Learning R

R is a very powerful and flexible statistics package and programming language.

This repository contains a number of 'howto' files aimed to providing an introduction to R and some os its possibilities.

Some other great sites for learning R are:

• OpenIntro statistics with a number of good statistics 'labs' in R
• Quick-R with explanations and sample code for a wide array of applications
• Advanced R Programming for (much) more information on what is really going on.

To install R and RStudio, please see lab 0 of the OpenIntro statistics book.

General Howto's

• Getting started
  • using R as a calculator (source)
  • playing with data in R (source)
• Data preparation
  • Organizing data (source)
  • Transforming and merging data (source)
• Data analysis
  • Plotting your data (source)
  • Data modeling: T-tests to linear models (source)
• Advanced modeling
  • Time series models (source)
  • Multi-level models (source)

Dealing with textual data

• Corpus Analysis: Term Document Matrices (source)
• LDA topic modeling (source)
• Lemmatization (source)
• Machine Learning with RTextTools (source)

For textual data, we have also developed two R packages to communicate with the AmCAT text analysis framework and to deal with corpus analysis and topic models. See the following howto's from these packages:

• Querying AmCAT (source)
• Document-Term Matrices (source)
• LDA Topic Models (source)

Network Analysis

• Using igraph(source)
• Communication networks(source)
• Content Similarity Networks(source)