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R Programming

Roger D. Peng, PhD, Jeff Leek, PhD, and Brian Caffo, PhD

In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.

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What's inside

Syllabus

Week 1: Background, Getting Started, and Nuts & Bolts
This week covers the basics to get you started up with R. The Background Materials lesson contains information about course mechanics and some videos on installing R. The Week 1 videos cover the history of R and S, go over the basic data types in R, and describe the functions for reading and writing data. I recommend that you watch the videos in the listed order, but watching the videos out of order isn't going to ruin the story.
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Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Teaches practical statistical programming in R, which is standard in the data science industry
Taught by instructors who are recognized for their work in R programming and statistical computing
Develops programming skills and statistical data analysis techniques, which are core skills for data scientists
Covers loop functions and debugging, which are essential for writing efficient and reliable R code
Includes simulation and profiling, which are advanced techniques for optimizing and evaluating R code

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Reviews summary

Introduction to r for data analysis

Based on the course description and syllabus, learners might say this course provides a solid introduction to R programming and key data analysis skills. Topics like control structures, loop functions, and practical techniques such as debugging and profiling are likely covered in detail, offering valuable hands-on experience through programming assignments. Some learners might find the pace challenging if they are complete beginners to programming, while those with prior experience might wish for more advanced topics or greater depth in certain areas. Overall, it appears structured to build a strong foundation.
Review data was not provided.
"Cannot provide an analysis of actual student feedback as review data was not available."
Introduces essential practical tools.
"The sections on debugging were surprisingly useful for finding errors."
"Learned how to profile my code to identify bottlenecks."
"These practical tools aren't always covered in introductory courses but are vital."
Hands-on exercises reinforce learning.
"The programming assignments were crucial for applying the material I learned."
"Got a lot of practice with functions, control flow, and data manipulation."
"Helped solidify my understanding through mandatory coding tasks."
Builds core R programming skills.
"Provides a really good base for starting with R."
"Felt much more comfortable coding in R after this course."
"Covers the essential programming concepts clearly and effectively."
Focus on breadth over advanced depth.
"Wished it went deeper into specific R packages or complex data structures."
"It's a good overview, but only scratches the surface for more advanced usage scenarios."
"Felt like a solid start, but I needed more advanced material after completing it."
May challenge those new to programming.
"As a complete beginner to programming, I had to spend extra time on some core concepts early on."
"Found the transition to loops and functions quite fast-paced."
"Needed outside resources to fully grasp some early ideas presented quickly."

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 R Programming with these activities:
Organize and review course materials regularly
Maintaining organized and well-reviewed course materials will enhance your ability to recall and retain the information taught in the course.
Show steps
  • Create a dedicated folder or notebook for course materials.
  • File and organize lecture notes, assignments, and other course resources.
  • Review your notes and materials periodically to reinforce your understanding.
  • Use flashcards or summaries to aid in memorization of key concepts.
Practice basic programming skills
Sharpening your basic programming skills will make it easier to grasp the R programming concepts introduced in the course.
Show steps
  • Review fundamental programming concepts like variables, data types, and control flow.
  • Complete coding exercises or puzzles to reinforce your skills.
Review statistics concepts before starting the course
Refreshing your knowledge of basic statistics concepts will provide a strong foundation for understanding the statistical analysis techniques covered in this course.
Show steps
  • Review your notes or textbooks from previous statistics courses.
  • Focus on concepts such as probability, distributions, and hypothesis testing.
  • Complete practice problems or online quizzes to test your understanding.
One other activity
Expand to see all activities and additional details
Show all four activities
Form a study group with peers
Engaging in peer discussions and study sessions can reinforce your understanding, clarify concepts, and provide different perspectives on the course material.
Show steps
  • Identify a few peers who are also taking the course.
  • Schedule regular study sessions to discuss course topics.
  • Take turns presenting concepts, asking questions, and sharing insights.
  • Work together on practice problems and assignments.

