andrewheiss

# Monkeys, Shakespeare, typewriters, &c. ----

library(tidyverse)

# Use 9 CPUs
mirai::daemons(9)

# For computability, loop through 200,000,000 seeds
number_of_seeds <- 2e8

andrewheiss

# Get latest 1.9
VER=$(curl -sL https://quarto.org/docs/download/_prerelease.json | grep -o '"version": "[^"]*' | grep -o '[^"]*$')
mkdir -p ~/quarto
curl -L "https://github.com/quarto-dev/quarto-cli/releases/download/v${VER}/quarto-${VER}-linux-amd64.tar.gz" | tar -xz -C ~/quarto --strip-components=1

# Add to system path so terminal uses it
echo 'export PATH="$HOME/quarto/bin:$PATH"' >> ~/.profile

# Add to .Rprofile path so RStudio uses it
echo 'Sys.setenv(PATH = paste0(Sys.getenv("HOME"), "/quarto/bin:", Sys.getenv("PATH")))' >> ~/.Rprofile

andrewheiss

# Adapted from Matt Parker's video here: https://www.youtube.com/watch?v=kahGSss6SsU
# Based on this paper here: https://arxiv.org/abs/2602.14487

library(tidyverse)
library(readxl)

# A "run" ends when cumulative heads first exceed 50% of flips
# The expected value of heads/total at that stopping time is pi/4

# Extract the heads/total ratio at the end of each run

andrewheiss

library(tidyverse)
library(parameters)
library(ggdist)

# :(
lm(body_mass ~ 0 + species, data = penguins) |>
  model_parameters() |>
  ggplot(aes(x = Coefficient, y = Parameter, color = Parameter)) +
  geom_pointrange(

andrewheiss

library(tidyverse)

ggplot(data = tibble(x = 1, y = "A"), aes(x = x, y = y)) +
  geom_pointrange(aes(xmin = 0.5, xmax = 1.5), color = "#1D6996") +
  annotate(
    geom = "text",
    label = "geom_pointrange()",
    x = 1,
    y = 1.5,
    hjust = 0.5,

andrewheiss

#!/usr/bin/env bash

# Required parameters:
# @raycast.schemaVersion 1
# @raycast.title Create Positron playground project
# @raycast.mode silent

# Optional parameters:
# @raycast.argument1 { "type": "text", "placeholder": "Project folder name" }
# @raycast.icon 🛝

andrewheiss

In this paper, the authors explore ████ ███████ ██ ███ ██████████ ██ ███████ ███ █████ ██████ ██ ████████ ██████ ███████████ ███ █████ ██ ██████████ ████ ██ ███████████ ████ ███ ██████ ██ ██████ █████████ █████████ █████ ███ ███ █████ ██ ███ ████ █████████ ███ ████████ █████ ████ ███ ███████████ █████████ █████ ██████████████ ███████ ██ ███ ████ ██████. This is important work!

The theory is strong and the qualitative work is well done and complements the experiment. I have some reservations and questions about the methods and analysis, but these issues are all fixable. I list my observations and comments below.

---

praise: This is an excellent and tightly written experiment that tests a specific, narrow, important hypothesis in a rigorous way. This is all incredibly fascinating and well done!

---

andrewheiss

library(tidyverse)
library(tidytext)
library(schrute)
library(rcartocolor)
library(ggh4x)

# Get all the words as single rows
all_words <- schrute::theoffice |> 
  mutate(season_cat = factor(season)) |> 
  unnest_tokens(output = word, input = text) |> 

andrewheiss

library(tidyverse)

# Download the annual HadCRUT.5.1.0.0 data from
# https://www.metoffice.gov.uk/hadobs/hadcrut5/data/HadCRUT.5.1.0.0/download.htm
hadcrut <- read_csv(
  "HadCRUT.5.1.0.0.analysis.summary_series.global.annual.csv"
) |>
  rename(
    anomaly = `Anomaly (deg C)`,
    conf_low = `Lower confidence limit (2.5%)`,

andrewheiss

library(tidyverse)
library(sf)
library(rnaturalearth)
library(ggtext)

clr_ocean <- "#d9f0ff"
clr_land <- "#facba6"

world <- ne_countries(scale = 110) |>
  filter(admin != "Antarctica")