I’ve talked about the retailpocalypse before and this morning I was greeted with the news about Dressbarn closing all 650 stores as I fired up a browser.
I tweeted some pix and data but not everyone is on Twitter so I’m just posting a blog-blurb here with the code and data links.
Code is below and at https://paste.sr.ht/~hrbrmstr/af6da1af0314426255c65bc2fc254e0abb2190c3.
Data is at https://rud.is/dl/dressbarn-locations.json.gz.
Images are in a gallery below the code.
library(rvest)
library(stringi)
library(urltools)
library(worldtilegrid) # install from sh/gl/gh or just remove the theme_enhange_wtg() calls
library(statebins)
library(tidyverse)
# this is the dressbarn locations directory page
pg <- read_html("https://locations.dressbarn.com/")
# this is the selector to get the main links
html_nodes(pg, "a.Directory-listLink") %>%
html_attr("href") -> locs
# PRE-NOTE
# No sleep() code (I looked at the web site, saw how many self-requests it makes for all DB
# resources and concluded that link scrapes + full page captures would not be burdensome
# plus they're going out of business)
# basic idea here is to get all the main state location pages
# some states only have one store so the link goes right to that so handle that condition
# for ones with multiple stores get all the links on the state index page
# for links on state index page that have multiple stores in one area,
# grab all those; then, concatenate all the final target store links into one
# character vector.
keep(locs, ~nchar(.x) == 2) %>%
sprintf("https://locations.dressbarn.com/%s", .) %>% # state has multiple listings
map(
~read_html(.x) %>%
html_nodes("a.Directory-listLink") %>%
html_attr("href") %>%
sprintf("https://locations.dressbarn.com/%s", .)
) %>%
append(
keep(locs, ~nchar(.x) > 2) %>% sprintf("https://locations.dressbarn.com/%s", .) # state has one store
) %>%
flatten_chr() %>%
map_if(
~stri_count_fixed(.x, "/") == 4, # 4 URL parts == there's another listing page layer
~read_html(.x) %>%
html_nodes("a.Teaser-titleLink") %>%
html_attr("href") %>%
stri_replace_first_fixed("../", "") %>%
sprintf("https://locations.dressbarn.com/%s", .)
) %>%
flatten_chr() -> listings
# make a tibble with the HTML source for the final store location pages
# so we don't end up doing multiple retrievals
tibble(
listing = listings,
html_src = map_chr(listings, ~httr::GET(.x) %>% httr::content(as = "text"))
) -> dress_barn
# save off our work in the event we have a (non-R-crashing) issue
tf <- tempfile(fileext = ".rds")
print(tf)
saveRDS(dress_barn, tf)
# now, get data from the pages
#
# first, turn all the character vectors into something we can get HTML nodes from
#
# dressbarn web folks handliy put an "uber" link on each page so we get lon/lat for free in that URL
# they also handily used an <address> semantic tag in the proper PostalAddress schema format
# so we can get locality and actual address, too
mutate(
dress_barn,
parsed = map(html_src, read_html),
uber_link =
map_chr(
parsed, ~html_nodes(.x, xpath=".//a[contains(@href, 'uber')]") %>%
html_attr("href")
),
locality = map_chr(
parsed, ~html_node(.x, xpath=".//address/meta[@itemprop = 'addressLocality']") %>%
html_attr("content")
),
address = map_chr(
parsed, ~html_node(.x, xpath=".//address/meta[@itemprop = 'streetAddress']") %>%
html_attr("content")
),
state = stri_match_first_regex(
dress_barn$listing,
"https://locations.dressbarn.com/([[:alpha:]]+)/.*$"
)[,2]
) %>%
bind_cols(
param_get(.$uber_link, c("dropoff%5Blatitude%5D", "dropoff%5Blongitude%5D")) %>%
as_tibble() %>%
set_names(c("lat", "lon")) %>%
mutate_all(as.double)
) -> dress_barn
# save off our hard work with the HTML source so we can do more later if need be
select(dress_barn, -parsed) %>%
saveRDS("~/Data/dressbarn-with-src.rds")
# save off something others will want
select(dress_barn, -parsed, -html_src, -listing) %>%
jsonlite::toJSON() %>%
write_lines("~/Data/dressbarn-locations.json.gz")
# simple map
ggplot(dress_barn, aes(lon, lat)) +
geom_jitter(size = 0.25, color = ft_cols$yellow, alpha = 1/2) +
coord_map("polyconic") +
labs(
title = "Locations of U.S. Dressbarn Stores",
subtitle = "All 650 locations closing",
caption = "Source: Dressbarn HTML store listings;\nData: <https://rud.is/dl/dressbarn-locations.json.gz> via @hrbrmstr"
) +
theme_ft_rc(grid="") +
theme_enhance_wtg()
unlink(tf) # cleanup
count(dress_barn, state) %>%
left_join(tibble(name = state.name, state = tolower(state.abb))) %>%
left_join(usmap::statepop, by = c("name"="full")) %>%
mutate(per_capita = (n/pop_2015) * 1000000) %>%
arrange(desc(per_capita)) %>%
select(name, n, per_capita) %>%
arrange(desc(per_capita)) %>%
complete(name = state.name) %>%
statebins(state_col = "name", value_col = "per_capita", ) +
labs(title = "Dressbarn State per-capita closings") +
theme_ipsum_rc(grid="") +
theme_enhance_wtg()
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