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CS50's Introduction to Programming with R

David J. Malan and Carter Zenke

An introduction to programming using a language called R, a popular language for statistical computing and graphics in data science and other domains. Learn to use RStudio, a popular integrated development environment (IDE). Learn to represent real-world data with vectors, matrices, arrays, lists, and data frames. Filter data with conditions, via which you can analyze subsets of data. Apply functions and loops, via which you can manipulate and summarize data sets. Write functions to modularize code and raise exceptions when something goes wrong. Tidy data with R’s tidyverse and create colorful visualizations with R’s grammar of graphics. By course’s end, learn to package, test, and share R code for others to use. Assignments inspired by real-world data sets.

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What's inside

Learning objectives

  • R
  • Rstudio
  • Vectors
  • Matrices
  • Arrays
  • Lists
  • Data frames
  • Conditions
  • Functions
  • Loops
  • Exceptions
  • Tidyverse

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Explores R, which is highly relevant for statistical computing and graphics in data science and other domains
Taught by David J. Malan, Carter Zenke, who are recognized for their work in R
Provides a strong foundation for beginners in R and statistical computing
Develops skills in data manipulation, visualization, and analysis using R's tidyverse and grammar of graphics

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Reviews summary

Foundational introduction to r programming

According to learners, CS50's Introduction to Programming with R provides a solid and comprehensive foundation for those new to R. Many highlight the engaging lectures and well-structured problem sets as particularly effective for learning. Students appreciate the course's focus on practical application of R for data science, including strong coverage of tidyverse and data visualization with ggplot2. While the pace can be challenging for absolute beginners, especially those without prior programming experience, the thorough explanations and supportive problem-solving structure help overcome difficulties. Some found certain assignments demanding, yet ultimately rewarding. The course is seen as a highly valuable stepping stone into data analysis.
Lectures are clear and engaging, making complex topics accessible.
"The instructor did an amazing job breaking down complex R concepts into understandable chunks."
"I found the lectures captivating and easy to follow, a true testament to great teaching."
"The way the material was presented, with practical examples, really helped me grasp R quickly."
Strong coverage of modern R practices and graphics.
"The segment on tidyverse was incredibly useful; it's exactly what I needed for my data analysis work."
"I finally understood ggplot2 thanks to this course, and now I can create really professional-looking plots."
"Learning to 'tidy' data was a game-changer for me, and the course explained it very clearly."
Hands-on assignments reinforce learning effectively.
"The problem sets were incredibly engaging and pushed me to truly understand the concepts, not just memorize them."
"I loved the labs; they were practical and helped solidify my understanding of RStudio and data manipulation."
"The assignments felt like real-world challenges, which made the learning experience much more rewarding."
Builds strong R fundamentals for beginners.
"This course gave me a very solid foundation in R, truly starting from scratch but quickly building up."
"I appreciate how thoroughly they covered basic R concepts before moving to more advanced topics like tidyverse."
"For someone completely new to R, I felt very well-equipped by the end of the course."
Learners may need to supplement with self-study.
"Don't expect to just watch lectures; you need to actively work on the problem sets and explore on your own."
"I had to spend a lot of time reviewing concepts and practicing outside of the assigned work to keep up."
"While the course provides a great base, truly mastering R requires consistent personal practice."
Can be fast-paced, especially for new programmers.
"While excellent, the pace was quite fast at times, and I often had to rewatch lectures or seek outside resources."
"If you're an absolute programming novice, be prepared to put in extra hours, as it moves quickly through concepts."
"I struggled with some of the later assignments due to the rapid introduction of new topics."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in CS50's Introduction to Programming with R with these activities:
Work through the R Tutorial
Exercises in the interactive tutorial will help you apply core concepts from the course.
Show steps
  • Visit the RStudio website and follow the tutorial.
Create a cheat sheet for R functions
Creating a cheat sheet will help you memorize and quickly reference important functions.
Browse courses on R Functions
Show steps
  • List the most commonly used R functions.
  • Describe the purpose and syntax of each function.
Create visualizations with ggplot2
ggplot2 is a powerful library for creating visualizations in R, enhancing your ability to communicate data insights.
Browse courses on Ggplot2
Show steps
  • Install the ggplot2 package.
  • Follow a tutorial on creating a basic plot.
Three other activities
Expand to see all activities and additional details
Show all six activities
Solve coding challenges on LeetCode
Solving coding challenges will enhance your problem-solving skills and deepen your understanding of R.
Browse courses on Coding Challenges
Show steps
  • Create an account on LeetCode.
  • Start solving easy problems in the R section.
Practice writing functions
Functions allow you to reuse code and write more modular and maintainable programs.
Browse courses on Functions
Show steps
  • Define a function to calculate the mean of a vector.
  • Call the function with different vectors.
Analyze a real-world dataset
Applying your skills to analyze real-world data will solidify your understanding and enhance your ability to solve problems.
Browse courses on Data Analysis
Show steps
  • Find a dataset that interests you.
  • Load the dataset into R.
  • Explore the data using summary statistics and visualizations.
  • Draw conclusions based on your analysis.

