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Mastering R has never been easier
Picking up R can be tough, even for seasoned statisticians and data analysts. R For Dummies, 2nd Edition provides a quick and painless way to master all the R you'll ever need. Requiring no prior programming experience and packed with tons of practical examples, step-by-step exercises, and sample code, this friendly and accessible guide shows you how to know your way around lists, data frames, and other R data structures, while learning to interact with other programs, such as Microsoft Excel. You'll learn how to reshape and manipulate data, merge data sets, split and combine data, perform calculations on vectors and arrays, and so much more.
R is an open source statistical environment and programming language that has become very popular in varied fields for the management and analysis of data. R provides a wide array of statistical and graphical techniques, and has become the standard among statisticians for software development and data analysis. R For Dummies, 2nd Edition takes the intimidation out of working with R and arms you with the knowledge and know-how to master the programming language of choice among statisticians and data analysts worldwide.
- Covers downloading, installing, and configuring R
- Includes tips for getting data in and out of R
- Offers advice on fitting regression models and ANOVA
- Provides helpful hints for working with graphics
R For Dummies, 2nd Edition is an ideal introduction to R for complete beginners, as well as an excellent technical reference for experienced R programmers.
- Sales Rank: #33011 in Books
- Brand: John Wiley & Sons Inc
- Published on: 2015-07-07
- Original language: English
- Number of items: 1
- Dimensions: 9.30" h x .90" w x 7.40" l, .0 pounds
- Binding: Paperback
- 432 pages
From the Back Cover
Learn to:
- Use R for data analysis and processing
- Write functions and scripts for repeatable analysis
- Create high-quality charts and graphics
- Perform statistical analysis and build models
All the R you need for statistical and data analysis, explained in plain English!
Do you have to use R on the job, but you don't know much about programming? Relax! Here's a quick and painless guide to using R for all sorts of statistical and data analysis. Through real-world examples and step-by-step exercises you'll learn how to reshape and manipulate data, perform calculations, create graphical representations, and more.
- Get started with R — acquire and install R, learn what makes R unique, compare code editors, and write your first R script
- Play well with others — see how R interacts with Excel and other programs and why statisticians and data analysts favor it
- R ABCs — make your code clear and explore functions and arguments
- A calculating side — take advantage of R's calculating power, working with numbers, infinity, missing values, mathematical functions, and vectors
- Get textual — use character vectors for text data, manipulate text, and work with strings and factors
- Data tells a tale — manipulate and process data using subsets, merge data from different sources, sort it, and summarize using statistical techniques
- Go graphic — present your data visually using plots and lattice graphics
Open the book and find:
- User packages that extend R
- Tips for getting data into and out of R
- How R handles basic arithmetic
- What to do with regression models
- Advice on managing the logical flow
- Effective debugging strategies
- How to incorporate dates and times
- Additional graphics packages you can use
About the Author
Andrie de Vries is a leading R expert and Business Services Director for Revolution Analytics. With over 20 years of experience, he provides consulting and training services in the use of R. Joris Meys is a statistician, R programmer and R lecturer with the faculty of Bio-Engineering at the University of Ghent.
Most helpful customer reviews
23 of 25 people found the following review helpful.
Not the best
By I Teach Typing
I teach statistics and statistical programming for a living and while this is not a bad book, it is so far from optimal I would not recommend it for my students. Typically people approach R because they have a dataset (in Excel or coming from REDCap) and they want to do basic summaries, graphics or make inferences. In everyday use that data gets imported into an R object called a data frame and then people play with the columns inside of the data frame. Rather than starting with real data imported into data frames, this book begins with manually typing data and doing basic operations on vectors. The authors do not get to data frames until page 132. Excel is not covered until page 229.
Instead of first covering the basic tasks, like importing and describing data the authors cover key programming ideas, which are critical for a deeper understanding of R. That coverage is not bad at all but it could be better. All the material is hampered by the authors not sweating the details on their code. R is a case sensitive language and the authors are not consistent in how they name objects. For example in chapter 3, where they cover R naming, they include objects called Intro, Total.baskets, baskets.of.Granny. Because R pays attention to what is capitalized, readers of the book who are trying to follow along will have to waste time fixing the authors inconsistent capitalization.
