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Philip S. Boonstra
This course provides a first look at the R statistical environment. Beginning with step-by-step instructions on downloading and installing the software, learners will first practice navigating R and its companion, RStudio. Then, they will read data into the R environment and prepare it for summary and analysis. A wide variety of concepts will be covered, including sorting rows of data, grouping by variables, summarizing over variables, pivoting, and creating new variables. Then, learners will visualize their data, creating publication-ready plots with relatively little effort. Finally, learners will understand how to set up a project workflow for their own analyses. All concepts taught in this course will be covered with multiple modalities: slide-based lectures, guided coding practice with the instructor, and independent but structured practice.
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What's inside
Syllabus
Become knowledgeable about and conversant in the R environment
Module 1 will cover all of the tasks to get you up and running in R. You’ll learn how to access R, how to navigate it, how to install R packages, and how to create scripts that keep a record of your work. We will also learn about The Global Findex Database 2017, a population-based survey and report that provides a wealth of information on financial access for persons all over the world. Your assessments will use data from The Global Findex Database 2017 to create a table and figure from the report.
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Syllabus
Become knowledgeable about and conversant in the R environment
Module 1 will cover all of the tasks to get you up and running in R. You’ll learn how to access R, how to navigate it, how to install R packages, and how to create scripts that keep a record of your work. We will also learn about The Global Findex Database 2017, a population-based survey and report that provides a wealth of information on financial access for persons all over the world. Your assessments will use data from The Global Findex Database 2017 to create a table and figure from the report.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Introduces beginners to the R statistical environment
Covers data manipulation and preparation tasks in R using the tidyverse packages
Guides learners on data visualization methods in R
Suitable for individuals interested in extracting insights from datasets using the R environment
Involves hands-on practice with the instructor and self-paced exercises
Emphasizes good coding practices and project workflow management in R
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Reviews summary
Foundational r for data arrangement and visualization
According to learners, this course offers a largely positive experience, especially for those new to the R statistical environment. Students praise its clear, step-by-step instructions and hands-on coding practice, which helps in mastering data wrangling with the Tidyverse and creating publication-ready plots using ggplot2. The practical examples, such as those drawn from the Global Findex Database, are frequently highlighted as being highly relevant and helpful. While the course provides a strong foundation and teaches a practical RMarkdown workflow, some experienced R users found the pace to be too slow, suggesting it is most ideal for absolute beginners.
Ideal for novices; might be slow for those with R experience.
"Good course for beginners, but if you have some prior experience with R, parts of it might feel a bit slow."
"It felt a bit too elementary for my needs, but it did cover what it promised. It's truly for absolute beginners."
"I had some prior R knowledge and found this course way too basic and slow-paced. If you already know some R, this is probably not for you."
Instructor's explanations are clear and easy to follow.
"The instructor's explanations were incredibly clear... Highly recommend for absolute beginners."
"Excellent content and delivery. The instructor breaks down difficult concepts into manageable parts."
"The videos are clear and concise, making it easy to grasp complex topics."
Covers setting up projects and sharing results effectively.
"The project workflow module was a bonus, teaching me how to set up my analyses properly."
"I appreciate the focus on 'R hygiene' and learning about RMarkdown to share my work."
"The course teaches crucial skills for organizing and exporting tables and figures from R onto your computer."
Emphasizes practical coding and real-world data tasks.
"The hands-on exercises really solidified my understanding. I especially loved learning about ggplot2; my plots look so much better now."
"The use of the Tidyverse packages made complex tasks feel approachable. The project workflow module was a bonus..."
"I learned how to read data, transform it, and create meaningful visuals... The practical examples using the Findex Database were very relevant."
Provides a clear, gentle introduction to R and RStudio.
"This course was exactly what I needed to get started with R. The instructor's explanations were incredibly clear..."
"As someone completely new to R, I found this course very well-structured. The pace was just right..."
"I now understand how to read data, transform it, and create meaningful visuals. This is a solid foundation."
Some users reported difficulties with software installation.
"I struggled a bit with the initial R and RStudio installation on my specific OS, but once past that, the course content was great."
"Found the initial setup very frustrating and time-consuming. The instructions weren't clear enough for troubleshooting common issues."
"I wish there was more dedicated tech support or clearer guides for installation issues, as this was a significant hurdle."
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 Arranging and Visualizing Data in R with these
activities:
Review statistical inference and probability
Show steps
Refreshes understanding of statistical inference and probability, which are foundational to R programming and statistical modeling.
Browse courses on
Statistical Inference
Show steps
-
Review notes or textbooks on statistical inference and probability
-
Solve practice problems on hypothesis testing, confidence intervals, and probability distributions
Follow tutorials on R basics
Show steps
Provides hands-on practice with R syntax and basic coding concepts, building a foundation for the course.
