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R Programming Fundamentals

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

There is one session available:

After a course session ends, it will be archived.
Starts Jul 28
Estimated 6 weeks
2–3 hours per week
Self-paced
Progress at your own speed
Free
Optional upgrade available

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

What you'll learn

Skip What you'll learn

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.

About the instructors

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