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.

You can install R and RStudio with the following links:

• R: https://cran.rstudio.com/
• Rstudio: https://www.rstudio.com/products/rstudio/download/#download

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.

General Howto's

(see also this short overview of useful R functions)

• 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

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. We also wrote two relevant howto's:

• Corpus Analysis: Term document Matrices, frequency analysis, and topic modeling (source))
• Claues Analysis: Using grammatical analysis for semantic network analysis (source))

Below are also some handouts that do not depend on AmCAT, based on a Dutch data set:

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

Network Analysis

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

(The last part of the 'semantic network analysis' demo above also has a simplistic network analysis at the end)