This is a dummy blog posts (A)

This is a test post. In this post, I try out different functionalities

Heading 1

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.

Merriweather

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.

3 - Code

This is inline code plus a small code chunk.

library(tidyverse)
ggplot(mpg) +
  geom_jitter(aes(cty, hwy), size = 4, alpha = 0.5) 

3 - 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)'
  )

4 - Some math stuff

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

2 - 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
)

2 - Margin 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?