This interactive maps permits users to compare the number of hotels and equivalent Air b n b rentals across all London boroughs.
library(sf)## Linking to GEOS 3.6.1, GDAL 2.2.3, PROJ 4.9.3library(tmap)
library(leafpop)
library(leaflet)
library(tmaptools)
library(tidyverse)## -- Attaching packages -------- tidyverse 1.2.1 --## v ggplot2 3.2.1 v purrr 0.3.2
## v tibble 2.1.3 v dplyr 0.8.3
## v tidyr 0.8.3 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.4.0## -- Conflicts ----------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(plyr)## -------------------------------------------------------------------------## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)## -------------------------------------------------------------------------##
## Attaching package: 'plyr'## The following objects are masked from 'package:dplyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following object is masked from 'package:purrr':
##
## compactOSM <- st_read("data/gis_osm_pois_a_free_1.shp")## Reading layer `gis_osm_pois_a_free_1' from data source `C:\Users\ucfnmac\OneDrive - University College London\Teaching\PUBLISHEXAMPLE\data\gis_osm_pois_a_free_1.shp' using driver `ESRI Shapefile'
## Simple feature collection with 35197 features and 4 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -0.5108706 ymin: 51.28117 xmax: 0.322123 ymax: 51.68948
## epsg (SRID): 4326
## proj4string: +proj=longlat +datum=WGS84 +no_defsLondonborough <- st_read("data/London_Borough_Excluding_MHW.shp")## Reading layer `London_Borough_Excluding_MHW' from data source `C:\Users\ucfnmac\OneDrive - University College London\Teaching\PUBLISHEXAMPLE\data\London_Borough_Excluding_MHW.shp' using driver `ESRI Shapefile'
## Simple feature collection with 33 features and 7 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: 503568.2 ymin: 155850.8 xmax: 561957.5 ymax: 200933.9
## epsg (SRID): NA
## proj4string: +proj=tmerc +lat_0=49 +lon_0=-2 +k=0.999601272 +x_0=400000 +y_0=-100000 +datum=OSGB36 +units=m +no_defsAirbnb <- read_csv("data/listings.csv")## Parsed with column specification:
## cols(
## id = col_double(),
## name = col_character(),
## host_id = col_double(),
## host_name = col_character(),
## neighbourhood_group = col_logical(),
## neighbourhood = col_character(),
## latitude = col_double(),
## longitude = col_double(),
## room_type = col_character(),
## price = col_double(),
## minimum_nights = col_double(),
## number_of_reviews = col_double(),
## last_review = col_date(format = ""),
## reviews_per_month = col_double(),
## calculated_host_listings_count = col_double(),
## availability_365 = col_double()
## )# plot xy data
Airbnb <- st_as_sf(Airbnb, coords = c("longitude", "latitude"),
crs = 4326)
# reproject
OSM <- st_transform(OSM, 27700)
Airbnb <- st_transform(Airbnb, 27700)
# we don't need to reproject Londonborough, but it
# doesn't have a CRS..you could also use set_crs
# it needs to have one for the next step
Londonborough<- st_transform(Londonborough, 27700)
#select hotels only
OSM <- OSM[OSM$fclass == 'hotel',]
Airbnb <- Airbnb[Airbnb$room_type == 'Entire home/apt' &
Airbnb$availability_365=='365',]
# make a function for the join
# functions are covered in practical 7
# but see if you can work out what is going on
# hint all you have to do is replace data1 and data2
# with the data you want to use
Joinfun <- function(data1, data2) {
# join OSM and London boroughs
joined <- st_join(data1, data2, join = st_within)
# count the number of hotels per borough
countno <- as.data.frame(count(joined$GSS_CODE))
# join the count back to the borough layer
counted <-left_join(data2, countno, by=c("GSS_CODE"="x"))
return(counted)
}
# use the function for hotels
Hotels <- Joinfun(OSM, Londonborough)
# then for airbnb
Airbnb <- Joinfun(Airbnb, Londonborough)
# now try to arrange the plots with tmap
breaks = c(0, 5, 12, 26, 57, 286)
#change the column name from freq for the legend
colnames(Hotels)[colnames(Hotels)=="freq"] <- "Accom count"
#join data
ti<-st_join(Airbnb, Hotels)
ti<-st_transform(ti,crs = 4326)
#remove the geometry for our pop up boxes to avoid
#the geometry field
ti2<-ti
st_geometry(ti2)<-NULL
popairbnb=popupTable(ti2, zcol=c("NAME.x", "GSS_CODE.x", "freq"))
pophotels=popupTable(ti2, zcol=c("NAME.x", "GSS_CODE.x", "Accom count"))
tmap_mode("view")## tmap mode set to interactive viewing# set the colour palettes using our previously defined breaks
pal <- colorBin(palette = "YlOrRd", domain=ti2$freq, bins=breaks)
pal2 <- colorBin(palette = "YlOrRd", domain=ti2$`Accom count`, bins=breaks)
map<- leaflet(ti) %>%
# add basemap options
addTiles(group = "OSM (default)") %>%
addProviderTiles(providers$Stamen.Toner, group = "Toner") %>%
addProviderTiles(providers$Stamen.TonerLite, group = "Toner Lite") %>%
addProviderTiles(providers$CartoDB.Positron, group = "CartoDB")%>%
#add our polygons, linking to the tables we just made
addPolygons(color="white",
weight = 2,
opacity = 1,
dashArray = "3",
popup = popairbnb,
fillOpacity = 0.7,
fillColor = ~pal(freq),
group = "Airbnb")%>%
addPolygons(fillColor = ~pal(`Accom count`),
weight = 2,
opacity = 1,
color = "white",
dashArray = "3",
popup = pophotels,
fillOpacity = 0.7,group = "Hotels")%>%
# add a legend
addLegend(pal = pal2, values = ~`Accom count`, group = c("Airbnb","Hotel"),
position ="bottomleft") %>%
# specify layers control
addLayersControl(
baseGroups = c("OSM (default)", "Toner", "Toner Lite", "CartoDB"),
overlayGroups = c("Airbnb", "Hotels"),
options = layersControlOptions(collapsed = FALSE)
)
# plot the map
map