MAP ASSIGNMENT

Quarto

Quarto enables you to weave together content and executable code into a finished document. To learn more about Quarto see https://quarto.org.

Running Code

When you click the Render button a document will be generated that includes both content and the output of embedded code. You can embed code like this:

library(tidyverse)
library(ggplot2)
library(plotly)
library(htmlwidgets)
library(dygraphs)
library(dplyr)
library(lubridate, warn.conflicts = FALSE)
library(anytime)
library(xts)
library(rworldmap)
library(sf)
library(rgeos)
library(ggdendro)
library(viridis)
library(tigris)
library(leaflet)

Description of the data and data source

For this assignment we use the data produces by the National Association of Realtors (NAR). NAR produces housing statistics on the national, regional, and metro-marketlevel where data is available. we use the national historical data (view US data) on https://www.realtor.com/research/data/ (Data from 2016 to 2022)

what you are trying to communicate Through the MAP we want to show how the median listing price vary from state to state.

what decisions you made and why you made those to best communicate the data

I made decision to create MAP with plotly, Add points to the map and also create dynamic MAP with leaflet (Add popup table). I chose those MAP, its help me to display additional information including data values and tables.

house= read.csv("RDC_Inventory_Core_Metrics_State_History.csv",header = TRUE)
house=house%>% rename(Date=month_date_yyyymm)
house$Date=anydate(house$Date)
house$Date=as.Date(house$Date,"%y-%m-%d")
house=house %>% drop_na()

Format MAP

#Use left_join to combine data
us_states_house <- map_data("state")
House = left_join(us_states_house,house, by = c("region"= "state"))

## Categorize annual average data
median_listing_price = House %>%
  group_by(region, state_id) %>%
  summarise(median_listing_price=mean(median_listing_price),
            Latitude=mean(lat),
            Longitude=mean(long)) %>%
  mutate(Median_listing = ifelse(median_listing_price<296500,"A",
                                 ifelse(median_listing_price<364990,"B",
                                        "C")))
## Make US outline
p_house_1 <- ggplot()
p_house_1 <- p_house_1 + geom_polygon(data = map_data("state"),
                                      mapping = aes(x = long, y = lat,
                                                    group = group),
                                      color="black", fill=NA)
## Add points
p_house_1 <- p_house_1 + geom_point(data=median_listing_price,
                                    aes(x=Longitude,
                                        y=Latitude,
                                        color=Median_listing))
##Format legend
p_house_1 <- p_house_1 + guides(color=guide_legend(title=expression(paste("Median listing price",'($)'))))
p_house_1<- p_house_1 + scale_color_manual(values = c("green", "yellow", "red"),
                                           labels = c("<296500","296500-364990","\u2265 364990"))

p_house_1 <- p_house_1 + theme_minimal()
p_house_1 <- p_house_1 + theme(axis.title = element_blank(),
                               legend.position = c(0.9,0.2))
p_house_1 <- p_house_1 + ggtitle(expression(paste('Median listing price in the USA')))

Save MAP as pdf

pdf("MAP_1.pdf", height=4, width=6)
print(p_house_1)
dev.off()

quartz_off_screen 
                2 

Display Figure in HTML

p_house_1

Dynamic Maps with leaflet

library(leafpop)
# Function to assign colors
make_color <- function(x) {
  sapply(x$median_listing_price,function(y) {
    if(y <= 296500) {
      "green"
      } else if(y <= 364990) {
        "orange"
    } else {
      "red"
    } })
}

# create icon format
icons <- awesomeIcons(
  icon = 'ios-close',
  iconColor = 'black',
  library = 'fa',
  text=round(median_listing_price$median_listing_price),
markerColor = make_color(median_listing_price)
)

## Add popup table
p_pm_popuptable <- leaflet(median_listing_price) %>%
  addTiles() %>%
  addAwesomeMarkers(~Longitude, ~Latitude,
  popup = popupTable(median_listing_price[,c("region","state_id","median_listing_price")],
                     row.numbers=FALSE,
                     feature.id=FALSE),
  icon=icons)

Save MAP as pdf

pdf("MAP_2.pdf", height=4, width=6)
dev.off()

quartz_off_screen 
                2 

Display Figure in HTML

p_pm_popuptable