Add Dressbarn to the Continued Retailpocalypse

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

Data is at

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() callslibrary(statebins)library(tidyverse)# this is the dressbarn locations directory pagepg <- read_html("")# this is the selector to get the main linkshtml_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("", .) %>% # state has multiple listings  map(    ~read_html(.x) %>%       html_nodes("a.Directory-listLink") %>%       html_attr("href") %>%       sprintf("", .)  ) %>%   append(    keep(locs, ~nchar(.x) > 2) %>% sprintf("", .) # 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("", .)  )  %>%   flatten_chr() -> listings# make a tibble with the HTML source for the final store location pages# so we don't end up doing multiple retrievalstibble(  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) issuetf <- 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, toomutate(  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$listing,     "[[:alpha:]]+)/.*$"  )[,2]) %>%   bind_cols(    param_get(.$uber_link, c("dropoff%5Blatitude%5D", "dropoff%5Blongitude%5D")) %>%       as_tibble() %>%       set_names(c("lat", "lon")) %>%      mutate_all(as.double)  ) -> dress_barn# save off our hard work with the HTML source so we can do more later if need beselect(dress_barn, -parsed) %>%   saveRDS("~/Data/dressbarn-with-src.rds")# save off something others will wantselect(dress_barn, -parsed, -html_src, -listing) %>%   jsonlite::toJSON() %>%   write_lines("~/Data/dressbarn-locations.json.gz")# simple mapggplot(dress_barn, aes(lon, lat)) +   geom_jitter(size = 0.25, color = ft_cols$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: <> via @hrbrmstr"  ) +  theme_ft_rc(grid="") +  theme_enhance_wtg()unlink(tf) # cleanup count(dress_barn, state) %>%   left_join(tibble(name =, state = tolower( %>%   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 = %>%   statebins(state_col = "name", value_col = "per_capita", ) +  scale_fill_r7c("Closing\nper-capita") +  labs(title = "Dressbarn State per-capita closings") +  theme_ipsum_rc(grid="") +  theme_enhance_wtg()

Dressbarn closings visualizations

*** This is a Security Bloggers Network syndicated blog from authored by hrbrmstr. Read the original post at: