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sea-viewer.rmd
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---
title: "sea-viewer"
output:
flexdashboard::flex_dashboard:
orientation: rows
theme: bootstrap
logo: logo.svg
source_code: "https://github.com/hesscl/sea-viewer"
---
```{r setup, echo = FALSE, include = FALSE}
library(flexdashboard)
library(tidyverse)
library(sqldf)
library(lubridate)
library(imputeTS)
library(xts)
library(highcharter)
library(forecast)
library(rgdal)
library(leaflet)
library(zoo)
library(DT)
library(sf)
library(yaml)
#data
#store credentials read in from YAML
cred <- read_yaml("H:/natrent0.yaml")
#create a pool for database connections
DB <- dbConnect(
drv = RPostgres::Postgres(),
dbname = "natrent",
host = "natrent0.csde.washington.edu",
user = names(cred),
password = cred[[names(cred)]],
bigint = "numeric"
)
#query the clean table
cl <- st_read(DB, query = "SELECT * FROM clean WHERE listing_loc_abb = 'sea' AND listing_date >= '2017-02-06'")
#separate the geometry col temporarily since it interferes with dplyr
cl_geo <- cl %>%
select(seq_id, geometry)
#dedupe and listwise delete
cl <- cl %>%
st_drop_geometry() %>%
mutate(listing_date = as.Date(listing_date),
listing_moyr = as.Date(as.yearmon(listing_date)),
cat_beds = NA,
cat_beds = ifelse(clean_beds %in% 0:4, clean_beds, cat_beds),
cat_beds = ifelse(clean_beds >= 5, "5+", cat_beds)) %>%
filter(!is.na(clean_beds), !is.na(clean_rent), !is.na(clean_sqft)) %>%
filter(listing_date != Sys.Date()) %>% #since day-of is incomplete
arrange(desc(listing_date)) %>%
distinct(listing_moyr, match_address, match_address2, clean_beds, clean_sqft, .keep_all = T)
#restore geometry to table, obj will be sf
cl <- inner_join(cl_geo, cl)
#remove low quality geocode matches
cl <- cl %>%
filter(!match_type %in% c("No Address Found", "Google Maps Lat/Long"))
#save the CRS to use for reprojecting other data
cl_crs <- st_crs(cl_geo$geometry)$epsg
#load shapefiles and transform to AEA
sea_shp <- read_sf(dsn = "../data/geo/sea_tract_2010/sea_tract_2010.shp")
sea_shp <- st_transform(sea_shp, cl_crs)
sea_shp <- st_set_crs(sea_shp, cl_crs)
kc_shp <- read_sf(dsn = "../data/geo/KCTract2010/KCTract2010.shp")
kc_shp <- st_transform(kc_shp, cl_crs)
kc_shp <- st_set_crs(kc_shp, cl_crs)
neigh_shp <- read_sf(dsn = "../data/geo/neighborhoods/WGS84/neighborhoods.shp")
neigh_shp <- st_transform(neigh_shp, cl_crs)
neigh_shp <- st_set_crs(neigh_shp, cl_crs)
neigh_shp <- neigh_shp %>%
mutate(HOOD = ifelse(L_HOOD == "NO BROADER TERM", toupper(S_HOOD), L_HOOD))
#dummy for seattle
cl$seattle <- st_within(cl, sea_shp) %>% lengths > 0 %>% as.numeric()
#dummy fo kc
cl$kingcty <- st_within(cl, kc_shp) %>% lengths > 0 %>% as.numeric()
#append tract IDs
cl <- st_join(cl, kc_shp[, "GISJOIN"])
#append clerk's office neigh defs
cl <- st_join(cl, neigh_shp[,c("L_HOOD", "S_HOOD", "HOOD")])
#remove the geometry from the cl table since agg fields now exist
cl$geometry <- NULL
#convert shapes to WGS84 for mapping with leaflet
sea_shp <- st_transform(sea_shp, 4326)
kc_shp <- st_transform(kc_shp, 4326)
neigh_shp <- st_transform(neigh_shp, 4326)
#functions for widgets
computeListings <- function(x){
y <- x %>%
filter(listing_date %in% seq(Sys.Date()-30, Sys.Date(), by = 1)) %>%