Career center

Learners who complete CS50's Introduction to Programming with R will develop knowledge and skills that may be useful to these careers:
Data Analyst
Data Analysts use their skills with programming and data to solve problems. This course provides an introduction to programming using a language called R, a popular language for statistical computing and graphics in data science and other domains. It helps build a foundation for understanding how to use statistical analysis to solve problems.
See salaries and explore the career path for Data Analyst
Statistician
Statisticians use their knowledge of data analysis to help businesses and organizations make informed decisions. This course provides an introduction to programming using R, a popular language for statistical computing and graphics in data science and other domains.
See salaries and explore the career path for Statistician
Data Scientist
Data Scientists use programming and data to solve problems and create insights. This course provides an introduction to programming using a language called R, a popular language for statistical computing and graphics in data science and other domains. It helps build a foundation for understanding how to use data to solve problems.
See salaries and explore the career path for Data Scientist
Machine Learning Engineer
Machine Learning Engineers use programming and data to build predictive models. This course provides an introduction to programming using a language called R, a popular language for statistical computing and graphics in data science and other domains. It helps build a foundation for understanding how to use R to develop and deploy machine learning models.
See salaries and explore the career path for Machine Learning Engineer
Quantitative Analyst
Quantitative Analysts use programming and data to solve problems in the financial industry. This course provides an introduction to programming using a language called R, a popular language for statistical computing and graphics in data science and other domains. It helps build a foundation for understanding how to use R to analyze financial data and develop trading strategies.
See salaries and explore the career path for Quantitative Analyst
Software Engineer
Software Engineers use programming to develop and maintain software applications. This course provides an introduction to programming using a language called R, a popular language for statistical computing and graphics in data science and other domains. It helps build a foundation for understanding how to use programming to develop and maintain software applications.
See salaries and explore the career path for Software Engineer
Data Engineer
Data Engineers use programming to manage and process data. This course provides an introduction to programming using a language called R, a popular language for statistical computing and graphics in data science and other domains. It helps build a foundation for understanding how to use R to manage and process large datasets.
See salaries and explore the career path for Data Engineer
Business Analyst
Business Analysts use data to help businesses understand their customers and make better decisions. This course provides an introduction to programming using a language called R, a popular language for statistical computing and graphics in data science and other domains. It helps build a foundation for understanding how to use R to analyze business data and make recommendations.
See salaries and explore the career path for Business Analyst
Operations Research Analyst
Operations Research Analysts use programming and data to solve problems in the transportation, healthcare, and manufacturing industries. This course provides an introduction to programming using a language called R, a popular language for statistical computing and graphics in data science and other domains. It helps build a foundation for understanding how to use R to develop and implement operations research models.
See salaries and explore the career path for Operations Research Analyst
Financial Analyst
Financial Analysts use programming and data to analyze financial data and make investment recommendations. This course provides an introduction to programming using a language called R, a popular language for statistical computing and graphics in data science and other domains. It helps build a foundation for understanding how to use R to analyze financial data and develop investment strategies.
See salaries and explore the career path for Financial Analyst
Biostatistician
Biostatisticians use programming and data to analyze medical data and design clinical trials. This course provides an introduction to programming using a language called R, a popular language for statistical computing and graphics in data science and other domains. It helps build a foundation for understanding how to use R to analyze medical data and design clinical trials.
See salaries and explore the career path for Biostatistician
Actuary
Actuaries use programming and data to assess risk and develop insurance products. This course provides an introduction to programming using a language called R, a popular language for statistical computing and graphics in data science and other domains. It helps build a foundation for understanding how to use R to assess risk and develop insurance products.
See salaries and explore the career path for Actuary
Software Developer
Software Developers use programming to develop and maintain software applications. This course provides an introduction to programming using a language called R, a popular language for statistical computing and graphics in data science and other domains.
See salaries and explore the career path for Software Developer
Web Developer
Web Developers use programming to develop and maintain websites. This course provides an introduction to programming using a language called R, a popular language for statistical computing and graphics in data science and other domains.
See salaries and explore the career path for Web Developer
Database Administrator
Database Administrators use programming to manage and maintain databases. This course provides an introduction to programming using a language called R, a popular language for statistical computing and graphics in data science and other domains.
See salaries and explore the career path for Database Administrator

