R for Everyone: Advanced Analytics and Graphics(Addison-Wesley Data & Analytics Series)

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. Organised 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 visualisation; 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, generalised 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 Analyse 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:
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

About the Author :
Jared P. Lander is the owner of Lander Analytics, a statistical consulting firm based in New York City, the organizer of the New York Open Statistical Programming Meetup and an adjunct professor of statistics at Columbia University. He is also a tour guide for Scott’s Pizza Tours and an advisor to Brewla Bars, a gourmet ice pop startup. With an M.A. from Columbia University in statistics and a B.A. from Muhlenberg College in mathematics, he has experience in both academic research and industry. His work for both large and small organizations spans politics, tech startups, fund raising, music, finance, healthcare, and humanitarian relief efforts. He specializes in data management, multilevel models, machine learning, generalized linear models, visualization, data management, and statistical computing.