Introduction to R Programming
CSE-41097
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
More Information: For more information about this course, please contact unex-techdata@ucsd.edu
Course Information
Course sessions
Section ID:
Class type:
This course is entirely web-based and to be completed asynchronously between the published course start and end dates. Synchronous attendance is NOT required.
You will have access to your online course on the published start date OR 1 business day after your enrollment is confirmed if you enroll on or after the published start date.
Textbooks:
No textbook required.
Policies:
- No refunds after: 4/7/2025
Schedule:
Instructor: Arthur Li, Master of Science
Biostatistician, City of Hope; Instructor, Department of Preventative Medicine, USC
Arthur Li holds an M.S. in Biostatistics from the University of Southern California and serves as a biostatistician at City of Hope National Medical Center, where he supports cancer research by analyzing clinical and genomic data. At USC, he developed and taught SAS and R programming courses and occasionally taught a linear regression course, helping students build data analysis skills. At UC San Diego Division of Extended Studies, Li developed and teaches the Biostatistical Methods series courses, transitioned from SAS to R, assisting learners in exploring biostatistics, alongside other R programming courses. He authored the Handbook of SAS® DATA Step Programming (CRC Press, 2013), a resource for data management in SAS. In his spare time, Li enjoys traveling, cooking, and exploring new cultures.
Section ID:
Class type:
This course is entirely web-based and to be completed asynchronously between the published course start and end dates. Synchronous attendance is NOT required.
You will have access to your online course on the published start date OR 1 business day after your enrollment is confirmed if you enroll on or after the published start date.
Textbooks:
No textbook required.
Policies:
- No refunds after: 6/30/2025
Schedule:
Instructor: Arthur Li, Master of Science
Biostatistician, City of Hope; Instructor, Department of Preventative Medicine, USC
Arthur Li holds an M.S. in Biostatistics from the University of Southern California and serves as a biostatistician at City of Hope National Medical Center, where he supports cancer research by analyzing clinical and genomic data. At USC, he developed and taught SAS and R programming courses and occasionally taught a linear regression course, helping students build data analysis skills. At UC San Diego Division of Extended Studies, Li developed and teaches the Biostatistical Methods series courses, transitioned from SAS to R, assisting learners in exploring biostatistics, alongside other R programming courses. He authored the Handbook of SAS® DATA Step Programming (CRC Press, 2013), a resource for data management in SAS. In his spare time, Li enjoys traveling, cooking, and exploring new cultures.