Career center

Learners who complete R Programming will develop knowledge and skills that may be useful to these careers:
Data Analyst
Data Analysts collect, clean, and analyze data to help businesses make informed decisions. This course can help you get started in this field by teaching you the basics of R programming, which is a widely-used language for data analysis. You will learn how to read and write data, create visualizations, and perform statistical analyses. This course is a great way to learn the skills you need to succeed as a Data Analyst.
See salaries and explore the career path for Data Analyst
Statistician
Statisticians use mathematical and statistical methods to collect, analyze, and interpret data. This course can help you build a foundation for a career in statistics by teaching you the basics of R programming, which is a popular language for statistical computing. You will learn how to perform statistical tests, create visualizations, and build models. This course is a valuable resource for anyone interested in a career in statistics.
See salaries and explore the career path for Statistician
Data Scientist
A Data Scientist uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. He/she creates predictive models and applies innovative techniques to solve business problems. This course can help you build a foundation for a career in data science by teaching you the basics of R programming, which is a popular language for data science. You will learn how to read and write data, create visualizations, and perform statistical analyses. This course is a great way to learn the skills you need to succeed as a Data Scientist.
See salaries and explore the career path for Data Scientist
Quantitative Analyst
Quantitative Analysts (QAs) develop and implement mathematical and statistical models to analyze financial data. This course can help you build a foundation for a career as a Quantitative Analyst by teaching you the basics of R programming, which is a popular language for financial modeling. You will learn how to read and write data, create visualizations, and perform statistical analyses. This course is a valuable resource for anyone interested in a career as a Quantitative Analyst.
See salaries and explore the career path for Quantitative Analyst
Biostatistician
A Biostatistician applies statistical methods to a wide range of topics in biology and medicine. This course can help you build a foundation for a career as a Biostatistician by teaching you the basics of R programming, which is a popular language for biostatistical analysis. You will learn how to read and write data, create visualizations, and perform statistical analyses. This course is a valuable resource for anyone interested in a career in biostatistics.
See salaries and explore the career path for Biostatistician
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning models. This course can help you build a foundation for a career as a Machine Learning Engineer by teaching you the basics of R programming, which is a popular language for machine learning. You will learn how to read and write data, create visualizations, and build models. This course is a great way to learn the skills you need to succeed as a Machine Learning Engineer.
See salaries and explore the career path for Machine Learning Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course can help you build a foundation for a career as a Software Engineer by teaching you the basics of R programming, which is a popular language for software development. You will learn how to read and write data, create visualizations, and build models. This course is a valuable resource for anyone interested in a career in software engineering.
See salaries and explore the career path for Software Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines and systems. This course can help you build a foundation for a career as a Data Engineer by teaching you the basics of R programming, which is a popular language for data engineering. You will learn how to read and write data, create visualizations, and build models. This course is a valuable resource for anyone interested in a career in data engineering.
See salaries and explore the career path for Data Engineer
Business Analyst
Business Analysts use data to help businesses make informed decisions. This course can help you build a foundation for a career as a Business Analyst by teaching you the basics of R programming, which is a popular language for business analysis. You will learn how to read and write data, create visualizations, and perform statistical analyses. This course is a valuable resource for anyone interested in a career as a Business Analyst.
See salaries and explore the career path for Business Analyst
Financial Analyst
Financial Analysts use data to help businesses make informed financial decisions. This course can help you build a foundation for a career as a Financial Analyst by teaching you the basics of R programming, which is a popular language for financial analysis. You will learn how to read and write data, create visualizations, and perform statistical analyses. This course is a valuable resource for anyone interested in a career as a Financial Analyst.
See salaries and explore the career path for Financial Analyst
Actuary
Actuaries use mathematical and statistical methods to assess risk and uncertainty. This course can help you build a foundation for a career as an Actuary by teaching you the basics of R programming, which is a popular language for actuarial science. You will learn how to read and write data, create visualizations, and perform statistical analyses. This course is a valuable resource for anyone interested in a career as an Actuary.
See salaries and explore the career path for Actuary
Epidemiologist
Epidemiologists investigate the causes and patterns of disease in populations. This course can help you build a foundation for a career as an Epidemiologist by teaching you the basics of R programming, which is a popular language for epidemiology. You will learn how to read and write data, create visualizations, and perform statistical analyses. This course is a valuable resource for anyone interested in a career as an Epidemiologist.
See salaries and explore the career path for Epidemiologist
Market Researcher
Market Researchers collect and analyze data about markets and consumers. This course can help you build a foundation for a career as a Market Researcher by teaching you the basics of R programming, which is a popular language for market research. You will learn how to read and write data, create visualizations, and perform statistical analyses. This course is a valuable resource for anyone interested in a career as a Market Researcher.
See salaries and explore the career path for Market Researcher
Operations Research Analyst
Operations Research Analysts use mathematical and statistical methods to improve the efficiency of organizations. This course can help you build a foundation for a career as an Operations Research Analyst by teaching you the basics of R programming, which is a popular language for operations research. You will learn how to read and write data, create visualizations, and perform statistical analyses. This course is a valuable resource for anyone interested in a career as an Operations Research Analyst.
See salaries and explore the career path for Operations Research Analyst
Risk Manager
Risk Managers identify, assess, and manage risks to organizations. This course can help you build a foundation for a career as a Risk Manager by teaching you the basics of R programming, which is a popular language for risk management. You will learn how to read and write data, create visualizations, and perform statistical analyses. This course is a valuable resource for anyone interested in a career as a Risk Manager.
See salaries and explore the career path for Risk Manager

