Learning R: A Step-by-Step Function Guide to Data Analysis / Cotton, Richard

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

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