Let R fly: Visualizing Export Data using R. Having been using R for 5+ years, visiting www.r-bloggers.com daily, learning cool tricks from it and knowing cool UseRs from it, I finally decide to quit being an observer and start to be a contributor to this wonderful community. In this very first of my blog, I would like to demonstrate how to use a very cool R GUI — RAnalyticFlow and three useful R packages — rworldmap, gregmisc and TeachingDemos to visualize New Zealand primary export data (the data used in this post are freely and publicly available from www.stats.govt.nz). RAnalyticFlow is a free GUI for R. It lets you to create flow charts of R codes. I find it really useful to break down big chunks of R codes into small manageable pieces. It also allows you to put non-essential codes (plots or checking data) out of essential ones.
I am not going to details about this GUI but I do encourage you to download and have a play (runs on Mac, Windows and Linux). My goal here is to plot two world maps that show. The Comprehensive R Archive Network. RStudio: My thoughts. Let me get this out of the way: I just love RStudio. Created by a team lead by JJ Allaire, a name that should ring a bell if you were involved in web development during the Clinton administration, RStudio is an R IDE that is actually designed for R from the ground up.
RStudio works on Linux, Mac, and Windows platforms, and can even run over the web. The movement of commands back and forth from console to editor is another task that other editors made unnecessarily difficult - the old Mac R GUI console would not let you copy-and-paste a subset of the history, ESS was always geared to having users write code in the editor then executing lines but never writing code in the console then committing to the script. RStudio provides means of easily going in either direction. Control over multiple plots (solving both the overwritten X-Window and the annoying type=Cairo PNG problem on OS X) is a welcome relief. RStudio has already garnered a good number of suggestions. The R Project for Statistical Computing. Contributed Packages. The R Journal.