Communication

Creating Quarto Extensions

Author

Julianne Clina

Published

July 29, 2026

Assignment. Create your own Quarto extension and practice what you have learned in the import, transform, visualize, and program chapters.

Create Your Own Quarto Extension

  1. Navigate to the code folder under your name in the 2026-RLAB-Practical-Exercises folder.

  2. Create an _extensions folder and a rlab folder inside extensions.

  3. Create an _extensions.yml and custom.css file inside the rlab folder.

  4. Add the following yaml to _extensions.yml:

---
title: RLAB Quarto Format
author: Morgan Brucks
contributes:
  formats:
    html: 
      title-block-banner: true
      theme: lumen
      css: custom.css
      embed-resources: true
      date-format: long
      code-copy: false
---
  1. Add the format and extension to the yaml of the communication.qmd file.
---
format:
  rlab-html: default
---

  1. Copy the css from this custom.css file into the custom.css file that you just created.

  2. Optional. Experiment with ths css styling in custom.css by changing the images, colors, font sizes, etc. to make it your own.

Data Science Review

Import Data

In this exercise, you will create an in-memory DuckDB database, copy the nycflights13 datasets into it, and use dplyr to create a new data frame for analysis.

Step 1

Create an in-memory DuckDB database by:

  • Connecting to DuckDB with DBI::dbConnect().
  • Using “:memory:” as the database location.
  • Copying the nycflights13 datasets into the database with dbplyr::copy_nycflights13().
con <- DBI::dbConnect(duckdb::duckdb(), dbdir = ":memory:")

dbplyr::copy_nycflights13(con)

Step 2

Using the tables stored in DuckDB, create a new object named flights that:

  • Starts with the weather table.
  • Selects only the origin, time_hour, temp, and wind_speed columns.
  • Joins the flights table using both origin and time_hour as the matching variables.
  • Collects the resulting data into your R session using collect().

After completing these steps, the flights object should contain the selected weather variables along with the matching flight information, ready for use in later exercises.

# Code here...

Write Your Own Filter Function

Create a function named custom_filter() that filters a data frame based on a user-specified variable and value.

Your function should have three arguments:

  • data: the data frame to filter.
  • filter_by: the variable to use for filtering.
  • value: the value to compare against.

Inside the function:

  • Use dplyr::filter() to keep observations where the selected variable is greater than or equal to value.
  • Use embracing ({{ }}) so that filter_by accepts an unquoted column name.

After writing your function, test it by creating a new object named filtered_flights that contains only rows where wind_speed is greater than or equal to 30 miles per hour.

If your function is written correctly, you should be able to call it by supplying a data frame, an unquoted variable name, and a comparison value.

# Code here...

Write Your Own Visualization Function

In this exercise, you will create a reusable function that produces a scatterplot using ggplot2. Your function should allow the user to choose the dataset, the variables to plot, and any optional arguments supported by ggplot2::geom_point().

Step 1

Create a function named custom_visualization() with four arguments:

  1. data: the data frame to plot.
  2. xvar: the variable to display on the x-axis.
  3. yvar: the variable to display on the y-axis.
  4. … additional arguments that will be passed directly to ggplot2::geom_point().

Step 2

Before creating the plot, remove any rows where both the x and y variables are missing (NA).

  • Use dplyr::filter().
  • Inside filter(), use dplyr::if_any().
  • Use embracing ({{ }}) so that the function accepts unquoted variable names.
  • The condition should only keep rows that do not have an NA.

Hint: The expression inside if_any() should refer to the range from xvar to yvar, and the predicate should be is.na.

Step 3

Pipe the filtered data into ggplot2::ggplot().

Use ggplot2::aes() to map:

  • x to xvar
  • y to yvar
  • Remember to use embracing ({{ }}) for both variables.

Step 4

Add a scatterplot layer using ggplot2::geom_point().

Instead of specifying arguments like color or size yourself, forward all optional arguments using .... This allows users to customize the points by passing any arguments accepted by geom_point(), such as: color, size, shape, fill, alpha, stroke, etc.

Step 5

Test your function using the filtered_flights data frame. Create a scatterplot of:

  • temp on the x-axis
  • wind_speed on the y-axis

Customize the points by passing several optional arguments through …, for example:

color = “blue” size = 4 shape = 21 stroke = 2 fill = “red”

If your function is written correctly, these arguments should be passed automatically to ggplot2::geom_point() without modifying the function itself.

# Code here...