You have probably noticed I’m not much into R (though I’m slowly coming around to it). It goes back to when I was in my graduate statistics program, where we were required to use SPlus (R’s parent language—as far as I can tell, it’s the same thing, but with customer support).
We were given a half hour tutorial and an incomprehensible text, and sent off to figure it out how to use SPlus on graduate level stats.
Not fun.
And since I was already fluent in SAS, SPSS, and BMDP (may it rest in peace), I resisted SPlus. A lot.
I actually wish R had been around, and I wish all the great resources for learning it that exist now, existed then.
Here’s one of them, created by our very own R instructor, David Lillis. An R tutorial series that will get you started with R. Enjoy.
R is Not So Hard! A Tutorial Part 1: Syntax
R is Not So Hard! A Tutorial Part 2: Variable Creation
R Is Not So Hard! A Tutorial Part 3: Regressions and Plots
R Is Not So Hard! A Tutorial Part 4: Fitting a Quadratic Model
R Is Not So Hard! A Tutorial Part 5: Fitting an Exponential Model
R Is Not So Hard! A Tutorial Part 6: Basic Plotting in R
R Is Not So Hard! A Tutorial Part 7: More Plotting in R
R Is Not So Hard! A Tutorial Part 8: Basic Commands
R Is Not So Hard! A Tutorial Part 9: Sub-setting
R Is Not So Hard! A Tutorial Part 10: Creating Summary Tables with aggregate()
R Is Not So Hard! A Tutorial Part 11: Creating Bar Charts
R is Not So Hard! A Tutorial Part 12: Creating Histograms & Setting Bin Widths
R Is Not So Hard! A Tutorial Part 13: Box Plots
R Is Not So Hard! A Tutorial Part 14: Pie Charts
R Is Not So Hard! A Tutorial Part 15: Counting Elements in a Data Set
R Is Not So Hard! A Tutorial Part 16: Counting Values within Cases
R Is Not So Hard! A Tutorial Part 17: Testing for Existence of Particular Values
R Is Not So Hard! A Tutorial Part 18: Re-Coding Values
R Is Not So Hard! A Tutorial Part 19: Multiple Graphs and par(mfrow=(A,B))
R is Not So Hard! A Tutorial Part 20: Useful Commands for Exploring Data
R is Not So Hard! A Tutorial Part 21: Pearson and Spearman Correlation
R is Not So Hard! A Tutorial Part 22: Creating and Customizing Scatter Plots
Graphing Non-Linear Mathematical Expressions in R
Doing Scatterplots in R
R Graphics: Plotting in Color With qplot
R Graphics: Plotting in Color With qplot Part 2
Linear Models in R: Plotting Regression Lines
Linear Models in R: Diagnosing Our Regression Model
Linear Models in R: Improving Our Regression Model
Review of Generalized Linear Models in R Part 1
Review of Generalized Linear Models in R Part 2
Review of Generalized Linear Models in R Part 3
Review of Generalized Linear Models in R Part 4
If you want to learn more about the syntax and techniques for data analysis and graphics using R, check out our upcoming 6-hour online workshop: Intro to R!
The resources to learn R now make it feasible for people like me (non-statisticians) to pick it up. The online documentation provided is way too terse. One web site I like for R is , not to be confused with John Quick’s page you link too above.
I switched from Data Desk 5 (rest in peace) as a graduate student to Stata 7 as a post-doc in 2002. At the time I tried R, but found the lack of documentation at that time a road block. Stata’s documentation is really good, by comparison.
I notice Stata is not feature much on this site. Stata 11 is a wonderful product, btw. I know find a combination of Stata and R the most useful tools for me. I use Stata for data manipulation, analysis, and graphics, and I use R for things not in Stata, and simulation.
Hi Dave,
I agree that Stata is a wonderful program. I don’t feature it much because I just haven’t used it as much, and don’t use it regularly.
And because Stata’s documentation IS so good, people don’t need help in the same way they do for something like SPSS, which has pretty bad manuals. 🙂
I would be very open to guest post submissions on Stata, though.
Karen
John’s approach to simple main effects following between subjects two-way ANOVA seems a little conservative, in that he is using only the data subsets he creates for the error, not the error from the original model. I made a comment on this on his web site, where I suggested this approach with repeated measures ANOVAs, but not between subjects ANOVAs, might be better? Just a thought.
Hi Dave,
It is a bit conservative, and the usual method of dealing with simple effects is to use the model MSE, as you suggest. (Geoffrey Keppel’s book Design And Analysis has a nice chapter on this).
It’s trickier, though in repeated measures ANOVA, because there isn’t a single MSE for the whole model. Within and between subjects effects have different error terms. It’s another reason that mixed models work better for repeated measures data–it doesn’t have that problem.
Karen