLisp-Stat. It had a number of features that R didn’t have at all, most notably dynamic graphics.
But ultimately, there were only so many parentheses that I could type, and still all of the research-level statistics was being done in S-PLUS. The department didn’t really have a lot of copies of S-PLUS lying around so I turned back to R. When I looked around at my fellow students, I realized that I was basically the only one who had any experience using R. Since there was a budding interest in R around the department, I decided to start a “brown bag” series where every week for about an hour I would talk about something you could do in R (which wasn’t much, really). People seemed to like it, if only because there wasn’t really anyone to turn to if you wanted to learn about R.
By the time I left grad school in 2003, the department had essentially switched over from XLisp-Stat to R for all its work (although there were a few hold outs). Jan discusses the rationale for the transition in a paper in the Journal of Statistical Software.
In the next step of my career, I went to the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health, where I have been for the past 17 years. When I got to Johns Hopkins people already seemed into R. Most people had abandoned S-PLUS a while ago and were committed to using R for their research. Of all the available statistical packages, R had the most powerful and expressive programming language, which was perfect for someone developing new statistical methods.
However, we didn’t really have a class that taught students how to use R. This was a problem because most of our grad students were coming into the program having never heard of R. Most likely in their undergraduate programs, they used some other software package. So along with Rafael Irizarry, Brian Caffo, Ingo Ruczinski, and Karl Broman, I started a new class to teach our graduate students R and a number of other skills they’d need in grad school.
The class was basically a weekly seminar where one of us talked about a computing topic of interest. I gave some of the R lectures in that class and when I asked people who had heard of R before, almost no one raised their hand. And no one had actually used it before. The main selling point at the time was “It’s just like S-PLUS but it’s free!” A lot of people had experience with SAS or Stata or SPSS. A number of people had used something like Java or C/C++ before and so I often used that as a reference frame. No one had ever used a functional-style of programming language like Scheme or Lisp.
To this day, I still teach the class, known a Biostatistics 140.776 (“Statistical Computing”). However, the nature of the class has changed quite a bit over the years. The population of students (mostly first-year graduate students) has shifted to the point where many of them have been introduced to R as undergraduates. This trend mirrors the overall trend with statistics where we are seeing more and more students do undergraduate majors in statistics (as opposed to, say, mathematics). Eventually, by 2008–2009, when I’d asked how many people had heard of or used R before, everyone raised their hand. However, even at that late date, I still felt the need to convince people that R was a “real” language that could be used for real tasks.
R has grown a lot in recent years, and is being used in so many places now, that I think it’s essentially impossible for a person to keep track of everything that is going on. That’s fine, but it makes “introducing” people to R an interesting experience. Nowadays in class, students are often teaching me something new about R that I’ve never seen or heard of before (they are quite good at Googling around for themselves). I feel no need to “bring people over” to R. In fact it’s quite the opposite–people might start asking questions if I weren’t teaching R.
This book comes from my experience teaching R in a variety of settings and through different stages of its (and my) development. Much of the material has been taken from by Statistical Computing class as well as the R Programming class I teach through Coursera.
I’m looking forward to teaching R to people as long as people will let me, and I’m interested in seeing how the next generation of students will approach it (and how my approach to them will change). Overall, it’s been just an amazing experience to see the widespread adoption of R over the past decade. I’m sure the next decade will be just as amazing.