group_by(listing_date) %>%
summarize(n = n()) %>%
summarize(median = median(n)) %>%
pull(median)
y <- prettyNum(y, big.mark = ",")
return(y)
}
computeKCMedian30 <- function(x){
y <- x %>%
filter(listing_date %in% seq(Sys.Date()-30, Sys.Date(), by = 1)) %>%
filter(seattle == 0) %>%
summarize(median = median(clean_rent)) %>%
pull(median)
y <- prettyNum(y, big.mark = ",")
return(y)
}
computeSeaMedian30 <- function(x){
y <- x %>%
filter(listing_date %in% seq(Sys.Date()-30, Sys.Date(), by = 1)) %>%
filter(seattle == 1) %>%
summarize(median = median(clean_rent)) %>%
pull(median)
y <- prettyNum(y, big.mark = ",")
return(y)
}
#function for Overview graphic
computeTS <- function(x){
y <- x %>%
group_by(listing_date) %>%
filter(clean_beds == 1) %>%
summarize(median = median(clean_rent))
full_dates <- seq(from = min(y$listing_date), to = max(y$listing_date), by = 1)
index <- seq(from = 1, length.out = length(full_dates), by = 1)
dateTable <- cbind.data.frame(full_dates, index)
medRent <- NULL
for(i in 1:length(full_dates)){
if(full_dates[i] %in% y$listing_date){
medRent[i] <- y$median[y$listing_date == full_dates[i]]
} else{
medRent[i] <- NA
}
}
ts <- cbind.data.frame(medRent, full_dates)
ts <- na.interpolation(ts, option = "linear")
ts <- zoo(ts$medRent, order.by = full_dates) %>% as.xts
return(ts)
}
compute90 <- function(x){
y <- x %>%
filter(listing_date %in% seq(Sys.Date()-91, Sys.Date()-1, 1)) %>%
group_by(cat_beds) %>%
summarize(n = n(),
quant0.025 = prettyNum(round(quantile(clean_rent, .025)), big.mark = ","),
medRent = prettyNum(round(median(clean_rent)), big.mark = ","),
quant0.975 = prettyNum(round(quantile(clean_rent, .975)), big.mark = ","),
medRentPerSqft = prettyNum(round(median(clean_rent/clean_sqft), 2), big.mark = ","))
return(y)
}
aggTrends <- function(x){
y <- x %>%
filter(cat_beds != "5+") %>%
mutate(listing_moyr = as.Date(as.character(listing_moyr))) %>%
group_by(listing_moyr, cat_beds) %>%
summarize(medRent = median(clean_rent),
n = n())
return(y)
}
prepShape <- function(x, bed.type = "1", shp = NULL, group = "GISJOIN"){
y <- cl %>%
filter(cat_beds == bed.type) %>%
mutate(GISJOIN = as.character(GISJOIN)) %>%
group_by_(group) %>%
summarize(n = n(),
medRent = median(clean_rent)) %>%
filter(n > 15)
shp <- left_join(shp, y)
}
ts_thm <- hc_theme_merge(
hc_theme_google(),
hc_theme(colors = RColorBrewer::brewer.pal(3, "Set1")[-1]))
sea_thm <- hc_theme_merge(
hc_theme_google(),
hc_theme(colors = RColorBrewer::brewer.pal(5, "Blues")[-1]))
kc_thm <- hc_theme_merge(
hc_theme_google(),
hc_theme(colors = RColorBrewer::brewer.pal(5, "Greens")[-1]))
#load SHA VPS table
vps <- read_csv("./sha_vps.csv")
#join to cl
cl <- left_join(cl, vps)
#restrict to KC
cl <- cl %>%
filter(kingcty == 1)
```
Home {data-icon="fa-home"}
=====================================
Row
-------------------------------------
### Welcome!
This website provides the latest information from the UW team's rental market database. The navigation bar provides access to detailed reports for Seattle, King County and specific neighborhoods.
Contact the project team at [[email protected]](mailto://[email protected]) for more information about this report or to suggest new features.
