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)
Tables
This lesson is locked
This lesson is called Tables, part of the R in 3 Months (Spring 2022) course. This lesson is called Tables, part of the R in 3 Months (Spring 2022) course.
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Transcript
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Your Turn
Change the default data frame printing method to use kable
Choose one of the table packages and make an attractive table that shows the top 10 districts with the highest percentage of Hispanic/Latino students (hint: use the
slice_max()
function to get the top 10 and thefmt_percent()
function if you’re usinggt
or thepercent()
function otherwise to display the percentage of Hispanic/Latino students in each)
Please note that in order to use the slice_max()
function, you need to have dplyr
1.0 or greater installed.
Learn More
If you want to read more about df_print options, you can find that here.
If you’re looking for an overview of all of the table packages discussed in this lesson, check out this comprehensive blog post I wrote.
That blog post includes links to some great tutorials on the various packages. Some additional tutorials have come out since then, especially on the gt
package. See, for example, tutorials by:
In case it’s not already clear, the gt
package is really popular, in large part because of this endorsement from Hadley Wickham.
gt = grammar of tables. It aims to do for tables what ggplot2 did for graphics. It’s still early days and tables are surprisingly complicated, but this is a very exciting package by a skilled developer! #rstats https://t.co/138FrCy5th
— Hadley Wickham (@hadleywickham) April 8, 2020
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Abby Isaacson
May 25, 2021
I'm not sure at what step my years got off, but my enrollment_by_race_ethnicity dataset has always displayed '2018-2019' for one year and '2017-18' format for the other. I tried using col_labels to rename a numeric variable but it didn't work. Can I rename in gt, or do I have to go back to the dataset?
Jessica Sickler
May 26, 2021
Is it possible to print a formatted table as an image to insert in report or document outside of R?
Jody Oconnor
May 29, 2021
I'd like to create the 'your turn' table using flextable, but I'm not having much luck with the syntax. Could you post example code using flextable instead of gt please? Thanks!
Atlang Mompe
June 25, 2021
Hi David, it seems that I cannot load the gt library - I have tried to follow your video? Please advise. > library(gt) Error in library(gt) : there is no package called ‘gt’
Andrew Paquin
May 29, 2023
Hi David, As I worked through this stuff today, I received several warnings that "vars" has been deprecated and that I should used c() instead. Has vars gone the way of the pet rock? Is there a hard date after which it simple won't work at all?