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95 lines
3.3 KiB
95 lines
3.3 KiB
library(sf)
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library(rgeocodio)
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library(rvest)
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library(stringi)
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library(pdftools)
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library(hrbrthemes)
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library(albersusa)
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library(tidyverse)
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library(magrittr)
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if (!file.exists(here::here("data/russe.rds"))) {
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russe_pg <- read_html("https://www.businessinsider.com/charlotte-russe-bankruptcy-stores-closing-list-2019-2")
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html_nodes(russe_pg, xpath=".//p[contains(., 'of the closing')]/following-sibling::ul/li") %>%
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html_text() -> russe
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russe_g <- rgeocodio::gio_batch_geocode(russe)
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saveRDS(russe_g, here::here("data/russe.rds"))
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}
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if (!file.exists(here::here("data/sears.rds"))) {
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sears_pg <- read_html("https://www.businessinsider.com/sears-closes-80-more-stores-2018-12")
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html_nodes(sears_pg, xpath=".//span[contains(., 'of the latest')]/../../p") %>%
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html_text() %>%
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keep(stri_detect_regex, "^(Sears|Kmart)") %>%
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stri_replace_first_regex("^(Sears[\\*]*|Kmart)", "") %>%
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stri_trim_both() -> sears
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sears2_pg <- read_html("https://www.businessinsider.com/sears-kmart-stores-closing-list-2018-10")
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html_nodes(sears2_pg, xpath=".//h2[text()='Sears' or text()='Kmart']/following-sibling::ul/li ") %>%
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html_text() %>%
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stri_trim_both() -> sears2
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sears_g <- rgeocodio::gio_batch_geocode(c(sears, sears2))
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saveRDS(sears_g, here::here("data/sears.rds"))
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}
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dressbarn <- as_tibble(jsonlite::stream_in(gzcon(url("https://rud.is/dl/dressbarn-locations.json.gz"))))
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payless <- read_csv("http://rud.is/dl/2019-payless-store-closings.csv")
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saveRDS(dressbarn, here::here("data/dressbarn.rds"))
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saveRDS(payless, here::here("data/payless.rds"))
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bind_rows(
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filter(sears_g, map_lgl(response_results, ~nrow(.x) > 0)) %>%
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mutate(ll = map(response_results, ~select(.x, location.lng, location.lat) %>% slice(1))) %>%
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select(ll) %>%
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unnest(ll) %>%
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set_names(c("lng", "lat")) %>%
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mutate(brand = "Sears/Kmart"),
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filter(russe_g, map_lgl(response_results, ~nrow(.x) > 0)) %>%
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mutate(ll = map(response_results, ~select(.x, location.lng, location.lat) %>% slice(1))) %>%
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select(ll) %>%
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unnest(ll) %>%
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set_names(c("lng", "lat")) %>%
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mutate(brand = "Russe"),
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select(payless, lng = longitude, lat=latitude) %>%
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mutate(brand = "Payless"),
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select(dressbarn, lng = lon, lat) %>%
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mutate(brand = "Dressbarn")
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) %>%
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filter(lng > -130, lat > 21) -> continental
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usa <- usa_sf("laea") %>% filter(!(name %in% c("Alaska", "Hawaii")))
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st_as_sf(continental, coords = c("lng", "lat"), crs = us_longlat_proj) %>%
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st_transform(albersusa::us_laea_proj) -> continental
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ggplot() +
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geom_sf(data = usa, fill = "#252525", size = 0.125, color = "#b2b2b277") +
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geom_sf(data = continental, aes(color = brand), size = 0.25, alpha = 1/3, show.legend = "point") +
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ggthemes::scale_color_tableau(name = NULL) +
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coord_sf(datum = NA) +
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guides(
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colour = guide_legend(
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override.aes = list(size = 2, alpha=1)
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)
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) +
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labs(
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x = NULL, y = NULL,
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title = "Places of the 2019 Retail Apocalpyse",
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subtitle = "Locations of four major brands store closings in 2019 alone (~3,100 stores in total)",
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caption = "Data source: (various + {rgeocodio})\nhttps://git.rud.is/hrbrmstr/y2019-30daymapchallenge • #30DayMapChallenge"
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) +
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theme_ft_rc(grid="") +
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theme(legend.position = c(0.5, 0.95)) +
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theme(legend.direction = "horizontal") +
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theme(axis.text = element_blank())
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