Testing out the stories collection.

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Data Visualization

Quarto works well with library(urbnthemes) – the Urban Institute’s R data visualization theme.

Consider an examples using the cars dataset, which contains speed and dist for 50. ?@fig-histogram shows two histograms displaying the distributions of speed and dist individually.

ggplot(cars, aes(x = speed)) +
  geom_histogram(bins = 15) +
  labs(title = "Histogram of speeds")

ggplot(cars, aes(x = dist)) +
  geom_histogram(bins = 15) +
  labs(title = "Histogram of distances")

Histograms of individual variables

Data Tables

The default for df-print is kable. This is the only type of table that works with the table references. kable works well until there is tons of data, where paged thrives.

Table 1 displays basic summary statistics for these two variables.

cars %>%
  summarise(
    `Median speed` = median(speed),
    `IQR speed` = IQR(speed),
    `Median dist` = median(dist),
    `IQR dist` = IQR(dist),
    `Correlation, r` = cor(speed, dist)
  ) %>%
  kable(digits = c(0, 0, 0, 0, 2))

Diagrams

Quarto has access to Mermaid and Graphviz for creating diagrams. Here is a simple example from the Quarto documentation:

flowchart LR
  A[Hard edge] --> B(Round edge)
  B --> C{Decision}
  C --> D[Result one]
  C --> E[Result two]

Graphviz

Equations

First Model

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

Table 2 shows the regression output for this model.

dist_fit <- lm(dist ~ speed, data = cars)
  
dist_fit %>%
  tidy() %>%
  kable(digits = c(0, 0, 2, 2, 2))

Second Model

Let’s fit a more complicated multiple linear regression model of the form shown in Equation 2.

Table 3 shows the regression output for this model.

dist_fit2 <- lm(dist ~ poly(speed, degree = 2, raw = TRUE), data = cars)
  
dist_fit2 %>%
  tidy() %>%
  kable(digits = c(0, 0, 2, 2, 2))

knitr::kable(
  mtcars[1:6, 1:10]
)