The Book of R: A First Course in Programming and Statistics

I almost never write Amazon reviews, but feel compelled to in the case of this gem of a textbook. Quick about me: -solid general computer skills (e.g. Excel), but NO programming background -took statistics in high school, barely passed calculus in college -was inspired to learn R after seeing a colleague use it for data analysis -have been disappointed by R texts that I've encountered thus far, finding them either too advanced or poorly organized/written Why I am giving this book 5 stars: -it is structured very logically, starting with the basics of the language before moving onto programming, statistics, and more advanced concepts -it is accessible to a wide audience, yet goes into the necessary level of detail -it is well written, in an easy, clear voice -an appropriate balance of text vs code examples and graphics -it has effective exercises at logical points to ensure the reader not only understands, but can execute the concepts learned -it is aesthetically very nice -- the spine doesn't crack, the book doesn't warp over time, the font and text size are very readable As a sidenote, I ordered R for Dummies as well. I didn't give it an especially thorough glance, but quickly flipped through it and decided to return it because I knew on first glance that The Book of R was far superior.

I've got a very good stats background from grad school and a long tech industry career in research. I've used SPSS for decades and made heavy use of SPSS syntax, so I delayed moving to R because I could do so much in SPSS so easily. But with some down time and having never gotten over the hump with online tutorials--including datacamp--I bought this book to work through R skills ground up. What a great decision. I can't recommend this book highly enough. I was transfixed with working through each example in the text and each practical exercise at the section breaks despite it being 400+ pages before I encountered a statistical concept/explanation I wasn't already very familiar with. I was re-learning, re-thinking, refreshing the way I go about working with data. And I was wishing I had done this a long time ago. Take your time, do it right, learn as you go, take notes for yourself on the side, save code snippets for future-you to leverage in your work, and enjoy the learning. I worked through this as I would a textbook despite being long out of school. I suppose it might make a good reference without doing that, but it will be a much better reference/reminder for me now that I have thoroughly internalized the way of working with R it provided me.

Was great help as I am trying to self teach myself R.

This wasn't the book for my needs, but it is still an excellent book on R. I'm not a super user of statistical software and I was hoping to use R for checking for normalized data and maybe running a standard deviation. And since tableau can pass scripts through to R, I thought it was time to learn it. I was hoping for a quick "go-to" book to look up just the information I needed, but this is a full, step by step course to learning everything about R. It is well written and has a great combination of learning and practice sessions. If I was inclined to teach myself everything about R, this would be the way to go. The author has a fun way of explaining statistics and program language that makes it a bit more fun to read and he brings some creative names into examples. I didn't realize it was such a hefty book, it's the size of a full text book. So even though this wasn't exactly what I was looking for, it will likely be exactly what 90% of readers are looking for. If you want to learn all the ins and outs of R, this is the book to purchase.

Great book! Very helpful in learning R! Would recommend to others due to its coding examples. It also has a unique approach for learners.

I come back to this book monthly. This book makes learning programming in R fun. The book also made me more comfortable with Statistics and Probability (Part 3) as well as Statistical Testing and Modeling (Part 4). I know I will be a much better Computational Statistician and Quantitative Analyst because of this book.

Love This book! It’s on my nightstand mom

This isn't a bad book, just not what I was looking for. I was looking for a book that easily walked me thru the syntax and process of R programming as a R novice. This seemed to be, for me, not so much a primer on data importation, cleaning up and transforming your data or production of quick & visually compelling gplot2 graphs and table making, rather it was more of a this is how to program for regression and statistical testing. It was tedious for me to extract the specific information I wanted and needed. I wanted to know the time, and this book told me how to build a clock.

Good for getting a strong hold on R

This book is wonderful, It explains everything clearly and has many exercises with solutions. The only drawback is the color of the book. It is in black and white which is very inconvenient when working with plots. When we have 3 plots overlay, different colors would have made a significant difference. I would rather pay more for another book in color. Anyway, it is a very good book.

I think this is by far the best book to start with R, beginning from the very basics of the code and ending up with how to plot a "sprinkled doughnut" in R. Even though it has about 800 pages in total, it is totally worth going through.

No doubt, the book deserves my five stars. If you’re a Data Science in the making, the first part (programming) might cover more than you ever need while the second part (statistics) is a great start. In other words, if you’re into Data Science with R, this is one of the few must-have books. My personal must have: # 1 - R for Data Science # 2 – Statistical Inference via Data Science (inference based on the tidy concept) # 3 – The Book of R If you’re a Data Scientist in R, you’re really in luck. Unlike using Python, in R there is one book that trumps them all: “R for Data Science”. On the other hand, I feel like all machine learning books in R suck, especially material on classification. Using Python, there are fantastic machine learning books such as “Python Machine Learning” or, of course, “Hands on Machine Learning …”. I fear the subtitle “a first course in programming and statistics” might be misleading. While the book starts more or less at zero knowledge, it covers a lot of ground. As others mentioned, the binding for this thick book is amazing. If you ever considered R vs. Python, I believe “The Book of R” makes your decision easy. Want to do statistics/EDA/visualizations? R is your choice. Want to do Deep Learning? Python is your choice. If you love R, you most likely have a deep appreciation and love for statistics. In this case, you will most likely love “The Book of R.” Without books like “The Book of R” and the people from R-Studio, we all would do Data Science in Python.

Excelent book.