# R statistics

## R-statistics blog

Guest post by Marek Hlavac Since its first introduction on this blog, stargazer, a package for turning R statistical output into beautiful LaTeX and ASCII text tables, has made a great deal of progress. Compared to available alternatives (such as apsrtable or texreg), the latest version (4.0) of stargazer supports the broadest range of model objects. In particular, it can create side-by-side regression tables from statistical model objects created by packages AER, betareg, dynlm, eha, ergm, gee, gmm, lme4, MASS, mgcv, nlme, nnet, ordinal, plm, pscl, quantreg, relevent, rms, robustbase, spdep, stats, survey, survival and Zelig.Someone on the R mailing list (link) asked: how can you easily (daily) collect data from many people into a spreadsheet and then analyse it using R. The answer people gave to it where on various ways of using excel. But excel files (at least for now), are not “on the cloud”.
Google spreadsheets + google forms + R = Easily collecting and importing data for analysis | R-statistics blog

R is an elegant and comprehensive statistical and graphical programming language. Unfortunately, it can also have a steep learning curve. I created this website for both current R users, and experienced users of other statistical packages (e.g., SAS, SPSS, Stata) who would like to transition to R.

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