R Programming

About the Course

In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.

Course Syllabus

The course will cover the following material each week:

• Week 1: Overview of R, R data types and objects, reading and writing data
• Week 2: Control structures, functions, scoping rules, dates and times
• Week 3: Loop functions, debugging tools
• Week 4: Simulation, code profiling

Recommended Background

Some familiarity with programming concepts will be useful as well basic knowledge of statistical reasoning; Data Scientist's Toolbox

Suggested Readings

• Software for Data Analysis: Programming with R (Statistics and Computing) by John M. Chambers (Springer)
• S Programming (Statistics and Computing) Brian D. Ripley and William N. Venables (Springer)

Course Format

There will be weekly lecture videos, quizzes, and programming assignments.

As part of this class you will be required to set up a GitHub account. GitHub is a tool for collaborative code sharing and editing. During this course and other courses in the Specialization you will be submitting links to files you publicly place in your GitHub account as part of peer evaluation. If you are concerned about preserving your anonymity you will need to set up an anonymous GitHub account and be careful not to include any information you do not want made available to peer evaluators.