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R Programming Fundamentals

4.4 Filled star Filled star Filled star Filled star Empty star
Based on 48 ratings
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Susan Holmes

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.

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What's inside

Learning objectives

  • 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.

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Suitable for data analysts, statisticians, and data scientists seeking to learn the basics of R programming
Taught by one of the creators of the R language, Robert Gentleman, ensuring high-quality instruction
Covers essential topics like data manipulation, visualization, and statistical functions, providing a comprehensive foundation
Introduces the popular ggplot2 package for data visualization, equipping learners with industry-standard tools
Provides hands-on experience with data import, manipulation, and visualization, ensuring practical application of concepts

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Reviews summary

R programming for absolute beginners

According to students, R Programming Fundamentals is a highly effective entry point for absolute beginners in the world of R. Learners consistently praise the course for its clear explanations and accessible pace, making complex concepts easy to grasp. The course provides a solid foundation in R basics, including essential data structures, data import/export, and a valuable introduction to ggplot2. While it is less suitable for those with prior programming experience seeking advanced topics, it excels at providing the necessary building blocks for future data analysis endeavors, though supplemental practice may be needed for real-world application.
Valued insights from the R co-creator.
"Robert Gentleman's insights were interesting."
"Hearing from one of the co-creators of R was a unique and valuable aspect of the course."
Covers essential R basics and key data analysis tools.
"A solid foundational course. It covers all the essential basics of R programming."
"I especially appreciated the modules on data frames and ggplot2. The `ggplot2` introduction was brilliant."
"The data import section was particularly helpful for my work."
Provides an accessible and clear introduction to R.
"Excellent introduction to R. The concepts were explained very clearly, and the hands-on exercises really helped solidify my understanding."
"As a complete beginner, I found it perfectly paced. Highly recommend for anyone starting with R."
"I had no prior programming experience and this course made R accessible. The instructor broke down complex ideas into manageable pieces."
"Perfect for beginners! I was intimidated by R, but this course guided me step-by-step."
Could benefit from more complex examples or integrated projects.
"My only minor critique is that it could benefit from a few more complex examples or perhaps a small project to integrate all the concepts."
"It felt more like a tour than a deep dive, but it got me started."
"I would have liked more hands-on coding challenges to solidify the material."
Too basic for those with prior programming knowledge.
"Found this course to be very basic. If you have any prior programming experience, you might find it redundant."
"I was hoping for more challenging problems or deeper statistical applications. Disappointing for an intermediate learner."
"It focuses too much on installation and simple syntax... didn't really delve deep enough into data manipulation techniques for real-world scenarios."

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 Fundamentals with these activities:
Review basic statistics
Brush up on essential statistical concepts to improve understanding throughout the course
Browse courses on Descriptive Statistics
Show steps
  • Review notes or textbooks on basic statistics
  • Complete practice problems or quizzes to test comprehension
Read "The R Book"
Enhance understanding and expand R knowledge through a comprehensive reference book
View The R Book on Amazon
Show steps
  • Read through specific chapters or sections relevant to the course
  • Take notes and highlight important concepts
  • Work through practice exercises to reinforce learning
  • Reference the book as needed throughout the course
Work through R exercises and tutorials
Reinforce R programming concepts through guided exercises
Show steps
  • Find online R tutorials or exercises
  • Practice writing R code to solve problems
  • Debug and refine your code to improve accuracy
Five other activities
Expand to see all activities and additional details
Show all eight activities
Participate in online discussion forums
Engage with peers, ask questions, and contribute to a collaborative learning environment
Show steps
  • Join online discussion forums or groups related to R
  • Actively participate in discussions by asking questions, sharing insights, and responding to others
  • Review and reflect on discussions to reinforce concepts
Explore R packages and resources
Expand R knowledge by exploring additional packages and resources
Show steps
  • Identify specific areas or tasks where additional R functionality is needed
  • Research and find relevant R packages or resources
  • Install and explore the selected packages or resources
  • Experiment with the new functionality to enhance data analysis and visualization
Develop a data visualization project
Apply R skills to create a visual representation of data, enhancing comprehension
Show steps
  • Choose a dataset and define the visualization goal
  • Explore and clean the data
  • Select appropriate R libraries and functions for visualization
  • Create and refine the visualization
  • Present or share your visualization
Write a blog post or article on R application
Consolidate understanding by creating a written resource that demonstrates R skills
Show steps
  • Choose a topic related to R that you want to share
  • Research and gather information on the topic
  • Write a well-structured blog post or article that includes examples and code snippets
  • Edit, proofread, and publish your written piece
  • Promote and share your blog post or article
Assist in R-related projects or initiatives
Gain practical experience and contribute to the R community
Show steps
  • Identify opportunities to volunteer with R projects or initiatives
  • Contact the project or initiative and express interest in volunteering
  • Contribute your skills and knowledge to the project or initiative
  • Reflect on your experience and its impact on your understanding of R

