Introduction to R Programming

Statistical computing is employed within a diverse range of industries.

In recent years, an open source project, R, has emerged as the preeminent statistical computing platform.

With its unsurpassed library of freely available packages, R is capable of addressing almost every statistical inference problem.

In this course, you will learn the most commonly-used (roughly 100) functions and operators from the R Base Package, which serves as the fundamental tools for accessing data from multiple sources, manipulating different types of R objects, performing character manipulation, and generating reports.

Furthermore, you will also learn how to write your own functions by using different types of control structures.

Course Highlights: R objects: Vectors, matrices, arrays, lists, and data frame Subsetting objects Data manipulations and aggregation Writing user-defined functions Character manipulations Course Learning Outcomes: Understand essential R functions from the Base R package Write R programs Manipulating different data types Implement newly gained statistical skills in academic research Develop programming skills used across different types of industries Prerequisites: This course assumes that prospective students have no prior knowledge of R.

While having some general programming experience may be beneficial to learning another, it is not a requirement for this course.

Software: R , a free software environment for statistical computing and graphics, is used for this course.

Textbook: Course notes are available to download for free for registered students.

Course typically offered: Online, every quarter Next Steps: Upon completion of this class, consider enrolling in other required coursework in the R for Data Analytics specialized certificate program