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)
Advanced YAML
This lesson is locked
This lesson is called Advanced YAML, part of the R in 3 Months (Spring 2022) course. This lesson is called Advanced YAML, part of the R in 3 Months (Spring 2022) course.
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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
Add a table of contents and make it floating
Adjust default figure height, width, and captions
Add a parameter to the YAML and use it in the body of your report to dynamically create a table of the top 10 districts by various race/ethnicity categories
Learn More
As I was making this course, I was confused about when you would define things like figure height and width in the YAML versus in the setup code chunk. Yihui Xie, developer of RMarkdown, helpfully laid out the differences for me in this RStudio Community thread.
I mention parameterized reporting in the video. This is the idea of making multiple reports at once. The Urban Institute has a nice walkthrough of how this works.
If you're looking for more YAML options, check out RMarkdown: The Definitive Guide as well as the ymlthis
package.
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Abby Isaacson
May 26, 2021
FYI I got this error AFTER I had successfully changed the variable names, so I updated: columns = vars(...)` has been deprecated in gt 0.3.0:
columns = c(...)
insteadcolumns = vars(...)
has been deprecated in gt 0.3.0:columns = c(...)
insteadAbby Isaacson
May 26, 2021
Also, any ideas why my test caption doesn't show up? YAML: output: html_document: toc: TRUE toc_depth: 2 toc_float: TRUE fig_width: 8 fig_height: 10 fig_caption: TRUE
CODE:
Matt M
December 12, 2021
the floating TOC in this course and elsewhere (e.g., https://bookdown.org/yihui/rmarkdown/html-document.html) keep the location within the TOC when I click to a different portion. But I’ve noticed that the TOC for R Graphics Cookbook always goes back to the top upon click (which I find annoying). What setting dictates whether the TOC “stays put” vs “returns to top”?
Elan Sykes
December 31, 2021
How do I know where I can put params$parameter_of_choice into the report and have it knit correctly? I tried to put it into the cols_label() function within quotes and it just showed up as "params$race_ethnicity" in the html doc: enrollment_race_ethnicity_with_percentages %>% filter(race_ethnicity == params$race_ethnicity) %>% filter(schoolyear == "2018_2019") %>% slice_max(percent_ethnicity, n = 10) %>% select(district, percent_ethnicity) %>% gt() %>% cols_label( district = "District", percent_ethnicity = "2018-2019 params$race_ethnicity Population Share" )
Sara Cifuentes
June 9, 2022
Hi, Although I have included this information in my YAML: output: html_document: toc: TRUE toc_depth: 2 toc_float: TRUE
I can't see the table of contents. Should I activate any specific library? Thank you in advance.
Emma S
December 24, 2022
How could you dynamically pick the district with the most growth for the highlight_district data frame in the ggplot chart? We previously explicitly typed "Douglas ESD" as the one with the most growth for the Hispanic/Latino population then highlighted it in orange in the chart. Is there a way to automatically highlight the district with the most growth, based on the race_ethnicity parameter?