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Introductory R: A Beginner's Guide to Data Visualisation, Statistical Analysis and Programming in R, by 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.
- Sales Rank: #133958 in eBooks
- Published on: 2013-03-13
- Released on: 2013-03-13
- Format: Kindle eBook
Most helpful customer reviews
26 of 28 people found the following review helpful.
Excellent Introduction And Advised For Quick Understanding Of Basic R Capability
By Chris Harding
Hello,
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.
References:
[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
12 of 13 people found the following review helpful.
An excellent introduction to the subject
By Beth Clare
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.
11 of 12 people found the following review helpful.
The book to start with
By Air
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!
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