Learning a programming language can sometimes feel a little like climbing a never-ending mountain with always seemingly more to learn and always a lot to remember.
Let’s get one thing (that took me a long time to discover myself) out in the open straight away. Learning to code in Python and R happens by doing not reading.
If you’re starting out from scratch, learning to program Python and R code is all about the practical experience, tackling problems head-on, getting your hands dirty, and generally tweaking and hacking your code until you’ve designed an awesome solution to a seemingly challenging problem.
It’s due to this, that I always advocate carrying out your own coding projects whenever possible over following generic “hello world” style tutorials if you want to make actual progress in learning to code in any programming language and become a true #codeboss.
With that being said, there are some truly excellent coding books for Python and R which range from complete beginners guides and tutorial books, to more advanced reference manuals and dictionaries and if you’re looking to get serious in learning either language, you’re going to want to get some of the best reading material on your shelf regardless.
While learning to code (in any language) is near impossible from books alone, they still play a seriously important role for helping start out or brush up on generic skills and learning programming syntax – what I call the ‘foundations’.
These kinds of books also serve as a handy reference point for grabbing when you either a.) forget something, or b.) have an idea that something is doable, but just don’t know how to get it done.
The following are my go-to choices and recommendations for the best books for coding in Python and R and which I’ve found (and continue to find) invaluable in my own programming journey.
Python Coding Books for Beginners
If you’re just starting out on the path to coding greatness, Python is one of the best programming languages to learn.
Getting started with Python is much like getting started with any other programming language – practical experience, experimentation, and regular ‘real-world’ practice is the key to success.
If you’re an absolute beginner and have never written a line of code in your life, the great news is, it’s super-easy to get going and one of the best ways to do so is to adopt a dual approach of what I call practical project learning, and book learning.
For the first of these, Elle Knows Machines has got your back. For the second, the following are some of the best books for learning Python from scratch.
Python Crash Course
This introduction to Python for beginners by Eric Matthews is one of my top recommendations for learning to code in Python as it not only introduces you to the basic elements of the language, but then actually puts this into practice by focusing on writing programs through specific projects later in the book.
As mentioned earlier, this is ultimately the only way in which you’re actually going to learn to code in any programming language and Python Crash Course recognizes this fact and provides a good stepping stone for getting comfortable with your own projects later on.
The projects in this book are simple but effective if you’re dipping your toe into learning Python for the first time and cover a nice range of practical exercises from making a simple game using the pygame package to working with data visualization and statistical analysis using matplotlib.
If you’re looking for a good Python book for absolute beginners, you could do a lot worse than Python Crash Course and I found it very accessible when starting out in the language myself.
Learn Python 3 the Hard Way
Learning Python “the hard way” is actually a really useful approach to learning to code in the language as it confronts you with the kinds of problems and solutions you’ll encounter when programming in real life.
Through a collection of over 50 exercises, Learn Python 3 the Hard Way requires you to type in code yourself, identify and correct the purposefully placed mistakes in the code, and then see how your now fixed code actually runs.
Combining exercises that are both short and instructive, Learn Python 3 the Hard Way is designed in a way that encourages you to think carefully about the logic behind the code you’ve written down to work out how it’s supposed to work and what needs changing in order to make it work.
While this certainly is the hard way of learning to program in Python, it’s also one of the best (and most realistic in real world terms) if you want to learn to both write and understand Python code.
Head First Python
The Head First series of books utilize a nice approach to teaching technical concepts and programming basics and the latest edition of their beginners guide to Python is a good example of this.
Breaking the topic down into smaller, individual sections, Head First Python focuses on an overarching project that it has you build upon as you learn each subsequent skill in Python and this is a nice approach if you’re looking to see things laid out sequentially.
This book also favors a more visual approach to learning rather than some of the usual reams of text which can risk you getting ‘code-blindness’ or overwhelming you from the offset and if you’re a visual learner, then this could be a good choice for you.
R Coding Books for Beginners
When it comes to data science and statistical analysis, the R programming language is widely considered to be the coding basis of choice by many in academia as well as in the industry.
Alongside the well-established Python, R is quickly gaining traction as an increasingly popular choice for data science applications due to the power of the language when it comes to analysing large amounts of data and making sense of it in a meaningful way.
R is considered by some to be slightly trickier to get a handle on than Python, but if you’re looking to learn R as your eventual primary goal anyway, there’s little reason not to just dive in to learning R right off the bat.
With all of this being said, you’re undoubtedly going to want a playlist of resources to hand that you can use to get familiar with the language as well as referencing if and when you inevitably get stuck along the way.
As I’ve been through all of the trials and tribulations of learning this powerful language from scratch, Elle Knows Machines will obviously be your natural starting point as you begin your quest to R greatness (after all, that’s why I created the site!), but as part of your code-creating inventory, I’d highly recommend picking up some of the books that I found the most useful on my own journey learning R.
These are the books I recommend to anyone looking to get started in R.
R for Data Science
O’Reilly are one of the go-to names for coding professionals and beginners alike thanks to their technical reference guides which have been a mainstay of those in the industry for years.
When it comes to R, one of the very best resources for those getting underway in the language is R for Data Science by data scientists Hadley Wickham and Garrett Grolemund (Wickham is also the head scientist at RStudio and adjunct professor of statistics at Stanford, Rice, and the University of Auckland).
If your trying to break into data science and plan on using R as your coding weapon of choice, then few books out there will equip you for the task quite as well as this one.
R for Data Science covers the foundations of transforming your data into knowledge and insight and lays out the essential elements of everything from getting started with the R programming language to using RStudio and the tidyverse collection of packages for streamlining your R coding experience.
One of the really awesome things about this book is that the authors have also provided a digital version of the book online (find it here) which is great if you want to follow the contents on your screen. With that being said however, the sheer usefulness of this volume makes it probably the essential beginners R reference for your bookshelf, so if you’re serious about getting ahead in the language, it’s almost certainly going to be an investment you’ll want to make for future programming posterity.
