Exploring pricing patterns

library(nyclodging)
library(echarts4r)
library(leaflet)
library(dplyr)

listings %>%
  filter(price >= 1000) %>% 
  mutate(label = glue::glue("
    <b>price</b>: {scales::dollar(price)}<br>
    <b>description</b>: {list_description}<br>
    <b>room type</b>: {room_type}<br>
    <b>neighorboud</b>: {neighbourhood}<br>
    <b>neighorboud group</b>: {neighbourhood_group}<br>
    <b>reviews</b>: {reviews}<br>
    <b>minimum nights</b>: {min_nights}<br>
    <b>last review date</b>: {last_review_date}<br>
    <b>list id</b>: {list_id}
  ")) %>% 
  leaflet() %>% 
  addTiles() %>% 
  addProviderTiles(providers$Esri.WorldTopoMap) %>% 
  addCircleMarkers(
    clusterOptions = markerClusterOptions(maxClusterRadius = 30),
    fill = ~ price, 
    popup = ~ label)

library(nycgeo)
library(sf)
library(tmap)

listings_sf <- listings %>% 
  st_as_sf(coords = c(lon = "lon", lat = "lat")) %>% 
  st_set_crs(4326)

nyc <- nyc_boundaries(geography = "tract") %>% 
  st_transform(4326)

map_df <- nyc %>% 
  st_join(listings_sf) %>% 
  group_by(tract_id) %>% 
  summarise(
    price = mean(price, na.rm = TRUE)
  )

tmap_mode("view")

tm_shape(map_df) + 
  tm_polygons(col = "price", 
              palette = rev(rcartocolor::carto_pal(n = 7, "ag_Sunset")),
              breaks = c(0, 100, 200, 300, 500, 1000, Inf))

Qiushi Yan