## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 6,
  fig.height = 4,
  dpi = 300,
  dev = "ragg_png"
)
library(gridmicrotex)
library(ggplot2)

## ----geom-basic, out.width="70%"----------------------------------------------
df <- data.frame(
  x = 1:3,
  y = 1:3,
  eq = c(r"($x^2$)", r"(\frac{a}{b})", r"($\sum_{i=1}^n x_i$)"),
  col = c("red", "blue", "green")
)

ggplot(df, aes(x, y, 
               label = eq, 
               colour = col, 
               size = c(14, 18, 14))) +
  geom_latex() +
  scale_colour_identity() +
  scale_size_identity() +
  labs(
    x = r"($\beta_1 \cdot x + \beta_0$)",
    y = r"($\mathrm{mpg}$)"
  ) +
  theme(
    axis.title.x = element_latex(fontsize = 14),
    axis.title.y = element_latex(fontsize = 14)
  )

## ----regression-annotation, out.width="70%"-----------------------------------
fit <- lm(mpg ~ wt, data = mtcars)
b0 <- round(coef(fit)[1], 1)
b1 <- round(coef(fit)[2], 1)
r2 <- round(summary(fit)$r.squared, 3)

eq_label <- sprintf(r"($\hat{y} = %s %s x, \quad R^2 = %s$)", b0, b1, r2)

ggplot(mtcars, aes(wt, mpg)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE) +
  annotate("latex", x = 4, y = 30, label = eq_label, size = 12) +
  theme_minimal()

## ----markdown, out.width="70%"------------------------------------------------
df <- data.frame(
  x   = 1:3,
  y   = c(2, 3, 1),
  lab = c("**bold**", r"(*slope* $\beta_1$)", "`code` and $x^2$")
)

ggplot(df, aes(x, y, label = lab)) +
  geom_point() +
  geom_markdown(fontsize = 12, vjust = -0.6) +
  ylim(0.5, 3.6) +
  labs(
    title = r"(*Fitted* model: $\hat{y} = \beta_0 + \beta_1 x$)",
    x     = "**weight** in $10^3$ lbs",
    y     = r"(*efficiency* $\eta$)"
  ) +
  theme(
    plot.title   = element_markdown(fontsize = 14),
    axis.title.x = element_markdown(),
    axis.title.y = element_markdown()
  )

## ----markdown-annotation, out.width="70%"-------------------------------------
fit <- lm(mpg ~ wt, data = mtcars)

note <- sprintf(
  r"(**Linear fit**<br>$\hat{y} = %s %s x$<br>$R^2 = %s$, *p* < 0.001)",
  round(coef(fit)[1], 1),
  round(coef(fit)[2], 1),
  round(summary(fit)$r.squared, 3)
)

ggplot(mtcars, aes(wt, mpg)) +
  geom_point(colour = "grey65") +
  geom_smooth(method = "lm", se = FALSE, colour = "#1F6FB2") +
  annotate("markdown", x = 4.1, y = 32, label = note, size = 11,
           style = "strong { color: #B22222 }") +
  theme_minimal()

## ----style, out.width="70%"---------------------------------------------------
css <- "
  body   { color: #33475B }
  strong { color: #B22222 }
  code   { color: #1F6FB2 }
  .unit  { color: grey55; font-size: smaller }
"

df <- data.frame(
  x = 1:3, y = c(2, 3, 1),
  lab = c("**bold** is red", "`code` is blue",
          'plain <span class="unit">with a note</span>')
)

ggplot(df, aes(x, y, label = lab)) +
  geom_point() +
  geom_markdown(style = css, fontsize = 11, vjust = -0.8) +
  ylim(0.5, 3.8) +
  labs(title = r"(**Styled** labels and $\beta_1$)",
       x = 'weight <span class="unit">(1000 lbs)</span>') +
  theme(
    plot.title   = element_markdown(style = css, fontsize = 14),
    axis.title.x = element_markdown(style = css)
  )

## ----blocktitle, out.width="70%"----------------------------------------------
ggplot(mtcars, aes(wt, mpg)) +
  geom_point(colour = "grey40") +
  labs(title = r"(## Fuel economy falls with weight

- slope $\beta_1 = -5.34$, *p* < 0.001
- $R^2 = 0.75$ over $n = 32$ cars)") +
  theme(plot.title = element_markdown(style = "
    body { background: #F4F7FB; padding: 10px;
           border: 1px solid #C7D6E5; border-radius: 4px }
  "))

