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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.
Register for this course and see more details by visiting:
OpenCourser.com/course/wpxlr6/cs50
What's inside
Learning objectives
- R
- Rstudio
- Vectors
- Matrices
- Arrays
- Lists
- Data frames
- Conditions
- Functions
- Loops
- Exceptions
- Tidyverse
- R
- Rstudio
- Vectors
- Matrices
- Arrays
- Lists
- Data frames
- Conditions
- Functions
- Loops
- Exceptions
- Tidyverse
Traffic lights
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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.
Work through the R Tutorial
Show steps
Exercises in the interactive tutorial will help you apply core concepts from the course.
Show steps
Show steps
Show steps
- Visit the RStudio website and follow the tutorial.
Create a cheat sheet for R functions
Show steps
Creating a cheat sheet will help you memorize and quickly reference important functions.
Browse courses on
R Functions
Show steps
Show steps
Show steps
- List the most commonly used R functions.
- Describe the purpose and syntax of each function.
Create visualizations with ggplot2
Show steps
ggplot2 is a powerful library for creating visualizations in R, enhancing your ability to communicate data insights.
Browse courses on
Ggplot2
Show steps
Show steps
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
Show steps
Solving coding challenges will enhance your problem-solving skills and deepen your understanding of R.
Browse courses on
Coding Challenges
Show steps
Show steps
Show steps
- Create an account on LeetCode.
- Start solving easy problems in the R section.
Practice writing functions
Show steps
Functions allow you to reuse code and write more modular and maintainable programs.
Browse courses on
Functions
Show steps
Show steps
Show steps
- Define a function to calculate the mean of a vector.
- Call the function with different vectors.
Analyze a real-world dataset
Show steps
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
Show steps
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 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.
Statistician
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.
Data Scientist
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.
Machine Learning Engineer
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.
Quantitative Analyst
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.
Software Engineer
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.
Data 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.
Business Analyst
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.
Operations Research 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.
Financial 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.
Biostatistician
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.
Actuary
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.
Software Developer
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.
Web 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.
Database Administrator
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.
For more career information including salaries, visit:
OpenCourser.com/course/wpxlr6/cs50
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/wpxlr6/cs50
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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
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