Using R for Data Management, Statistical Analysis, and Graphics (Anglais) Relié – 2 août 2010
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Description du produit
Présentation de l'éditeur
Quick and Easy Access to Key Elements of Documentation
Includes worked examples across a wide variety of applications, tasks, and graphics
Using R for Data Management, Statistical Analysis, and Graphics presents an easy way to learn how to perform an analytical task in R, without having to navigate through the extensive, idiosyncratic, and sometimes unwieldy software documentation and vast number of add-on packages. Organized by short, clear descriptive entries, the book covers many common tasks, such as data management, descriptive summaries, inferential procedures, regression analysis, multivariate methods, and the creation of graphics.
Through the extensive indexing, cross-referencing, and worked examples in this text, users can directly find and implement the material they need. The text includes convenient indices organized by topic and R syntax. Demonstrating the R code in action and facilitating exploration, the authors present example analyses that employ a single data set from the HELP study. They also provide several case studies of more complex applications. Data sets and code are available for download on the book’s website.
Helping to improve your analytical skills, this book lucidly summarizes the aspects of R most often used by statistical analysts. New users of R will find the simple approach easy to understand while more sophisticated users will appreciate the invaluable source of task-oriented information.
Biographie de l'auteur
Nicholas J. Horton is an associate professor in the Department of Mathematics and Statistics at Smith College in Northampton, Massachusetts. His research interests include longitudinal regression models and missing data methods, with applications in psychiatric epidemiology and substance abuse research.
Ken Kleinman is an associate professor in the Department of Population Medicine at Harvard Medical School in Boston, Massachusetts. His research deals with clustered data analysis, surveillance, and epidemiological applications in projects ranging from vaccine and bioterrorism surveillance to observational epidemiology to individual-, practice-, and community-randomized interventions.
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Commentaires client les plus utiles sur Amazon.com
This particular text served me well in both learning to use R and also supplementing the instruction I was receiving in my stats course. The book is well organized, clearly written, and combines statistics knowledge with software instruction so that you can better understand when and how it is appropriate to use particular functions. The book covers topics from installation and add on libraries (which you will need unless you want to write your own code for common mathematical functions, etc.) to data management and manipulation and analysis, to plotting and printing, but you can see everything it covers in the table of contents on the product page.
It's a heavy, hard back text that, again, is written very clearly and is easy to understand.
It's just unfortunate that open source is looked down upon in research or this book would be getting a great deal more use from me. I love R and the book made it a breeze to learn to use the language. The language has a great deal of functionality though the user interface is not as intuitive as the interface in something like SPSS. You really have to program everything a you would in SAS.
Some additional things I like about this book (and R) are that you can use them with a Mac and the book explains how to do some things on a Mac that make using R *really* simple, like dragging and dropping your data files into the program to attach them for analysis. (I don't have a PC and didn't pay much attention to whether or not this feature exists for PCs also.)
If you need to learn R, buy this book.
The only downside of this nicely written book is in its brevity. It may not be an exaggeration to say that packing too much in less than 300 pages would amount to overload. But the flip side to that is that the uninitiated is likely to be left hanging with the numerous skeletal codes introducing many packages to fit different shades of models. That said, Using R for Data Management, Statistical Analysis, and Graphics provides a competent stepping stone into using the open-source language to both professionals and students.
If you want to benefit from the beauty of applied statistics implemented using R, this is the book for you. It will pave the learning route so systematically so much so that your destination to other advanced books with suffer no knowledge gaps.