Discovering Statistics Using R

Honestly such a helpful book. Of course we are in the era of AI but it doesn’t explain everything well. This book explains the stats, the code, and why you’re doing xyz. It’s a great book and puts it all in simple terms. Here I am in my post doc and I still refer to it!

This book fills a niche that very much needed to be filled. It is both a review of basic statistical concepts and directions as to how to perform the corresponding analyses/tests in R. It's light on theory of course, but supplying proofs and in-depth descriptions isn't what this book is about. Although I'm a bit rusty, I've had a great deal of graduate level statistics, none of which emphasized application. This book is an excellent guide as to how to actually apply statistics. Extremely welcome is its emphasis on underlying assumptions. In my theoretical statistics classes, the Central Limit Theorem was the answer to almost all questions involving assumptions. As the authors point out, even with a sample size that's sufficiently large, the CLT does not always guarantee normality. I also like that the authors give complete steps in each chapter. Thus the entire coding to accomplish something is present and you don't have to go looking for how to accomplish some preliminary step before you can do the current procedure. At the end of each chapter is a list of what R packages and functions have been used. The authors do include some sophomoric humor, maybe to make this more palatable to undergraduates, but this doesn't become annoying. Finally the authors appear to like cats, a mark in their favor. One word of warning, Field may not provide a context for something—a test, a transformation, etc. Readers are advised to look at the references he provides at the ends of the chapters. For instance, his later presentations on bootstrapping will make a lot more sense if you’ve read the paper by Wright, London, & Field he suggests. This can be found online. When presenting the Fisher transformation of Pearson’s r to a z-score in Sect. 6.3.3, he doesn’t tell you that it should be used only in tests of null hypotheses rho = some constant not = to 0 or to 1; where .3 < |rho| < 1, r’s sampling distribution will tend to be skewed, making the Fisher transformation necessary. Not knowing this context, given in Chen and Popovich, one of the references at the end of Chapter 6, could cause a reader to use the Fisher transformation inappropriately.

The writing style is highly accessible, fun, varied, and rich in detail. Simply a superb way to get going quickly in R AND in statistics, but even if you have considerable stat under you belt, as I do, it provides an excellent review of concepts, and their implementation in R. I am pleased in every way with this massive survey of the field. With this in hand I know I can go off in whatever direction of specialization I require. There is simply no question in my mind that this the best starter book for both stat and R (and learning the two together, these days, just makes sense). It turns out to be far better than I expected. Loaded with extra information, plenty of fine-grained detail, well worked-out examples, and unexpected humor, this makes its subject just about as accessible as can be done. A great value!

I must start by saying that this is an excellent book and one of the most approachable that I've found for teaching students new to R. This could be a five-star book if the author would consider a few things, many of which would shorten the length of the book considerably or as an even better alternative the author could place even more code and data examples in the book with the saved space: 1) Ditch the discussion and elements of using R commander. This is a hold-over GUI need from the author's last book on SPSS. Learning R well means getting into the code and the command line and staying there. GUI based add -on's like R commander just get in the way and you cannot even run all of the available stats in it anyway. Users are buying this book to learn the code, so teach them the code only! 2) Dispense with the witty banter. The author clearly has a sense of humor and likes it, but there are needles pages in the book, essentially the start of each chapter, that are just silly asides that serve no purpose and take up a LOT of space. The humorous examples for many of the datasets are great and a welcome change to a stuffy stats book but the excessive chatter and jokes end up taking up more space that could be used for more practical examples or hints for the new R user. I also found these witty asides distracting once I was knee-deep in the methods of running a test. 3) Dispense with the repetitive instructions and inefficient methods. Do we really need to see the code for loading a new data set over and over? Does that data set really need to be a .dat file? It would be better if the author would stick with conventional excel files (.csv) that most people are commonly going to use. Provide an early chapter for getting data into R and move on already! If you are coming at this topic from the life sciences (biological, ecological etc.) the author tends to favor planned contrasts a lot more than post hoc multiple comparisons which are much more common in these sciences. I would strongly recommend that you buy this book, but find a good bio stats book to read in tandem so you can get your head around more biological examples.

日本にはこのような本がないので、大変参考になります。

Perfecto si eres principiante en el mundo de R y la estadística. Un libro muy útil y ameno para leer. Lo recomiendo.

I highly recommend this book for beginners. Although the explanations are a little lengthy they are very clear and pertinent. This book is aimed at readers with a moderate proficiency in maths (for example matrix calculus is avoided). However, it explains the history, backgound and main insights behind statistics reasonning very well. As a result, from an intuitive viewpoint, this book would also be helpful background reading to more mathematically minded readers such as Engineering and Physics students. My hearty congratulations to the authors for having produced such a helpful and well thought out teaching book.

It is a very clear introduction to the use of R for conducting the main statistical techniques. It appears very useful for psychologist, since the book covers a number of techniques used in psychological research. A strength of the book is that it covers a number of techniques that are little known, also among specialists. For example, the robust linear models and multilevel analysis are explained very clearly. The book is written in a very slight style, so the reader is not frustrated by the difficulties of some arguments. A very good manual!

I started reading this book so far I found it very helpful and the language is easy to understand that creates clarity to understand the concepts