Quarto Tutorials

This is a test of quarto

Recreating examples from the quarto website

Part I: Penguins

This is the code from the penguins tutorial

Note

This is a super basic intro.

Note

Note that there are five types of callouts, including: note, tip, warning, caution, and important.

Tip With Caption

This is an example of a callout with a caption.

EMERGENCY, AWFL ON THE LOOSE, WRECKING AMERKWA

BAP Out

Meet Quarto

Quarto enables you to weave together content and executable code into a finished document. To learn more about Quarto see https://quarto.org.

Meet the penguins

The penguins data from the palmerpenguins package contains size measurements for 344 penguins from three species observed on three islands in the Palmer Archipelago, Antarctica.

The plot below shows the relationship between flipper and bill lengths of these penguins.

ggplot(penguins, 
       aes(x = flipper_length_mm, y = bill_length_mm)) +
  geom_point(aes(color = species, shape = species)) +
  scale_color_manual(values = c("darkorange","purple","cyan4")) +
  labs(
    title = "Flipper and bill length",
    subtitle = "Dimensions for penguins at Palmer Station LTER",
    x = "Flipper length (mm)", y = "Bill length (mm)",
    color = "Penguin species", shape = "Penguin species"
  ) +
  theme_minimal()

A basic scatter plot of flipper length versus bill length

Part II: Computations

This is the code from the computations

This dataset contains a subset of the fuel economy data from the EPA. Specifically, we use the mpg dataset from the ggplot2 package.

The visualization below shows a positive, strong, and linear relationship between the city and highway mileage of these cars. Additionally, mileage is higher for cars with fewer cylinders.

ggplot(mpg, aes(x = hwy, y = cty, color = cyl)) +
  geom_point(alpha = 0.5, size = 2) +
  scale_color_viridis_c() +
  theme_minimal()

There are 234 observations in our data.

The average city mileage of the cars in our data is 16.86 and the average highway mileage is 23.44.

The plots in Figure Figure 1 show the relationship between city and highway mileage for 38 popular models of cars. In Figure Figure 1 (a) the points are colored by the number of cylinders while in Figure Figure 1 (b) the points are colored by engine displacement.

ggplot(mpg, aes(x = hwy, y = cty, color = cyl)) +
  geom_point(alpha = 0.5, size = 2) +
  scale_color_viridis_c() +
  theme_minimal()

ggplot(mpg, aes(x = hwy, y = cty, color = displ)) +
  geom_point(alpha = 0.5, size = 2) +
  scale_color_viridis_c(option = "E") +
  theme_minimal()

(a) Color by number of cylinders

(b) Color by engine displacement, in liters

Figure 1: City and highway mileage for 38 popular models of cars.

Part III: Authoring

Introduction

In this analysis, Authoring using housing prices, we build a model predicting sale prices of houses based on data on houses that were sold in the Duke Forest neighborhood of Durham, NC around November 2020. Let’s start by loading the packages we’ll use for the analysis.

library(openintro)  # for data
library(tidyverse)  # for data wrangling and visualization
library(knitr)      # for tables
library(broom)      # for model summary

We present the results of exploratory data analysis in Section 3.2 and the regression model in Section 3.3.

We’re going to do this analysis using literate programming [@knuth1984].

Exploratory data analysis

The data contains 98 houses. As part of the exploratory analysis let’s visualize and summarize the relationship between areas and prices of these houses.

Data visualization

Figure 2 shows two histograms displaying the distributions of price and area individually.

ggplot(duke_forest, aes(x = price)) +
  geom_histogram(binwidth = 50000) +
  labs(title = "Histogram of prices")

ggplot(duke_forest, aes(x = area)) +
  geom_histogram(binwidth = 250) +
  labs(title = "Histogram of areas")

(a) Histogram of prices

(b) Histogram of areas

Figure 2: Histograms of individual variables

Figure 3 displays the relationship between these two variables in a scatterplot.

ggplot(duke_forest, aes(x = area, y = price)) +
  geom_point() +
  labs(title = "Price and area of houses in Duke Forest")

Figure 3: Scatterplot of price vs. area of houses in Duke Forest

Summary statistics

Table 1 displays basic summary statistics for these two variables.

duke_forest %>%
  summarise(
    `Median price` = median(price),
    `IQR price` = IQR(price),
    `Median area` = median(area),
    `IQR area` = IQR(area),
    `Correlation, r` = cor(price, area)
    ) %>%
  kable(digits = c(0, 0, 0, 0, 2))

Table 1: Summary statistics for price and area of houses in Duke Forest

Median price	IQR price	Median area	IQR area	Correlation, r
540000	193125	2623	1121	0.67

Modeling

We can fit a simple linear regression model of the form shown in Equation 1.

price = \hat{\beta}_0 + \hat{\beta}_1 \times area + \epsilon \tag{1}

Table 2 shows the regression output for this model.

price_fit <- lm(price ~ area, data = duke_forest)
  
price_fit %>%
  tidy() %>%
  kable(digits = c(0, 0, 2, 2, 2))

Table 2: Linear regression model for predicting price from area

term	estimate	std.error	statistic	p.value
(Intercept)	116652	53302.46	2.19	0.03
area	159	18.17	8.78	0.00

Note

This is a pretty incomplete analysis, but hopefully the document provides a good overview of some of the authoring features of Quarto!

References