Reading list

We've selected 12 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in CS50's Introduction to Programming with R.
Cover image
The R Book
Save
Comprehensive introduction to the R programming language. It covers all of the basics, from data types and operators to functions and loops. It also includes a chapter on graphics, which is essential for data visualization. This book great choice for beginners who want to learn R from the ground up.
The R Book
Hardcover
$$$
The R Book
Kindle Edition
$$$
Cover image
R for Data Science
Save
Practical guide to using R for data science. It covers a wide range of topics, from data manipulation and visualization to statistical modeling and machine learning. This book great choice for intermediate users who want to learn more about R's capabilities.
R for Data Science: Import, Tidy, Transform,...
Paperback
$$
(Deutsch) R für Data Science: Daten importieren, bereinigen,...
Kindle Edition
$$$
Cover image
Information Theory, Inference and Learning...
Save
Comprehensive guide to information theory, inference, and learning algorithms. It covers a wide range of topics, from entropy to Bayesian inference. This book great choice for intermediate users who want to learn more about information theory and its applications.
Information Theory, Inference and Learning...
Hardcover
$$$
Cover image
Deep Learning
Save
Comprehensive guide to deep learning. It covers a wide range of topics, from neural networks to deep learning models. This book great choice for intermediate users who want to learn more about deep learning.
Deep Learning
Paperback
$$$
Deep Learning (Adaptive Computation and Machine...
Hardcover
$$$
Deep Learning (Adaptive Computation and Machine...
Kindle Edition
$$$
Cover image
Reinforcement Learning, second edition
Save
Comprehensive guide to reinforcement learning. It covers a wide range of topics, from Markov decision processes to deep reinforcement learning. This book great choice for intermediate users who want to learn more about reinforcement learning.
Reinforcement Learning, second edition: An...
Hardcover
$$$
Reinforcement Learning, second edition: An...
Kindle Edition
$$$
Cover image
Distributed Optimization and Statistical Learning...
Save
Comprehensive guide to convex optimization. It covers a wide range of topics, from linear programming to conic programming. This book great choice for intermediate users who want to learn more about convex optimization.
Distributed Optimization and Statistical Learning...
Paperback
$$$
Cover image
Machine Learning with R
Save
Practical guide to using R for machine learning. It covers a wide range of topics, from supervised learning to unsupervised learning. This book great choice for intermediate users who want to learn more about R's machine learning capabilities.
Machine Learning with R - Fourth Edition: Learn...
Paperback
$$
Machine Learning with R - Second Edition: Expert...
Kindle Edition
$$
Machine Learning with R - Third Edition: Expert...
Paperback
$$
(Italiano) Machine Learning con R: Conoscere le tecniche per...
Kindle Edition
$$
R: Unleash Machine Learning Techniques
Kindle Edition
$$$
Machine Learning with R: Learn How to Use R to...
Paperback
$$
(中文) Machine learning and R language (3rd edition of the...
Paperback
$$
Cover image
Regression and Other Stories
Save
Modern take on statistical inference, using data science tools. It covers a wide range of topics, from Bayesian inference to machine learning. This book great choice for intermediate users who want to learn more about statistical inference.
Regression and Other Stories (Analytical Methods...
Paperback
$$
Regression and Other Stories (Analytical Methods...
Kindle Edition
$$
Cover image
The Elements of Statistical Learning
Save
Classic introduction to statistical learning. It covers a wide range of topics, from linear regression to machine learning. This book great choice for intermediate users who want to learn more about statistical learning.
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Kindle Edition
$$$
Cover image
R Graphics Cookbook
Save
Collection of recipes for creating graphics in R. The recipes cover a wide range of topics, from basic plots to complex visualizations. This book great choice for intermediate users who want to learn more about R's graphics capabilities.
R Graphics Cookbook
Paperback
Check
R Graphics Cookbook
Kindle Edition
Check
Cover image
R Markdown
Save
Comprehensive guide to R Markdown, a format for creating dynamic reports in R. R Markdown great way to share your work with others, and this book will teach you how to use it effectively.
R Markdown
Paperback
Check
R Markdown
Kindle Edition
Check
Cover image
R Packages
Save
Comprehensive guide to the advanced features of R. It covers topics such as object-oriented programming, high-performance computing, and debugging. This book great choice for experienced users who want to learn more about R's advanced capabilities.
R Packages
Paperback
Check
R Packages
Kindle Edition
Check

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Course image
Effort
6 to 12 hours per week for 7 weeks
Level
Introductory
Via
edX
Institution
Harvard University
Instructors
David J. Malan
Carter Zenke
Language
English
Transcript
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Explores R, which is highly relevant for statistical computing and graphics in data science and other domains
Taught by David J. Malan, Carter Zenke, who are recognized for their work in R
Provides a strong foundation for beginners in R and statistical computing
Develops skills in data manipulation, visualization, and analysis using R's tidyverse and grammar of graphics
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