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Not for everyone, only good for starters. This book is basically 2-distinct books: The first 13-chapters are the basics of R. They are OK only if you are new to R. The book "R for everyone" has many positive reviews on Amazon.
Among advantages of the book are (1) it covers RStudio and ggplot2 in some details. Chapters are short and can be converted to lessons using one chapter one lesson principle.
The author used to teach a course at Columbia, which is a plus, but the standard of coverage of programming language in this book is very low as this is book mainly directed toward non professionals. Some online resources and one chapter for the book are at http://www.jaredlander.com/r-for-everyone/ (no code examples was posted by the author which is "big no no" signifying lack of respect for readers ):
The idea is to provide 80% of functionality by explaining only 20% of the language is great, but it's implementation if far from perfect.
It OK for very beginning level of study, when the person does not have a programming experience in any language, but even in this case there might be better books.
The author introduces way too many packages. Uses examples that are way too diverse and generally do not care about consistency from one chapter to another.
I do not recommend this book for people who already know other scripting language such as Perl. The R book by Michael J. Crawley is a better deal.
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JoeT on January 27, 2014To get the negatives out of the way: it's unfortunate that, having invested in appealing graphic design - the book looks just so much nicer than the spartan O'Reilly titles - Addison Wesley have not provided the author with similarly solid editorial support, resulting in a book that definitely feels rushed. There are typos, cosmetic blemishes (one regular annoyance is a table that's too wide for a page - one could fit it using a smaller font, but instead the table ends up split across twice as many rows), a couple of statistical blunders (on pp. 172 and 263), things that could have been left out, things that should have been included (oddly, the chapter on joins never mentions outer joins, and, in fact, does not explain what a join is) - and, finally, time and again, things that should have been explained better. I do not feel that "R for Everyone" is the best available introduction to R, and continue to endorse Robert Kabacoff's high-quality "R in Action" in that capacity.
Where "R for Everyone" differs from "R in Action" - and, coming to the positives, where it wins out - is in intermediate-R territory. One important example is coverage of "ggplot2". Whereas "R in Action" discusses the "old school" R graphics, "R for Everyone" goes with "ggplot2", becoming the second popular book (after Winston Chang's "R Graphics Cookbook") to discuss the package - and although its explanation of "ggplot2" syntax is sketchy, the samples found throughout the book do build into a useful "ggplot2" gallery that actually brought me over the fence.
"plyr" package, an important data-manipulation aid, is another example, and another "R in Action" no-show. So is "data.table". So is "knitr", used to produce reports. So is "rcpp", used to interface R and C++. So is R package-building. (You will notice that the topics become more advanced. These are introductions rather than substantial explorations, but awareness is a valuable thing).
In the book's second half, when discussion moves from R to statistics-with-R, the author continues to manage to find original material; statistical explanations may be brief - this is not a textbook - but examples, and pointers to useful R utilities, are much appreciated.
I own just one R book - literally, "The R Book", by Crawley - but "R for Everyone" will be joining it; this has got to be a compliment. Kudos to Jared Lander for writing an original, substantial, useful book
This was probably the hardest book to rate of any I have rated on Amazon.For what it's worth, I am an R user and I like to pick up books on R to see how other people do things. The fact that I was exposed to packages I have never used was a plus and definitely make the book worthwhile.
This book is basically 2-distinct books: The first 13-chapters are the basics of R. They are quite good and if you are new to R you will find them extremely useful.
Virtually all the remainder of the book is using R for various statistical techniques. This is where I had my problem. If you get this book with the assumption that you will learn statistics at the same time, then you will be disappointed. The problem is that while the book does tell you HOW to do the test, that's about it. There isn't much in terms of explaining what it is you did or how to interpret the results. I suppose if you look at it as a book to show you how to use the various R commands to run a t-test or an ANOVA, then that's OK, but I don't see value if you do something, get a value and not understand what it's for. But, if you are already statistically savvy, then this might not be an issue.
One thing I did not like though is the use of ggplot. Now, I fully appreciate that ggplot will in fact generate far better graphics than the core plot routines in R. No question. But, ggplot in itself is a book, and in many cases, I just cut-and-pasted the code into R to see what happens. There wasn't really a whole bunch of explanations as to why you were doing what you were doing. Given that this is more an intro book (given the initial chapters of R that gives me this impression), I would have considered using the core plot routines instead. More work and less attractive I know, but if your audience are people who are new to R, then why not stay with the core routines?
Paulo C. Rios Jr.on April 28, 2015
In spite of the good reviews, the reality is very different. This is a bad R book with many shortcomings that range from bad explanations of the basics (functions, data types) to no explanation at all when showing the use of R in data analysis.The book has a nice layout in color, but this is misleading. Long R output without proper formatting for a textbook is always displayed because the author wrote the book directly in the code as he himself states and printed it out as it is. And it feels like. Most of the text looks just like comments in a program code.
