I have become a complete knitr addict of late and have been using it in combination with RStudio’s R markdown support on a regular basis. In fact I wrote this post using it! It then dawned on me how great it would be if I … Continue reading →

When I am working in new institutions and I am asking: “Do you have a document management system?” I often get the answer:”Yap, we are using folders” … OKAY. Making analysis, developing applications and keeping an eye on code, data and applications make this even harder as it has to be. Of course not many

One of Rs great strengths compared to other statistic solutions or programming languages is the amount of possibilities for creating well-designed publication-quality plots. Almost all plot types can be created with any amount of fine tuning. R works on small data sets as well as on big data. In addition to Rs base-graphics various add-on

Arguably, knitr (CRAN link) is the most outstanding R package of this year and its creator, Yihui Xie is the star of the useR! conference 2012. This is because the ease of use comparing to Sweave for making reproducible report. Integration of knitR and R Studio has made reproducible research much more convenience, intuitive and easier to

Where do these come from? Since most statistical packages calculate these estimates automatically, it is not unreasonable to think that many researchers using applied econometrics are unfamiliar with the exact details of their computation. For the purposes of illustration, I am going to estimate different standard errors from a basic linear regression model: , using the

Hi, our group of R users from INSEE, aka FLR, meets monthly in Paris. Next meeting is on Wed 13 (tomorrow), 1-2 pm, room 539 (an ID is needed to come in, map to access INSEE R), about ggplot2 and parallel computing. Since the first meeting in February, presentations have included hot topics like webscrapping, C in R, RStudio, SQLite

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