Career center

Learners who complete R Programming Fundamentals will develop knowledge and skills that may be useful to these careers:
Data Analyst
Data Analysts play a key role in maximizing use of data to make informed decisions. They compile, process, and analyze large quantities of data from a variety of sources. This course provides a solid base of knowledge for individuals seeking to enter or advance in this field. From learning how to work with data to creating visualizations, you'll gain essential skills for becoming a successful Data Analyst.
See salaries and explore the career path for Data Analyst
Statistician
Statisticians apply mathematical and statistical methods to collect, analyze, interpret, and present data. They develop models to analyze complex datasets and draw evidence-based conclusions. Those interested in becoming a Statistician will gain a valuable understanding of statistical functions and techniques through taking this course. It covers theoretical foundations and practical applications, making it an excellent starting point.
See salaries and explore the career path for Statistician
Data Scientist
Data Scientists use advanced analytical techniques and machine learning algorithms to extract insights from large datasets. They collaborate with teams to solve business challenges and drive decision-making. Those aspiring to become Data Scientists will find this course a valuable resource. It introduces core concepts and tools necessary for success, such as data manipulation, statistical modeling, and visualization.
See salaries and explore the career path for Data Scientist
Software Engineer
Software Engineers design, develop, and maintain software applications. They work with stakeholders to understand requirements, translate them into technical specifications, and implement solutions. This course provides a foundation in programming and data analysis, which are essential skills for Software Engineers. It introduces R, a widely-used language in the field, and covers key concepts such as data manipulation and visualization.
See salaries and explore the career path for Software Engineer
Machine Learning Engineer
Machine Learning Engineers design, implement, and maintain machine learning models. They collaborate with teams to deploy models into production systems and monitor their performance. This course provides a solid foundation for those interested in pursuing a career in Machine Learning Engineering. It introduces R, a popular language for machine learning, and covers fundamental concepts such as data preprocessing and model evaluation.
See salaries and explore the career path for Machine Learning Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines and infrastructure. They work with large datasets, ensuring data quality and accessibility for analysis and reporting. This course provides a foundation in data management and analysis, which are essential skills for Data Engineers. It covers data structures, data manipulation techniques, and data visualization.
See salaries and explore the career path for Data Engineer
Business Analyst
Business Analysts use data and analytical techniques to solve business problems and improve decision-making. They work with stakeholders to identify opportunities and develop solutions. This course provides a foundation in data analysis and visualization, which are valuable skills for Business Analysts. It introduces R, a popular language for data analysis, and covers fundamental concepts such as data exploration and presentation.
See salaries and explore the career path for Business Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. They play a key role in risk management and portfolio optimization. This course provides a foundation in statistical modeling and data analysis, which are essential skills for Quantitative Analysts. It introduces R, a widely-used language in the field, and covers key concepts such as regression analysis and time series analysis.
See salaries and explore the career path for Quantitative Analyst
Market Researcher
Market Researchers conduct surveys, analyze data, and provide insights into consumer behavior. They help businesses understand market trends and make informed decisions. This course provides a foundation in data analysis and visualization, which are essential skills for Market Researchers. It introduces R, a popular language for data analysis, and covers fundamental concepts such as data exploration and presentation.
See salaries and explore the career path for Market Researcher
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty. They work in various industries, including insurance, finance, and consulting. This course provides a foundation in statistical modeling and data analysis, which are essential skills for Actuaries. It introduces R, a widely-used language in the field, and covers key concepts such as survival analysis and risk assessment.
See salaries and explore the career path for Actuary
Epidemiologist
Epidemiologists investigate the causes and patterns of disease in populations. They design and conduct studies to identify risk factors and develop prevention strategies. This course provides a foundation in statistical modeling and data analysis, which are essential skills for Epidemiologists. It introduces R, a widely-used language in the field, and covers key concepts such as survival analysis and risk assessment.
See salaries and explore the career path for Epidemiologist
Biostatistician
Biostatisticians apply statistical methods to medical and health-related data. They collaborate with researchers to design studies, analyze data, and interpret results. This course provides a foundation in statistical modeling and data analysis, which are essential skills for Biostatisticians. It introduces R, a widely-used language in the field, and covers key concepts such as clinical trial design and survival analysis.
See salaries and explore the career path for Biostatistician
Financial Analyst
Financial Analysts evaluate financial data and make recommendations for investment decisions. They work with individuals and organizations to create financial plans and manage investments. This course provides a foundation in data analysis and visualization, which are essential skills for Financial Analysts. It introduces R, a popular language for data analysis, and covers fundamental concepts such as financial modeling and risk assessment.
See salaries and explore the career path for Financial Analyst
Risk Manager
Risk Managers identify, assess, and manage risks faced by organizations. They develop and implement strategies to mitigate risks and protect assets. This course provides a foundation in statistical modeling and data analysis, which are essential skills for Risk Managers. It introduces R, a widely-used language in the field, and covers key concepts such as risk assessment and portfolio optimization.
See salaries and explore the career path for Risk Manager
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to optimize business processes and improve efficiency. They work with organizations to identify inefficiencies and develop solutions. This course provides a foundation in statistical modeling and data analysis, which are essential skills for Operations Research Analysts. It introduces R, a widely-used language in the field, and covers key concepts such as linear programming and simulation.
See salaries and explore the career path for Operations Research Analyst