Show steps
-
Find tutorials on R installation, data types, and basic operations
-
Follow the tutorials step-by-step and experiment with the code
Read 'R for Data Science'
Show steps
Provides a comprehensive review of R programming, data wrangling, and data analysis, complementing the course content.
View
R for Data Science: Import, Tidy, Transform,...
on Amazon
Show steps
-
Read selected chapters of 'R for Data Science'
-
Complete exercises and work through examples in the book
Four other activities
Expand to see all activities and additional details
Show all seven activities
Complete R coding exercises
Show steps
Reinforces understanding of R commands and functions through repetitive practice, improving coding proficiency.
Browse courses on
R Programming
Show steps
-
Find online coding exercises or problem sets on R
-
Solve coding problems independently, referring to documentation or tutorials when needed
Create an R cheat sheet
Show steps
Enhances recall of R commands and concepts by creating a concise summary of key information.
Browse courses on
R Programming
Show steps
-
Review R documentation and tutorials
-
Identify and organize essential commands, functions, and concepts
-
Create a cheat sheet using a text editor or online tool
Answer questions and support peers in online forums
Show steps
Deepens understanding and strengthens problem-solving skills by helping others, reinforcing concepts through articulation.
Browse courses on
R Programming
Show steps
-
Participate in R-related online forums
-
Read and respond to questions posed by other learners
Develop an R project for data visualization
Show steps
Applies and extends course concepts through a hands-on project, fostering practical skills and creativity.
Browse courses on
Data Visualization
Show steps
-
Identify a dataset for visualization
-
Create visualizations using R's graphical capabilities
-
Write a brief report summarizing the insights gained from the visualization
Review statistical inference and probability
Show steps
Refreshes understanding of statistical inference and probability, which are foundational to R programming and statistical modeling.
Browse courses on
Statistical Inference
Show steps
Show steps
Show steps
- Review notes or textbooks on statistical inference and probability
- Solve practice problems on hypothesis testing, confidence intervals, and probability distributions
Follow tutorials on R basics
Show steps
Provides hands-on practice with R syntax and basic coding concepts, building a foundation for the course.
Show steps
Show steps
Show steps
- Find tutorials on R installation, data types, and basic operations
- Follow the tutorials step-by-step and experiment with the code
Read 'R for Data Science'
Show steps
Provides a comprehensive review of R programming, data wrangling, and data analysis, complementing the course content.
View
R for Data Science: Import, Tidy, Transform,...
on Amazon
Show steps
Show steps
Show steps
- Read selected chapters of 'R for Data Science'
- Complete exercises and work through examples in the book
Four other activities
Expand to see all activities and additional details
Show all seven activities
Complete R coding exercises
Show steps
Reinforces understanding of R commands and functions through repetitive practice, improving coding proficiency.
Browse courses on
R Programming
Show steps
Show steps
Show steps
- Find online coding exercises or problem sets on R
- Solve coding problems independently, referring to documentation or tutorials when needed
Create an R cheat sheet
Show steps
Enhances recall of R commands and concepts by creating a concise summary of key information.
Browse courses on
R Programming
Show steps
Show steps
Show steps
- Review R documentation and tutorials
- Identify and organize essential commands, functions, and concepts
- Create a cheat sheet using a text editor or online tool
Answer questions and support peers in online forums
Show steps
Deepens understanding and strengthens problem-solving skills by helping others, reinforcing concepts through articulation.
Browse courses on
R Programming
Show steps
Show steps
Show steps
- Participate in R-related online forums
- Read and respond to questions posed by other learners
Develop an R project for data visualization
Show steps
Applies and extends course concepts through a hands-on project, fostering practical skills and creativity.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Identify a dataset for visualization
- Create visualizations using R's graphical capabilities
- Write a brief report summarizing the insights gained from the visualization
Career center
Learners who complete Arranging and Visualizing Data in R will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to help businesses make informed decisions. This course provides a strong foundation in the R programming language, which is widely used by data analysts. Learners of this course will develop the skills needed to wrangle, visualize, and analyze data.
Data Scientist
Data Scientist
Data Scientists use statistical and machine learning techniques to extract insights from data. This course provides a foundation in the R programming language and tidyverse packages, which are essential tools for data scientists. Learners will develop the skills needed to clean, manipulate, and visualize data.
Data Engineer
Data Engineer
Data Engineers design and build systems to manage and process data. This course provides a foundation in the R programming language, which is commonly used for data engineering tasks such as data cleaning, transformation, and integration.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. This course provides a foundation in the R programming language, which is widely used by statisticians for data analysis and visualization. Learners will develop the skills needed to clean, manipulate, and analyze data.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. This course provides a foundation in the R programming language, which is widely used by quantitative analysts for data analysis and modeling. Learners will develop the skills needed to clean, manipulate, and analyze data.
Information Security Analyst
Information Security Analyst
Information Security Analysts plan and implement security measures to protect an organization's information systems. This course provides a foundation in the R programming language, which is increasingly used for data analysis and visualization in the field of information security.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data to make investment recommendations. This course provides a foundation in the R programming language, which is widely used by financial analysts for data analysis and modeling. Learners will develop the skills needed to clean, manipulate, and analyze financial data.
