Introduction of R Programming:
R Programming is probably every data scientist’s preferred programming language (besides Python and SAS) to build prototypes, visualize data, or run analyses on data sets.
There are so many libraries, applications and techniques exist to explore data in R that I’m sure even experts don’t know them all!
Aspiring data, scientists who are reading this though, fear not, for you are well on your way to understanding these secrets.
Books about the R programming language fall into different categories:
- Learning R
- Reference books for the professional R programmer
- Books about data science or visualization, using R to illustrate the concepts
Books are a great way to learn a new programming language. Code samples is another great tool to start learning R, especially if you already use a different programming language. You might also want to check our DSC articles about R: they also include cheat sheets. If you are unsure about learning R, read about R versus Python.
Books to learn R
- Learning R– Learn how to perform data analysis with the R language and software environment, even if you have little or no programming experience. With the tutorials in this hands-on guide, you’ll learn how to use the essential R tools you need to know to analyze data, including data types and programming concepts.
- R in a Nutshell– If you’re considering R for statistical computing and data visualization, this book provides a quick and practical guide to just about everything you can do with the open source R language and software environment. You’ll learn how to write R functions and use R packages to help you prepare, visualize, and analyze data. Author Joseph Adler illustrates each process with a wealth of examples from medicine, business, and sports.
- Introduction to Data Science with R– Learn practical skills for visualizing, transforming, and modeling data in R. This comprehensive video course shows you how to explore and understand data, as well as how to build linear and non-linear models in the R language and environment. It’s ideal whether you’re a non-programmer with no data science experience, or a data scientist switching to R from other software such as SAS or Excel.
1. An Introduction To Statistical Learning – With Applications in R
Authored by: Trevor Hastie and Rob Tibshirani, recognized Stanford professors and authors of “The Elements of Statistical Learning”
What you’ll learn: Implementation of statistical and machine learning techniques in R
This book will teach you what you need to know, without harassing you much about the math behind it all. Even if you’re a novice at machine learning and don’t know R, I’d highly recommend reading this book from cover to cover, to gain both, a theoretical and practical understanding of many important machine learning and statistical techniques
2. The Elements Of Data Analytical Thinking
Authored by: Jeff Leek
What you’ll learn: Methods of analyzing data
Why: Data analysis is a process of inspecting, cleaning, transforming and modeling data with the goal of finding useful information, suggesting conclusions and supporting decision-making. This book explores data analysis methods that sometimes fall through the cracks in traditional statistical methods.
The book is based on the authors’ blog posts, lecture materials, and tutorials on simply stats and Coursera.
3. R Programming For Data Science
Authored by: Roger D.Peng
What you’ll learn: The basics of R, for a non-programmer
Why: This is the ideal book for someone with no prior programming experience. It doesn’t talk about statistics or machine learning. It is solely dedicated to the fundamentals of R programming.
Below are some concepts you’ll familiarize yourself with, over the course of the book:
- How to install R on your computer
- Cleaning messy data sets
- Manage data frames with dplyr packages
- The coding standards for R
There is even a case study at the end of the book in which the author explains how to process and analyze a raw data set using R. The book is based on the famous data science specialization course on Coursera.
4.Exploratory Data Analysis With R
Authored by: Roger D.Peng
What you’ll learn: Data visualization
Why: Data visualization is a must-have skill for a data scientist, and this book will walk you through some crucial techniques to visualize data in R.
Topics discussed in this book include plotting systems in R, basic principles of constructing informative data graphics, making exploratory and analytical graphs, clustering methods and visualizing high dimensional data.
5.Learning RStudio For R Statistical Computing
Authored by: Mark P.J.van der Loo
What you’ll learn: Quickly and efficiently create and manage statistical analysis projects, import data, develop R scripts, and generate reports and graphics
Why: This book is different from the others in the list in the sense that it teaches you how to user on the popular IDE R Studio rather than on the standard R software. The book is aimed at R developers and analysts who wish to do R statistical development while taking advantage of RStudio functionality.