R Programming Tutorial: Learn the Basics of Statistical Computing

Name: R Programming Tutorial: Learn the Basics of Statistical Computing
Availability: InStock

2 hours 4.6(9,200 reviews)Free

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Rating: Rating 4.6
Hours: 2
Cost: Free

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freeCodeCamp's R Programming Tutorial teaches R, the language for statistical computing and data analysis. You learn syntax, data structures, visualization, and statistics through video and interactive examples. The entire course is free on freeCodeCamp.org. After finishing, you can load datasets, create visualizations, and perform statistical analysis in R. R is the tool of choice for statisticians, data scientists in academia, and anyone doing serious statistical work. The main limitation: R has a steeper learning curve than Python for beginners. Also, for business analytics, Python is increasingly preferred.

R is used by 3.3% of professional developers but is dominant in academic statistics and specialized research (source: Stack Overflow Developer Survey 2025).

About This Course

freeCodeCamp's 2-hour R tutorial with Barton Poulson of datalab.cc. A hands-on overview of the statistical programming language R: syntax, data types, vectors, data frames, and statistical analysis. Free on YouTube.

Course Details

Platform: freeCodeCamp
Language: R
Level: beginner
Duration: 2h
Certificate: No
Cost: Free

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Frequently Asked Questions

Is freeCodeCamp's R course free?

Yes, completely free on freeCodeCamp.org in video format.

Is R a good first language?

Not ideal. Python is gentler. Learn R if you specifically need statistical computing or are in an academic environment.

What's the difference between R and Python for data science?

R is stronger in statistics and specialized visualization. Python is more general-purpose and easier to learn. Pick based on your use case.

Can I get a job knowing just R?

Yes, especially in academia, research, and specialized analytics roles. In business, Python is more common.

What's the next step after R fundamentals?

Learn ggplot2 (visualization), dplyr (data manipulation), and RMarkdown (reporting). Then build analyses on real datasets.

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