R for Everyone: Advanced Analytics and Graphics

by Jared P. Lander

Statistical Computation for Programmers, Scientists, Quants, Excel Users, and Other Professionals
Using the open source R language, you can build powerful statistical models to answer many of your most challenging questions. R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone is the solution. Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you'll need to accomplish 80 percent of modern data tasks. Lander's self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. You'll download and install R; navigate and use the R environment; master basic program control, data import, and manipulation; and walk through several essential tests. Then, building on this foundation, you'll construct several complete models, both linear and nonlinear, and use some data mining techniques. By the time you're done, you won't just know how to write R programs, you'll be ready to tackle the statistical problems you care about most. COVERAGE INCLUDES - Exploring R, RStudio, and R packages - Using R for math: variable types, vectors, calling functions, and more - Exploiting data structures, including data.frames, matrices, and lists - Creating attractive, intuitive statistical graphics - Writing user-defined functions - Controlling program flow with if, ifelse, and complex checks - Improving program efficiency with group manipulations - Combining and reshaping multiple datasets - Manipulating strings using R's facilities and regular expressions - Creating normal, binomial, and Poisson probability distributions - Programming basic statistics: mean, standard deviation, and t-tests - Building linear, generalized linear, and nonlinear models - Assessing the quality of models and variable selection - Preventing overfitting, using the Elastic Net and Bayesian methods - Analyzing univariate and multivariate time series data - Grouping data via K-means and hierarchical clustering - Preparing reports, slideshows, and web pages with knitr - Building reusable R packages with devtools and Rcpp - Getting involved with the R global community

Genres: Programming, Nonfiction, Computer Science, Technology, Technical, Mathematics, Coding

465 pages, ebook

First published December 1, 2013

Reviews

[Arthur · Rating 3 out of 5]

Comprehensive and because if covers a little bit of everything. Too shallow to my taste though. It was highly recommended to me, but there are better books now. Nonetheless, no regrets because it is written by Jared himself.

[Erin · Rating 4 out of 5]

This is one I would definitely consider adding to my work-related library.

[Michael · Rating 4 out of 5]

This is a solid introduction to R.
Well written and lots of examples.
Some of the examples point to data that no longer exists (e.g. world bank map data), but you can find other sources/samples easy enough.
Few chapters assume advanced stats knowledge.

As with all these types of books, the best way to learn is to apply the knowledge quickly in real world situations.

[Roger · Rating 5 out of 5]

This is a very good first book for learning R, and to a certain extent, R Studio. It can be used as a tutorial book as well as a reference. As with most software, learning the foundation is often the most difficult part; after that one can learn incrementally as needed. I find that many other resources and articles about R are either so high level to be not useful, or delve too deeply into the weeds where anyone new to R gets lost quickly. Jared Lander has done a good job striking a balance between these two. The book is not perfect, but I'm not sure a perfect book is possible, as people have different needs for a first book in R.

[Emily · Rating 3 out of 5]

Somewhat helpful, in the sense that there’s code for everything, and it’s comprehensive, but shallow on the explanations and theory. You may learn how to execute something without understanding what it means.

[Vivian Nguyen · Rating 5 out of 5]

Stopped at 5.3

[David Gunter]

Great book. Lots of information and a great source of R goodness

[Sebastian Gebski · Rating 4 out of 5]

It took me some time to find whether I love this book or hate it, I don't really think any option in between could apply. But in the end, there's just too many good things about it. However, to fully enjoy it, you have to set proper expectations upfront:

1.) you've got to refresh your statistics knowledge on your own, this book doesn't even pretend that it's going to teach you much in that matter

2.) this is 'by example' book - it doesn't guide you smoothly through syntax meanders, it dives straight to the examples with minimum comments; I admit - in majority of cases it doesn't work, but this book is an exception: code formatting is perfect (proper syntax coloring, readable paragraph width, clear fonts), there are many visualisations (that make sense & answer the question text itself doesn't)

This convention makes reading it quite a challenge - mainly because R's syntax may be tricky (& it doesn't resemble other languages), but in the end you feel that the effort is not wasting & you're learning really awesome stuff: all the cases (examples) are very interesting & easy to map into other challenges you may be facing IRL.

In general - I think it's the best book about R I've read so far.

[Marc · Rating 4 out of 5]

Really liked the format of the book. Was much better formated for my way of learning. The only down was that the graphics side wasn't enough. Would love a ggplot2/shiny book from the author. Also the editing was lacking with several misses of typos and sentence structure. Well worth over looking those slight flaws.