COJohns Hopkins University · on Coursera
R Programming
Beginner 57 hours English Course Certificate Certificate
About this course
This Johns Hopkins course is part of the Data Science Specialization and covers R from fundamentals: vectors, matrices, data frames, lists, functions, and control structures. Students work with real datasets throughout, applying R's functional programming model and learning the tidyverse and base R approaches.
The course is practical and assignment-heavy — students write R scripts that clean, transform, and analyze real datasets, building the foundation for statistical computing and data science work in R.
What you'll learn
Write R code using vectors, lists, matrices, and data frames
Apply functional programming patterns with lapply, sapply, and mapply
Load, clean, and transform data using base R and tidyverse tools
Write R functions with proper error handling and documentation
Work with dates, strings, and regular expressions in R
This course includes
57h
On-demand video
Yes
Certificate
Yes
Mobile access
English
Language
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Instructor
RP
Roger Peng / Jeff Leek
Coursera instructor
680K+ learners10 courses4.5 instructor rating
Taught by Roger Peng, Jeff Leek, and Brian Caffo from Johns Hopkins Bloomberg School of Public Health — pioneers of data science education on Coursera.
Requirements
- No programming experience required; statistical curiosity helpful
Who this course is for
- Statisticians and researchers who use or want to use R for data analysis
- Data analysts transitioning from Excel or SPSS to programmatic analysis
- Students in statistics, public health, or social science programs
About this provider
CO
Coursera
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Frequently asked questions
Python dominates in industry; R dominates in academia and statistics. If you're going into research or working with statisticians, learn R.
Introduces both — deep ggplot2 and tidyverse coverage comes in later courses in the Hopkins Data Science Specialization.