jeffwong/recipes

An R Cookbook. Step by step guide for data analysis in R.

#Recipes

Recipes aims to be an open cookbook for doing data analysis in R. The goal is to provide a tutorial for R, but also to provide real world examples on how data analysis is done with R, showing real world insight. I hope to gather these recipes and place them here in a central repository so others can learn the R language, and also gain the skills needed to draw insight from their own data.

##What's Missing?

One thing that is missing from other guides is how to take disorganized data, and transform/clean it to a workable state. Great data analysis is both very elegant with its math and very messy with its data munging. Data manipulation is probably the biggest barrier into data science, the adage goes "the work of a statistician is 90% preparing the data, and 10% statistics".
In these recipes let's show the full data analysis, end to end, and remove all the barriers for others.

Much like the explosiveness of d3, I hope others will help to develop a community of open data science and contribute recipes of their own.

##Other R Cookbooks

R is a difficult language to learn since it is developed by a team of volunteers. Compared to other languages, it is also in its infancy, or maybe teenage years, and is still being rapidly developed. During this time, there is very little documentation. As far as I know, there have been two big efforts in writing R guides

Winston Chang's RCookbook

Paul Teetor's RCookbook

Chang's R cookbook is a great tutorial to R and to data visualization, and is my favorite reference. Unfortunately, most of the data is just artificially generated. Paul Teetor's book addresses lots of data analysis problems with real world motivation behind each, but it isn't open, and consequently will be hard to update as R and R packages evolve. Let's instead develop Recipes here, a practical, data analytics community.