List of best books to learn R

Best Books to Learn R

R is probably every data scientist’s preferred programming language
(besides Python and SAS) to build prototypes, visualize data, or run analyses on data sets. Many libraries, applications and techniques exist to explore data in R programming language. So here is our recommendation for the best Book to learn R and become a master of the technology.

a. A Handbook of programming with R by Garrett Grolemund

It is best suited for people new to R. This book teaches you to learn
how to load data, assemble and disassemble data objects, navigate R’s
environment system, write your own functions, and use all of R’s
programming tools. Here you will understand how to write functions &
loops in R, rather than just juggling with packages. The book language
is simple to understand and examples can be reproduced easily.

b. The Art of R programming by Norman Matloff

This book teaches how to do software development with R, from basic
types and data structures to advanced topics. No statistical knowledge
is required, and your programming skills can range from hobbyist to pro.

c. An Introduction To Statistical Learning With Applications in R by Trevor Hastie and Rob Tibshirani

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.

d. Learning RStudio For R Statistical Computing by Mark P.J.van der Loo

This book is different from the others in the list in the sense that
it teaches you how to use R on the popular IDE RStudio rather than on
the standard R software. The book is for R developers and analysts who
want to do R statistical development using RStudio functionality. So you
can Quickly and efficiently create and manage statistical analysis
projects, import data, develop R scripts, and generate reports and
graphics.

e. Practical Data Science with R by Nina Zumel & John Mount

It focuses on data science methods and their applications in real
world. It’s different in itself. None of the books listed above, talks
about real world challenges in model building, model deployment, but it
does. The author focusses on establishing a connection between ML and
its impact on real world activities. It’s a must read for freshers who
are yet to enter analytics industry.

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