Get access to all lessons in this course.
-
Week 1: Getting Started with R
- Welcome to Getting Started with R
- Install R
- Install RStudio
- Projects
- Files in R
- Packages
- Import Data
- Objects and Functions
- Examine our Data
- Import Our Data Again
- Getting Help
- Wrapping Up
- R in 3 Months Spring 2022 Week 1 Live Session
-
Week 2: Fundamentals of R (RMarkdown)
- Welcome to Fundamentals of R
- RMarkdown Overview
- YAML
- Text
- Code Chunks
- Wrapping Up
- R in 3 Months Spring 2022 Week 2 Project Assignment
- R in 3 Months Spring 2022 Week 2 Office Hours
- R in 3 Months Spring 2022 Week 2 Live Session
-
Week 3: Fundamentals of R (Data Wrangling and Analysis)
- Getting Started
- The Tidyverse
- select
- mutate
- filter
- summarize
- group_by
- count
- arrange
- Create a New Data Frame
- Crosstabs
- Wrapping Up
- R in 3 Months Spring 2022 Week 3 Office Hours
- R in 3 Months Spring 2022 Week 3 Live Session
- R in 3 Months Spring 2022 Week 3 Project Assignment
-
Week 4: Fundamentals of R (Data Visualization)
- An Important Workflow Tip
- The Grammar of Graphics
- Scatterplots
- Histograms
- Bar Charts
- color and fill
- scales
- Text and Labels
- Plot Labels
- Themes
- Facets
- Save Plots
- Wrapping Up
- You Did It!
- R in 3 Months Spring 2022 Week 4 Office Hours
- R in 3 Months Spring 2022 Week 4 Live Session
- R in 3 Months Spring 2022 Week 4 Project Assignment
-
Week 5: Catch-Up Week
- R in 3 Months Spring 2022 Week 5 Office Hours
- R in 3 Months Spring 2022 Week 5 Project Assignment: ASSIGNMENT AMNESTY
-
Week 6: Git + GitHub
- What is Git? What is GitHub?
- Why Should You Learn to Use Git and GitHub?
- Update Everything
- Install Git
- Configure Git
- Create a Local Git Repository
- Commits
- Commit History
- GitHub Repositories
- Connect RStudio and GitHub
- Push an RStudio Project to a GitHub Repository
- Pull a GitHub Repository to an RStudio Project
- Keep RStudio and GitHub in Sync
- R in 3 Months Spring 2022 Week 6 Office Hours
- R in 3 Months Spring 2022 Week 6 Live Session
- R in 3 Months Spring 2022 Week 6 Project Assignment
-
Week 7: Going Deeper with R (Advanced Data Wrangling, Part 1)
- Overview
- Importing Data
- Tidy Data
- Reshaping Data
- Dealing with Missing Data
- Changing Variable Types
- Advanced Variable Creation
- Advanced Summarizing
- Binding Data Frames
- R in 3 Months Spring 2022 Week 7 Office Hours
- R in 3 Months Spring 2022 Week 7 Project Assignment
- R in 3 Months Spring 2022 Week 7 Live Session
-
Week 8: Going Deeper with R (Advanced Data Wrangling, Part 2)
- Functions
- Merging Data
- Renaming Variables
- Quick Interlude to Reorganize our Code
- Exporting Data
- R in 3 Months Spring 2022 Week 8 Office Hours
- R in 3 Months Spring 2022 Week 8 Live Session
- R in 3 Months Spring 2022 Week 8 Project Assignment
-
Week 9: Catch-Up Week
- R in 3 Months Spring 2022 Week 9 Office Hours
- R in 3 Months Spring 2022 Week 9 - Assignment Amnesty
-
Week 10: Going Deeper with R (Advanced Data Visualization, Part 1)
- Data Visualization Best Practices
- Tidy Data
- Pipe Data Into ggplot
- Reorder Plots to Highlight Findings
- Line Charts
- Use Color to Highlight Findings
- Declutter
- Use the scales Package for Nicely Formatted Values
- Use Direct Labeling
- R in 3 Months Spring 2022 Week 10 Office Hours
- R in 3 Months Spring 2022 Week 10 Live Session
- R in 3 Months Spring 2022 Week 10 Project Assignment
-
Week 11: Going Deeper with R (Advanced Data Visualization, Part 2)
- Use Axis Text Wisely
- Use Titles to Highlight Findings
- Use Color in Titles to Highlight Findings
- Use Annotations to Explain
- Tweak Spacing
- Customize Your Theme
- Customize Your Fonts
- Try New Plot Types
- R in 3 Months Spring 2022 Week 11 Live Session
- R in 3 Months Spring 2022 Week 11 Office Hours
- R in 3 Months Spring 2022 Week 11 Project Assignment
-
Week 12: Going Deeper with R (Advanced RMarkdown)
- Advanced Markdown Text Formatting
- Tables
- Advanced YAML
- Inline R Code
- Making Your Reports Shine: Word Edition
- Making Your Reports Shine: HTML Edition
- Making Your Reports Shine: PDF Edition
- Presentations
- Dashboards
- Other Formats
- You Did It!