The treatment of functions is very poor (they are also very rarely used in the book) and the explanation of the different R data types lacks depth and is misguided.
Silly examples are used to show the basics as in printing the author's name. The later chapters get even worse, literally damaging all the more interesting parts, where the book leaves the very basics and moves on to data handling and then to advanced data analytics in R.
The part of the book that deals with data analytics is sincerely a bit of a tragedy. Rushed text with no clear or sometimes whatsoever explanations of what is actually being done, with just little text and lots of code output and charts taking most of the space. Ironically the book that is "for everyone" makes hard for "everyone" to understand anything that uses statistics, about 60% of the book!.
It is hard even for those trained on statistics or related "hard" sciences.
For example, In chapter 22, right in the beginning the author uses a value for the predicted number of clusters in the data under analysis. This value is taken out of the blue and only later it is shown how this value can be found using two methods. The first method doesn't bring any useful value (and you wonder why it is shown). The second method does bring a good value but it is not explained in the text how this method's results should be interpreted to determine this value. Apart from two rushed sentences that speak of a standard deviation being used, whatever this standard deviation is coming from as the author says nothing about the algorithm clusGap that he used for such. I did some research and found out that the author's LiveLesson video course, that follows the book almost page by page, does mention, albeit quickly, how to interpret the second method's result above. But not in his book… Unfortunately this video course also suffers from the same problems that the book does, as it is mostly a live reading of the book with the author typing the code.
In fact, almost anything related to data analytics is very poorly explained, if at all. Another example, out of many, is the section 20.3 on Generalized Adaptive Models. After preprocessing the raw data used for the analysis, a few charts are displayed (without much explanation of the code used for which) and then the data analysis code output is shown without any explanation. Two features of the data, CreditAmount and Age, are displayed in charts where they are smoothed, but there is no explanation about what for. And the analysis stops right there without any further explanation. What could be said in a few sentences is left out.
Most of the data analytics examples also show very poor performance, leading the user to think why data analysis is used if it performs so badly and, if it performs well, why the author didn't select any better example.
There are also many pedagogical errors, minor and major ones. I will just mention a few taken from chapter 12 as an example:
1) Many variables are created with the function assign in a loop but actually only two of them are used. What for? On top of it, the same loop is coded again later with just a different variable name.
2) The function merge is used with the same column names, although the author states that "the ability to specify different column names (..) is the most useful feature of merge" before doing so with the function join from the library plyr. Then you wonder what difference is being shown.
3) It gets worse. In a rather convoluted way to show how to merge different data frames, the author introduces two new features of R, eval and parse, just by passing and without any specific examples or further explanation. In this same convoluted example the author also uses the R function Reduce in the most complicated way with the dots, without first showing simple examples and what it is for. Only then later down in the text he goes on to explain what Reduce does but fails to mention that it can only be applied to binary functions. The text states that "Reduce can be a difficult function to grasp". If it is, it would deserve a better treatment, not as a side note, explained in an example that is related to something else (how to merge data frames). It should also have a full explanation of how it can be used.
R is a beautiful language that can be well explained. It is not hard to show its power in data analysis with short but clear explanations. It's regrettable that this book misses its stated goals so badly, when it could have done otherwise brilliantly, as its author seems capable to do a much better job. So I can't recommend this book.
There is actually a shortage of good R books in the market, but "R in Action" (second edition is coming), "The Art of R Programming" and "The R Book" are much superior choices.
Amazon Customer on December 5, 2014
Not for everyone, only good for starters
This book covers a lot of basic R materials. It consists of 24 short chapters with detailed appendices, making it a good reference for newbies and pragmatic users. No single book could possibly cover much of what's in R (4000+ packages) and this book is not an exception. As such, one mostly finds basic stuff here which rarely get deep enough. Many useful recent R libraries are missing.
If one already knows some statistics (and no R) the book may help with implementation, otherwise one shouldn't expect learning much stats from this book. The book's breadth is fine but imbalanced; too much emphasis on graphics, some obvious basics concepts are missing (pi, exp). Disappointingly, Chapter 8 "Writing R Functions" is totally useless. One doesn't need developing functions to print "Hello Jared", or to calculate StDev when R already does that. Function are needed to carry out muilti-level calculations usually as a part of algorithms. None of this is addressed in chapter 8. The author promises expanding upon this in later chapters, but that doesn't seem to happen either.
In summary, an absolute beginner may benefit from this book, but an advanced user would not. The price is currently ~ $27.00 which is reasonable compared to many R books with similar caliber and bulk.
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