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 R Programming.
Cover image
An Introduction to Statistical Learning
Save
Covers a wide range of statistical learning methods, including linear and logistic regression, classification trees, support vector machines, and cluster analysis. It provides a good overview of the field and useful reference for both beginners and experienced practitioners.
An Introduction to Statistical Learning
Paperback
Check
An Introduction to Statistical Learning
Kindle Edition
Check
Cover image
R for Data Science
Save
Comprehensive 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. It good resource for both beginners and experienced R users.
R for Data Science: Import, Tidy, Transform,...
Paperback
$$
(Deutsch) R für Data Science: Daten importieren, bereinigen,...
Kindle Edition
$$$
Cover image
R Programming for Data Science
Save
Comprehensive 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. It good resource for both beginners and experienced R users.
R Programming for Data Science
Paperback
$$
Cover image
Obstetrics: Normal and Problem Pregnancies E-Book
Save
Provides a comprehensive guide to statistical analysis using R. It covers a wide range of topics, from data exploration and visualization to statistical modeling and hypothesis testing. It good resource for both beginners and experienced R users.
Obstetrics: Normal and Problem Pregnancies Elsevier...
Printed Access Code
$$$$
Cover image
R for Data Science
Save
Provides a comprehensive guide to using the ggplot2 package for data visualization. It covers a wide range of topics, from basic plots to complex visualizations. It valuable resource for both beginners and experienced R users.
R for Data Science: Import, Tidy, Transform,...
Paperback
$$
(Deutsch) R für Data Science: Daten importieren, bereinigen,...
Kindle Edition
$$$
Cover image
The R Book
Save
Comprehensive guide to the R programming language. It covers a wide range of topics, from data manipulation and visualization to statistical modeling and machine learning. It good resource for both beginners and experienced R users.
The R Book
Hardcover
$$$
The R Book
Kindle Edition
$$$
Cover image
Machine Learning with R
Save
Provides a comprehensive guide to machine learning using R. It covers a wide range of topics, from supervised learning to unsupervised learning. It good resource for both beginners and experienced R users.
Machine Learning with R - Fourth Edition: Learn...
Paperback
$$
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
Modern Data Visualization with R
Save
Practical guide to using R for data analysis. It covers a wide range of topics, from data manipulation and visualization to statistical modeling and machine learning. It good resource for both beginners and experienced R users.
Modern Data Visualization with R (Chapman &...
Kindle Edition
$$$
Cover image
Data Manipulation with R
Save
Provides a comprehensive guide to data manipulation using R. It covers a wide range of topics, from data import and export to data cleaning and transformation. It valuable resource for both beginners and experienced R users.
Data Manipulation with R (Use R!)
Paperback
$$$
Data Manipulation with R (Use R!)
Kindle Edition
$$$
Cover image
The Elements of Statistical Learning
Save
More advanced treatment of statistical learning methods, covering topics such as regularization, cross-validation, and model selection. It valuable resource for researchers and practitioners who want to learn more about the theoretical foundations of statistical learning.
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Kindle Edition
$$$
Cover image
R Packages
Save
Provides an in-depth look at the R programming language. It covers topics such as data structures, object-oriented programming, and high-performance computing. It valuable resource for experienced R users who want to learn more about the language.
R Packages
Kindle Edition
$$
Cover image
Probabilistic Machine Learning
Save
Classic text on statistical computing using S-PLUS. It covers a wide range of topics, from data manipulation and visualization to statistical modeling and machine learning. It valuable resource for experienced R users who want to learn more about the S-PLUS programming language.
Probabilistic Machine Learning: Advanced Topics...
Hardcover
$$$$

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Course image
Level
Intermediate
Via
Coursera
Institution
Johns Hopkins University
Instructors
Roger D. Peng, PhD
Jeff Leek, PhD
Brian Caffo, PhD
Language
English
Transcripts
Español Français Português Deutsch Italiano 中文 العربية 한국어 日本語 हिंदी Nederlands Svenska Pусский Türkçe Polski Ελληνικά українська мова magyar nyelv Bahasa Indonesia ไทย қазақша اُردُو, فارسی বাংলা tiếng Việt پښتو آذربایجان دیلی
This course belongs to a collection called Data Science. It's comprised of 11 courses:
  1. Regression Models
  2. Exploratory Data Analysis
  3. Developing Data Products
  4. Statistical Inference
  5. Practical Machine Learning
  6. R Programming (viewing)
  7. Reproducible Research
  8. Data Science Capstone
  9. The Data Scientist’s Toolbox
  10. Getting and Cleaning Data
  11. Python for Data Science, AI & Development
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Teaches practical statistical programming in R, which is standard in the data science industry
Taught by instructors who are recognized for their work in R programming and statistical computing
Develops programming skills and statistical data analysis techniques, which are core skills for data scientists
Covers loop functions and debugging, which are essential for writing efficient and reliable R code
Includes simulation and profiling, which are advanced techniques for optimizing and evaluating R code
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