Chapter 1. Introduction to R

How we analyze data has changed dramatically in recent years. With the advent of personal computers and the internet, the sheer volume of data we have available has grown enormously. Companies have terabytes of data about the consumers they interact with, and governmental, academic, and private research institutions have extensive archival and survey data on every manner of research topic. Gleaning information (let alone wisdom) from these massive stores of data has become an industry in itself. At the same time, presenting the information in easily accessible and digestible ways has become increasingly challenging.

The science of data analysis (statistics, psychometrics, econometrics, and machine learning) has kept pace with this explosion of data. Before personal computers and the internet, new statistical methods were developed by academic researchers who published their results as theoretical papers in professional journals. It could take years for these methods to be adapted by programmers and incorporated into the statistical packages widely available to data analysts. Today, new methodologies appear daily. Statistical researchers publish new and improved methods, along with the code to produce them, on easily accessible websites.

1.1. Why use R?

1.2. Obtaining and installing R

1.3. Working with R

1.4. Packages

1.5. Batch processing

1.6. Using output as input: reusing results

1.7. Working with large datasets

1.8. Working through an example

1.9. Summary