Chapter 2. Introduction to R Programming Language and Statistical Environment

In Chapter 1, The era of "Big Data", you have become familiar with the most useful Big Data terminology, and a small selection of typical tools applied to unusually large or complex data sets. You have also gained essential insights into how R was developed and how it became the leading statistical computing environment and programming language favored by technology giants and the best universities in the world. In this chapter you will have the opportunity to learn some most important R functions from base R installation and well-known third party packages used for data crunching, transformation, and analysis. More specifically in this chapter you will:

• Understand the landscape of available R data structures
• Be guided through a number of R operations allowing you to import data from standard and proprietary data formats
• Carry out essential data cleaning and processing activities such as subsetting, aggregating, creating contingency tables, and so on
• Inspect the data by implementing a selection of Exploratory Data Analysis techniques such as descriptive statistics
• Apply basic statistical methods to estimate correlation parameters between two (Pearson's r) or more variables (multiple regressions) or find the differences between means for two (t-tests) or more groups Analysis of Variance (ANOVA)
• Be introduced to more advanced data modeling tasks like logistic and Poisson regressions