Two days ago, I wrote an introduction to this series.
Today, I will discuss ease of learning. Unlike the earlier post (and, I hope, most of the ones to come) this one is inherently subjective. “Ease of learning” is not the same for everyone – indeed, one thing I’d like to explore here and in the comments is why some people find SAS easier to learn, while others find R easier to learn. (Note that I am only discussing ease of use for statistical analysis and data management necessary to do that analysis).
Personally, I have found and continue to find SAS much easier to learn. There may be several reasons for this.
First, I learned SAS before I learned R. I’ve been using SAS for nearly 20 years (scary thought!) and R for about 5. Second, I did a lot of learning of SAS in graduate school, where many of my professors gave us the programs they had written. I learn best by example. With R, though, I have mostly learned on my own. Third, I’ve been to a lot of conferences about SAS, and none about R – purely because there have been SAS conferences that were convenient for me to go to, and there have not been such R conferences (although one is coming up this summer). Those three reasons might be lumped together into a “happenstance” group – None of them are inherently about the software.
Reasons that are due to my own traits
Next, I consider some of my own traits that might relate to my feelings about ease of learning:
First: I am not a programmer. I took one programming course way back when I was an undergraduate (in 1977). We learned a bit of ALGOL. But this was in the days of punchcards, and frequent machine outages, and freshman were on the bottom of the priority stack. I didn’t learn that much (although I got an A) and some of what I learned was about binary arithmetic and AND gates and stuff. Not that useful for programming in R.
Second: I like voluminous help files. In a later post, I will discuss getting help, but there is little doubt that SAS documentation is lengthier than R documentation.
Reasons due to differences in the software
Finally, some reasons that I think relate to differences in software.
First, R is object oriented (amended per comments: R is a function language that is object oriented), SAS is more a procedural language (amended for clarity). I’m not too sure about this, but I think that some people may have brains that prefer object oriented code, while others have brains that prefer procedural code. I find procedural code easier to understand. I also think people who are trained as programmers may find object oriented easier – but that is just a guess based on casual observation.
Second, all of SAS is written by one group of people. There are, to be sure, some inconsistencies in how things work, but there is an overall style. The base packages of R were also written by one group, but they are supplemented by a huge number of other packages, written by different people – often, these packages duplicate each other or base R in terms of their basic goals, but differ in how they approach getting to those goals.
Questions for commenters
I’d like this to be a conversation. So … to start us off:
1. Do you find SAS easier or R easier?
2. Why? Does it have to do with your background, or differences in the packages, or both?
3. Do you prefer terse help files, or lengthy ones?
4. What might make R easier to learn?
5. What might make SAS easier to learn?
[learn_more caption="Author Bio"] I specialize in helping graduate students and researchers in psychology, education, economics and the social sciences with all aspects of statistical analysis. Many new and relatively uncommon statistical techniques are available, and these may widen the field of hypotheses you can investigate. Graphical techniques are often misapplied, but, done correctly, they can summarize a great deal of information in a single figure. I can help with writing papers, writing grant applications, and doing analysis for grants and research.
Specialties: Regression, logistic regression, cluster analysis, statistical graphics, quantile regression.