library(readxl)
library(ggplot2)
df<- read_xlsx("/Users/unalt/Desktop/MEF/Data Analytics Essentials/week3/EVDS_istanbul_property_data.xlsx",skip=36,n_max=94,
               col_names=c('Date','TotalSales','EncumberedSales','FirsthandSales',
                           'SecondhandSales','ForeignSales','NewHousePriceIndex','HouseLevel','HouseUnitPrice'))


p<- ggplot(df, aes(Date, TotalSales, color=NewHousePriceIndex)) + 
      geom_point(aes(size=HouseUnitPrice)) +
      ggtitle("Istanbul Property Trend","2012-2020 Istanbul Housing Sales&Price&Index Data") +
      xlab("Date") + ylab("Total House Sales")
p + theme(
  plot.title = element_text(color="darkred", size=15, face="bold"),
  plot.subtitle = element_text(color="darkred", size=12),
  axis.title.x = element_text(color="dimgrey", size=13, face="bold"),
  axis.title.y = element_text(color="dimgrey", size=13, face="bold"),
  axis.text.x = element_text(angle=90, size=9),
  legend.text = element_text(size = 12),
  legend.title = element_text(size=12)
)
## Warning: Removed 2 rows containing missing values (geom_point).

When the real estate data of Istanbul between 2012-2020 were examined, the following findings were obtained: