R for Data Analysis (In Easy Steps)

Author: Mike McGrath
Publisher: Easy Steps
Date: March 2018
Pages: 192
ISBN: 978-1840787955
Print: 1840787953
Audience: Would-be R programmers
Rating: 5
Reviewer: Mike James
R is a good language for data analysis, but so many books try to teach you the analysis rather than the R.

When a language is targeted at a particular use the problem for any book is how much do you cover the use and how much the language. In the case of R it is fairly unreasonable to expect a book to teach you the basics of R and all of the data analysis techniques you might was to use via it. If you know some data analysis then showing how these are implemented in R is a reasonable compromise, but it won't suit every reader.

This particular book is a very simple approach to using the R language and using it to do simple things - that often turn out to be harder than you might expect. This book explains in detail how to do those simple things. It is worth pointing out that it is in full color and remarkable value for money. It is organized, like all Easy Steps books, into short chapters divided into even shorter steps, complete with icons for helpful comments at the side of the page. There are also lots of screen dumps.


There are ten chapters totalling 186 pages. Chapter 1 is a brief explanation of why you might want to use R and how to install it. It makes heavy use of RStudio, which is by far the best way to use R. Chapter 2 is where the real work starts and we find out about using variables, data types and simple data structures. To show that it is all worth learning you get to plot a simple graph. Most of the examples involve creating charts of one sort or another which is a good choice. Everyone understands charts and you can find out about R without getting lost in the details of an Anova or regression. Chapters 3 and 4 move on to using expressions and conditions to manipulate data.



Chapters 6 and 7 deal with the real workhorses of data analysis - the matrix and more importantly the data frame. After these two you more or less know enough to start using R for real. Chapter 9 is called story telling with data and its about creating charts but it goes beyond the basics to explain how to make the chart look good. Chapter 10 continues in this vein to work towards a graphic that is presentable to a public. 

If you are know another programming language you will most likely find this approach a little slow but if you don't know R at all it is an easy way into the language. I repeat - this is a fairly basic introduction to R and it doesn't reach many of the features and facilities of the language. You can learn these either on the job or with another book..

It is also important to realize that this is not about statistics or modelling. You don't get any information on linear models, descriptive statistics, contingency tables, etc.  The most it does is show you how to draw custom charts which is a still a skill well worth having. The book is an easy read and easy on the eye without going in for the excesses of the "Head First" or "Dummy's" type books.

Within its limits, this is a very good book and you should ignore any reviews that say otherwise simply because they have the level and intent of the book wrong. At the price you can take a risk, even if you aren't 100% sure that it is for you.


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Essential C# 12 (Pearson)

Author: Mark Michaelis
Publisher: Addison-Wesley
Date: December 3, 2023
Pages: 1232
ISBN: 978-0138219512
Print: 0138219516
Kindle: B0CLKY8GNV
Audience: C# developers
Rating: 5
Reviewer: Mike James
The latest edition of a highly recommended book that combines reference and tutorial material.

Deep Learning (No Starch Press)

Author: Andrew Glassner
Publisher: No Starch Press
Date: July 2021
Pages: 750
ISBN: 978-1718500723
Print: 1718500726
Kindle: ‎ B085BVWXNS
Audience: Developers interested in deep learning
Rating: Mike James
Reviewer: 5
A book on deep learning wtihout an equation in sight?

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Last Updated ( Tuesday, 01 January 2019 )