- Taschenbuch: 406 Seiten
- Verlag: John Wiley & Sons; Auflage: 1. Auflage (20. Juli 2012)
- Sprache: Englisch
- ISBN-10: 1119962846
- ISBN-13: 978-1119962847
- Größe und/oder Gewicht: 18,8 x 2,2 x 23,6 cm
- Durchschnittliche Kundenbewertung: 3 Kundenrezensionen
- Amazon Bestseller-Rang: Nr. 137.462 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)
- Komplettes Inhaltsverzeichnis ansehen
R For Dummies (Englisch) Taschenbuch – 20. Juli 2012
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Master the programming language of choice among statisticians and data analysts worldwide
Coming to grips with R can be tough, even for seasoned statisticians and data analysts. Enter R For Dummies, the quick, easy way to master all the R you'll ever need. Requiring no prior programming experience and packed with practical examples, easy, step-by-step exercises, and sample code, this extremely accessible guide is the ideal introduction to R for complete beginners. It also covers many concepts that intermediate-level programmers will find extremely useful.
* Master your R ABCs -- get up to speed in no time with the basics, from installing and configuring R to writing simple scripts and performing simultaneous calculations on many variables
* Put data in its place -- get to know your way around lists, data frames, and other R data structures while learning to interact with other programs, such as Microsoft Excel
* Make data dance to your tune -- learn how to reshape and manipulate data, merge data sets, split and combine data, perform calculations on vectors and arrays, and much more
* Visualize it -- learn to use R's powerful data visualization features to create beautiful and informative graphical presentations of your data
* Get statistical -- find out how to do simple statistical analysis, summarize your variables, and conduct classic statistical tests, such as t-tests
* Expand and customize R -- get the lowdown on how to find, install, and make the most of add-on packages created by the global R community for a wide variety of purposes
Open the book and find:
* Help downloading, installing, and configuring R
* Tips for getting data in and out of R
* Ways to use data frames and lists to organize data
* How to manipulate and process data
* Advice on fitting regression models and ANOVA
* Helpful hints for working with graphics
* How to code in R
* What R mailing lists and forums can do for you
* Use R for data analysis and processing
* Write functions and scripts for repeatable analysis
* Create high-quality charts and graphics
* Perform statistical analysis and build models
Über den Autor und weitere Mitwirkende
Andrie de Vries is a market research consultant specializing in surveys, statistical analysis, and strategy.
Joris Meys is a statistician and R programmer with the faculty of bio-engineering at the University of Ghent.
Note that this book won't teach you how to do statistics, and rather beats some subjects like flow control to death, but if you have never used R before and need a way in, this book will show you the way.
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As stated, my slow learning curve is in part due to the nature of R. R is not a software application (like Excel). It is a full-fledged computer program like C++, Python, etc... (A recent conversation with a colleague suggests that R is actually relatively simple and is a lot more like Python than C++ that is much more code intensive). R is also comparable with what we could call a hybrid (halfway in between a software application and a computer program) like SAS. In other words, any of those programs take a lot longer to learn. They don't have user friendly windows and menu driven commands. They instead rely on codes, syntax, etc... You have to graduate from being a software end user to becoming a computer coder. It is not an easy transition.
Additionally, the book as structured does not facilitate learning. Its emphasis on vector calculations is wasteful. Also, now that I have had some experience with R, the book omits plenty of key formatting to render .csv file correctly readable in R (omit $ sign; %; and ","; otherwise R does not read your original Excel formatted data correctly). Those tips should be the first ones mentioned regarding how to import an Excel file into R (pg. 211 in book). Instead, the author has omitted those key tips and I have wasted probably over 10 hours of trial-and-error and research in uncovering those. From this standpoint, I don't feel this book lives up to its "quick and easy" way to learn R advertisement at all (see back cover).
For my part, based on my firsthand experience I have to question whether I went about this the right way. At this stage, my 30 hour investment does not seem fruitful. As others have mentioned there is a ton of information on R available for free. Maybe a better way to learn R was not as I attempted having a somewhat in depth view of all the wonderful things that R can do from a rather poor book at that; but, instead focus on one specific thing you really want to do with R that you can't do with Excel (using abundant free information on any specific topic you are looking for). And, then develop proficiency into this one thing. Once you have expertise in this one thing add on by learning adjacent concepts to your first objective. Before you know it, you may have developed expertise in half a dozen things that you can't readily do in Excel.
Another strategy is to try another book. The following book seems intriguing Learn R in a Day.
Added in January 2015: after more experience with R and R books, with more perspective on the subject I feel this book is adequate despite the mentioned weaknesses. It can serve as a good reference on topics. That is especially true regarding the chapters on graphics. The latter are among the most powerful tools and more complex ones that R has. And, the author does a good job of describing them. In view of that, I have upgraded the book's rating from a 1 to a 3.
I'll start this review off by saying up-front that this book's approach to teaching R wasn't my preferred way of learning a programming language. Most of this book, from the start, explains the features/functions of R. Then, towards the back (from about chapter 12), it gets into task-related uses for R. Overall, their approach is to explain how all the "bits" of R work.
My preferred way of learning is to be given a scenario to be guided through. The author leads you through the scenario, introducing and explaining R functions along the way. By the time you have finished the scenario, you have learned a handful of functions and how they are used. However, this is not the way "R for Dummies" is structured.
So why do I like this book?
(1) The explanations are easy to understand
There was only one topic I didn't understand in the whole book (in case the authors read this review, the topic was "levels" and "labels"). This is about two paragraphs in the whole book I had difficulty with - which is pretty good!
The way the authors explain, and the examples they choose to illustrate their explanations, are excellent. Much better than the freely available introductory R guides on the internet.
(2) The functions and features the authors choose to describe in their book are exactly the most useful ones required in my day-to-day work.
This book has been my main learning resource for R. I'm still using it as my main reference source.
If I was to start learning R from scratch - given what I know now - I would start with a very basic scenario-based beginner's book (there are a few around), and then transition to "R for Dummies". The scenario-based book would give me a quicker jump-start on the style in which R works when carrying out work-oriented tasks. R for Dummies would then flesh out the details. I consider "R for Dummies" a must-have for continued R learning.
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