library(tidyverse)
library(paletteer)
library(ggforce)
library(scales)
tuesdata <- tidytuesdayR::tt_load(2026, week = 42)
ai_salary_majors <- tuesdata$ai_salary_majors
#fields by number of graduates
fields_df <- ai_salary_majors %>%
group_by(broad_field) %>%
summarise(total_n_grads = sum(n_graduates, na.rm = TRUE)) %>%
slice_max(total_n_grads, n = 6)
#plotting
scatter <- ggplot(ai_salary_majors %>%
filter(broad_field %in% fields_df$broad_field),
aes(ai_exposure, median_starting_salary, color = broad_field)) +
geom_point(size = 4) +
xlim(-1.5, 1.5) +
geom_mark_hull(aes(fill = broad_field),
concavity = 5,
alpha = 0.03,
size = 0.2) +
scale_color_paletteer_d("MetBrewer::Klimt") +
labs(x = "AI exposure",
y = "Median starting salary") +
scale_y_continuous(labels = label_dollar())+
theme_minimal(base_size = 16) +
theme(legend.position = "none")
legend <- ggplot(fields_df,
aes(total_n_grads,
fct_reorder(broad_field, total_n_grads),
fill = broad_field)) +
geom_bar(stat = "identity") +
scale_fill_paletteer_d("MetBrewer::Klimt") +
scale_x_continuous(labels = label_comma()) +
labs(x = "Total graduates",
y = NULL) +
theme_minimal(base_size = 16) +
theme(legend.position = "none")
#finish assembling using Figma
