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R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics

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With more than 200 practical recipes, this book helps you perform data analysis with R quickly and efficiently. The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and outpu With more than 200 practical recipes, this book helps you perform data analysis with R quickly and efficiently. The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression. Each recipe addresses a specific problem, with a discussion that explains the solution and offers insight into how it works. If you're a beginner, R Cookbook will help get you started. If you're an experienced data programmer, it will jog your memory and expand your horizons. You'll get the job done faster and learn more about R in the process. Create vectors, handle variables, and perform other basic functions Input and output data Tackle data structures such as matrices, lists, factors, and data frames Work with probability, probability distributions, and random variables Calculate statistics and confidence intervals, and perform statistical tests Create a variety of graphic displays Build statistical models with linear regressions and analysis of variance (ANOVA) Explore advanced statistical techniques, such as finding clusters in your data Wonderfully readable, R Cookbook serves not only as a solutions manual of sorts, but as a truly enjoyable way to explore the R language--one practical example at a time.--Jeffrey Ryan, software consultant and R package author


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With more than 200 practical recipes, this book helps you perform data analysis with R quickly and efficiently. The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and outpu With more than 200 practical recipes, this book helps you perform data analysis with R quickly and efficiently. The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression. Each recipe addresses a specific problem, with a discussion that explains the solution and offers insight into how it works. If you're a beginner, R Cookbook will help get you started. If you're an experienced data programmer, it will jog your memory and expand your horizons. You'll get the job done faster and learn more about R in the process. Create vectors, handle variables, and perform other basic functions Input and output data Tackle data structures such as matrices, lists, factors, and data frames Work with probability, probability distributions, and random variables Calculate statistics and confidence intervals, and perform statistical tests Create a variety of graphic displays Build statistical models with linear regressions and analysis of variance (ANOVA) Explore advanced statistical techniques, such as finding clusters in your data Wonderfully readable, R Cookbook serves not only as a solutions manual of sorts, but as a truly enjoyable way to explore the R language--one practical example at a time.--Jeffrey Ryan, software consultant and R package author

30 review for R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics

  1. 4 out of 5

    Louis

    A part of the cookbook series is expected to provide a multitude of examples of useful tasks. The R Cookbook does this, but also more. This provides more, teaching about R beyond what reference books and most tutorials. One weakness of R compared to other data analysis environments and programming languages is it's lack of coherence that comes from a central design. Instead it seems like a set of constructs, each designed differently. As an example, the multiple packages for graphic. Every user o A part of the cookbook series is expected to provide a multitude of examples of useful tasks. The R Cookbook does this, but also more. This provides more, teaching about R beyond what reference books and most tutorials. One weakness of R compared to other data analysis environments and programming languages is it's lack of coherence that comes from a central design. Instead it seems like a set of constructs, each designed differently. As an example, the multiple packages for graphic. Every user of R soon picks up idioms from tutorials or trial and error. Book authors use their favorites. But the effect is that it is hard to know what you do not know. And one does not realize the realm of the possible. The R Cookbook does this. In having multiple related recipes together what it provides are a number of closely related tasks, done in different ways. And using different idioms. And I have taken advantage of it, learning more ways of working with various data structures, the apply family of functions and other data transforms. This makes the R Cookbook even more valuable then the typical member of the O'Reilly Cookbook series. Well recommended. I receive a free electronic of this book as part of the O'Reilly Blogger program. More information on the book can be found at O'Reilly Press

  2. 4 out of 5

    Sara Dahaabović

    Amazing resource!

  3. 5 out of 5

    Arun Mahendrakar

    I agree that this is not a book for the beginners, but nevertheless it started with the very basics. I was impressed about it though it is from a Cookbook series. The approach in the book is: if this is your problem, here's the solution, followed by a discussion which provides great amount of detail about the concept or function. This format allows the book to be used as a reference guide as well as it will assist us to jump to a point directly. The book illustrates plenty of functions of R in th I agree that this is not a book for the beginners, but nevertheless it started with the very basics. I was impressed about it though it is from a Cookbook series. The approach in the book is: if this is your problem, here's the solution, followed by a discussion which provides great amount of detail about the concept or function. This format allows the book to be used as a reference guide as well as it will assist us to jump to a point directly. The book illustrates plenty of functions of R in this fashion. There are parts of the book that educate us about Statistics itself, but prior knowledge of some Statistics is a must if you want to make the best of this book. The author shows examples from a variety of libraries - MASS, Cars93, zoo, XML to name a few. Enthusiastic readers will explore the data sets in these libraries and become more hands-on. The chapter on Useful Tricks exposed many of the "helper" functions. These add to the convenience while working with R. The code samples for the book seemed incomplete when I downloaded them. But this worked for my benefit in the sense that I spent more time actually writing the code or creating the data myself - helped me get a little more comfortable around it's syntax. Blessing in disguise I guess.