Row
-------------------------------------
### Daily 1-bedroom median rent time-series (90-day rolling average)
```{r overview.ts, echo = FALSE, warning = F, message = F, dpi = 300}
ts <- computeTS(cl %>% filter(seattle == 0))
ts_sea <- computeTS(cl %>% filter(seattle == 1))
ts <- xts(rollapply(as.zoo(ts), FUN = mean, 90, na.rm = T, partial = T))
ts_sea <- xts(rollapply(as.zoo(ts_sea), FUN = mean, 90, na.rm = T, partial = T))
mts <- cbind(ts_sea, ts)
highchart() %>%
hc_add_series(mts[,1], name = "Seattle") %>%
hc_add_series(mts[,2], name = "King County") %>%
hc_yAxis(title = list(text = "Median Listing Rent"), opposite = F) %>%
hc_xAxis(type = "datetime") %>%
hc_tooltip(pointFormat = "<b>{series.name}</b> <br> Rent: {point.y:%.0f}") %>%
hc_add_theme(ts_thm) %>%
hc_exporting(enabled = TRUE)
```
Row
-------------------------------------
### Seattle median listings/day (past 30 days)
```{r overview.list}
listings <- computeListings(cl %>% filter(seattle == 1))
valueBox(listings,
color = "secondary",
icon = "fa-building")
```
### Seattle median listing rent (past 30 days)
```{r overview.seamed}
median <- computeSeaMedian30(cl)
valueBox(paste0("\U0024", median),
color = "secondary",
icon = "fa-dollar")
```
### King County median listing rent (past 30 days)
```{r overview.kcmed}
median <- computeKCMedian30(cl)
valueBox(paste0("\U0024", median),
color = "secondary",
icon = "fa-dollar")
```
Overview {data-navmenu="Seattle"}
=====================================
Row
-------------------------------------
### 90-day summary for Seattle listings
```{r sea.90day}
DT::datatable(compute90(cl %>% filter(seattle == 1)),
rownames = F,
colnames = c("Bedroom Size", "N Listings", "2.5% Quantile", "Median Rent", "97.5% Quantile", "Median Rent/Sqft"),
class = "cell-border stripe",
style = "bootstrap",
autoHideNavigation = T,
extensions = 'Buttons',
options = list(dom = 'Bfrtip',
buttons = c('copy', 'csv', 'excel', 'pdf', 'print'),
paging = F,
searching = F,
autoWidth = T,
columnDefs = list(list(className = 'dt-right', targets = 1:5))
))
```
Row
-------------------------------------
### Trend analysis
```{r sea.trend}
agg_sea <- aggTrends(cl %>% filter(seattle == 1, cat_beds %in% c("0", "1", "2", "3")))
hchart(agg_sea, type = "line",
hcaes(x = listing_moyr, y = medRent, group = cat_beds)) %>%
hc_yAxis(title = list(text = "Median Listing Rent")) %>%
hc_xAxis(title = list(text = "Month")) %>%
hc_add_theme(sea_thm) %>%
hc_tooltip(pointFormat = "<b>{series.name} Bedroom Units</b> <br> Rent: {point.y:%.0f} <br> N Listings: {point.n}") %>%
hc_exporting(enabled = TRUE)
```
0B Rent Map {data-navmenu="Seattle"}
=====================================
```{r, sea.0Bmap, warning = FALSE}
sea_map <- prepShape(x = cl, bed.type = "0", shp = sea_shp)
labels <- sprintf(
"<strong>%s</strong><br/>Median 0B Rent: %g",
sea_map$NAMELSAD10, sea_map$medRent
) %>% lapply(htmltools::HTML)
pal <- colorBin("Blues", domain = sea_map$medRent, bins = 6)
leaflet(sea_map) %>%
setView(lng = -122.332460, lat = 47.610629, zoom = 11) %>%
addProviderTiles(providers$CartoDB.Positron) %>%
addPolygons(color = "#444444", weight = 1, smoothFactor = 0.5,
opacity = 1.0, fillOpacity = 0.5,
fillColor = ~pal(medRent),
highlight = highlightOptions(
weight = 5,
color = "#666",
fillOpacity = 0.7,
bringToFront = TRUE),
label = labels,
labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px"),
textsize = "15px",
direction = "auto")) %>%
addLegend(pal = pal, values = ~medRent, opacity = 0.7, title = "0B Median Rent",
