Plots and graphs, some modeling, and maybe some data exploration depending on the data set and where I want to go with it. R: Simulations, simulations, and more simulations. It's the first program I open up with a new data set. SAS: basic data exploration, analysis, and modelling. Sure, you can have a preference, but I think it's a bit closed-minded for someone to be "only an R programmer" or "only a SAS programmer.". In all honesty, they're all good in their own way. sometimes too much info though, especially if you don't know what it all means (seriously, outside of actual statisticians, it's not like anyone knows the difference between -2LogL, AIC, BIC, CAIC, HQIC, Chi-Square etc., and which to look at depending on the goals of their model). On the other hand, each of those procedures give me a hell of a lot of information for 4-5 lines of short code, more than I'll get from R. So I can definitely see how SAS can be confusing, especially to new users. The stupid thing about it is, I will use a number of those on a single data set because of what they output beyond the basic ANOVA table and how it's output. Seriously, I can get the same results for some ANOVA models using PROC ANOVA, PROC GLM, PROC GENMOD, PROC MIXED, and PROC GLIMMIX. R-bloggers - blog aggregator with statistics articles generally done with R software.
SAS VS SPSS SOFTWARE
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