In EvaMaeRey/ggmemory: What the Package Does (One Line, Title Case)

# This is the recommended set up for flipbooks
# you might think about setting cache to TRUE as you gain practice --- building flipbooks from scratch can be time consuming
knitr::opts_chunk$set(fig.width = 6, message = FALSE, warning = FALSE, comment = "", cache = F)
library(flipbookr)
library(tidyverse)

library(tidyverse)
nurses_raw <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-10-05/nurses.csv")

nurses_raw %>% 
  janitor::clean_names() ->
nurses

options(scipen = 10)

nurses %>% 
  filter(state != "Guam" &
           state!= "Virgin Islands") %>% 
  ggplot() + 
  aes(x = year) + 
  aes(y = total_employed_rn) + 
  aes(group = state) + 
  geom_line(color = "magenta") +
  geom_point(size = 4,
             alpha = .7,
             shape = 21,
             fill = "magenta",
             color = "white") + 
  scale_y_log10() + 
  facet_wrap(facets = vars(state))

nurses %>% 
  filter(state != "Guam" &
           state!= "Virgin Islands") %>% 
  ggplot() + 
  aes(x = year) + 
  aes(y = hourly_wage_avg) + 
  aes(group = state) + 
  geom_line(color = "magenta") +
  geom_point(size = 4,
             alpha = .7,
             shape = 21,
             fill = "magenta",
             color = "white") + 
  facet_wrap(facets = vars(state))


nurses %>% 
  filter(state != "Guam" &
           state!= "Virgin Islands"&
           state != "Puerto Rico") %>% 
  filter(year %in% range(year)) %>% 
  select(state, year, hourly_wage_median) %>% 
  pivot_wider(names_from = year, values_from = hourly_wage_median) %>% 
  ggplot() + 
  aes(x = `1998`, y = `2020`) + 
  geom_abline(slope = 1, 
              intercept = 0,
              linetype = "dashed") + 
  coord_equal(ylim = c(0,60),
              xlim = c(0,40)) + 
  labs(title = "Median hourly wage, 1998 and 2020",
       subtitle = "50 US States and District of Columbia") + 
  geom_segment(aes(xend = `1998`, yend = 0),
               color = "grey")  + 
  geom_segment(aes(xend = `1998`, yend = `1998`),
               color = "grey35") + 
  geom_point() + 
  ggxmean::geom_lm() +
  labs(x = "Hourly wage in 1998, $US") +
  labs(y = "Hourly wage in 2020, $US") + 
  ggrepel::geom_text_repel(aes(label = state %>% 
                                 str_wrap(12),
                               lineheight = .8
                               ),
                           data = . %>% sample_frac(.2)) + 
  scale_y_continuous(labels=scales::dollar_format()) + 
  scale_x_continuous(labels=scales::dollar_format()) + 
  theme_minimal() + 
  ggstamp::stamp_arrow(x = 35, y = 35, 
                       xend = 35, yend = 60,
                       size = .75) + 
  ggstamp::stamp_text(x = 38, y = 50,
                      char_width = 12,
                      size = 4, 
                      label = "Gain median wage",
                      angle = -90)

nurses %>% 
  filter(state != "Guam" &
           state!= "Virgin Islands" &
         state!= "Puerto Rico") %>% 
  filter(year %in% range(year)) %>% 
  select(state, year, hourly_wage_median) %>% 
  pivot_wider(names_from = year, values_from = hourly_wage_median) %>% 
  arrange(`1998`) %>% 
  mutate(state = fct_inorder(state)) %>% 
  ggplot() + 
  aes(x = `1998`, y = state) + 
  geom_point(color = "plum3") + 
  geom_segment(aes(xend = `2020`, yend = state), color = "plum3") + 
  geom_point(aes(x = `2020`), 
             color = "plum4")