Unlocking the power of data visualization with R

library(tidyverse)

artists <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-09-27/artists.csv')
# artists%>%View

states <- map_data("state")

my_artists <- artists%>%
  mutate(region=tolower(state))%>%
  left_join(states,by=c("region"))

ggplot(states,aes(long,lat,group=group))+
  geom_polygon(fill="grey80",color="grey40")+
  geom_point(data=my_artists,aes(color=race))

Make a tree map

df <- artists%>%
  group_by(state)%>%
  summarise(tot=sum(artists_n,na.rm = TRUE))

# Create data
group <- df$state
value <- df$tot
data <- data.frame(group,value)

# install.packages("Polychrome")
library(Polychrome)
# https://colorbrewer2.org/#type=sequential&scheme=Greens&n=9
# build-in color palette
values <- createPalette(52,  c("#f7fcfd", "#9ebcda", "#f7fcfd"))

# library
library(treemap)
png(filename="w39_us_artists.png",width=1400, height=1700)
treemap(dtf = data,index = "group",vSize="value",type="index",
        title = "STATE ARTISTS",
        border.col = "grey70",
        border.lwds = 2,
        title.legend = "US States",
        fontsize.title=80,
        fontfamily.labels = "Roboto Condensed",
        fontfamily.title = "Roboto Condensed",
        force.print.labels = TRUE,
        fontface.labels = 2,
        fontsize.labels = data$value,
        palette = values
            )
dev.off()