andrewheiss

Teaching

• https://r-primers.andrewheiss.com/
• https://evalsp25.classes.andrewheiss.com/
• https://datavizsp25.classes.andrewheiss.com/

Blogging

• https://www.andrewheiss.com/blog/
• https://www.andrewheiss.com/blog/2025/02/19/ggplot-histogram-legend/

andrewheiss

matches:
  # Markdown / HTML things
  - trigger: ";mdl"
    replace: "[$|$]({{clipb}})"
    vars:
      - name: "clipb"
        type: "clipboard"

  - trigger: ";br"
    replace: "<br>"

andrewheiss

library(tidyverse)
library(gutenbergr)

constitution_raw <- gutenberg_download(5)

constitution <- constitution_raw |> 
  slice(36:546) |> 
  filter(text != "") |> 
  mutate(text_lc = str_to_lower(text)) |> 
  mutate(is_article = str_starts(text_lc, "article")) |> 

andrewheiss

library(tidyverse)
library(readxl)
library(tinytable)

# Load data downloaded from https://www.systemicpeace.org/inscrdata.html
polity <- read_excel("~/Downloads/p5v2018.xls") |> 
  mutate(polity_change = polity2 - lag(polity2), .by = scode) |> 
  filter(year > 1800)

# Find the biggest drops in polity scores since 1985

andrewheiss

#!/bin/bash

# Required parameters:
# @raycast.schemaVersion 1
# @raycast.title Toggle Litra
# @raycast.mode silent

# Optional parameters:
# @raycast.icon 💡

andrewheiss

library(tidyverse)

# Colors too dark
mtcars |> 
  mutate(carb = factor(carb)) |> 
  ggplot(aes(carb, fill = carb)) +
  geom_bar() +
  scale_fill_viridis_d(
 option = "magma", begin = 0.1, end = 0.8

andrewheiss

library(tidyverse)
library(sf)
#> Linking to GEOS 3.11.0, GDAL 3.5.3, PROJ 9.1.0; sf_use_s2() is TRUE
library(rnaturalearth)

# All countries
countries <- ne_countries(scale = 50) |>
  filter(iso_a3 != "ATA")

andrewheiss

library(tidyverse)

# Example data
plot_data <- mpg |> 
  mutate(cyl = factor(cyl)) |> 
  group_by(cyl) |> 
  summarize(n = n())

# ew that yellow is gross

andrewheiss

---
title: Panel tabset from list of plots
---

```{r}
#| warning: false
#| message: false
library(tidyverse)
library(glue)

andrewheiss

library(tidyverse)

mean_sales <- c(dairy = 5364.5846, meat = 5059.6955, fish = 764.4324, deli = 1744.4206, cheese = 364.5226)
sd_sales <- c(dairy = 1192.3751, meat = 1560.7741, fish = 333.7008, deli = 509.8426, cheese = 127.2061)

# This is from the lesson---this is how you iterate over two things
map2(mean_sales, sd_sales, \(.x, .y) rnorm(30, .x, .y))
#> $dairy
#>  [1] 5260.738 5948.625 4823.229 5332.571 5087.384 3951.635 3381.243 4245.053