position = "bottomright")
```
1B Rent Map {data-navmenu="Seattle"}
=====================================
```{r, sea.1Bmap, warning = FALSE}
sea_map <- prepShape(x = cl, bed.type = "1", shp = sea_shp)
pal <- colorBin("Blues", domain = sea_map$medRent, bins = 6)
labels <- sprintf(
"<strong>%s</strong><br/>Median 1B Rent: %g",
sea_map$NAMELSAD10, sea_map$medRent
) %>% lapply(htmltools::HTML)
leaflet(sea_map) %>%
addProviderTiles(providers$CartoDB.Positron) %>%
addPolygons(color = "#444444", weight = 1, smoothFactor = 0.5,
opacity = 1.0, fillOpacity = 0.5,
fillColor = ~pal(medRent),
highlight = highlightOptions(
weight = 5,
color = "#666",
fillOpacity = 0.7,
bringToFront = TRUE),
label = labels,
labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px"),
textsize = "15px",
direction = "auto")) %>%
addLegend(pal = pal, values = ~medRent, opacity = 0.7, title = "1B Median Rent",
position = "bottomright")
```
2B Rent Map {data-navmenu="Seattle"}
=====================================
```{r, sea.2Bmap, warning = FALSE}
sea_map <- prepShape(x = cl, bed.type = "2", shp = sea_shp)
pal <- colorBin("Blues", domain = sea_map$medRent, bins = 6)
labels <- sprintf(
"<strong>%s</strong><br/>Median 2B Rent: %g",
sea_map$NAMELSAD10, sea_map$medRent
) %>% lapply(htmltools::HTML)
leaflet(sea_map) %>%
setView(lng = -122.332460, lat = 47.610629, zoom = 11) %>%
addProviderTiles(providers$CartoDB.Positron) %>%
addPolygons(color = "#444444", weight = 1, smoothFactor = 0.5,
opacity = 1.0, fillOpacity = 0.5,
fillColor = ~pal(medRent),
highlight = highlightOptions(
weight = 5,
color = "#666",
fillOpacity = 0.7,
bringToFront = TRUE),
label = labels,
labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px"),
textsize = "15px",
direction = "auto")) %>%
addLegend(pal = pal, values = ~medRent, opacity = 0.7, title = "2B Median Rent",
position = "bottomright")
```
3B Rent Map {data-navmenu="Seattle"}
=====================================
```{r sea.3Bmap, warning = FALSE}
sea_map <- prepShape(x = cl, bed.type = "3", shp = sea_shp)
pal <- colorBin("Blues", domain = sea_map$medRent, bins = 6)
labels <- sprintf(
"<strong>%s</strong><br/>Median 3B Rent: %g",
sea_map$NAMELSAD10, sea_map$medRent
) %>% lapply(htmltools::HTML)
leaflet(sea_map) %>%
setView(lng = -122.332460, lat = 47.610629, zoom = 11) %>%
addProviderTiles(providers$CartoDB.Positron) %>%
addPolygons(color = "#444444", weight = 1, smoothFactor = 0.5,
opacity = 1.0, fillOpacity = 0.5,
fillColor = ~pal(medRent),
highlight = highlightOptions(
weight = 5,
color = "#666",
fillOpacity = 0.7,
bringToFront = TRUE),
label = labels,
labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px"),
textsize = "15px",
direction = "auto")) %>%
addLegend(pal = pal, values = ~medRent, opacity = 0.7, title = "3B Median Rent",
position = "bottomright")
```
Overview {data-navmenu="King County"}
=====================================
Row
-------------------------------------
### 90-day summary for King County listings
```{r kc.90day}
DT::datatable(compute90(cl %>% filter(seattle == 0)),
rownames = F,
colnames = c("Bedroom Size", "N Listings", "2.5% Quantile", "Median Rent", "97.5% Quantile", "Median Rent/Sqft"),
class = "cell-border stripe",
style = "bootstrap",
autoHideNavigation = T,
extensions = 'Buttons',
options = list(dom = 'Bfrtip',
buttons = c('copy', 'csv', 'excel', 'pdf', 'print'),
paging = F,
searching = F,
autoWidth = T,
columnDefs = list(list(className = 'dt-right', targets = 1:5))
