If you’re clever, R is wonderful for this and free. There’s a fairly large working group in the stat department here at Iowa State dedicated to statistical graphics, and they exclusively use R. An alum designed the ggplot2 package.
I would love to see some postings exhibiting clever ways to use R to model and present ideas in Bayesian reasoning, data analysis, or evolutionary optimization.
There are plenty of tutorials for R—just google. I’m sure someone else has written a much better tutorial than I could write.
R is a full blown programming language without a simple to use GUI (at least not in the base package), so if you don’t have any programming experience it might be slow going. But the freedom of a programming language makes the learning curve worth it (if you’ve used SAS, you understand what I mean)
My grad program offers 1 credit course in R to first year grad students, and much of it is available online. When it comes to statistical graphics, the stuff on ggplot2 is particularly relevant, and in the first 2-3 sets of lecture notes.
If you’re clever, R is wonderful for this and free. There’s a fairly large working group in the stat department here at Iowa State dedicated to statistical graphics, and they exclusively use R. An alum designed the ggplot2 package.
I would love to see some postings exhibiting clever ways to use R to model and present ideas in Bayesian reasoning, data analysis, or evolutionary optimization.
Is there a good existing tutorial for R? If not, do you have the time and background to write one?
There are plenty of tutorials for R—just google. I’m sure someone else has written a much better tutorial than I could write.
R is a full blown programming language without a simple to use GUI (at least not in the base package), so if you don’t have any programming experience it might be slow going. But the freedom of a programming language makes the learning curve worth it (if you’ve used SAS, you understand what I mean)
My grad program offers 1 credit course in R to first year grad students, and much of it is available online. When it comes to statistical graphics, the stuff on ggplot2 is particularly relevant, and in the first 2-3 sets of lecture notes.
See also the ggplot2 reference manual
My personal favorite: Quick-R