download.file("https://s3-us-west-2.amazonaws.com/econresearch/Reports/Core/RDC_InventoryCoreMetrics_State.csv",
destfile = "State.csv")
download.file("https://s3-us-west-2.amazonaws.com/econresearch/Reports/Core/RDC_InventoryCoreMetrics_Metro.csv",
destfile = "Metro.csv")
library(ggplot2)
State_data<-read.csv("State.csv")
Metro_data<-read.csv("Metro.csv")
taland
Metro_prices<-Metro_data [which(Metro_data$Hhrank<21),]
Metro_top<-subset(Metro_data,Metro_data$Hrank <21)
Metro_top
ratio_yy<-Metro_prices$price_increased_count_yy/Metro_prices$price_reduced_count_yy
ratio_mm<-Metro_prices$price_increased_count_mm/Metro_prices$price_reduced_count_mm
barplot(ratio_yy, main='Ratio of price increase to decrease in Top 20 cities Y to Y', col="blue",
names.arg=Metro_prices$cbsa_title, cex.axis=0.7, cex.names = 0.6, las=2)
barplot(ratio_mm, main='Ratio of price increase to decrease in Top 20 cities M to M', col="red",
names.arg=Metro_prices$cbsa_title, cex.axis=0.7, cex.names = 0.6, las=2)
barplot(Metro_prices$Median.Listing.Price, main='Median Prices for Top 20 cities', col="blue",
names.arg=Metro_prices$cbsa_title, cex.axis=0.7,
cex.names = 0.5, las=2)
barplot(Metro_prices$Median.Listing.Price.Y.Y, main='Median Prices for Top 20 cities Y to Y', col="red",
names.arg=Metro_prices$cbsa_title, cex.axis=0.7,
cex.names = 0.6, las=2)
barplot(Metro_prices$Median.Listing.Price.M.M, main='Median Prices for Top 20 cities M to M', col="blue",
names.arg=Metro_prices$cbsa_title, cex.axis=0.7,
cex.names = 0.6, las=2)
barplot(Metro_prices$Active.Listing.Count.Y.Y, main='Active Listing for Top 20 cities Y to Y', col="blue",
names.arg=Metro_prices$cbsa_title, cex.axis=0.7,
cex.names = 0.6, las=2)
barplot(Metro_prices$Active.Listing.Count.M.M, main='Active Listing for Top 20 cities M to M', col="blue",
names.arg=Metro_prices$cbsa_title, cex.axis=0.7,
cex.names = 0.6, las=2)
barplot(Metro_prices$Days.on.Market.Y.Y, main='Days on Market for Top 20 cities Y to Y', col="red",
names.arg=Metro_prices$cbsa_title, cex.axis=0.7,
cex.names = 0.6, las=2)
barplot(Metro_prices$price_increased_count_yy, main='Price Increased count for Top 20 cities Y to Y', col="green",
names.arg=Metro_prices$cbsa_title, cex.axis=0.7,
cex.names = 0.6, las=2)
barplot(Metro_prices$pending_ratio_yy, main='Price Pending ratio for Top 20 cities Y to Y', col="green",
names.arg=Metro_prices$cbsa_title, cex.axis=0.7,
cex.names = 0.5, las=2)
ggplot(Metro_prices, aes(x=factor(Hhrank), y=Median.Listing.Price.Y.Y)) +
geom_col(fill="lightblue", colour="red")
geom_text(aes(label=Median.Listing.Price.Y.Y), vjust=-0.2)
ggplot(Metro_prices, aes(x=Metro_prices$Median.Listing.Price.Y.Y)) +
geom_histogram()
barplot(Metro_prices[order(Metro_prices$Median.Listing.Price.Y.Y)], horiz=T)
hist(Metro_prices$Median.Listing.Price)
hist(Metro_data$Median.Listing.Price.Y.Y)
summary(Metro_data$Median.Listing.Price.Y.Y)
barplot(Metro_prices$Median.Listing.Price.Y.Y, cex.names = 0.9, las=2)
# Select Housing Prices for top 20 cities
Housing<- subset(Metro_data, Metro_data$Hhrank <'20')
Housing<-Housing[order(Housing$Month),]
ratio=Housing$Avg.Listing.Price/Housing$Median.Listing.Price
barplot(Housing$Median.Listing.Price)
d <- density(ratio)
plot(d, main="Average Prices / Median Prices")
polygon(d, col="red", border="blue")
var0<-Housing$Median.Listing.Price
var1<-Housing$Avg.Listing.Price
date <- seq(as.Date("2012-05-01"), by="1 month", length.out=84)
# Creating Charts
ggplot() + geom_line(aes(x=date,y=var0),color='red') +
geom_line(aes(x=date,y=var1),color='blue') +
ylab('Housing Prices')+xlab('Date')+
labs(title=" Median Listing Prices (in Red) and Average Listing Prices (in Blue)")
# Percent Chagnes
var2<-Housing$Median.Listing.Price.Y.Y
var3<-Housing$Avg.Listing.Price.Y.Y
date <- seq(as.Date("2012-05-01"), by="1 month", length.out=84)
ggplot() + geom_line(aes(x=date,y=var2),color='red') +
geom_line(aes(x=date,y=var3),color='blue') +
ylab('Housing Prices')+xlab('Date')+
labs(title=" Median Listing Prices (in Red) and Average Listing Prices Y to Y (in Blue)")
barplot(Housing$Days.on.Market, main="Days on Market"
,
names.arg = Housing$Month, cex.names = 0.3 )
barplot(Housing$Days.on.Market.Y.Y, main="Days on Market Y to Y"
,
names.arg = Housing$Month, cex.names = 0.3 )
barplot(Housing$Total.Listing.Count.Y.Y, main="Total Listing Y to Y"
,
names.arg = Housing$Month, cex.names = 0.5 )
barplot(Housing$Pending.Listing.Count.Y.Y, main="Pending Listing Y to Y"
,
names.arg = Housing$Month, cex.names = 0.5 )
# Basic line plot with points
ggplot(data=Housing, aes(x=Housing$Month, y=ratio, group=1)) +
geom_line()+
geom_point()+
labs(title=" Ratio ( Average Price / Median Price) ")
# Basic line plot with points
ggplot(data=Housing, aes(x=Housing$Month, y=Housing$Active.Listing.Count.Y.Y, group=1)) +
geom_line(linetype="dashed")+
geom_point()+
labs(title=" Active Listing Count")
