Associate Professor, Biostatistics
Arranging and Visualizing Data in R
What You'll Learn
- Become knowledgeable about and conversant in the R environment
- Format and manipulate data within R into suitable formats
- Develop an intuition for doing exploratory data analysis
- Develop a workflow in R
4 Modules
20 Hours
5 hrs per module (approx.)
Rating
About Arranging and Visualizing Data in R
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.
Skills You'll Gain
- Data Presentation
- Data Visualization
- Data Wrangling
- RStudio
- Statistical Graphics
What You'll Earn
- Certificate of Completion:
- Certificates of completion acknowledge knowledge acquired upon completion of a non-credit course or program.
Welcome Message
Welcome to Arranging and Visualizing Data in R! This course introduces learners to the R statistical environment and teaches essential skills for managing and visualizing tabular data. You will learn to navigate R and RStudio, manipulate and summarize datasets, create publication-ready plots, and develop an efficient project workflow. This course is ideal for researchers, public health professionals, and data-driven decision makers with little or no prior experience in R.
This abbreviated syllabus description was created with the help of AI tools and reviewed by staff. The full syllabus is available to those who enroll in the course.
Course Schedule
Module 1: Become Knowledgeable About and Conversant in the R Environment
- Video: Data Science for Health Research: Specialization Introduction
- Reading: Meet Your Instructor
- Reading: Welcome & Course Syllabus
- Discussion Prompt: Meet Your Fellow Global Classmates
- Reading: Pre-Course Survey
- Video: Working with data in R
- Video: Course Roadmap
- Reading: Introduction to and how to use Independent Guides
- Reading: The Global Findex Report 2017 [PDF]
- Graded Assignment: 1.1 Practice Quiz
- Video: R and RStudio: What they are and how to get them Part 1
- Video: R and RStudio: What they are and how to get them Part 2
- Reading: 1.2 Independent Guide
- Graded Assignment: 1.2 Practice Quiz
- Video: Objects and Assignments in R
- Video: Scripts
- Video: Installing R Packages
- Video: RStudio Projects
- Reading: 1.3 Independent Guide
- Graded Assignment: 1.3 Practice Quiz
- Reading: Reminder: Global Findex Database 2017
- Graded: Module 1 Quiz
Module 2: Format and Manipulate Data Within R into Suitable Formats
- Video: Using the Tidyverse to Read in your Data
- Reading: 2.1 Independent Guide
- Reading: R for Data Science: Chapter 11
- Graded Assignment: 2.1 Practice Quiz
- Reading: 2.1 Post-Quiz code document
- Video: Filter
- Video: Grouping and Summarizing
- Video: Grouping and Summarizing Guided Practice
- Reading: 2.2 Independent Guide
- Reading: R for Data Science: Chapter 5
- Reading: Introduction to weighted.mean Function
- Graded Assignment: 2.2 Practice Quiz
- Reading: 2.2 Post-Quiz Code Document
- Video: Understanding R Functions (1)
- Video: Data Pivoting
- Video: Data Pivoting Guided Practice
- Video: Creating New Variables with Mutate
- Video: Creating New Variables with Mutate Guided Practice
- Video: Selecting Variables and Arranging Rows
- Video: Selecting Variables and Arranging Rows Guided Practice
- Reading: 2.3 Independent Guide
- Reading: R for Data Science: Chapter 12 & 5
- Graded Assignment: 2.3 Practice Quiz
- Reading: 2.3 Post-Quiz Code Document
- Video: Joining Data
- Video: Joining Data Guided Practice
- Video: Understanding R Functions (2)
- Video: Understanding R Functions (2) Guided Practice
- Reading: 2.4 Independent Guide
- Reading: R for Data Science: Chapter 13
- Graded Assignment: 2.4 Practice Quiz
- Reading: 2.4 Post-Quiz Code Document
- Discussion Prompt: Module 2 Reflection
- Graded: Module 2 Quiz
Module 3: Develop Intuition for Doing Exploratory Data Analysis
- Video: Visualizing Data in ggplot2
- Video: The Grammar of Graphics
- Video: The Grammar of Graphics Guided Practice
- Video: A First Look at Geoms
- Video: A First Look at Geoms Guided Practice
- Video: Layering Geoms and Optional Aesthetics
- Video: Layering Geoms and Optional Aesthetics Guided Practice
- Reading: 3.1 Independent Guide
- Reading: ggplot2: Chapter 2
- Reading: Countries by Income Category
- Graded Assignment: 3.1 Practice Quiz
- Video: Histograms
- Video: Histograms Guided Practice
- Video: Bar Plots
- Video: Bar Plots Guided Practice
- Video: Boxplots
- Video: Boxplots Guided Practice
- Reading: 3.2 Independent Guide
- Reading: ggplot2: Chapter 4
- Graded Assignment: 3.2 Practice Quiz
- Video: Complex Plots with Tidy Code
- Video: Complex Plots with Tidy Code Guided Practice
- Video: Tidy Plots
- Video: Tidy Plots Guided Practice
- Video: Faceting
- Video: Faceting Guided Practice
- Reading: 3.3 Independent Guide
- Reading: ggplot2: Chapters 10, 11, 12, 16
- Graded Assignment: 3.3 Practice Quiz
- Graded: Module 3 Quiz
Module 4: Develop a Workflow in R
- Video: Sharing your Work
- Video: Sharing your Work Guided Practice
- Reading: 4.1 Independent Guide
- Video: R Markdown
- Video: R Markdown Guided Practice
- Reading: 4.2 Independent Guide
- Video: Working with Data in R: Altogether
- Reading: Bonus Independent Guide
- Reading: Post-Course Survey
- Graded: Recreated Indicator Table and Figure 1.1
Grading Policy
To earn a certificate, learners must achieve an overall grade of 80%. Grading is based entirely on quizzes, with three module quizzes each worth 20% of your final grade, and the quiz in the last module worth 40% of your final grade.
Professor of Epidemiology
Course content developed by U-M faculty and managed by the university. Faculty titles and affiliations are updated periodically.
Beginner Level
There are no formal requirements to take this course. It is expected that learners understand data are important for public health.
