> Previously I used SAS for 23 years and now R/S-Plus for 17. SAS is > effective for large datasets (in my work > 500,000 subjects) but except > for that, R is far superior to SAS for data management and manipulation. > Just four of the reasons are that you can > > - merge data frames multiple ways and compare the results > - deal with arrays (lists) of datasets using high-level operators > - easily do complex calculations on serial data such as find the highest > blood pressure per subject that is measured before something else is > measured > - sense the type of a variable (character, factor, date, discrete > numeric, continuous numeric, etc.) while analyzing it, and tailor the > analysis to the type of variable
And one more: * you can trust that R will do the correct thing with missing values (propagate them by default) Hadley -- http://had.co.nz/ ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.