R for Everyone

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
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Auteur

Jared P. Lander is the Chief Data Scientist of Lander Analytics, a New York-based data science firm that specializes in statistical consulting and training services; the organizer of the New York Open Statistical Programming Meetup-the world's largest R meetup-and the New York R Conference; and an adjunct professor of statistics at Columbia University. With an M.A. from Columbia University in statistics and a B.S. from Muhlenberg College in mathematics, he has experience in both academic research and industry. Very active in the data community, Jared is a frequent speaker at conferences, universities, and meetups around the world. His writings on statistics can be found at jaredlander.com and his work has been featured in publications such as Forbes and the Wall Street Journal.

Contenu

• Chapter 1: Getting R 11.1 Downloading R
• Chapter 2: The R Environment
• Chapter 3: R Packages
• Chapter 4: Basics of R
• Chapter 5: Advanced Data Structures
• Chapter 6: Reading Data into R
• Chapter 7: Statistical Graphics
• Chapter 8: Writing R Functions
• Chapter 9: Control Statements
• Chapter 10: Loops, the Un-R Way to Iterate
• Chapter 11: Group Manipulation
• Chapter 12: Data Reshaping
• Chapter 13: Manipulating Strings
• Chapter 14: Probability Distributions
• Chapter 15: Basic Statistics
• Chapter 16: Linear Models
• Chapter 17: Generalized Linear Models
• Chapter 18: Model Diagnostics
• Chapter 19: Regularization and Shrinkage
• Chapter 20: Nonlinear Models
• Chapter 21: Time Series and Autocorrelation
• Chapter 22: Clustering
• Chapter 23: Reproducibility, Reports and Slide Shows with knitr
• Chapter 24: Building R Packages
• Appendix A: Real-Life Resources
• A.1 Meetups
• A.2 Stackoverflow
• A.3 Twitter
• A.4 Conferences
• A.5 Web Sites
• A.6 Documents
• A.7 Books
• A.8 Conclusion
• Appendix B: Glossary