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R for Everyone: Advanced Analytics and Graphics
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, Second Edition, 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, manipulation, and visualization; 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. After all this you’ll make your code reproducible with LaTeX, RMarkdown, and Shiny.
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
Explore R, RStudio, and R packages Use R for variable types, vectors, calling functions, and more Exploit data structures, including data.frames, matrices, and lists Read many different types of data Create attractive, intuitive statistical graphics Write user-defined functions Control program flow with if, ifelse, and complex checks Improve program efficiency with group manipulations Combine and reshape multiple datasets Manipulate strings using R’s facilities and regular expressions Create normal, binomial, and Poisson probability distributions Build linear, generalized linear, and nonlinear models Program basic mean, standard deviation, and t-tests Train machine learning models Assess the quality of models and variable selection Prevent overfitting and perform variable selection, using the Elastic Net and Bayesian methods Analyze univariate and multivariate time series data Group data via K-means and hierarchical clustering Prepare reports, slideshows, and web pages with knitr Display interactive data with RMarkdown and htmlwidgets Implement dashboards with Shiny Build reusable R packages with devtools and Rcpp Register your product at informit.com/register for convenient access to downloads, updates, and corrections as they become available.
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, Second Edition, 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, manipulation, and visualization; 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. After all this you’ll make your code reproducible with LaTeX, RMarkdown, and Shiny.
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
Explore R, RStudio, and R packages Use R for variable types, vectors, calling functions, and more Exploit data structures, including data.frames, matrices, and lists Read many different types of data Create attractive, intuitive statistical graphics Write user-defined functions Control program flow with if, ifelse, and complex checks Improve program efficiency with group manipulations Combine and reshape multiple datasets Manipulate strings using R’s facilities and regular expressions Create normal, binomial, and Poisson probability distributions Build linear, generalized linear, and nonlinear models Program basic mean, standard deviation, and t-tests Train machine learning models Assess the quality of models and variable selection Prevent overfitting and perform variable selection, using the Elastic Net and Bayesian methods Analyze univariate and multivariate time series data Group data via K-means and hierarchical clustering Prepare reports, slideshows, and web pages with knitr Display interactive data with RMarkdown and htmlwidgets Implement dashboards with Shiny Build reusable R packages with devtools and Rcpp Register your product at informit.com/register for convenient access to downloads, updates, and corrections as they become available.
1603 pages, Kindle Edition
First published December 1, 2013
118 people are currently reading
319 people want to read
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Displaying 1 - 9 of 9 reviews
December 27, 2015
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.
February 10, 2015
This is one I would definitely consider adding to my work-related library.
September 6, 2014
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.
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.
September 8, 2019
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.
March 26, 2019
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.
August 7, 2018
Stopped at 5.3
Read
April 19, 2024Great book. Lots of information and a great source of R goodness
January 22, 2015
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.
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.
May 29, 2014
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.
Displaying 1 - 9 of 9 reviews