Book Image

Learning R Programming

By : Kun Ren
Book Image

Learning R Programming

By: Kun Ren

Overview of this book

R is a high-level functional language and one of the must-know tools for data science and statistics. Powerful but complex, R can be challenging for beginners and those unfamiliar with its unique behaviors. Learning R Programming is the solution - an easy and practical way to learn R and develop a broad and consistent understanding of the language. Through hands-on examples you'll discover powerful R tools, and R best practices that will give you a deeper understanding of working with data. You'll get to grips with R's data structures and data processing techniques, as well as the most popular R packages to boost your productivity from the offset. Start with the basics of R, then dive deep into the programming techniques and paradigms to make your R code excel. Advance quickly to a deeper understanding of R's behavior as you learn common tasks including data analysis, databases, web scraping, high performance computing, and writing documents. By the end of the book, you'll be a confident R programmer adept at solving problems with the right techniques.
Table of Contents (21 chapters)
Learning R Programming
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface

Analyzing data


In practical data analysis, most time is spent on data cleansing, that is, to filter and transform the original data (or raw data) to a form that is easier to analyze. The filtering and transforming process is also called data manipulation. We will dedicate an entire chapter to this topic.

In this section, we directly assume that the data is ready for analysis. We won't go deep into the models, but will apply some simple models to leave you an impression of how to fit a model with data, how to interact with fitted models, and how to apply a fitted model to make predictions.

Fitting a linear model

The simplest model in R is the linear model, that is, we use a linear function to describe the relationship between two random variables under a certain set of assumptions. In the following example, we will create a linear function that maps xto 3 + 2 * x. Then we generate a normally-distributed random numeric vector x, and generate y by f(x) plus some independent noise:

f <- function...