))
```
Row
-------------------------------------
### Trend analysis
```{r, kc.trend}
agg_kc <- aggTrends(cl %>% filter(cat_beds %in% c("0", "1", "2", "3", seattle == 0)))
hchart(agg_kc, type = "line",
hcaes(x = listing_moyr, y = medRent, group = cat_beds)) %>%
hc_yAxis(title = list(text = "Median Listing Rent")) %>%
hc_xAxis(title = list(text = "Month")) %>%
hc_add_theme(kc_thm) %>%
hc_tooltip(pointFormat = "<b>{series.name} Bedroom Units</b> <br> Rent: {point.y:%.0f} <br> N Listings: {point.n}") %>%
hc_exporting(enabled = TRUE)
```
0B Rent Map {data-navmenu="King County"}
=====================================
```{r, kc.0Bmap, warning = FALSE}
kc_map <- prepShape(x = cl %>% filter(seattle == 0), bed.type = "0", shp = kc_shp)
labels <- sprintf(
"<strong>%s</strong><br/>Median 0B Rent: %g",
kc_map$NAMELSAD10, kc_map$medRent
) %>% lapply(htmltools::HTML)
pal <- colorBin("Greens", domain = kc_map$medRent, bins = 6)
leaflet(kc_map) %>%
setView(lng = -122.332460, lat = 47.610629, zoom = 11) %>%
addProviderTiles(providers$CartoDB.Positron) %>%
addPolygons(color = "#444444", weight = 1, smoothFactor = 0.5,
opacity = 1.0, fillOpacity = 0.5,
fillColor = ~pal(medRent),
highlight = highlightOptions(
weight = 5,
color = "#666",
fillOpacity = 0.7,
bringToFront = TRUE),
label = labels,
labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px"),
textsize = "15px",
direction = "auto")) %>%
addLegend(pal = pal, values = ~medRent, opacity = 0.7, title = "0B Median Rent",
position = "bottomright")
```
1B Rent Map {data-navmenu="King County"}
=====================================
```{r, kc.1Bmap, warning = FALSE}
kc_map <- prepShape(x = cl %>% filter(seattle == 0), bed.type = "1", shp = kc_shp)
pal <- colorBin("Greens", domain = kc_map$medRent, bins = 6)
labels <- sprintf(
"<strong>%s</strong><br/>Median 1B Rent: %g",
kc_map$NAMELSAD10, kc_map$medRent
) %>% lapply(htmltools::HTML)
leaflet(kc_map) %>%
setView(lng = -122.332460, lat = 47.610629, zoom = 11) %>%
addProviderTiles(providers$CartoDB.Positron) %>%
addPolygons(color = "#444444", weight = 1, smoothFactor = 0.5,
opacity = 1.0, fillOpacity = 0.5,
fillColor = ~pal(medRent),
highlight = highlightOptions(
weight = 5,
color = "#666",
fillOpacity = 0.7,
bringToFront = TRUE),
label = labels,
labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px"),
textsize = "15px",
direction = "auto")) %>%
addLegend(pal = pal, values = ~medRent, opacity = 0.7, title = "1B Median Rent",
position = "bottomright")
```
2B Rent Map {data-navmenu="King County"}
=====================================
```{r, kc.2Bmap, warning = FALSE}
kc_map <- prepShape(x = cl %>% filter(seattle == 0), bed.type = "2", shp = kc_shp)
pal <- colorBin("Greens", domain = kc_map$medRent, bins = 6)
labels <- sprintf(
"<strong>%s</strong><br/>Median 2B Rent: %g",
kc_map$NAMELSAD10, kc_map$medRent
) %>% lapply(htmltools::HTML)
leaflet(kc_map) %>%
setView(lng = -122.332460, lat = 47.610629, zoom = 11) %>%
addProviderTiles(providers$CartoDB.Positron) %>%
addPolygons(color = "#444444", weight = 1, smoothFactor = 0.5,
opacity = 1.0, fillOpacity = 0.5,
fillColor = ~pal(medRent),
highlight = highlightOptions(
weight = 5,
color = "#666",
fillOpacity = 0.7,
bringToFront = TRUE),
label = labels,
labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px"),
textsize = "15px",
direction = "auto")) %>%
addLegend(pal = pal, values = ~medRent, opacity = 0.7, title = "2B Median Rent",
position = "bottomright")
```