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 Fundamentals.
Cover image
R for Data Science
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Provides a comprehensive introduction to R for data science. It covers the basics of R, as well as more advanced topics such as data manipulation, visualization, and modeling.
R for Data Science: Import, Tidy, Transform,...
Paperback
$$
(Deutsch) R für Data Science: Daten importieren, bereinigen,...
Kindle Edition
$$$
Cover image
R Cookbook
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Collection of recipes for common R tasks. It valuable reference for R users of all levels.
R Cookbook: Proven Recipes for Data Analysis,...
Paperback
$$$
R Cookbook: Proven Recipes for Data Analysis,...
Kindle Edition
$$$
Cover image
Modern Data Visualization with R
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Provides a practical introduction to R for data analysis. It covers the basics of R, as well as more advanced topics such as data manipulation, visualization, and modeling.
Modern Data Visualization with R
Paperback
Check
Modern Data Visualization with R
Kindle Edition
Check
Cover image
Modern Statistics for Modern Biology
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Covers the basics of statistics, with a focus on applications in biology. It valuable resource for students and researchers in the life sciences.
Modern Statistics for Modern Biology
Paperback
$$$
Cover image
Statistical Methods in Bioinformatics
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Provides a comprehensive introduction to statistical methods in bioinformatics. It covers the basics of statistics, as well as more advanced topics such as sequence analysis, gene expression analysis, and population genetics.
Statistical Methods in Bioinformatics: An...
Hardcover
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Statistical Methods in Bioinformatics: An...
Kindle Edition
$$$
Cover image
Machine Learning with R
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Provides a comprehensive introduction to machine learning with R. It covers the basics of machine learning, as well as more advanced topics such as supervised learning, unsupervised learning, and deep learning.
Machine Learning with R - Fourth Edition: Learn...
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Machine Learning with R - Second Edition: Expert...
Kindle Edition
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Machine Learning with R - Third Edition: Expert...
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(Italiano) Machine Learning con R: Conoscere le tecniche per...
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R: Unleash Machine Learning Techniques
Kindle Edition
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Machine Learning with R: Learn How to Use R to...
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(中文) Machine learning and R language (3rd edition of the...
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Cover image
AI-ML for Decision and Risk Analysis
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Provides a comprehensive introduction to data analysis with R. It covers the basics of data analysis, as well as more advanced topics such as data mining, text mining, and statistical modeling.
AI-ML for Decision and Risk Analysis: Challenges...
Hardcover
$$$$
AI-ML for Decision and Risk Analysis: Challenges...
Kindle Edition
$$$$
Cover image
Advanced R, Second Edition
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Provides a comprehensive introduction to ggplot2, a popular R package for data visualization. It covers the basics of ggplot2, as well as more advanced topics such as creating custom visualizations and working with large datasets.
Advanced R, Second Edition (Chapman & Hall/CRC The...
Paperback
$$$
Advanced R, Second Edition (Chapman & Hall/CRC The...
Kindle Edition
$$$
Cover image
Fast Policy
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Provides a gentle introduction to R for beginners. It covers the basics of R, as well as more advanced topics such as data manipulation, visualization, and modeling.
Fast Policy
Paperback
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Fast Policy
Kindle Edition
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Cover image
The Art of Machine Learning
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Provides a comprehensive introduction to R for programmers. It covers the basics of R, as well as more advanced topics such as object-oriented programming, data structures, and algorithms.
The Art of Machine Learning: A Hands-On Guide to...
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The Art of Machine Learning: A Hands-On Guide to...
Kindle Edition
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Cover image
Using R for Introductory Statistics
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Provides a comprehensive introduction to RStudio, a popular integrated development environment for R. It covers the basics of RStudio, as well as more advanced topics such as working with projects, debugging code, and creating presentations.
Using R for Introductory Statistics (Chapman &...
Hardcover
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Using R for Introductory Statistics (Chapman &...
Hardcover
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Using R for Introductory Statistics (Chapman &...
Kindle Edition
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Cover image
R for Everyone
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Provides a gentle introduction to R for beginners. It covers the basics of R, as well as more advanced topics such as data manipulation, visualization, and modeling.
R for Everyone: Advanced Analytics and Graphics...
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R for Everyone: Advanced Analytics and Graphics...
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R for Everyone: Advanced Analytics and Graphics...
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Course image
Rating
4.4 Filled star Filled star Filled star Filled star Empty star
Effort
2 to 3 hours per week for 6 weeks
Level
Introductory
Via
edX
Institution
Stanford University
Instructor
Susan Holmes
Language
English
Transcript
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Suitable for data analysts, statisticians, and data scientists seeking to learn the basics of R programming
Taught by one of the creators of the R language, Robert Gentleman, ensuring high-quality instruction
Covers essential topics like data manipulation, visualization, and statistical functions, providing a comprehensive foundation
Introduces the popular ggplot2 package for data visualization, equipping learners with industry-standard tools
Provides hands-on experience with data import, manipulation, and visualization, ensuring practical application of concepts
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