In general the writing is good to very good but it could be better. For example the coverage of methods vs functions is not crystal clear and it needs to be.
If you are coming to R with a programming background there is some very nice stuff here. The authors do regularly point out things that are different about R (for example its coverage of vectorized functions and recycling) and they have useful technical points throughout the book. If you are coming to R from a database background you will find the lack of coverage on R and SQL integration disappointing.
While each has its own flaws, readers are better off getting Data Manipulation with R (Use R!) to learn to slice and dice data and perhaps OpenIntro Statistics: Third Edition to learn statistics with R (or SAS) and/or The Art of R Programming: A Tour of Statistical Software Design to learn how R actually works.
16 of 18 people found the following review helpful.
If You Are Learning R For "Data Science" Purposes, Do Not Miss This Review
By Big Data Paramedic
R Programming language has become very prominent these days ,because it is the goto language for "Data Science" and by extension "Data Scientist", the sexiest job of 21st century according to Harvard Business Review.
WHY R FOR ME ( YOU )
Probably you are looking at changing your career to Data Science. If this is the case, I have signed up with coursera (John Hopkins) which has a specialization track called "Data Science" . It gets you a certificate and a live project as well. They use R in the entire training curriculum.
Here are the subjects
1. The Data Scientist's Toolbox
2. R Programming
3. Getting and Cleaning Data
4. Exploratory Data Analysis
5. Reproducible Research
6. Statistical Inference
7. Regression Models
8. Practical Machine Learning
9. Developing Data Products
10 . Data Science Capstone ... A real project.
Also, if money is not an issue, Harvard is providing a data science course online (8 grand) and UC Berkley is also having one. Please check out them as well and many more will certainly crop up. Before you begin to study, a good statistics knowledge will get you beyond a 'mediocre' Data scientist.In fact at Harvard Data science course, Introduction to Statisitics is a prerequisite.
R ISSUES ONLINE:
This is how I got into R Programming. I am still studying and the problem with this Coursera is that they assume a level of intelligence most of us do not have. Few slides of R and then there is a project with very little help from there on. It is very frustrating and no doubt many drop out. By the way, if passing the test and get certificate is all you need, answers for all the exercises are available online. Old Fashioned, I did not feel like getting it that way!
ABOUT THIS BOOK:
This book is what I call the bridge before you can enroll in any data science course. It just walks you through simple step by step and goes on . The only issue I have with this book is that it does not take you with the same example through out the book, First few chapters have granny, then it is months and so on. Not a biggie,but certainly a dent in this real good book.
SUMMARY:
I will have no hesitation to recommend this book as the first step to anyone who wants to get into Data Science.. and use R as the Vehicle to get there.I highly recommend you reading Data Science for Business: What you need to know about data mining and data-analytic thinking.
7 of 7 people found the following review helpful.
Good introduction to R that corrects many errors from the first edition
By Epilady
Based on R version 3.2.0 (current version is 3.2.1 as of 8/11/2015 - not any major changes impacting what's in this book), this is the second edition of R For Dummies. There are a lot of improvements over the earlier version, including corrections to syntax and other errors (e.g. using double quotes for strings vs single). They also have an R package that can be downloaded and installed in r (rfordummies) that contain all the code examples in the book.
It uses R Studio (also freeware) to demo the concepts, which is a nice touch, as RStudio is similar across operating system environments, making it more universal.
This book is best for people who are interested in getting started with R, and have at least a basic understanding of rudimentary statistical concepts. It covers downloading the software, getting started, the basics of the language, how to interact with other programs (such as Excel), sorting and ordering data, subsetting and merging data, working with graphics (primarily plots, charts, and facets), and then suggests 10 different packages that can be downloaded (but are not part of the base program). It's a decent collection to begin (there are more than 6000 packages that can be downloaded from CRAN).
Overall, it's a good introduction to R, and is relatively decent at explaining some of R's oddities, including a really nice job of explaining the difference between an array and a list, which non-programmers seem to struggle over when being introduced to R for the first time.
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