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StanfordOnline: R Programming Fundamentals

4.6 stars
25 ratings

This course covers the basics of R: a free programming language and software environment used for statistical computing and graphics. R is widely used by data analysts, statisticians, and data scientists around the world. This course covers an introduction to R, from installation to basic statistical functions. You will learn to work with variable and external data sets, write functions, and hear from one of the co-creators of the R language, Robert Gentleman.

R Programming Fundamentals
6 weeks
2–3 hours per week
Self-paced
Progress at your own speed
Free
Optional upgrade available

There is one session available:

38,615 already enrolled! After a course session ends, it will be archivedOpens in a new tab.
Starts Mar 28
Ends Aug 31

About this course

Skip About this course

This course covers the basics of R: a free programming language and software environment used for statistical computing and graphics. R is widely used by data analysts, statisticians, and data scientists around the world. This course covers an introduction to R, from installation to basic statistical functions. You will learn to work with variable and external data sets, write functions, and hear from one of the co-creators of the R language, Robert Gentleman.

At a glance

  • Institution: StanfordOnline
  • Subject: Computer Science
  • Level: Introductory
  • Prerequisites:
    • Basic familiarity with computers and productivity software
    • Helpful but not required: experience/background in statistics, a scientific or engineering discipline
  • Language: English
  • Video Transcript: English
  • Associated skills:R (Programming Language), Computational Statistics

What you'll learn

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We will cover:

1. How to download and install R.

2. How to use R in an interactive and easy-to-understand environment.

3. What the key objects are and how we manipulate them in R.

4. Where the objects are stored and how to save our work.

5. All the important data structures: data frames, lists, matrices.

6. How to import data into R and how to save your work.

7. How to manipulate and preprocess data and work with missing values.

8. How to plot your data – an introduction to ggplot2.

9. How to use the wealth of contributed packages to achieve a specific task.

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