Market Researcher
Market Researcher
Market Researchers conduct surveys and analyze data to understand consumer behavior. This course provides a foundation in the R programming language, which is widely used by market researchers for data analysis and visualization. Learners will develop the skills needed to clean, manipulate, and analyze data.
Business Analyst
Business Analyst
Business Analysts use data to identify problems and opportunities for businesses. This course provides a foundation in the R programming language, which is increasingly used by business analysts for data analysis and visualization. Learners will develop the skills needed to clean, manipulate, and analyze data.
Biostatistician
Biostatistician
Biostatisticians apply statistical methods to solve problems in biology and medicine. This course provides a foundation in the R programming language, which is widely used by biostatisticians for data analysis and visualization. Learners will develop the skills needed to clean, manipulate, and analyze data.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course provides a foundation in the R programming language, which is increasingly used for data analysis and visualization in the software development process. Learners will develop the skills needed to clean, manipulate, and analyze data to inform software design and development.
Product Manager
Product Manager
Product Managers oversee the development and launch of new products. This course provides a foundation in the R programming language, which is increasingly used by product managers for data analysis and visualization. Learners will develop the skills needed to clean, manipulate, and analyze data to inform product design and development.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to improve the efficiency of operations. This course provides a foundation in the R programming language, which is increasingly used by operations research analysts for data analysis and modeling. Learners will develop the skills needed to clean, manipulate, and analyze data to inform operational decision-making.
Actuary
Actuary
Actuaries assess risk and uncertainty. This course provides a foundation in the R programming language, which is increasingly used by actuaries for data analysis and modeling. Learners will develop the skills needed to clean, manipulate, and analyze data to inform risk assessment and mitigation.
Economist
Economist
Economists study the production, distribution, and consumption of goods and services. This course provides a foundation in the R programming language, which is increasingly used by economists for data analysis and modeling. Learners will develop the skills needed to clean, manipulate, and analyze data to inform economic decision-making.
For more career information including salaries, visit:
OpenCourser.com/course/6z4kez/arranging
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
Arranging and Visualizing Data in R .
Save
Comprehensive introduction to the R programming language for data science. It covers all the basics of R, from data import and manipulation to statistical modeling and visualization. It valuable resource for anyone who wants to learn more about R.
More advanced guide to R programming. It covers topics such as object-oriented programming, debugging, and performance optimization. It valuable resource for anyone who wants to improve their R programming skills.
Save
Comprehensive reference guide to the R programming language. It covers all the basics of R, from data import and manipulation to statistical modeling and visualization. It valuable resource for anyone who wants to learn more about R.
Save
Collection of recipes for common tasks in R. It covers a wide range of topics, from data import and manipulation to statistical modeling and visualization. It valuable resource for anyone who wants to learn more about R.
Comprehensive guide to the ggplot2 package for data visualization in R. It covers all the basics of ggplot2, from creating simple plots to creating complex visualizations. It valuable resource for anyone who wants to learn more about data visualization.
Comprehensive guide to teaching quantitative methods using R. It covers all the basics of R, from data import and manipulation to statistical modeling and visualization. It valuable resource for anyone who wants to learn more about teaching quantitative methods.
Save
Comprehensive guide to the R package ecosystem. It covers all the basics of R packages, from creating and installing packages to using and developing packages. It valuable resource for anyone who wants to learn more about R packages.
Comprehensive guide to applied statistics using the S-PLUS software. It covers all the basics of applied statistics, from data import and manipulation to statistical modeling and visualization. It valuable resource for anyone who wants to learn more about applied statistics.
Comprehensive guide to statistical methods for psychology. It covers all the basics of statistical methods, from data import and manipulation to statistical modeling and visualization. It valuable resource for anyone who wants to learn more about statistical methods for psychology.
Save
Comprehensive guide to data visualization. It covers all the basics of data visualization, from data import and manipulation to creating and designing visualizations. It valuable resource for anyone who wants to learn more about data visualization.
Comprehensive guide to statistical learning. It covers all the basics of statistical learning, from data import and manipulation to statistical modeling and prediction. It valuable resource for anyone who wants to learn more about statistical learning.
Comprehensive guide to machine learning using R. It covers all the basics of machine learning, from data import and manipulation to model training and evaluation. It valuable resource for anyone who wants to learn more about machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/6z4kez/arranging
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Similar courses
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Effort
4 weeks
Level
Intermediate
Via
Coursera
Institution
University of Michigan
Instructor
Philip S. Boonstra
Language
English
This course belongs to a
collection
called
Data Science for Health Research. It's comprised of three courses:
- Linear Regression Modeling for Health Data
- Logistic Regression and Prediction for Health Data
- Arranging and Visualizing Data in R (viewing)
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