new_post.qmd

---
title: 'This is a dummy blog posts'
date: '2022-06-01'
categories: ['123', 'Second Tag']
description: 'This is a test post. In this post, I try out different functionalities'
execute: 
  message: false
  warning: false
editor_options: 
  chunk_output_type: console
---

# 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](https://github.com/twbs/bootstrap-sass/blob/master/assets/stylesheets/bootstrap/_variables.scss) 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.

```{r}
library(tidyverse)

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


### 3 - Tabsets

```{r, echo = F}
knitr::opts_chunk$set(collapse = T)
library(tidyverse)
dat <- palmerpenguins::penguins %>% 
  filter(!is.na(sex))

lm.mod <- dat %>%
  mutate(
    sex = if_else(sex == 'male', 1, 0),
  ) %>% 
  lm(data = ., sex ~ body_mass_g + bill_length_mm + species) 

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


::: panel-tabset
### Transforming OLS estimates

```{r}
#| code-fold: true
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

```{r}
#| code-fold: true
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

::: {layout="[[1,1]]"}

```{r}
#| eval: false
#| echo: true
geom_density(
  mapping = NULL,
  data = NULL,
  stat = "density",
  position = "identity",
  ...,
  na.rm = FALSE,
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE,
  outline.type = "upper"
)
```

```{r}
#| eval: false
#| echo: true
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

```{r}
#| fig-cap: "Bla bla bla. This is a caption in the margin. Super cool isn't it?"
#| fig-cap-location: margin
ggplot(data = gapminder::gapminder, mapping = aes(x = lifeExp, fill = continent)) +
  stat_density(position = "identity", alpha = 0.5)
```