Free Learning R Ebooks Online
Learn how to perform data analysis with the R language and software environment, even if you have little or no programming experience. With the tutorials in this hands-on guide, you’ll learn how to use the essential R tools you need to know to analyze data, including data types and programming concepts.The second half of Learning R shows you real data analysis in action by covering everything from importing data to publishing your results. Each chapter in the book includes a quiz on what you’ve learned, and concludes with exercises, most of which involve writing R code.Write a simple R program, and discover what the language can doUse data types such as vectors, arrays, lists, data frames, and stringsExecute code conditionally or repeatedly with branches and loopsApply R add-on packages, and package your own work for othersLearn how to clean data you import from a variety of sourcesUnderstand data through visualization and summary statisticsUse statistical models to pass quantitative judgments about data and make predictionsLearn what to do when things go wrong while writing data analysis code
Paperback: 400 pages
Publisher: O'Reilly Media; 1 edition (September 26, 2013)
Language: English
ISBN-10: 1449357105
ISBN-13: 978-1449357108
Product Dimensions: 7 x 0.8 x 9.2 inches
Shipping Weight: 1.8 pounds (View shipping rates and policies)
Average Customer Review: 3.8 out of 5 stars See all reviews (13 customer reviews)
Best Sellers Rank: #111,242 in Books (See Top 100 in Books) #24 in Books > Computers & Technology > Computer Science > Bioinformatics #64 in Books > Computers & Technology > Databases & Big Data > Data Mining #65 in Books > Computers & Technology > Databases & Big Data > Data Modeling & Design
If you want to start learning R, there several things you may want to consider. There are two kind of skills you want to cultivate: programming skills and actual data analysis ones. In principle, you can learn techniques alone, but then your creativity as a data analyst will be limited and probably you will end up writing poor code. Alternatively focusing only on coding may make you a good programmer, but it will be hard to get started on putting your skill into practice. Any book should strike a tradeoff in where to stand between training you in these two topics. Cotton's book try its best in this and does a pretty good job. The first part of the book, covering the intricacies of the language is the one I found most useful. I has all sort of good advise and explanations on the data structures and functions you can use. It is appropriately applied - not just about computation and programming, but actually links how they are applied in the actual data analysis. In this sense, this was the most original and interesting part of the book. The second part of the book, covering data analysis techniques was more conventional but still good. As such, there are perhaps better books if you are interested on any of the two sides ("machine learning for hackers" is very good to learn how to apply the techniques and seeing them in action; "Introduction to statistical learning" is a bit more theoretical; Advanced R or The Art of R Computing are unbeatable about teaching the language, although a bit dray).The approach of Cotton is really instructive. He is friendly, he write well in a easygoing fashion and the book is full of useful tips that helped me to understand how the language merge with the technique.
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