The R Cookbook by Paul Teetor (O'Reilly Media) aims and succeeds in introducing programmers to the technical aspects of the R language. R is a programming language designed for Statistical work, therefore it might not share too much ground with other typical languages like Python or C. The R Cookbook gives a tour to programmers to this wonderful language, from the very screen output beginnings, through ploting, to the creation of statistical models and Time Series.
The author does a great job exposing the differences and similarities of R to other languages. For example, the author, explains how in R the single brackets applied to a vector (myvector[1]) mean "give me a vector of one item from myvector", he then explains that the correct way to get a single item is myvector[1]. Comparisons to other languages like SQL are dispersed through the chapters.
Mr. Teetor provides a throughout explanation of other aspects of R that might not be common in other computer languages. For example, in Chapter 5, he explains in detail and with clear examples what the Recycling Rule is. I doubt I would have been able to grasp such concepts so easily without an explanation like his.
This book however, is not a book about Statistics. The author goes ankle deep in Statistics Theory, just deep enough to have a general understanding of the examples. That is fine because he even states at the beginning of the book that this is not a book on Statistics. There are other books that deal with the theory, some of them are referenced in the book as a pointer to further reading. I recommend reading an introductory Statistical analysis book such as Data Science for Business before reading the cookbook.
This book fits very nicely as a step between an introductory book to Statistics and a book for Regression Modeling. I recommend this book for those programmers that have some basic notion of Statistical modeling and want to learn how to implement those concepts in R. This is exactly the place I was when I started reading the R Cookbook.
The author does a great job exposing the differences and similarities of R to other languages. For example, the author, explains how in R the single brackets applied to a vector (myvector[1]) mean "give me a vector of one item from myvector", he then explains that the correct way to get a single item is myvector[1]. Comparisons to other languages like SQL are dispersed through the chapters.
Mr. Teetor provides a throughout explanation of other aspects of R that might not be common in other computer languages. For example, in Chapter 5, he explains in detail and with clear examples what the Recycling Rule is. I doubt I would have been able to grasp such concepts so easily without an explanation like his.
This book however, is not a book about Statistics. The author goes ankle deep in Statistics Theory, just deep enough to have a general understanding of the examples. That is fine because he even states at the beginning of the book that this is not a book on Statistics. There are other books that deal with the theory, some of them are referenced in the book as a pointer to further reading. I recommend reading an introductory Statistical analysis book such as Data Science for Business before reading the cookbook.
This book fits very nicely as a step between an introductory book to Statistics and a book for Regression Modeling. I recommend this book for those programmers that have some basic notion of Statistical modeling and want to learn how to implement those concepts in R. This is exactly the place I was when I started reading the R Cookbook.
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