I think that R/S's biggest advantage is in the ways you can store data. It does not force you to fit your data to a single spreadsheet like table, but rather encourages you to think about your data and find the correct way to store it. Lists and objects are a great advantage for keeping related things combined together. I can have multiple data sets available all at the same time, but still in separate objects.
Also the results of routines can be kept in a way that makes working with them easy. I remember working with programs that just had one big spreadsheet and ending up with 3 different columns of residuals from 3 different models, but then forgot which residuals matched which model. With R/S each lm object has the residuals stored with it including the call to remind us what model and options were used. One plot that I like to make when exploring different models is: > plot( fitted(model1), fitted(model2) ); abline(0,1) That is simple and straight forward in R/S, but much more difficult in other programs. I also like the fact that the graphics system will let me create anything I want. There are tools to create the standard plots (and I really like the simplicity of calling plot on an lm object and getting a standard set of diagnostics), but there are also the tools to create any plot I can imagine, or add any information I feel useful to an existing plot. -- Gregory (Greg) L. Snow Ph.D. Statistical Data Center Intermountain Healthcare [email protected] 801.408.8111 > -----Original Message----- > From: [email protected] [mailto:r-help-boun...@r- > project.org] On Behalf Of Michael Friendly > Sent: Wednesday, October 06, 2010 3:06 PM > To: R-help > Subject: [R] R: Tools for thinking about data analysis and graphics > > I'm giving a talk about some aspects of language and conceptual tools > for thinking about how > to solve problems in several programming languages for statistical > computing and graphics. I'm particularly > interested in language features that relate to: > > o expressive power: ease of translating what you want to do into the > results you want > o elegance: how well does the code provide a simple human-readable > description of what is done? > o extensibility: ease of generalizing a method to wider scope > o learnability: your learning curve (rate, asymptote) > > For R, some things to cite are (a) data and function objects, (b) > object-oriented methods (S3 & S4); (c) function mapping over data with > *apply methods and plyr. > > What other language features of R should be on this list? I would > welcome suggestions (and brief illustrative examples). > > -Michael > > > -- > Michael Friendly Email: friendly AT yorku DOT ca > Professor, Psychology Dept. > York University Voice: 416 736-5115 x66249 Fax: 416 736-5814 > 4700 Keele Street Web: http://www.datavis.ca > Toronto, ONT M3J 1P3 CANADA > > ______________________________________________ > [email protected] 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. ______________________________________________ [email protected] 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.
