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A First Course in Statistical Programming with R
This new color edition of Braun and Murdoch's bestselling textbook integrates use of the RStudio platform and adds discussion of newer graphics systems, extensive exploration of Markov chain Monte Carlo, expert advice on common error messages, motivating applications of matrix decompositions, and numerous new examples and exercises. This is the only introduction needed to start programming in R, the computing standard for analyzing data. Co-written by an R core team member and an established R author, this book comes with real R code that complies with the standards of the language. Unlike other introductory books on the R system, this book emphasizes programming, including the principles that apply to most computing languages, and techniques used to develop more complex projects. Solutions, datasets, and any errata are available from the book's website. The many examples, all from real applications, make it particularly useful for anyone working in practical data analysis.
230 pages, Paperback
First published January 28, 2007
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Displaying 1 - 2 of 2 reviews
April 24, 2019
Parts of the book provide a solid introduction to R and R Studio, especially regarding elements of the software. The authors seamlessly move from one aspect to the next.
Nonetheless, once things get into the nitty gritty of actually employing statistics using R packages and R command language, glossing over rapidly becomes the norm. I would have expected a book titled “A First Course in Statistical Programming with R” to start readers off with gradual easement into the GLM. Certainly, there are parts of the GLM brought into middle chapters, yet they are so abruptly examined before the authors delve into more intermediate-advanced statistical methods.
Before you know it, later chapters are diving into simulation models and complex algebra that utilize very specific (and arguably very esoteric) packages designed for narrowly focused fields of research; not exactly a keen idea for an introductory level book of such accessible length. In parallel, I was coincidentally proceeding through an online R tutorial designed by biomedical professionals – they did a much better job sticking to basics and avoiding coloring the lessons with their own proclivities.
Some parts of chapters unexpectedly dive into some really good tips on programming R scripts, managing R Studio routines, and reducing unnecessary code complexity. Yet those parts didn’t flow with the other sections surrounding it. Therefore, only the first few chapters flow well together, with bits & pieces of following chapters being of relevance to the beginner.
Nonetheless, once things get into the nitty gritty of actually employing statistics using R packages and R command language, glossing over rapidly becomes the norm. I would have expected a book titled “A First Course in Statistical Programming with R” to start readers off with gradual easement into the GLM. Certainly, there are parts of the GLM brought into middle chapters, yet they are so abruptly examined before the authors delve into more intermediate-advanced statistical methods.
Before you know it, later chapters are diving into simulation models and complex algebra that utilize very specific (and arguably very esoteric) packages designed for narrowly focused fields of research; not exactly a keen idea for an introductory level book of such accessible length. In parallel, I was coincidentally proceeding through an online R tutorial designed by biomedical professionals – they did a much better job sticking to basics and avoiding coloring the lessons with their own proclivities.
Some parts of chapters unexpectedly dive into some really good tips on programming R scripts, managing R Studio routines, and reducing unnecessary code complexity. Yet those parts didn’t flow with the other sections surrounding it. Therefore, only the first few chapters flow well together, with bits & pieces of following chapters being of relevance to the beginner.
February 28, 2022
R is a package that allows the user to use pre-existing packages to perform some mathematical operations. It is very easy to install and use with a user window that is very similar in appearance to that of Maple and Mathematica.
However, even the most intuitive of packages still presents some points of confusion, but with this book, you can be up and running, doing very advanced work with R in a matter of minutes. Using a series of code examples, the authors take you through many of the basic capabilities of the package. All that is needed to follow the examples is a basic understanding of control constructs such as the if-then, loops and functions as well as knowledge of the underlying mathematics.
Areas of mathematics that are supported by the basic R package include:
*) Statistical graphics
*) Computational linear algebra
*) Monte Carlo simulations using several different random number distributions such as the Poisson distribution
*) Numerical optimization
*) Linear programming
Windows versions also allow you to set the locale so that languages other than English can be used.
While it is rare to see in a book, denseness does not have to be difficult, and this book is an example of that. The authors are terse and effective as they clearly demonstrate how to use the R package. If you lack the budget for the purchase of a commercial computational mathematics package, then R with this textbook provides a very low cost alternative for many classes.
However, even the most intuitive of packages still presents some points of confusion, but with this book, you can be up and running, doing very advanced work with R in a matter of minutes. Using a series of code examples, the authors take you through many of the basic capabilities of the package. All that is needed to follow the examples is a basic understanding of control constructs such as the if-then, loops and functions as well as knowledge of the underlying mathematics.
Areas of mathematics that are supported by the basic R package include:
*) Statistical graphics
*) Computational linear algebra
*) Monte Carlo simulations using several different random number distributions such as the Poisson distribution
*) Numerical optimization
*) Linear programming
Windows versions also allow you to set the locale so that languages other than English can be used.
While it is rare to see in a book, denseness does not have to be difficult, and this book is an example of that. The authors are terse and effective as they clearly demonstrate how to use the R package. If you lack the budget for the purchase of a commercial computational mathematics package, then R with this textbook provides a very low cost alternative for many classes.
Displaying 1 - 2 of 2 reviews