Originally submitted at O'Reilly
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...
Useful R for Statistical Courses
By Eder Andrés Ávila Niño from Paipa, Colombia on 3/30/2012
Pros: Easy to understand, Well-written, Concise, Helpful examples
Best Uses: Student, Intermediate, Novice
Describe Yourself: Developer
This is a book about how to use the R programming language to solve several statistical problems like combinations, probabilities, frequencies, quantiles, means, correlations, regressions and ANOVA analysis, and as a plus, time series analysis. This cookbook is perfect to students and professors looking for a computing tool that helps them to understand and teach statistical courses, respectively. That's because many times it is important to solve complex exercises that shows the several applications that a statistical topic, like the normal distribution, has in surveys, engineering, manufacturing and commerce, which implies performing several activities related to data retrieval, storage, processing, analysis and visualization.
This cookbook explains the how-to of such activities providing a catalog of tasks to accomplish each of these, for example, how to read and write CVS files, prepare the data contained in those files, generate random samples, calculate the probability of X, test for normality, perform linear regressions and plot the histogram. Each task is described following this structure: Problem, Solution, Discussion and See Also, being the Discussion section the valuable resource of the R Cookbook, because this section explains the implications of using the given solution together with some tips and tricks. Those implications, tips and tricks makes the difference between the R Cookbook and a raw Internet search, so students and professors can learn about the rationale of a solving a statistical problem with R, not just the syntax of variables and functions.
However, it is important to clarify that the R Cookbook is not a book for learning the essence of statistics; instead the R Cookbook is a suggested companion resource for the course main textbook. The R Cookbook will help the explanation and understanding of the core topics, exercises and applications, and simulate different problem scenarios.
Finally and personally, I'd like to see a second edition, volume or update to include how to solve operations research problems using R, if possible. For instance, simplex method, duality, transport and network problems, and inventory and queuing systems are the most common ones in operations research.
Note: This review is in exchange of the O'Reilly Bloggers Review Program (oreilly.com/bloggers).
This cookbook explains the how-to of such activities providing a catalog of tasks to accomplish each of these, for example, how to read and write CVS files, prepare the data contained in those files, generate random samples, calculate the probability of X, test for normality, perform linear regressions and plot the histogram. Each task is described following this structure: Problem, Solution, Discussion and See Also, being the Discussion section the valuable resource of the R Cookbook, because this section explains the implications of using the given solution together with some tips and tricks. Those implications, tips and tricks makes the difference between the R Cookbook and a raw Internet search, so students and professors can learn about the rationale of a solving a statistical problem with R, not just the syntax of variables and functions.
However, it is important to clarify that the R Cookbook is not a book for learning the essence of statistics; instead the R Cookbook is a suggested companion resource for the course main textbook. The R Cookbook will help the explanation and understanding of the core topics, exercises and applications, and simulate different problem scenarios.
Finally and personally, I'd like to see a second edition, volume or update to include how to solve operations research problems using R, if possible. For instance, simplex method, duality, transport and network problems, and inventory and queuing systems are the most common ones in operations research.
Note: This review is in exchange of the O'Reilly Bloggers Review Program (oreilly.com/bloggers).
(legalese)
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