R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics (O'reilly Cookbooks)

With clear and concise explanations covering most of the important areas, this is a brilliant book. It can not only save you plenty of time, but also help you to gain new insights as you learn from comprehensive but short and clear discussions of so many different topics. They are all explained in a such a way that you can directly access anyone of them without having to read the rest of the book. Some highlights: - The chapter 2 on "Some Basics" is simple but mandatory for any beginner. - Chapter 5 on "Data Structures" is one of the best along with Chapter 12 on "Useful Tricks". Chapter 5 covers data structures in a clearer and better way than many other books on R. - Chapter 6 on "Data Transformations" shows the beauty and power of R in short but very good examples. - Statistics with R is covered in Chapter 8 on "Probability" and chapter 9 on "General Statistics", whereas Chapter 11 covers "Linear Regression and ANOVA". Here again the very clear prose and simple examples show these applications of R in a much better way than books on R and "advanced analytics" like "R for Everyone" (a book that is a big disappointment). - There is a good coverage of graphics in Chapter 10, whereas the author still found space to treat time series analysis in Chapter 14 with more brilliant examples. What is missing? A coverage of RStudio, a great and free development environment for R. There is also a lack of any examples covering statistical learning data analysis other than linear regression which actually belongs more to standard statistical analysis. But this is little in comparison to what it offers. This is a great book written in a masterful way not only in knowing the subject matter but also in knowing how to present it and teach it.

This is an excellent book. I read it from cover to cover. I did not try out the examples however. I found the writing to be very good and the book, although a cookbook, actually provides a great way to get an in depth overview of R. The R packages facilitate the use of the book examples by providing test data in the packages. The book is organized well, especially the file I/O and data structures, as well as the statistics sections. I have worked with statistics at various levels over the years and taken courses but I wanted to brush up on concepts and applications, and this book was really good for that. I think it is also a decent book for learning programming although one would start using the 1-based paradigm instead of 0-based for indexing and that is kind of nonstandard and used only for math software. But a beginner could learn quite a bit by just playing around with the examples. The explanations of the statistics concepts was particularly good. The author is very precise with his language and even repetitive (which I appreciated) about the rigorous interpretation of results. The R software thankfully provides a well designed, open source alternative to Matlab and this cookbook (with its references) is an ideal place to start learning for practical use at work or on projects. I thoroughly recommend it. I found very few typos which for me is one of many quality indicators. The author also writes in an entertaining style making the book fun to read - which is a challenge considering the subject matter could be considered dry (by some).

The R statistical analysis tool has much to recommend it to students, researchers, and commercial data analysts. It contains a powerful set of analysis and graphics commands and a constantly-growing number of add-on packages produced by its large user community. R and most of its add-ons are also available for free under an open source license. It is a realistic and readily available rival to major commercial tools such as SAS and SPSS. As with everything, there is a downside. R is accessed through a command line interface, has an overwhelming number of commands, and its syntax is difficult to learn and remember. R users, especially novices, will find this cookbook of tremendous help. It contains many brief sections, each of which lists example R code for a specific analysis task. Tasks supported range from downloading and installing R through more complex data analysis. The sections I found most useful were: - Finding Relevant Functions and Packages - Performing Matrix Operations - Editing a Data Frame - Generating Reproducible Random Numbers - Plotting Multiple Data Sets - Predicting a Binary-Valued Variable (Logistic Regression) Paul Teetor has produced a well-organized and useful reference book. The sections are straightforward and the example R code is no more complex than necessary. The explanations in each sections are instructive, yet concise. Numerous cross-links between sections allow readers to understand related tasks when writing more complex code. There are even a few sections on common R error messages and useful programming tricks. I recommend this book to anyone working with R who already has some background in data analysis with one or more other software tools. Note: The book comes with an offer from the published to purchase upgrades as new versions are released. This seems like a good idea, but I have no experience with this from O'Reilly.

Ein gutes Buch, setzt gute Englischkenntnisse voraus, schade, dass es dieses Buch nicht in deutsch gibt, da sind uns die USA halt immer ein Stück voraus., Preis ist angemessen, ist für Programmierer mit tiefer gehenden Ambitionen.

By far the best R book around. Excellent book for both absolute beginners looking to hit the ground running and advanced users looking to expand their set of tools with immediate results. If you're interested in quantitative finance, the time-series section (chapter 14) alone is worth a lot more than the price of the book. The 'recipe' based approach of the book truly sets it apart.

This is a concise and effective overview of the R programming basic aspects. A must have for quick solving issues.

Es un buen libro tanto tanto para iniciarse con el programa R como para profundizar en dicho lenguaje. Enseña con ejemplos como resolver problemas habituales que puedan surgir al analizar datos, como hacer gráficos, todo tipo de análisis estadísticos, modificar y transformar set de datos, etc

Used during my MSc. Found it very helpful - some of the websites I've looked for help with before are aimed at people who really know that they are doing with R, whereas this was at the right level for me. I found the sections explaining the results of GLMs particularly useful. I only wish I'd bought it sooner - t would have been so useful!