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Robert A. Muenchen, Joseph M. Hilbe, "R for Stata Users"
Springer | 2010 | ISBN: 1441913173 | 524 pages | PDF | 3,8 MB
Stata is the most flexible and extensible data analysis package available from a commercial vendor. R is a similarly flexible free and open source package for data analysis, with over 3,000 add-on packages available. This book shows you how to extend the power of Stata through the use of R. It introduces R using Stata terminology with which you are already familiar. It steps through more than 30 programs written in both languages, comparing and contrasting the two packages' different approaches. When finished, you will be able to use R in conjunction with Stata, or separately, to import data, manage and transform it, create publication quality graphics, and perform basic statistical analyses.
A glossary defines over 50 R terms using Stata jargon and again using more formal R terminology. The table of contents and index allow you to find equivalent R functions by looking up Stata commands and vice versa. The example programs and practice datasets for both R and Stata are available for download.
Who This Book Is For
This book is, of course, for people who already know Stata. It may also be useful to R users wishing to learn Stata. However, we explain none of the Stata programs, only the R ones and how the packages differ, so it is not ideal
for that purpose. This book is based on R for SAS and SPSS Users However, there is quite a bit of additional material covered here, and, of course, the comparative coverage is completely different.
Who This Book Is Not For
We make no effort to teach statistics or graphics. Although we briefly state the goal and assumptions of each analysis, we do not cover their formulas or derivations. We have more than enough to discuss without tackling those
topics too. This is also not a book about writing R functions, it is about using the thousands that already exist. We will write only a few very short functions. If you want to learn more about writing functions, we recommend
John Chamber’s Software for Data Analysis: Programming with R. However, if you know Stata, reading this book should ease your transition to more complex books like that.
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