R Programming – Intermediate
This course will teach experienced data analysts a systematic overview of R as a programming language, emphasizing good programming practices and the development of clear, concise code. After completing the course, students should be able to manipulate data programmatically using R functions of their own design.
Overview
This course is intended for experienced data analysts looking to unlock the power of R. Students should have at least one year of daily R use under their belts and a goal of using R as a serious statistical computing tool. This is an advanced course with an open, independent learning style. Students should be comfortable with a trial and error approach emphasizing good programming practices and the development of clear, concise code.
- Intermediate
- 4 Weeks
- Expert Instructor
- Tuiton-Back Guarantee
- 100% Online
- TA Support
Learning Outcomes
After completing this course students should be able to work with various data types, recognize different types of loops, and create and apply user-defined functions.
In contrast with the detailed step-by-step approach in an introductory course, this more advanced course will use a more open and independent learning style, and students should expect to occasionally wrestle a bit with the concepts and be comfortable with a trial and error approach.
- Efficiently deal with different data types and structures
- Recognize and code different types of loops
- Create user-defined functions
- Use functions to avoid loops
- Properly apply lexical scoping
Who Should Take This Course
Statistical analysts with at least one year of daily R experience and who want to use R as a serious statistical computing tool.
Our Instructors
Course Syllabus
Week 1
Data
- Quick review of R data types and data structures
- Importing data
- Recoding data
Week 2
Loops
- Measuring and monitoring Râs performance
- Different types of loops
- Fast loops
Week 3
Functions
- Creating user-defined functions
- Proper lexical scoping
Week 4
Avoiding Loops
- Using user-defined functions to avoid loops
Class Dates
2025
Instructors: Dr. Hongcheng Li
Instructors: Dr. Hongcheng Li
Prerequisites
Statistical analysts with at least one year of daily R experience or have used R as a statistical computing tool.
If you are new to R, you should start with R Programming Intro 1.
Introduction to R Programming
- Skill: Intermediate
- Credit Options: ACE, CAP, CEU
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Additional Information
Homework
Homework in this course consists of assigned readings, guided exercises in writing code, narrated slides, and supplemental readings available online.
Course Text
The course text is The Art of R Programming: A Tour of Statistical Software Design, by Norman Matloff. Relevant sections will be made available online in the course.
The following texts are not required for the course, but provide useful background and a handy reference once you are done. Each session will provide pointers to relevant material in the books.
- R in a Nutshell: A Desktop Quick Reference, by Joseph Adler.
- Data Manipulation with R, by Phil Spector.
Software
Participants should be familiar with and have access to R.
The recommended R editor in this course is eMacs. While RStudio is used in other courses, it uses a different R engine, resulting in functionality discrepancies that can be distracting in class.