R Through Excel
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From the reviews: “The book R Through Excel …offers a good entry for those just beginning with R through the familiar Microsoft Excel. …Will help those using Microsoft Excel on computers in MS Windows environments become more familiar with two programs designed to work with R. …All of the functions and many of the topics discussed… can be applied for independent R installations on Linux, Unix, or Apple computers. R is an intimidating but powerful program that assumes an intimate knowledge of data formats and terminology not familiar to many language testers. R Through Excel is a highly recommended first step into that program.” (Shiken: JALT Testing& Evaluation SIG Newsletter ) “Students, researchers, and others who wish to use R … . This book is essentially a manual for the RExcel software. … Most commonly a page consists of one or more screenshots showing how to use RExcel. The whole book is reproduced in color, on glossy paper. … Readers are guided through the menu system … to see how to carry out common statistical procedures. … For anyone wishing to learn RExcel this book would be a useful purchase.” (David J. Scott, International Statistical Review, Vol. 78 (2), 2010) “R Through Excel offers a concise introduction to statistical analysis for those with little prior experience in statistical software. The text provides a nontechnical introduction to the R programming language and the presentation is helpful for those who are averse to syntax commands. … an excellent manual to have on the shelf for anyone that is interested in integrating R and Excel. … For those in academia who teach introductory statistics and want to use R, this text provides a gentle manner for doing so.” (Philip Okoth, The American Statistician, Vol. 65 (4), November, 2011)
Product Description
In this book, the authors build on RExcel, a free add-in for Excel that can be downloaded from the R distribution network. RExcel seamlessly integrates the entire set of R's statistical and graphical methods into Excel, allowing students to focus on statistical methods and concepts and minimizing the distraction of learning a new programming language.
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Most Helpful Customer Reviews
0 of 1 people found the following review helpful
Format:Kindle Edition|Amazon Verified Purchase
Disappointed in the book in Kindle form. While the Kindle edtion of R Cookbook reads easily, this has too many diagrams and figures which prove difficult to read in Kindle format.
The contents are OKish but I expected more about programming in VBA and using macros instead of the rather simplistic treatment of basic statistical procedures.
The contents are OKish but I expected more about programming in VBA and using macros instead of the rather simplistic treatment of basic statistical procedures.
0 of 1 people found the following review helpful
Format:Paperback
This book is an excellent account of R through Excel. It is heavily biased towards the statistical analysis side, which is good if you need a refreshers coursein stats. I woul have liked to see some more information on R through VBA. There are many screenshot examples which makes the book easy to follow.
Most Helpful Customer Reviews on Amazon.com (beta)
Amazon.com:
3 reviews
27 of 27 people found the following review helpful
Good book and software but not much that is new
22 Aug 2009
Format:Paperback|Amazon Verified Purchase
Skip this paragraph if you know about R and Excel.... R is a powerful programming language that is useful for generating scientific graphics (both simple and extremely complex) and for statistical work. Unfortunately it has a steep learning curve and many say the help files are not particularly helpful for novices. Excel has a user friendly system for entering data and doing basic graphics but has relatively very limited tools for statistics or complex scientific graphics. Combining the strengths of the two is the goal of this book and the free software that goes with it.
With the tools described in this book the user can point and click their way to common analyses and graphics inside of Excel without having to learn to write code in the R programming language. Both the software and book are good but not great because they do not add much to the existing tools for R.
Years ago, John Fox wrote a point-and-click code-generating add-on package for R called R Commander that revolutionized the usability of R. Inside of the R programming environment you can download the Rcmdr package and type library(Rcmdr) and get practically all the same functionality as the tools provided here. What the authors of this book do is bring the functionality of Rcmdr into a Excel as an add-on to the Excel 2003 menus or 2007 ribbon. The implementation is surprisingly smooth (including adding nice right-click menu items) and bug free.
The book itself is mostly nicely rendered color pictures. Think of it as a very well annotated PowerPoint presentation. You will be able to quickly page through it and it is well indexed. The less than 10 page appendix which explains how to install the R packages and required services (or how to install from scratch) is probably the most useful part of the book. The authors do not focus much on the "behind the scenes" strengths of their work which allow you to recalculate and pass information into and out of R "on the fly." However, they do include a few worksheets that demonstrate the ability to pass information into R and return graphics effortlessly. Think of this as the ability to add sliders and push buttons to Excel and have instantly updated high quality graphics. Unfortunately, they only give one example where they use there new RApply function to return a calculated value from R into Excel. There clearly is a lot of functionality but the book does not explain it.
If you are just starting with R this book will probably be a HUGE help to you because it saves you from memorizing a lot of code and it will help you learn how to write code by showing you the commands that were used to generate analyses and graphics. However, if you have experience with R and Rcmdr you probably want to save your money.