3B Rent Map {data-navmenu="King County"}
=====================================
```{r kc.3Bmap, warning = FALSE}
kc_map <- prepShape(x = cl %>% filter(seattle == 0), bed.type = "3", shp = kc_shp)
pal <- colorBin("Greens", domain = kc_map$medRent, bins = 6)
labels <- sprintf(
"<strong>%s</strong><br/>Median 3B Rent: %g",
kc_map$NAMELSAD10, kc_map$medRent
) %>% lapply(htmltools::HTML)
leaflet(kc_map) %>%
setView(lng = -122.332460, lat = 47.610629, zoom = 11) %>%
addProviderTiles(providers$CartoDB.Positron) %>%
addPolygons(color = "#444444", weight = 1, smoothFactor = 0.5,
opacity = 1.0, fillOpacity = 0.5,
fillColor = ~pal(medRent),
highlight = highlightOptions(
weight = 5,
color = "#666",
fillOpacity = 0.7,
bringToFront = TRUE),
label = labels,
labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px"),
textsize = "15px",
direction = "auto")) %>%
addLegend(pal = pal, values = ~medRent, opacity = 0.7, title = "3B Median Rent",
position = "bottomright")
```
1B Map {data-navmenu="Neighborhoods"}
=====================================
```{r, neigh.1Bmap, warning = FALSE}
neigh_map <- prepShape(x = cl, bed.type = "1", shp = neigh_shp, group = "HOOD")
pal <- colorBin("Blues", domain = neigh_map$medRent, bins = 6)
labels <- sprintf(
"<strong>%s</strong><br/>Median 1B Rent: %g",
neigh_map$HOOD, neigh_map$medRent
) %>% lapply(htmltools::HTML)
leaflet(neigh_map) %>%
setView(lng = -122.332460, lat = 47.610629, zoom = 11) %>%
addProviderTiles(providers$CartoDB.Positron) %>%
addPolygons(color = "#444444", weight = 1, smoothFactor = 0.5,
opacity = 1.0, fillOpacity = 0.5,
fillColor = ~pal(medRent),
highlight = highlightOptions(
weight = 5,
color = "#666",
fillOpacity = 0.7,
bringToFront = TRUE),
label = labels,
labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px"),
textsize = "15px",
direction = "auto")) %>%
addLegend(pal = pal, values = ~medRent, opacity = 0.7, title = "1B Median Rent",
position = "bottomright")
```
Ballard {data-navmenu="Neighborhoods"}
=====================================
Row
-------------------------------------
### 90-day summary
```{r ballard.90day}
DT::datatable(compute90(cl %>% filter(L_HOOD == "BALLARD")),
rownames = F,
colnames = c("Bedroom Size", "N Listings", "2.5% Quantile", "Median Rent", "97.5% Quantile", "Median Rent/Sqft"),
class = "cell-border stripe",
style = "bootstrap",
autoHideNavigation = T,
extensions = 'Buttons',
options = list(dom = 'Bfrtip',
buttons = c('copy', 'csv', 'excel', 'pdf', 'print'),
paging = F,
searching = F,
autoWidth = T,
columnDefs = list(list(className = 'dt-right', targets = 1:5))
))
```
Row
-------------------------------------
### Trend Analysis
```{r ballard.trend}
agg_ball <- aggTrends(cl %>% filter(L_HOOD == "BALLARD", cat_beds %in% c("0", "1", "2")))
hchart(agg_ball, type = "line",
hcaes(x = listing_moyr, y = medRent, group = cat_beds)) %>%
hc_yAxis(title = list(text = "Median Listing Rent")) %>%
hc_xAxis(title = list(text = "Month")) %>%
hc_add_theme(sea_thm) %>%
hc_tooltip(pointFormat = "<b>{series.name} Bedroom Units</b> <br> Rent: {point.y:%.0f} <br> N Listings: {point.n}") %>%
hc_exporting(enabled = TRUE)
```
Beacon Hill {data-navmenu="Neighborhoods"}
=====================================
Row
-------------------------------------
### 90-day summary
```{r beacon.90day}
DT::datatable(compute90(cl %>% filter(L_HOOD == "BEACON HILL")),
rownames = F,
colnames = c("Bedroom Size", "N Listings", "2.5% Quantile", "Median Rent", "97.5% Quantile", "Median Rent/Sqft"),
