Quarto 101

Brian Cervantes Alvarez

What is Quarto?

Quarto allows you to

Present your findings in R, Python, Julia, & Observable
Use Revealjs (this presentation for example)
Have images, videos, and iframe backgrounds imbedded in your documents

“Publish reproducible, production quality articles, presentations, dashboards, websites, blogs, and books in HTML, PDF, MS Word, ePub, and more.” - quarto.org

Teaching with Quarto

# Set Parameters for Data Generation
set.seed(20242025)  # For reproducibility

classOverlap <- 0.2
proportionMajority <- 0.85
sampleSize <- 300
numericVariables <- 20
categoricalVariablePercentage <- 40
missingType <- 'none'
totalPercentageMissing <- 0

Teaching with Quarto

# Set Parameters for Data Generation
classOverlap <- 0.2
proportionMajority <- 0.85
sampleSize <- 300
numericVariables <- 20
categoricalVariablePercentage <- 40
missingType <- 'none'
totalPercentageMissing <- 0

# Generate and Store Simulated Datasets
datasetList <- list()  # Initialize list to store datasets

Teaching with Quarto

# Set Parameters for Data Generation
set.seed(20242025)  # For reproducibility

classOverlap <- 0.2
proportionMajority <- 0.85
sampleSize <- 300
numericVariables <- 20
categoricalVariablePercentage <- 40
missingType <- 'none'
totalPercentageMissing <- 0

# Generate and Store Simulated Datasets
datasetList <- list()  # Initialize list to store datasets

for (i in 1:5) {
  set.seed(100 + i)  # Different seed for each dataset to ensure variability
  
  # Generate datasets using the simtraindatfun function
  datasets <- simtraindatfun(
    classOverlap = classOverlap,
    proportionMajority = proportionMajority,
    sampleSize = sampleSize,
    numericVariables = numericVariables,
    categoricalVariablePercentage = categoricalVariablePercentage,
    missingType = missingType,
    totalPercentageMissing = totalPercentageMissing
  )
  
  # Store the generated datasets in the list with a unique name
  datasetList[[paste0("Dataset_", i)]] <- datasets
}

fizz_buzz <- function(fbnums = 1:50) {
  output <- dplyr::case_when(
    fbnums %% 15 == 0 ~ "FizzBuzz",
    fbnums %% 3 == 0 ~ "Fizz",
    fbnums %% 5 == 0 ~ "Buzz",
    TRUE ~ as.character(fbnums)
  )
  print(output)
}

def fizz_buzz(num):
  if num % 15 == 0:
    print("FizzBuzz")
  elif num % 5 == 0:
    print("Buzz")
  elif num % 3 == 0:
    print("Fizz")
  else:
    print(num)

function FizzBuzz(num)
  if num % 15 == 0
    println("FizzBuzz")
  elseif num % 5 == 0
    println("Buzz")
  elseif num % 3 == 0
    println("Fizz")
  else
    println(num)
  end
end

function fizzBuzz(num) {
  if (num % 15 === 0) {
    return "FizzBuzz";
  } else if (num % 5 === 0) {
    return "Buzz";
  } else if (num % 3 === 0) {
    return "Fizz";
  } else {
    return num;
  }
}

Shiny apps? No problem!

Serverless Shiny? Yep!

#| '!! shinylive warning !!': |
#|   shinylive does not work in self-contained HTML documents.
#|   Please set `embed-resources: false` in your metadata.
#| standalone: true
#| viewerHeight: 650
import matplotlib.pyplot as plt
import numpy as np
from shiny.express import ui, input, render

with ui.sidebar():
    ui.input_slider("n", "N", 0, 100, 20)


@render.plot(alt="A histogram")
def histogram():
    np.random.seed(19680801)
    x = 100 + 15 * np.random.randn(437)
    plt.hist(x, input.n(), density=True)

Wanna create a portfolio website?

Videos!

Wanna learn more?

Here are some pro tips before you jump into using quarto:

Understand how github works (push/pull/commits)
Learn and master how to use Rmarkdown
Understand how to create shiny/interactive applications
Then, jump into learning Quarto!

Wanna see everything that quarto’s got to offer? Click on the quarto logo!

References

https://quarto.org/

https://quarto.org/docs/presentations/revealjs/

https://quarto.org/docs/presentations/revealjs/advanced.html

https://quarto.org/docs/authoring/markdown-basics.html