- R in 3 Months Spring 2022 Week 12 Office Hours
- R in 3 Months Spring 2022 Week 12 Live Session
- R in 3 Months Spring 2022 Week 12 Project Assignment
-
Week 13: Final Assignment
- R in 3 Months Spring 2022 Week 13 Office Hours
- R in 3 Months Spring 2022 Week 13 Live Session
- R in 3 Months Spring 2022 Final Project Assignment
-
WEEK 14: Retrospective
R in 3 Months (Spring 2022)
scales
This lesson is locked
This lesson is called scales, part of the R in 3 Months (Spring 2022) course. This lesson is called scales, part of the R in 3 Months (Spring 2022) course.
If the video is not playing correctly, you can watch it in a new window
Transcript
Click on the transcript to go to that point in the video. Please note that transcripts are auto generated and may contain minor inaccuracies.
Your Turn
Complete the scales sections of the data-visualization-exercises.Rmd file.
Learn More
scales Resources
Want to see all the built-in colors you can use? Here's a cheatsheet.
There is information on the tidyverse website about the various scales (scale_color_brewer, scale_color_viridirs_d, scale_y_continuous, etc.).
Chapter 8 of Data Visualization: A Practical Introduction
There are a lot of other packages that give you color/fill palettes you can work with. See especially the paleteer package, which is a meta palette package, give you access to palettes from many other packages.
You need to be signed-in to comment on this post. Login.
David Keyes Founder
April 5, 2021
Yup! Check out the
seq()
function. For example, this:breaks = seq(from = 0, to = 8, by = 1))``` Does the same thing as: ```scale_y_continuous(limits = c(0, 8), breaks = c(0, 1, 2, 3, 4, 5, 6, 7, 8))```
Jeff Shandling
April 12, 2021
Hi David-
I'm a bit confused on where to find the distinct colors for each palette. For example, where did you find "Dark2"? Thanks Jeff
Lina Khan
October 6, 2021
For the change of colors (or fill) using scale_color_brewer, I typed palette.pals() to see the options available. It shows the option used in the solutions ("Dark2") as "Dark 2" with a space inside, which I when I ran didn't work. But Dark2 worked. I also tried "Okabe-Ito" as shown in the palette.pals() list, but that didn't work either. Anyway, why would the options show with the spacing (or whatever else?) not correct? Also, when would we need to worry about adding 'd' (like scale_color_veridis_d, as shown in the lesson), vs not? Thanks!
Juan Clavijo
October 18, 2021
Hi! I'm typing this code in to change the color of the bar graph but the color is not changing from the standard colors. However, when I copy and paste your code from the solutions (which looks the same at least to me) it does work. Why might that happen?
ggplot(data = sleep_by_gender, mapping = aes(x = gender, y = avg_sleep, fill = gender)) + geom_col() + scale_color_brewer(palette = "Dark2")
Ellen Wilson
January 11, 2023
The pre-set palettes are very cool! I'm wondering, though, how to use an organization's brand colors? How can that be set up? The ideal would be if there was a way to get sequential, qualitative, and diverging palette options based on brand colors...