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Introductory R: A Beginner's Guide to Data Visualisation, Statistical Analysis and Programming in R (English Edition) [Kindle Edition]

Robert Knell
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R is now the most widely used statistical software in academic science and it is rapidly expanding into other fields such as finance. R is almost limitlessly flexible and powerful, hence its appeal, but can be very difficult for the novice user. There are no easy pull-down menus, error messages are often cryptic and simple tasks like importing your data or exporting a graph can be difficult and frustrating. Introductory R is written for the novice user who knows a little about statistics but who hasn't yet got to grips with the ways of R. This new edition is completely revised and greatly expanded with new chapters on the basics of descriptive statistics and statistical testing, considerably more information on statistics and six new chapters on programming in R. Topics covered include

1) A walkthrough of the basics of R's command line interface
2) Data structures including vectors, matrices and data frames
3) R functions and how to use them
4) Expanding your analysis and plotting capacities with add-in R packages
5) A set of simple rules to follow to make sure you import your data properly
6) An introduction to the script editor and advice on workflow
7) A detailed introduction to drawing publication-standard graphs in R
8) How to understand the help files and how to deal with some of the most common errors that you might encounter.
9) Basic descriptive statistics
10) The theory behind statistical testing and how to interpret the output of statistical tests
11) Thorough coverage of the basics of data analysis in R with chapters on using chi-squared tests, t-tests, correlation analysis, regression, ANOVA and general linear models
12) What the assumptions behind the analyses mean and how to test them using diagnostic plots
13) Explanations of the summary tables produced for statistical analyses such as regression and ANOVA
14) Writing functions in R
15) Using table operations to manipulate matrices and data frames
16) Using conditional statements and loops in R programmes.
17) Writing longer R programmes.

The techniques of statistical analysis in R are illustrated by a series of chapters where experimental and survey data are analysed. There is a strong emphasis on using real data from real scientific research, with all the problems and uncertainty that implies, rather than well-behaved made-up data that give ideal and easy to analyse results.


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3.0 von 5 Sternen Ein wirklicher Beginners Guide 9. April 2015
Von Amaryllis
Verifizierter Kauf
Hatte man noch nichts damit zu tun, ist man hier genau richtig. Hat man schon Vorkenntnisse, muss man ein bisschen blättern, bis man die für einen selbst interessanten Sachen findet. Jedoch für das Geld ein hervorragendes Anleitungsbuch.
War diese Rezension für Sie hilfreich?
Die hilfreichsten Kundenrezensionen auf (beta) 4.3 von 5 Sternen  18 Rezensionen
12 von 12 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Excellent Introduction And Advised For Quick Understanding Of Basic R Capability 14. Mai 2014
Von Chris Harding - Veröffentlicht auf

First, there is much information available online about R, but much of the R literature is quite advanced. In addition, PhD statisticians, which write much of the R documentation, have a tendency to use very specific vocabulary words to describe a method. For the later reasons, the learning curve for the powerful open source R can be quite gradual when considering learning on the Y axis and Time on the X axis.

This Introductory course provides an opportunity to understand a massive amount of R for computing statistics. It is written for beginners but actually provides very useful R tools. The book provides knowledge to quickly become proficient in R, which can be challenging. If you google R, you will find there is much information on the Internet about the process of importing data as an example. It appears that many struggle with the latter task. This task and more are explained in this introductory book in a logical step-wise process.

Personally, I believe R, which is free and powerful, should be a part of every engineering and scientific statistical course. Why? As a chemical engineer, I have used statistics to optimize processes. Since validated statistical software was not distributed, I did my work in Microsoft Excel, which I was familiar with and I was rushed, but wish I would have known about well developed and introductory R documentation. This Introductory book would have been quite helpful.

Also, I appreciate that Dr. Knell also suggests books for more complicated topics. I think most people, myself included, forget that our professors kindly lead our learning. As such, we did not have to review textbooks, etc. Although the Internet is useful in this process, one can still stumble upon a difficult read. Since Dr. Knell does such a good job with explaining Introductory R in easy to understand language, I believe his suggested books are probably effective for quick learning as well. As such, I plan to us the Internet to evaluate his suggested readings before I buy my own.

In fact the US Food and Drug Administration (FDA) used R to evaluate part of the complicated relationship of heart toxicity and drugs[1]. The later is important because the FDA requires their regulated industries use "validated" statistical software packages. As such, FDA must have the opinion that R is appropriate data analysis software, which might calm some peoples fear about learning R.

Quote: "The limits of intra-subject day-to-day variability were calculated using R 2.12.0 (R Project for Statistical Computing, Vienna, Austria)."[1]

Finally, I also suggest scientist and engineers become more informed about "Linear Mixed Models," and R handles such models effectively.


[1] Johannesen, L; Vicente, J; Gray, RA; Galeotti, L; Loring, Z; Garnett, CE; Florian, J; Ugander, M; Stockbridge, N; Strauss, DG. Improving the Assessment of Heart Toxicity for All New Drugs through Translational Regulatory Science, Dec. 2013. Clin Pharmacol Ther[online]. 2013. Available from:: [...] 10.1038/clpt.2013.238
10 von 10 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen An excellent introduction to the subject 29. August 2013
Von Beth Clare - Veröffentlicht auf
As an R novice I am always on the look out for a good introduction. Knell's book provides a user friendly method of getting into the platform and a useful set of instructions for statistics in R and many visualization techniques. Perfect for the novice, students and those with R phobia. Certainly recommended.
8 von 8 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen The book to start with 17. November 2013
Von Air - Veröffentlicht auf
Verifizierter Kauf
If you are new to R, this is the book to start with. It covers basics, importing data, using the help files, R packages, graphs, record keeping, trouble shooting and basic programming, each with some examples. If you have questions, you can click on the link on the “how to use this book” section to author’s website and send the author an e-mail. You can count on his reply. Dr. Knell even offers to check my coding to help me solving one of my problems!
6 von 6 Kunden fanden die folgende Rezension hilfreich
4.0 von 5 Sternen Very practical guide 8. Dezember 2013
Von lmnieves - Veröffentlicht auf
Verifizierter Kauf
I recommend this book for all of us who are starting using R and at the same time want to start learning about data analysis. This is a brief introduction, but goes directly to the matter in a very understandable way.
2 von 2 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen This is one of the best. What distinguishes it is its practicality 22. November 2014
Von G. A Wheeler - Veröffentlicht auf
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I have several books on R, ranging from introductory to quite advanced. I don't use the language on a daily basis so I find that reading them helps reinforce my knowledge. This is one of the best. What distinguishes it is its practicality; while other books tend to be more high level ("what is data analytics") or technical/academic (some of the Springer ones, which are great but assume quite a bit of knowledge), this book is grounded in practice. For example, the author goes into some detail into how best to prep data in a spreadsheet before importing it into R. He covers a number of R error messages and what typically causes them. It's these practical tips from the trenches that make this a great book. The book grew out of lecture notes that have been revised and improved through several iterations of real use in education, so they have had lots of feedback and revision, and their pedagogical value is high. And the price is a bargain.
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