class = "cell-border stripe",
style = "bootstrap",
autoHideNavigation = T,
extensions = 'Buttons',
options = list(dom = 'Bfrtip',
buttons = c('copy', 'csv', 'excel', 'pdf', 'print'),
paging = F,
searching = F,
autoWidth = T,
columnDefs = list(list(className = 'dt-right', targets = 1:5))
))
```
Row
-------------------------------------
### Trend Analysis
```{r beacon.trend}
agg_beac <- aggTrends(cl %>% filter(L_HOOD == "BEACON HILL", cat_beds %in% c("0", "1", "2")))
hchart(agg_beac, type = "line",
hcaes(x = listing_moyr, y = medRent, group = cat_beds)) %>%
hc_yAxis(title = list(text = "Median Listing Rent")) %>%
hc_xAxis(title = list(text = "Month")) %>%
hc_add_theme(sea_thm) %>%
hc_tooltip(pointFormat = "<b>{series.name} Bedroom Units</b> <br> Rent: {point.y:%.0f} <br> N Listings: {point.n}") %>%
hc_exporting(enabled = TRUE)
```
Capitol Hill {data-navmenu="Neighborhoods"}
=====================================
Row
-------------------------------------
### 90-day summary
```{r capitol.90day}
DT::datatable(compute90(cl %>% filter(L_HOOD == "CAPITOL HILL")),
rownames = F,
colnames = c("Bedroom Size", "N Listings", "2.5% Quantile", "Median Rent", "97.5% Quantile", "Median Rent/Sqft"),
class = "cell-border stripe",
style = "bootstrap",
autoHideNavigation = T,
extensions = 'Buttons',
options = list(dom = 'Bfrtip',
buttons = c('copy', 'csv', 'excel', 'pdf', 'print'),
paging = F,
searching = F,
autoWidth = T,
columnDefs = list(list(className = 'dt-right', targets = 1:5))
))
```
Row
-------------------------------------
### Trend Analysis
```{r capitol.trend}
agg_cap <- aggTrends(cl %>% filter(L_HOOD == "CAPITOL HILL", cat_beds %in% c("0", "1", "2")))
hchart(agg_cap, type = "line",
hcaes(x = listing_moyr, y = medRent, group = cat_beds)) %>%
hc_yAxis(title = list(text = "Median Listing Rent")) %>%
hc_xAxis(title = list(text = "Month")) %>%
hc_add_theme(sea_thm) %>%
hc_tooltip(pointFormat = "<b>{series.name} Bedroom Units</b> <br> Rent: {point.y:%.0f} <br> N Listings: {point.n}") %>%
hc_exporting(enabled = TRUE)
```
Central District {data-navmenu="Neighborhoods"}
=====================================
Row
-------------------------------------
### 90-day summary
```{r central.90day}
DT::datatable(compute90(cl %>% filter(L_HOOD == "CENTRAL AREA")),
rownames = F,
colnames = c("Bedroom Size", "N Listings", "2.5% Quantile", "Median Rent", "97.5% Quantile", "Median Rent/Sqft"),
class = "cell-border stripe",
style = "bootstrap",
autoHideNavigation = T,
extensions = 'Buttons',
options = list(dom = 'Bfrtip',
buttons = c('copy', 'csv', 'excel', 'pdf', 'print'),
paging = F,
searching = F,
autoWidth = T,
columnDefs = list(list(className = 'dt-right', targets = 1:5))
))
```
Row
-------------------------------------
### Trend Analysis
```{r central.trend}
agg_cent <- aggTrends(cl %>% filter(L_HOOD == "CENTRAL AREA", cat_beds %in% c("0", "1", "2")))
hchart(agg_cent, type = "line",
hcaes(x = listing_moyr, y = medRent, group = cat_beds)) %>%
hc_yAxis(title = list(text = "Median Listing Rent")) %>%
hc_xAxis(title = list(text = "Month")) %>%
hc_add_theme(sea_thm) %>%
hc_tooltip(pointFormat = "<b>{series.name} Bedroom Units</b> <br> Rent: {point.y:%.0f} <br> N Listings: {point.n}") %>%
hc_exporting(enabled = TRUE)
```
Delridge {data-navmenu="Neighborhoods"}
=====================================
Row
-------------------------------------
### 90-day summary
```{r delridge.90day}
DT::datatable(compute90(cl %>% filter(L_HOOD == "DELRIDGE")),
