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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 class.

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 during each quarter.

Next Steps: Upon completion of this class, consider enrolling in other required coursework in the R for Data Analytics specialized certificate program.

More Information: For more information about this course, please contact

Course Number: CSE-41097
Credit: 3.00 unit(s)
Related Certificate Programs: BiostatisticsBusiness Intelligence AnalysisGeographic Information SystemsPython ProgrammingR for Data Analytics

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