Course Summary
This course teaches how to use the Tidyverse tools for data visualization in R. Students will learn how to create effective visualizations and communicate their insights to others.Key Learning Points
- Learn Tidyverse tools for data visualization in R
- Create effective visualizations to communicate insights
- Explore different types of plots and graphics
Related Topics for further study
Learning Outcomes
- Create effective visualizations using Tidyverse tools
- Communicate insights through data visualization
- Explore different types of plots and graphics
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of R programming
- Familiarity with data analysis concepts
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Data Visualization with ggplot2
- R Programming
Related Education Paths
Notable People in This Field
- David Robinson
- Julia Silge
Related Books
Description
Data visualization is a critical part of any data science project. Once data have been imported and wrangled into place, visualizing your data can help you get a handle on what’s going on in the data set. Similarly, once you’ve completed your analysis and are ready to present your findings, data visualizations are a highly effective way to communicate your results to others. In this course we will cover what data visualization is and define some of the basic types of data visualizations.
Knowledge
- D​istinguish between various types of plots and their uses
- U​se the ggplot2 R package to develop data visualizations
- B​uild effective data summary tables
- B​uild data animations for visual storytelling
Outline
- About This Course
- Data Visualization Background
- General Features of Plots
- Plot Types
- Plot Types
- Histogram
- Densityplot
- Scatterplot
- Barplot
- Boxplot
- Line Plots
- Plot Basics Quiz
- Making Good Plots
- Choose the Right Type of Plot
- Be Mindful When Choosing Colors
- Label the Axes
- Make Sure the Numbers Add Up
- Make Comparisons Easy on Viewers
- Use y-axes That Start at Zero
- Keep It Simple
- Good Plots Quiz
- Plot Generation Process
- Three Questions You Should Ask
- ggplot2 Basics
- ggplot2 Background
- Example Dataset: diamonds
- Scatterplots: geom_point()
- Aesthetics
- Facets
- Geoms
- EDA Plots
- Introduction to ggplot2 Quiz
- ggplot2: Customization
- Colors
- Labels
- Themes
- Custom Theme
- Legends
- Scales
- Coordinate Adjustment
- Annotation
- Vertical and Horizontal Lines
- ggplot2 Customization Quiz
- Tables
- Tables
- Tables in R
- Getting the Data in Order
- An Exploratory Table
- Improving the Table Output
- Annotating Your Table
- Tables in R Quiz
- ggplot2: Extensions
- ggrepel
- directlabels
- cowplot
- patchwork
- gganimate
- ggplot2 Extensions Quiz
- Case Studies
- Case Study #1: Health Expenditures
- Exploratory Data Analysis (EDA)
- Q1: Relationship between coverage and spending?
- Q2: Spending Across Geographic Regions?
- Q3: Coverage and Spending Change Over Time?
- Case Study #2: Firearms
- Exploratory Data Analysis (EDA)
- Q: Fatal Police Shootings and Legislation
- Project: Visualizing Data in the Tidyverse
Summary of User Reviews
Discover how to transform and visualize data with Tidyverse. This course has received positive reviews from users who found it to be an excellent resource for learning data visualization techniques.Pros from User Reviews
- Clear and concise explanations
- Great examples and exercises
- Practical applications for real-world data analysis
- Interactive quizzes to test understanding
Cons from User Reviews
- Lack of advanced topics
- Some users found the pace too slow
- No certification offered upon completion
Microsoft Power BI Masterclass - Expand Excellence
- 4.7
-
![]()
![]()
![]()
![]()
![]()
achieve the next Level of Business Intelligence with Microsoft Power BI Desktop. How to master Power BI Desktop Microsoft Power BI in 4 hours - Expand Excellence ...
Tidy Data: Updated Data Processing With tidyr and dplyr in R
- 4.5
-
![]()
![]()
![]()
![]()
![]()
Learn Data Preprocessing, Data Wrangling and Data Visualisation With the Two Most Happening R Data Science Packages Hello, My name is Minerva Singh....
Exploratory Data Analysis
- 4.7
-
![]()
![]()
![]()
![]()
![]()
This course covers the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models....
Explore Core Data Concepts in Microsoft Azure
- 4.8
-
![]()
![]()
![]()
![]()
![]()
You will identify and describe core data concepts such as relational, non-relational, big data, and analytics, and explore how this technology is implemented with Microsoft Azure....
