TidyTuesday 2021/W41

2021_41.R

library(tidyverse)
library(janitor)
library(ggtext)
library(geofacet)
library(biscale)
library(cowplot)

nurses <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-10-05/nurses.csv') %>% clean_names()

nurses$st <- state.abb[match(nurses$state, state.name)]

df1 = nurses %>% 
  filter(year==2020) %>%
  mutate(st=ifelse(state=="District of Columbia","DC",st)) %>%
  mutate(st=ifelse(state=="Puerto Rico","PR",st)) %>% 
  drop_na(st) %>%
  select(state, st, year, total_employed_rn, hourly_wage_median) %>%
  bi_class(x=hourly_wage_median, y=total_employed_rn, style="quantile",dim=3) 
  
# state tile map function reference: https://medium.com/@NickDoesData/visualizing-geographic-data-in-r-fb2e0f5b59c5
create_gradient_state_tile_map <- function(state, value, title, subtitle, caption, legend_title, state_grid='us_state_with_DC_PR_grid2') {
  
  df <- as.tibble(data.frame(state, value))
  
  fig <- df %>% 
    mutate(x = 1) %>% 
    mutate(label_y = .5) %>%  
    mutate(label_x = 1) %>% 
    ggplot()+
    geom_bar(mapping=aes(x=x, fill=value), width=.4)  +
    facet_geo(~ state, grid=state_grid) +
    labs(title=title, subtitle=subtitle, caption=caption) +
    geom_text(aes(x=label_x, y=label_y, label=state, color=value),size=3, show.legend=F, family="sans") 
  
  return(fig)
}

# main plot
p1 = create_gradient_state_tile_map(df1$st, df1$bi_class, 
                                    title='US-based Registered Nurses Employment and Wage in 2020', legend_title = "",
                                    subtitle="<span style = 'color:#012a4a;'><b>Total employed registered nurses</b></span> and <span style = 'color:#012a4a;'><b>median hourly wage</b></span>, by US state\n",
                               caption="Note: Data from Guam and Virgin Islands are not presented<br>#TidyTuesday Week 41 | Data from Data.World") + 
  bi_scale_fill(pal="DkCyan",dim=3, guide="none") +
  scale_color_manual(values=c("grey10","grey10","white","white","white","white","white","white","white")) +
  theme_void(base_size=10, base_family = "sans") + 
  theme(strip.text.x = element_blank(),
        plot.margin = unit(c(.5,4,.5,2), "cm"),
        plot.title=element_text(size=14, face="bold", color="#012a4a"),
        plot.subtitle=element_markdown(size=8, color="#011c31", margin=margin(t=5,b=18)),
        legend.title=element_text(size=9),
        plot.caption = element_markdown(size=5.6, color="#011c31",margin=margin(t=30), lineheight=1.5, hjust=0)) + 
  guides(fill = guide_colorbar(title="Count",
                              title.position = "top", 
                              barwidth = unit(.5, "lines"), 
                              barheight = unit(10, "lines"))) 
# legend                              
p2 = bi_legend(pal = "DkCyan", 
            dim = 3,
            ylab = "Total employed",
            xlab = "Median hourly wage",
            size = 2.5) + 
  theme(panel.border = element_blank(),
        axis.text = element_blank(),
        axis.title.x = element_text(size = 6, family="sans",
                                    color = "#011c31", margin=margin(t=-5)),
        axis.title.y = element_text(size = 6, family="sans",
                                    color = "#011c31", margin=margin(r=-5)),
        legend.text = element_text(size = 6),
        plot.background = element_blank(),
        legend.text.align = 0)
  
# combine plot and legend
ggdraw() +
 draw_plot(p1, 0, 0, 1, 1) +
 draw_plot(p2, 0.72, 0.04, 0.25, 0.25)