2021_04_13_post_offices.Rmd

---
title: "TidyTemplate"
date: 2021-04-13
output: html_output
---

```{r setup, include=FALSE}

knitr::opts_chunk$set(echo = TRUE)

library(tidyverse)
library(tidytuesdayR)
library(scales)
theme_set(theme_light())

```

# Load the weekly Data

Dowload the weekly data and make available in the `tt` object.

```{r Load}

tt <- tt_load("2021-04-13")

tt$post_offices %>%
  add_count(name, orig_name, state, county1, sort = TRUE) %>%
  filter(n >= 2) %>%
  arrange(name, orig_name, state, county1) %>%
  View()

post_offices <- tt$post_offices %>%
  select(name, state, county1, established, discontinued, continuous,
         stamp_index, id,
         coordinates,
         latitude,
         longitude,
         gnis_dist,
         gnis_county,
         gnis_state) %>%
  filter(established >= 1639,
         is.na(discontinued) | discontinued >= established)
```

```{r}
post_office_years <- post_offices %>%
  select(name, state, established, discontinued, latitude, longitude) %>%
  replace_na(list(discontinued = 2003)) %>%
  filter(established >= 1750,
         discontinued <= 2021) %>%
  mutate(year = map2(established, discontinued, seq)) %>%
  unnest(year)
```

```{r}
post_office_years %>%
  count(year, name = "n_post_offices") %>%
  arrange(desc(year)) %>%
  ggplot(aes(year, n_post_offices)) +
  geom_area() +
  labs(x = "Year",
       y = "# of post offices currently active in US")
```

```{r}
post_offices_cumulative <- post_office_years %>%
  count(year,
        state = fct_lump(state, 16),
        name = "n_post_offices")

post_offices_cumulative %>%
  mutate(state = fct_reorder(state, -n_post_offices, sum)) %>%
  filter(state != "Other") %>%
  ggplot(aes(year, n_post_offices, fill = state)) +
  geom_area() +
  labs(x = "Year",
       y = "# of post offices currently active in US") +
  facet_wrap(~ state) +
  theme(legend.position = "none")
```

```{r}
post_office_closures <- post_offices %>%
  filter(!is.na(discontinued)) %>%
  filter(discontinued >= 1750,
         established >= 1750,
         discontinued <= 2021) %>%
  count(state = fct_lump(state, 16),
        decade = 10 * (discontinued %/% 10),
        name = "n_closures")

post_office_closures %>%
  mutate(state = fct_reorder(state, -n_closures, sum)) %>%
  filter(state != "Other") %>%
  ggplot(aes(decade, n_closures, fill = state)) +
  geom_area() +
  labs(x = "Decade",
       y = "# of post offices closed in this decade") +
  facet_wrap(~ state) +
  theme(legend.position = "none") +
  labs(title = "When and where were the most post offices closed?")
```

```{r}
post_office_closures %>%
  filter(decade < 2000) %>%
  inner_join(post_offices_cumulative, by = c("state", "decade" = "year")) %>%
  mutate(pct_closed = n_closures / n_post_offices) %>%
  filter(n_post_offices >= 50) %>%
  filter(state %in% c("KY", "PA")) %>%
  ggplot(aes(decade, pct_closed, color = state)) +
  geom_line() +
  scale_y_continuous(labels = percent) +
  labs(y = "% of post offices open at start of decade that closed")
```

### Maps

```{r}
library(ggthemes)

states_map <- map_data("state") %>%
  as_tibble() %>%
  mutate(state = state.abb[match(region, str_to_lower(state.name))]) %>%
  replace_na(list(state = "DC"))

library(historydata)

state_pops <- us_state_populations %>%
  mutate(state = state.abb[match(state, state.name)]) %>%
  replace_na(list(state = "DC"))

post_office_years %>%
  filter(year == 2003) %>%
  count(state, sort = TRUE) %>%
  inner_join(states_map, by = "state") %>%
  ggplot(aes(long, lat, group = group, fill = n)) +
  geom_polygon() +
  scale_fill_gradient2(low = "blue", high = "red",
                       midpoint = 750) +
  theme_map() +
  labs(fill = "# of post offices")

post_office_years %>%
  filter(year == 2000) %>%
  count(state, sort = TRUE) %>%
  inner_join(state_pops %>% filter(year == 2000), by = "state") %>%
  mutate(post_office_density = n / (population / 1e6)) %>%
  inner_join(states_map, by = "state") %>%
  ggplot(aes(long, lat, group = group, fill = post_office_density)) +
  geom_polygon() +
  scale_fill_gradient2(low = "blue", high = "red",
                       midpoint = 250) +
  theme_map() +
  labs(fill = "Post offices / M people")
```

```{r}
post_office_cumulative_all <- post_office_years %>%
  count(year,
        state,
        name = "n_post_offices")

by_state_year <- post_office_cumulative_all %>%
  left_join(state_pops, by = c("year", "state")) %>%
  mutate(post_office_density = n_post_offices / (population / 1e6))

by_state_year %>%
  filter(year %in% c(1800, 1850, 1900, 1950)) %>%
  inner_join(states_map, by = "state") %>%
  ggplot(aes(long, lat, group = group, fill = n_post_offices)) +
  geom_polygon() +
  scale_fill_gradient2(low = "blue", high = "red",
                       mid = "pink",
                       midpoint = 2000) +
  facet_wrap(~ year) +
  theme_map() +
  labs(fill = "Post offices")
```

```{r}
library(gganimate)

by_state_year %>%
  inner_join(states_map, by = "state") %>%
  ggplot(aes(long, lat, group = group, fill = n_post_offices)) +
  geom_polygon() +
  scale_fill_gradient2(low = "blue", high = "red",
                       mid = "pink",
                       midpoint = 2000) +
  transition_manual(year) +
  theme_map() +
  labs(fill = "Post offices",
       title = "{ current_frame }")

anim_save("~/Desktop/post_offices_over_time.gif")
```

```{r}
post_office_years %>%
  filter(year == 1900,
         !state %in% c("HI", "AK")) %>%
  ggplot(aes(longitude, latitude)) +
  borders("state") +
  geom_point(size = .01, alpha = .25) +
  theme_map() +
  coord_map()

anim <- post_office_years %>%
  filter(year %% 4 == 0) %>%
  filter(!state %in% c("HI", "AK")) %>%
  ggplot(aes(longitude, latitude)) +
  borders("state", colour = "lightgray") +
  geom_point(size = .01, alpha = .25, color = "#004B87") +
  transition_manual(year) +
  labs(title = "Post office locations in the continental US: { current_frame }") +
  theme_map() +
  coord_map()

animate(
  anim,
  nframes = 30,
  fps = 6
)

anim_save("~/Desktop/post-office-dots-blue.gif")
```


We'll be doing an animated map
* with dots
* as a choropleth by state

* Maybe comparing to population (post offices per person in state or in county?)