Learning R

Learn how to perform data analysis with the R language and software environment, even if you have little or no programming experience. With the tutorials in this hands-on guide, you’ll learn how to use the essential R tools you need to know to analyze data, including data types and programming concepts.

The second half of Learning R shows you real data analysis in action by covering everything from importing data to publishing your results. Each chapter in the book includes a quiz on what you’ve learned, and concludes with exercises, most of which involve writing R code.

• Write a simple R program, and discover what the language can do
• Use data types such as vectors, arrays, lists, data frames, and strings
• Execute code conditionally or repeatedly with branches and loops
• Apply R add-on packages, and package your own work for others
• Learn how to clean data you import from a variety of sources
• Understand data through visualization and summary statistics
• Use statistical models to pass quantitative judgments about data and make predictions
• Learn what to do when things go wrong while writing data analysis code

• R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics
• Practical Statistics for Data Scientists: 50 Essential Concepts
• R For Dummies, 2nd Edition
• R Cookbook (O'Reilly Cookbooks)
• Hands-On Programming with R: Write Your Own Functions And Simulations
• Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
• R in Action: Data Analysis and Graphics with R
• The Book of R: A First Course in Programming and Statistics
• Keeping Up with the Quants: Your Guide to Understanding and Using Analytics
• R for Data Science: Import, Tidy, Transform, Visualize, and Model Data