  4. 5 out of 5

    Joshua Hruzik

    If you love learning by doing this is your book! R is the best statistical software package on the market (although it's free, so technically there is no market for it). Its functions outstrip those of SPSS and STATA by far. So naturally everyone doing statistical work should be familiar with R. The R Cookbook is designed for clear cut problem solving. You want something being done by R? This book will have a description how to do it and a detailed section of what you are actually doing. This boo If you love learning by doing this is your book! R is the best statistical software package on the market (although it's free, so technically there is no market for it). Its functions outstrip those of SPSS and STATA by far. So naturally everyone doing statistical work should be familiar with R. The R Cookbook is designed for clear cut problem solving. You want something being done by R? This book will have a description how to do it and a detailed section of what you are actually doing. This book will help you master the steep learning curve of R and within a short time you'll never want to go back to SPSS or STATA. A lot of the statistical techniques are explained in the detailed section, so even if you are relatively new to statistics you'll learn the most basic aspects by reading this book (I would strongly recommend a full stat intro) If you are doing more sophisticated analysis you'd still have to check the internet for additional resources. This book covers the basics, but lacks some of the statistical tests (e.g. White's test). Once you know how to handle R, implementing additional statistical procedures shouldn't be a problem.

  5. 4 out of 5

    Darryl Pendergrass

    Great R Reference Book The book contains numerous recipes for addressing specific topics related to R. I found several solutions to topics that I have faced while using R. I found the coverage of solutions strike a good balance for beginning and advanced users. The author directs the reader to other books where necessary to learn more about specific topics.

  6. 5 out of 5

    Yanick Champoux

    The recipes are short and start to the point. It however has the problem that not all basics and details of the syntax are explained. For example, at some point the notation "plot( x ~ y)" shows up without any indication of what the tilde does there. Good intermediate book, and would complement, but likely not replace, an introduction book. The recipes are short and start to the point. It however has the problem that not all basics and details of the syntax are explained. For example, at some point the notation "plot( x ~ y)" shows up without any indication of what the tilde does there. Good intermediate book, and would complement, but likely not replace, an introduction book.

  7. 5 out of 5

    Ohud Saud

    I love this book. Looking for your next great read, consider reading it. simple, clear and practical.

  8. 5 out of 5

    Daniel Morgan

    This was a great guide to figuring out how to do basic statistical functions in R.

  9. 4 out of 5

    Muthukumaran Panchaksaram

    This book provides all the basics of R programming. I also use it as a encyclopedia/dictionary to refer back any R functions or methods quickly whenever needed.

  10. 5 out of 5

    Maged M.

    learning by example, learning by practical recipes.

  11. 5 out of 5

    Francis McGuire

    Examples are a little dated

  12. 5 out of 5

    Ngaafare Ricardo

    loving this so far

  13. 4 out of 5

    Richard

    This is an excellent book on R. If you don't know anything about R, this isn't the book to start with. However, it will be really useful after you have learned some really basic concepts about R (basically, one week after you start learning R!). The book consists of user-oriented tasks. The reader looks up the task they are interested in performing from the contents pages at the front of the book. So the book is designed as a reference book. The strength of this book are: ** the range and number This is an excellent book on R. If you don't know anything about R, this isn't the book to start with. However, it will be really useful after you have learned some really basic concepts about R (basically, one week after you start learning R!). The book consists of user-oriented tasks. The reader looks up the task they are interested in performing from the contents pages at the front of the book. So the book is designed as a reference book. The strength of this book are: ** the range and number of tasks described (they include some really basic beginner's stuff right through to specific statistical analyses.) ** the explanations are easy to understand ** the explanations are full. By this I mean the reader who has never performed that task before can pick up everything they need to know to carry out that task themselves. For example, tips and illustrative examples are included. This book has saved me a lot of time and head-scratching! However, it's not designed to be the only R book you'll ever need. It's a fantastic reference meant for beginners to intermediate R users. And you will learn a lot from it!

