Get access to all lessons in this course.
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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WEEK 14: Retrospective
R in 3 Months (Spring 2022)
Reorder Plots to Highlight Findings
This lesson is locked
This lesson is called Reorder Plots to Highlight Findings, part of the R in 3 Months (Spring 2022) course. This lesson is called Reorder Plots to Highlight Findings, part of the R in 3 Months (Spring 2022) course.
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Transcript
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Your Turn
Make a bar chart that shows race/ethnicity in Beaverton SD 48J. As before, filter your data to only include 2018-2019 data and only include Beaverton SD 48J. Then, do the following:
Using the
reorder()
function, make a bar chart that shows the percent of race/ethnicity groups in descending orderMake the same bar chart using
mutate()
andfct_reorder()
to reorder the race/ethnicity groups
Learn More
I mention that the fct_reorder()
function comes from the forcats
package in the video. Here’s how the documentation lays out the goal of forcats
:
R uses factors to handle categorical variables, variables that have a fixed and known set of possible values. Factors are also helpful for reordering character vectors to improve display. The goal of the forcats package is to provide a suite of tools that solve common problems with factors, including changing the order of levels or the values.
As the tweet below demonstrates, using factors can be complicated.
making any bar graphs with factors/categories is still my least favorite thing in the world pic.twitter.com/Neg8qp9ueq
— Damie Pak (@pakdamie) May 12, 2020
If you want to dig into factors a bit more, start with Chapter 15 of R for Data Science and Chapter 10 of Stat 545.
I'd also suggest reading the 2017 article Wrangling Categorical Data in R by Amelia McNamara and Nicholas Horton. Amelia McNamara also gave a talk in 2019 at rstudio::conf about factors , which is very helpful.
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Alberto Espinoza
October 7, 2022
David, are there instances where you prefer to use reorder() vs. fct_reorder() or vice-versa? I understand both concepts but not sure in which situations to employ either. Or is it a matter of preference?