Customer Reviews

A Beginner's Guide to R (Use R!)

5 star:		(4)
4 star:		(3)
3 star:		(1)
2 star:		(1)
1 star:		(0)

 

 

If you want to start (from scratch) to learn to use R for data manipulation and graphics get this book. The authors say, in the forward, that it was designed to support a 3 day (8 hour per day) introduction to the R language without teaching statistics and it does a fabulous job. It covers R data types and data manipulation nearly as well as my favorite book on the topic Data Manipulation with R (Use R) but it also does a good job covering basic plots (all the typical one or two variable plots) out to intermediate graphics (making a lattice with lots of pictures) (a good next step for graphics is Data Analysis and Graphics Using R: An Example-based Approach (Cambridge Series in Statistical and Probabilistic Mathematics)) and also gives a very accessible introduction to applying functions to sets of variables and the R version of looping.

The organization and flow are excellent. Each chapter has many exercises and as you read their are pointers saying to go try the corresponding problems. There is no answer key for the problems but they are so closely tied to the material that should not be a major issue.

The index is okay but not good because there are functions used in the body of the book that are not indexed and it is missing an overall summary index/table that covers all the functions.

If the publisher will clean up the indexing, so that it is easier to return and find the information in the book, it is a solid 5 star product. Another possible complaint is that it is very expensive for a 200 page paperback but the quality of the writing offsets for the bloated price.

As the previous poster noted, if you are new to R, and especially if you are new to programming, this is the book for you. R is a very powerful statistical language with a steep learning curve if you attempt to master it on your own. For those who are used to black-box statistical programs, R can be intimidating since the user must enter syntax rather than relying on point-and-click programs.

The authors' experience in teaching R to all levels of users shines throughout this informative and friendly handbook. They show how to perform routine statistical tasks in a step-by-step format, describing common mistakes and remedies which alone are worth the price of the book.

I strongly advise that newcomers to R take an afternoon to work systematically through the book rather than treating it like a set of recipes for programming to pick through as needed. This approach will build confidence and competence, and will save considerable time when work is due. I wish I had had this book when I first encountered R, and even after years of use, I found more efficient methods in this book than ones I had picked up along the way. Excellent.

Firstly, I confess a possible lack of objectivity in this review as I have co-authored a couple of other books with the lead author of this book.

For anyone new to R, an occasional user of R, or indeed if you teach R, there seems to a recurring set of questions that need answered to get R to do what you want it to do. Many simple things seem to take a frustratingly large number of hours or even days to resolve. Not because they are difficult to do, just difficult to find out how to do.

Based on many years experience of using and teaching R, the authors have pulled together the answers to the types of questions that most beginners find themselves asking. There is no attempt at teaching statistics in this book, it is strictly a compilation of "how to do things in R". But with the added bonus of passing on many tips on the best ways of doing things, and a useful chapter of common errors that new, and sometimes not so new, users of R often make.

For the beginner, and occasional user who forgets how to do things in between R sessions, this book will be invaluable, and almost certainly save you hours of searching. Additionally, simply leafing through the book is likely to draw your attention to ways of using R, or ways of avoiding problems with R, that you are unlikely to come across when leafing through more comprehensive and wider ranging books on R.

If you are an R beginner and value your blood pressure and your time, this book is well worth the money.

This book should be required for everyone learning statistics with R assignments. Book focuses on basic formating and commands and provides a basic platform with which to proceed to stats assignments. What is most important, it is written on the assumption you know little or nothing about R and how to use it. Each chapter is concise but provides one with an understanding of what is being achieved by the basic commands and why the commands produce the needed result.

I like this book because it covers the basics of R without trying to teach statistics at the same time. If you work through all the examples you will have a solid beginning for programming with R and learn a lot about object-oriented programming (this was my first encounter with objects, my last gig was FORTRAN many years ago). My only criticism: I did not learn enough about outputting data into files.

I've been wanting to learn R for a while now, but teaching and meetings have gotten in the way. I've been using this book for about a month and spend an hour per day. I do the exercises and also type in all of the R code in the book to see what works (though you can download the code from the book's website). I have found the book very straightforward. Certainly R takes some getting used to and ultimately I'll be using it for statistical applications, but, as noted by the authors, this book is written to introduce the reader to R, not to statistics with R. The explanations for what you are doing (and why) are very clear. The authors have experience teaching a well-constructed workshop for learning R, and it shows in the flow of the book. The text is clearly written and the steps are easy to follow. Learning builds from chapter to chapter. I would suggest, however, that though the authors indicate you can skip chapter 4 on simple functions with the first read, enough of what comes later hints at chapter 4 that I found it made more sense to go back and read chapter 4 rather than wonder what was going on. I have not found the same need to read Chapter 6 on the first pass, though I expect it will help in later understanding how to run statistical tests with R.

I am an ecologist, and the authors use strictly ecological examples, which also makes the book very accessible. I've also been working on a data set of my own and it is VERY satisfying to take what I've learned in this book and apply it directly to my own data. At my university, we have limited access to major stats packages (Minitab - yes, Systat - no) and the draw to R, a free and very adaptable system, is strong.

Aside from R, another aspect of this book that I really like is that the authors use lots of instances of practical "exploratory data analysis" such as examination of data for outliers. These are steps that are easy to forget about and I like the fresh reminder. I'm also intrigued by the authors' hints at what they like and don't like about particular approaches that I've purchased their related book, Analyzing Ecological Data (hasn't arrived yet).

In sum - this is a great book for teaching yourself the basics of R.

I am desperately trying to learn R. But there just aren't any good self-teachinig intros to the language. I've tried several but either end up disgusted that they're too disconnected or are just too simplistic and more interested in being cute or clever rather than seriously teaching the language. This one is better; but is still a bit obtuse. Many of the examples don't work or they don't give results that match what is in the book. That is very disappointing and makes learning the language exceptionally difficult. On the other hand, the order and style of material presentation is pretty good and I've learned more than from any other similar introduction.

I have modified my initial review to a much more favorable one in light of the author's response. I have not worked through this book yet, but at this point I am trying to have an open mind about it. But here is my initial complaint below (modified somewhat), which I am leaving because I think it would be helpful to other potential readers:

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(Original comments)
Like many other reviewers, I have looked all over for a book that teaches R completely from scratch. It is kind of bizarre how this seeming easy request is so hard to fulfill. It must the fact that R is open source. There are many "tutorials" on the web for R, but they seem to assume a fairly high level of proficiency with it. I think the book could have been priced somewhat less given its relatively small size. Okay, so maybe it would be worth it if I could learn R. But right at the beginning, on P. 13, the authors highly recommend using TINN as an text editor to R, from which one can send code directly to R. Then they write "We refer to the online manuals of Tinn-R for their use with R." They don't say anything more about Tinn-R. I downloaded Tinn-R, but it is not at all intuitive. I could not get it to send commands to R. So I went back to the web to look for tutorials on how to use Tinn-R. But there is less information about Tinn-R than there is on R. I feel that the authors could have devoted at least a few pages to Tinn-R.

This book is frustrating to say the least. It just brushes certain topics, leaving out valuable information - that could have easily been included. For example the topic of passing a dataframe to a sapply or lapply is touched but the why, how, when,... etc are never explained. When reading this or many other sections in this book you cannot help but feel there is a lot of important information regarding these topics you simply still don't know about. Even for a Beginners Guide it leaves out far too much information to be really useful. Some parts might be quite good but the holes in the information this book presents are simply too glaringly huge - if you're looking for a good primer for R, better look somewhere else.