## Book description

**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 math: 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 statistics: 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

## Table of contents

- About This E-Book
- Title Page
- Copyright Page
- Dedication Page
- Contents
- Foreword
- Preface
- Acknowledgments
- About the Author
- 1. Getting R
- 2. The R Environment
- 3. R Packages
- 4. Basics of R
- 5. Advanced Data Structures
- 6. Reading Data into R
- 7. Statistical Graphics
- 8. Writing R functions
- 9. Control Statements
- 10. Loops, the Un-R Way to Iterate
- 11. Group Manipulation
- 12. Faster Group Manipulation with dplyr
- 13. Iterating with purrr
- 14. Data Reshaping
- 15. Reshaping Data in the Tidyverse
- 16. Manipulating Strings
- 17. Probability Distributions
- 18. Basic Statistics
- 19. Linear Models
- 20. Generalized Linear Models
- 21. Model Diagnostics
- 22. Regularization and Shrinkage
- 23. Nonlinear Models
- 24. Time Series and Autocorrelation
- 25. Clustering
- 26. Model Fitting with Caret
- 27. Reproducibility and Reports with knitr
- 28. Rich Documents with RMarkdown
- 29. Interactive Dashboards with Shiny
- 30. Building R Packages
- A. Real-Life Resources
- B. Glossary
- List of Figures
- List of Tables
- General Index
- Index of Functions
- Index of Packages
- Index of People
- Data Index
- Code Snippets

## Product information

- Title: R for Everyone: Advanced Analytics and Graphics, 2nd Edition
- Author(s):
- Release date: June 2017
- Publisher(s): Addison-Wesley Professional
- ISBN: 9780134546988

## You might also like

book

### R for Everyone: Advanced Analytics and Graphics

Statistical Computation for Programmers, Scientists, Quants, Excel Users, and Other Professionals Using the open source R …

book

### Statistical Rethinking, 2nd Edition

Statistical Rethinking: A Bayesian Course with Examples in R and Stan, Second Edition builds knowledge/confidence in …

book

### Storytelling with Data: A Data Visualization Guide for Business Professionals

Don't simply show your data—tell a story with it! Storytelling with Data teaches you the fundamentals …

book

### R for Data Science, 2nd Edition

Use R to turn data into insight, knowledge, and understanding. With this practical book, aspiring data …