Wednesday, November 26, 2014



You read already my first R post, this is a refresher.


You can find R in CRAN.

Setup Related

You can find related projects in CRAN.

Setup Packages

You can find ReShape2 in GitHub or
Reshape2 makes it easy to transform data between wide and long formats. reshape2 is based around two key functions: melt and cast: melt takes wide-format data and melts it into long-format data and cast takes long-format data and casts it into wide-format data.

You can find Dplyr in GitHub or
Dplyr is the next iteration of plyr, focussing on only data frames. dplyr is faster and has a more consistent API.

You can find DevTools or
Devtools makes package development a breeze: it works with R’s existing conventions for code structure, adding efficient tools to support the cycle of package development.

You can find PackRat or
A dependency management tool for R to make your R projects more isolated, portable, and reproducible.

You can  find KnitR. Elegant, flexible and fast dynamic report generation that combines R with TeX, Markdown, or HTML.

You can find RMarkdown Website or in GitHub or
R Markdown lets you insert R code into a markdown document. R then generates a final document that replaces the R code with its results.

You can find Shiny or
Shiny makes it incredibly easy to build interactive web applications with R.
You can find Ggplot2 or
An enhanced data visualization package for R.

You can find GgVis or
Is the next iteration of the popular ggplot2 graphics package. ggvis creates dynamic, interactive data visualizations.

You can find RforGoogleAdWord post.

Setup R-(D)COM

You can find R-(D)COM in CRAN. Also the R-(D)COM installer.

Setup RStudio

You can find it in it RStudio Website.

Setup PowerShell

You can find PowerShell R Interop in Codeplex.

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