Andrew Heiss 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!

library(tidyverse)
library(ggdiagram)

rect_width <- 1.75
rect_height <- 0.6

clrs <- MetBrewer::met.brewer("Tam")

# set_default_arrowhead(ggarrow::arrow_head_wings(offset = 30, inset = 60))

	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) \|>

	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%)`,

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

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

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

	---
	title: "Cross reference fun times"
	crossref:
	custom:
	- kind: float
	key: appfig
	latex-env: appfig
	reference-prefix: Figure A
	space-before-numbering: false
	latex-list-of-description: Appendix Figure

	library(tidyverse)

	# Here's some state-level data
	some_state_data <- tribble(
	~state, ~something,
	"Wyoming", 5,
	"North Carolina", 9,
	"Nevada", 10,
	"Georgia", 3,
	"Rhode Island", 1,

	library(tidyverse)
	library(ggtext)
	library(patchwork)
	library(scales)

	top_plot <- ggplot(penguins, aes(x = body_mass)) +
	geom_histogram(binwidth = 100, color = "white", boundary = 0) +
	scale_x_continuous(
	breaks = seq(2500, 6500, by = 1000),
	limits = c(2500, 6500),

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

	# World map
	world <- ne_countries(scale = "medium") \|>
	filter(adm0_a3 != "ATA") \|>
	st_transform(crs = "+proj=merc")

	# The center of the map is off the coast of Sardegna