We may earn an affiliate commission when you visit our partners.
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/mxo3iy/r
Three deals to help you save
We found three deals and offers that may be relevant to this course.
Save money when you learn.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
Use code at checkout.
Valid until December 4
Save on Select Programs
Stay ahead in a competitive job market and save with these programs.
Take
30%
off
EDXCYBER24
Valid for a limited time only
Future-proof your career
Access O'Reilly books, live events, courses, and more. Save with an annual subscription.
Take
15%
off
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.
- 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.
Good to know
Good to know
Know what's good ,
what to watch for , 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
Save this course
Save R Programming Fundamentals to your list so you can find it easily later:
Save
Save
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
Show steps
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"
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Review basic statistics
Show steps
Brush up on essential statistical concepts to improve understanding throughout the course
Browse courses on
Descriptive Statistics
Show steps
Show steps
Show steps
- Review notes or textbooks on basic statistics
- Complete practice problems or quizzes to test comprehension
Read "The R Book"
Show steps
Enhance understanding and expand R knowledge through a comprehensive reference book
View
The R Book
on Amazon
Show steps
Show steps
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
Show steps
Reinforce R programming concepts through guided exercises
Show steps
Show steps
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
Show steps
Engage with peers, ask questions, and contribute to a collaborative learning environment
Show steps
Show steps
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
Show steps
Expand R knowledge by exploring additional packages and resources
Show steps
Show steps
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
Show steps
Apply R skills to create a visual representation of data, enhancing comprehension
Show steps
Show steps
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
Show steps
Consolidate understanding by creating a written resource that demonstrates R skills
Show steps
Show steps
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
Show steps
Gain practical experience and contribute to the R community
Show steps
Show steps
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 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.
Statistician
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.
Data Scientist
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.
Software Engineer
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.
Machine Learning 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.
Data 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.
Business Analyst
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.
Quantitative 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.
Market Researcher
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.
Actuary
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.
Epidemiologist
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.
Biostatistician
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.
Financial Analyst
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.
Risk Manager
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.
Operations Research Analyst
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.
For more career information including salaries, visit:
OpenCourser.com/course/mxo3iy/r
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.
Save
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.
Save
Collection of recipes for common R tasks. It valuable reference for R users of all levels.
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.
Covers the basics of statistics, with a focus on applications in biology. It valuable resource for students and researchers in the life sciences.
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.
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.
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.
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.
Save
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.
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.
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.
Save
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.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/mxo3iy/r
Share
Similar courses
Here are nine courses similar to
R Programming Fundamentals.
R Programming
OpenCourser.com/course/mtu9ag/r
R Programming
Most relevant
Scraping Media from the Web with R
OpenCourser.com/course/655d6r/scraping
Scraping Media from the Web with R
Most relevant
Creating a Custom Function in R
OpenCourser.com/course/sppp0f/creating
Creating a Custom Function in R
Most relevant
Understanding R Function Scoping and Closures
OpenCourser.com/course/vatm3k/understanding
Understanding R Function Scoping and Closures
Most relevant
Importing Formatted Text Files: R Playbook
OpenCourser.com/course/42vexn/importing
Importing Formatted Text Files: R Playbook
Most relevant
Getting Started with Tidyverse
OpenCourser.com/course/7vp5yt/getting
Getting Started with Tidyverse
Most relevant
Foundations of strategic business analytics
OpenCourser.com/course/nyaqrh/foundations
Foundations of strategic business analytics
Most relevant
Using probability distributions for real world problems in R
OpenCourser.com/course/gl490z/using
Using probability distributions for real world problems...
Most relevant
Bayesian Statistics: Techniques and Models
OpenCourser.com/course/1lplko/bayesian
Bayesian Statistics: Techniques and Models
Most relevant
Effort
2 to 3 hours per week for 6 weeks
Level
Introductory
Via
edX
Institution
Stanford University
Instructor
Susan Holmes
Language
English
Transcript
English
Good to know
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
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser