andrewheiss

library(tidyverse)
library(broom)
library(marginaleffects)
library(palmerpenguins)

penguins <- penguins %>% drop_na(sex)

model1 <- lm(body_mass_g ~ flipper_length_mm + species, data = penguins)

andrewheiss

library(tidyverse)
library(lme4)
library(marginaleffects)

# ?ChickWeight
# weight = body weight in grams
# Time = days since birth
# Chick = chick ID
# Diet = one of 4 possible diets

andrewheiss

---
title: Table testing
format: 
  html: default
  pdf: default
---

```{r, warning=FALSE, message=FALSE}
library(tidyverse)
library(gt)

andrewheiss

---
title: "tikz in Quarto!"
format: html
---

```{r include=FALSE}
# Necessary for using dvisvgm on macOS
# See https://www.andrewheiss.com/blog/2021/08/27/tikz-knitr-html-svg-fun/
Sys.setenv(LIBGS = "/usr/local/share/ghostscript/9.53.3/lib/libgs.dylib.9.53")

andrewheiss

library(tidyverse)

withr::with_seed(1234, {
  Wave1_original <- tibble(
    AID = sample(1:51, 1000, replace = TRUE),
    H1NM12A = sample(0:1, 1000, replace = TRUE),
    H1NM12B = sample(0:1, 1000, replace = TRUE),
    H1NM12C = sample(0:1, 1000, replace = TRUE),
    H1NM12D = sample(0:1, 1000, replace = TRUE),

andrewheiss

# ------------------------------------------------------------------------------------------
# Basically all translated from the Python example at https://atproto.com/blog/create-post
# ------------------------------------------------------------------------------------------

library(httr2)

# Create a logged-in API session object
session <- request("https://bsky.social/xrpc/com.atproto.server.createSession") |> 
  req_method("POST") |> 
  req_body_json(list(

andrewheiss

library(reticulate)

# Run `pip install atproto` in the terminal to instally the Python atproto library
atproto <- import("atproto")

# Create a new empty client
client <- atproto$Client()

# Log into the API
profile <- client$login(Sys.getenv("BSKY_USER"), Sys.getenv("BSKY_PASS"))

andrewheiss

library(tidyverse)
library(palmerpenguins)
library(broom)

# Get rid of missing values
penguins <- penguins %>% drop_na(sex)

# Scatterplot
ggplot(penguins, aes(x = bill_length_mm, y = body_mass_g)) +
  geom_point() + 

andrewheiss

$$
\begin{align}
&\ \textbf{Registered provinces for INGO } i \\
\text{Count of provinces}\ \sim&\ \operatorname{Ordered\,Beta}(\mu_{i_j}, \phi_y, k_{0_y}, k_{1_y}) \\[8pt]
&\ \textbf{Model of outcome average} \\
% Put the huge equation in a nested \begin{aligned}[t] environment so that 
% \mathrlap{} can go around it so that the annotations in the priors can be 
% aligned closer to the math
\mu_i =&\ 
  \mathrlap{\begin{aligned}[t]

andrewheiss

library(tidyverse)

# Example data
df <- tibble(org_id = 1:10,
  outcome = 1:10,
  issue_1 = c("A", "B", "C", "D", "E", "F", "G", "H", "I", "J"),
  issue_2 = c(NA, NA, "X", NA, "Y", NA, NA, NA, NA, "Z"))
df
#> # A tibble: 10 × 4