With the tools described in this book the user can point and click their way to common analyses and graphics inside of Excel without having to learn to write code in the R programming language. Both the software and book are good but not great because they do not add much to the existing tools for R.
Years ago, John Fox wrote a point-and-click code-generating add-on package for R called R Commander that revolutionized the usability of R. Inside of the R programming environment you can download the Rcmdr package and type library(Rcmdr) and get practically all the same functionality as the tools provided here. What the authors of this book do is bring the functionality of Rcmdr into a Excel as an add-on to the Excel 2003 menus or 2007 ribbon. The implementation is surprisingly smooth (including adding nice right-click menu items) and bug free.
The book itself is mostly nicely rendered color pictures. Think of it as a very well annotated PowerPoint presentation. You will be able to quickly page through it and it is well indexed. The less than 10 page appendix which explains how to install the R packages and required services (or how to install from scratch) is probably the most useful part of the book. The authors do not focus much on the "behind the scenes" strengths of their work which allow you to recalculate and pass information into and out of R "on the fly." However, they do include a few worksheets that demonstrate the ability to pass information into R and return graphics effortlessly. Think of this as the ability to add sliders and push buttons to Excel and have instantly updated high quality graphics. Unfortunately, they only give one example where they use there new RApply function to return a calculated value from R into Excel. There clearly is a lot of functionality but the book does not explain it.
If you are just starting with R this book will probably be a HUGE help to you because it saves you from memorizing a lot of code and it will help you learn how to write code by showing you the commands that were used to generate analyses and graphics. However, if you have experience with R and Rcmdr you probably want to save your money.
19 of 20 people found the following review helpful
not intended to add to the R library of procedures
25 Aug 2009
Format:Paperback
Most people are familiar with Microsoft Excel and many have used macros to add functional capabilities. Peter Bruce's XL Miner is one fine example. R is a free software tool used mostly by statisticians for their research and is particularly popular in academia. It's origins are with the Bell Laboratories or AT&T Lucent (I am not sure what they call themselves since the divestiture). S was similarly a free package initiated by AT&T and is very similar to R.
However it sees limited use in industry particularly the pharmaceutical industry. The reason is that most applied statistician use SAS, SPSS or STATA for their analysis. In drugs and devices SAS is basically the only option.
I believe the authors have produced this product to broaden the user community for R. This would benefit applied statisticians and other users of statistical and graphical methods. SPlus developed a strong commercial market simply by putting a front end on S. These authors are doing the same thing with R. I met them recently at the 2009 Joint Statistical Meetings in Washington DC and I get a sense that they are not looking for a big commercial venture. My colleagues at GSK showed great interest in this and apparently had experience with the product before the book was published.
The other reviewer provides a good description of the book's content and is correct in assessing it as a huge help to R novices. But I think he/she misses the boat when criticizing it for not adding new procedures to the R libraries. That is not at all what the authors had in mind and it does not diminish what they have accomplished.
I expect this tool will get a lot of use especially for statisticians like myself who are interested in being able to use the capabilities of R and do not have the time or perhaps the interest to learn R.
However it sees limited use in industry particularly the pharmaceutical industry. The reason is that most applied statistician use SAS, SPSS or STATA for their analysis. In drugs and devices SAS is basically the only option.
I believe the authors have produced this product to broaden the user community for R. This would benefit applied statisticians and other users of statistical and graphical methods. SPlus developed a strong commercial market simply by putting a front end on S. These authors are doing the same thing with R. I met them recently at the 2009 Joint Statistical Meetings in Washington DC and I get a sense that they are not looking for a big commercial venture. My colleagues at GSK showed great interest in this and apparently had experience with the product before the book was published.
The other reviewer provides a good description of the book's content and is correct in assessing it as a huge help to R novices. But I think he/she misses the boat when criticizing it for not adding new procedures to the R libraries. That is not at all what the authors had in mind and it does not diminish what they have accomplished.
I expect this tool will get a lot of use especially for statisticians like myself who are interested in being able to use the capabilities of R and do not have the time or perhaps the interest to learn R.
7 of 7 people found the following review helpful
very good book
10 Nov 2009
Format:Paperback|Amazon Verified Purchase
This is a worth buying book. Clear and precise in conjunction with the appropriate software is a must for a statistician's library
It obviously doesn't apply for the advance statistician but is a successful and precise help for anybody who wants to work through excel. You do not need to know r language when you first buy the book.
It transforms a statistical jargon to a window interface.
Highly recommended.
It obviously doesn't apply for the advance statistician but is a successful and precise help for anybody who wants to work through excel. You do not need to know r language when you first buy the book.
It transforms a statistical jargon to a window interface.
Highly recommended.
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