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library(tidyverse)
library(palmerpenguins) # for `penguins` data set

# fit a model for each variable (bill length, bill depth, flipper length)
penguin_models <- penguins %>%
  pivot_longer(c(bill_length_mm, bill_depth_mm, flipper_length_mm),
               names_to = "outcome_name",
               values_to = "outcome") %>%
  group_by(outcome_name) %>%
  summarise(model = list(lm(outcome ~ species, data = cur_data())))

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library(tidyverse)
library(palmerpenguins) # for `penguins` data set

penguin_model_formulas <- tribble(
  ~name,               ~formula,
  "depth only",        "bill_length_mm ~ bill_depth_mm",
  "species only",      "bill_length_mm ~ species",
  "depth and species", "bill_length_mm ~ bill_depth_mm + species",
)

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# example_geo_data.R: example of spatial data which may be desirable to
# overlay on a map.
# Cameron Patrick <cameron.patrick@unimelb.edu.au>, 22 Jan 2022.

library(tidyverse)
library(rnaturalearth)
library(ragg)
# You will also need the natural earth data:
# remotes::install_github("ropensci/rnaturalearthhires")

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library(emmeans)
data(mtcars)

# in the example here, all models give the same point estimates and similar
# SEs because there is only a single, categorical, variable in the model.
# if you add a continuous predictor, they will no longer, because the relationship
# assumed by the model will be different for the three models!

m <- glm(am ~ vs, data = mtcars, family = binomial)
em1 <- emmeans(m, "vs")

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library(tidyverse)
library(lubridate)
library(glue)
library(jsonlite)
library(cowplot)

# thanks to u/doubleunplussed on reddit for this dataset
json_url <- "https://pastebin.com/raw/gxZAUJwd"
ib_rate_data_orig <- fromJSON(json_url)

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# Using emmeans with metafor objects - "quick and dirty reference grid"
# usage:
# emmeans(qdrg_metafor(my_metafor_object), "categorical_var")
qdrg_metafor <- function(object) {
  coefs <- coef(object)
  names(coefs)[1] <- "(Intercept)"
  vcovs <- vcov(object)
  rownames(vcovs)[1] <- "(Intercept)"
  colnames(vcovs)[1] <- "(Intercept)"
  qdrg(

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library(dplyr)
library(glmmTMB)

nobs <- 100
ngroup <- 5
dat <- tibble(
  x = sample(1:3, size = nobs, replace = TRUE, prob = c(0.15, 0.5, 0.35)),
  y = runif(nobs),
  z = case_when(
    x == 1 ~ 0,

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library(tidyverse)
library(ggrepel)
library(ragg)
library(showtext)

showtext_auto()
font_add("AVHershey Complex Medium", "AVHersheyComplexMedium.ttf")

data(Animals, package = "MASS")

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---
title: "CLT rule of thumb demo"
author: "Cameron Patrick"
date: "2024-03-15"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(
  echo = TRUE,