This is a Quarto blog

This is a test post based on the Quarto blog writing guide by Albert Rapp.

Intro

This post is to show some of Quarto’s blog features. I might revisit and edit this post from time to time to experiment with the layout and appearance of my blog. When I first created my blog I used this post to figure out the light and dark toggle options.

Section with subsections

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nam suscipit est nec dui eleifend, at dictum elit ullamcorper. Aliquam feugiat dictum bibendum. Praesent fermentum laoreet quam, cursus volutpat odio dapibus in. Fusce luctus porttitor vehicula. Donec ac tortor nisi. Donec at lectus tortor. Morbi tempor, nibh non euismod viverra, metus arcu aliquet elit, sed fringilla urna leo vel purus.

Inline Code

This is inline code plus a small code chunk.

library(dplyr)
library(ggplot2)
library(scales)

# inspired by tmwr.org
data(ames, package = "modeldata")

ggplot(ames, aes(x = Sale_Price)) + 
  geom_histogram(bins = 50, col= "white") + 
  scale_x_log10(labels = label_dollar(scale_cut = cut_long_scale())) +
  theme(
    axis.text.x  = element_text(size = 10),
    plot.margin = margin(0, 1, 0, 0, "cm")
  )

Ooh la la - Tabsets

Transforming OLS estimates

Code

preds_lm %>% 
  ggplot(aes(body_mass_g, bill_length_mm, col = correct)) +
  geom_jitter(size = 4, alpha = 0.6) +
  facet_wrap(vars(species)) +
  scale_color_manual(values = c('grey60', thematic::okabe_ito(3)[3])) +
  scale_x_continuous(breaks = seq(3000, 6000, 1000)) +
  theme_minimal(base_size = 12) +
  theme(
    legend.position = 'top', 
    panel.background = element_rect(color = 'black'),
    panel.grid.minor = element_blank()
  ) +
  labs(
    x = 'Body mass (in g)',
    y = 'Bill length (in mm)'
  )

Maximizing likelihood

Code

glm.mod <- glm(sex ~ body_mass_g + bill_length_mm + species, family = binomial, data = dat)

preds <- dat %>% 
  mutate(
    prob.fit = glm.mod$fitted.values,
    prediction = if_else(prob.fit > 0.5, 'male', 'female'),
    correct = if_else(sex == prediction, 'correct', 'incorrect')
  )


preds %>% 
  ggplot(aes(body_mass_g, bill_length_mm, col = correct)) +
  geom_jitter(size = 4, alpha = 0.6) +
  facet_wrap(vars(species)) +
  scale_x_continuous(breaks = seq(3000, 6000, 1000)) +
  scale_color_manual(values = c('grey60', thematic::okabe_ito(3)[3])) +
  theme_minimal(base_size = 10) +
  theme(
    legend.position = 'top', 
    panel.background = element_rect(color = 'black'),
    panel.grid.minor = element_blank()
  ) +
  labs(
    x = 'Body mass (in g)',
    y = 'Bill length (in mm)'
  )

Subsubsection - LaTeX stuff

\[ \int_0^1 f(x) \ dx \]

We’ve got columns

geom_density(
  mapping = NULL,
  data = NULL,
  stat = "density",
  position = "identity",
  ...,
  na.rm = FALSE,
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE,
  outline.type = "upper"
)

stat_density(
  mapping = NULL,
  data = NULL,
  geom = "area",
  position = "stack",
  ...,
  bw = "nrd0",
  adjust = 1,
  kernel = "gaussian",
  n = 512,
  trim = FALSE,
  na.rm = FALSE,
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE
)

We can even do captions

ggplot(data = gapminder::gapminder, mapping = aes(x = lifeExp, fill = continent)) +
  stat_density(position = "identity", alpha = 0.5)

Bla bla bla. This is a caption in the margin. Super cool isn’t it?