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.
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.
- Quick review of R data types and data structures
- Importing data
- Recoding data
- Measuring and monitoring Râs performance
- Different types of loops
- Fast loops
- Creating user-defined functions
- Proper lexical scoping
- Using user-defined functions to avoid loops
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.
The Statistics.com courses have helped me a lot, pushing me to the limit and making me learn much more than I expected I could. The knowledge I gained I could immediately leverage in my job … then eventually led to landing a job in my dream company – Amazon.
This program has been a life and work game changer for me. Within 2 weeks of taking this class, I was able to produce far more than I ever had before.
The material covered in the Analytics for Data Science Certificate will be indispensable in my work. I can’t wait to take other courses. Great work!
I learned more in the past 6 weeks than I did taking a full semester of statistics in college, and 10 weeks of statistics in graduate school. Seriously.
This is the best online course I have ever taken. Very well prepared. Covers a lot of real-life problems. Good job, thank you very much!
The more courses I take at Statistics.com, the more appreciation I have for the smart approach, quality of instructors, assistants, admin and program. Well done!
This course greatly benefited me because I am interested in working in AI. It has given me solid foundational knowledge…After completing this last course, I feel I have gained valuable skills that will enhance my employability in Data Science, opening up diverse career opportunities.
Frequently Asked Questions
What is your satisfaction guarantee and how does it work?
Can I transfer or withdraw from a course?
Who are the instructors at Statistics.com?
Visit our knowledge base and learn more.
Homework in this course consists of assigned readings, guided exercises in writing code, narrated slides, and supplemental readings available online.
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.
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.