  14. 5 out of 5

    Luís Gouveia

    Como a maior parte dos livros da O'Reilly, este possui muitos exemplos e é bastante orientado para a prática e realização em computador das suas propostas de trabalho. Constitui uma boa fonte para aprofundar o conhecimento desta linguagem de programação e programação de análise de dados / estatística, cada vez mais popular. Possui uma página de recursos na Web que facilita a sua aprendizagem: http://www.cookbook-r.com/ Como a maior parte dos livros da O'Reilly, este possui muitos exemplos e é bastante orientado para a prática e realização em computador das suas propostas de trabalho. Constitui uma boa fonte para aprofundar o conhecimento desta linguagem de programação e programação de análise de dados / estatística, cada vez mais popular. Possui uma página de recursos na Web que facilita a sua aprendizagem: http://www.cookbook-r.com/

  15. 4 out of 5

    Glenn

    The R programming language is well suited for exploring correlation in medium size data sets. If you already understand the null hypothesis and know which algorithms are appropriate to use for statistical variance testing, then this book will really boost your productivity with R. If you don't have much understanding of statistics and what purpose it serves, then this is not the first book that you should turn to. The R programming language is well suited for exploring correlation in medium size data sets. If you already understand the null hypothesis and know which algorithms are appropriate to use for statistical variance testing, then this book will really boost your productivity with R. If you don't have much understanding of statistics and what purpose it serves, then this is not the first book that you should turn to.

  16. 4 out of 5

    Paul Boal

    As someone who isn't a practicing statistician I was blown away by how accessible and informative this cookbook was. I expected examples to be inaccessible, but the author educated me on both R and applied statistics at the same time. As someone who isn't a practicing statistician I was blown away by how accessible and informative this cookbook was. I expected examples to be inaccessible, but the author educated me on both R and applied statistics at the same time.

  17. 4 out of 5

    Wouter

    Nice recipe reference for introductory statistics measures/tests.

  18. 5 out of 5

    Vladimir Chupakhin

    It is my Top-1 recommended book for people starting to study R. Concise and with very useful examples.

  19. 5 out of 5

    Sweemeng Ng

    Good reference for learning R surprisingly comprehensive

  20. 4 out of 5

    Michael Bond

    Seems to cover just the right topics for a book of its size. To get more info on graphics or prorgamming, you may need additional books, but this is the ideal cookbook.

  21. 5 out of 5

    Kier O'Neil

    Useless unless you need specific formulas for checking certain formats like driver's licence numbers per state. Useless unless you need specific formulas for checking certain formats like driver's licence numbers per state.

  22. 5 out of 5

    Karl

    Much better than "R in a Nutshell", even given what needs they're supposed to serve. Read this first if you're learning R (and already know some statistics). Much better than "R in a Nutshell", even given what needs they're supposed to serve. Read this first if you're learning R (and already know some statistics).

  23. 5 out of 5

    Zerthimon21

  24. 5 out of 5

    Paul Rosania

  25. 4 out of 5

    Martin Rosa

  26. 5 out of 5

    Stockfish

  27. 4 out of 5

    Scott J Pearson

    I picked up this book with the intention of learning intermediate R. I was past the novice stage of learning the language, but I was still short of learning Advanced R. This book gave me the confidence to read R code more quickly and to understand more nuance in this (fun) language. This book is written by a quant (Wall Street data analyst) who has Masters degrees in both statistics and computer science. I find his statistics section interesting and most helpful. His visualization section is date I picked up this book with the intention of learning intermediate R. I was past the novice stage of learning the language, but I was still short of learning Advanced R. This book gave me the confidence to read R code more quickly and to understand more nuance in this (fun) language. This book is written by a quant (Wall Street data analyst) who has Masters degrees in both statistics and computer science. I find his statistics section interesting and most helpful. His visualization section is dated as it should use ggplot instead of R's native plotting techniques. He analyzes several helpful methods; figuring out those methods constitutes the learning part of the book. The short script (this is a computer cookbook after all) were helpful to extend my knowledge and agility with the language. The statistics section consists of a plethora of helpful analytical techniques to get what you want out of R. The information in this section is unique to me and as such new/useful. It tells me what techniques to use for certain types of data (e.g., normal vs. non-normal). Short of a statistics textbook, that's all you can ask for from a computer script cookbook. So this book served its purpose well. I would not classify it as essential R reading, however. There are other texts which are more important. Some of the scripts are obvious, but this book provided good reading while I was eating lunch for a couple of weeks.

  28. 4 out of 5

    Matthew Racine

  29. 4 out of 5

    Ransala

  30. 5 out of 5

    Donald Benjamin Start

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