Series: For Dummies
Paperback: 432 pages
Publisher: For Dummies; 2 edition (July 7, 2015)
Product Dimensions: 7.4 x 0.9 x 9.3 inches
Shipping Weight: 1.2 pounds (View shipping rates and policies)
Average Customer Review: 4.3 out of 5 stars See all reviews (22 customer reviews)
Best Sellers Rank: #28,183 in Books (See Top 100 in Books) #16 in Books > Computers & Technology > Software > Mathematical & Statistical #71 in Books > Textbooks > Science & Mathematics > Mathematics > Statistics #95 in Books > Textbooks > Computer Science > Programming Languages
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.
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 Products10 . 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.
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