rownames = F,
colnames = c("Bedroom Size", "N Listings", "2.5% Quantile", "Median Rent", "97.5% Quantile", "Median Rent/Sqft"),
class = "cell-border stripe",
style = "bootstrap",
autoHideNavigation = T,
extensions = 'Buttons',
options = list(dom = 'Bfrtip',
buttons = c('copy', 'csv', 'excel', 'pdf', 'print'),
paging = F,
searching = F,
autoWidth = T,
columnDefs = list(list(className = 'dt-right', targets = 1:5))
))
```
Row
-------------------------------------
### Trend Analysis
```{r delridge.trend}
agg_del <- aggTrends(cl %>% filter(L_HOOD == "DELRIDGE", cat_beds %in% c("0", "1", "2")))
hchart(agg_del, type = "line",
hcaes(x = listing_moyr, y = medRent, group = cat_beds)) %>%
hc_yAxis(title = list(text = "Median Listing Rent")) %>%
hc_xAxis(title = list(text = "Month")) %>%
hc_add_theme(sea_thm) %>%
hc_tooltip(pointFormat = "<b>{series.name} Bedroom Units</b> <br> Rent: {point.y:%.0f} <br> N Listings: {point.n}") %>%
hc_exporting(enabled = TRUE)
```
Downtown {data-navmenu="Neighborhoods"}
=====================================
Row
-------------------------------------
### 90-day summary
```{r downtown.90day}
DT::datatable(compute90(cl %>% filter(L_HOOD == "DOWNTOWN")),
rownames = F,
colnames = c("Bedroom Size", "N Listings", "2.5% Quantile", "Median Rent", "97.5% Quantile", "Median Rent/Sqft"),
class = "cell-border stripe",
style = "bootstrap",
autoHideNavigation = T,
extensions = 'Buttons',
options = list(dom = 'Bfrtip',
buttons = c('copy', 'csv', 'excel', 'pdf', 'print'),
paging = F,
searching = F,
autoWidth = T,
columnDefs = list(list(className = 'dt-right', targets = 1:5))
))
```
Row
-------------------------------------
### Trend Analysis
```{r downtown.trend}
agg_down <- aggTrends(cl %>% filter(L_HOOD == "DOWNTOWN", cat_beds %in% c("0", "1", "2")))
hchart(agg_down, type = "line",
hcaes(x = listing_moyr, y = medRent, group = cat_beds)) %>%
hc_yAxis(title = list(text = "Median Listing Rent")) %>%
hc_xAxis(title = list(text = "Month")) %>%
hc_add_theme(sea_thm) %>%
hc_tooltip(pointFormat = "<b>{series.name} Bedroom Units</b> <br> Rent: {point.y:%.0f} <br> N Listings: {point.n}") %>%
hc_exporting(enabled = TRUE)
```
Lake City {data-navmenu="Neighborhoods"}
=====================================
Row
-------------------------------------
### 90-day summary
```{r lakecity.90day}
DT::datatable(compute90(cl %>% filter(L_HOOD == "LAKE CITY")),
rownames = F,
colnames = c("Bedroom Size", "N Listings", "2.5% Quantile", "Median Rent", "97.5% Quantile", "Median Rent/Sqft"),
class = "cell-border stripe",
style = "bootstrap",
autoHideNavigation = T,
extensions = 'Buttons',
options = list(dom = 'Bfrtip',
buttons = c('copy', 'csv', 'excel', 'pdf', 'print'),
paging = F,
searching = F,
autoWidth = T,
columnDefs = list(list(className = 'dt-right', targets = 1:5))
))
```
Row
-------------------------------------
### Trend Analysis
```{r lakecity.trend}
agg_lake <- aggTrends(cl %>% filter(L_HOOD == "LAKE CITY", cat_beds %in% c("0", "1", "2")))
hchart(agg_lake, type = "line",
hcaes(x = listing_moyr, y = medRent, group = cat_beds)) %>%
hc_yAxis(title = list(text = "Median Listing Rent")) %>%
hc_xAxis(title = list(text = "Month")) %>%
hc_add_theme(sea_thm) %>%
hc_tooltip(pointFormat = "<b>{series.name} Bedroom Units</b> <br> Rent: {point.y:%.0f} <br> N Listings: {point.n}") %>%
hc_exporting(enabled = TRUE)
```
Magnolia {data-navmenu="Neighborhoods"}
=====================================
Row
-------------------------------------