Assignment 3

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(ggplot2)
library(tidyverse)

## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --

## <U+221A> tibble  3.0.4     <U+221A> purrr   0.3.4
## <U+221A> tidyr   1.1.2     <U+221A> stringr 1.4.0
## <U+221A> readr   1.4.0     <U+221A> forcats 0.5.0

## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(readxl)

housedf <- read_xlsx("C:/Users/hüseyin/Desktop/Dersler/BDA503-Data Analytics Essentials/EVDS_istanbul_property_data (1).xlsx", col_names=c('Month','Total_Sales','Mortgaged_Sales','New_House_Sales',
                                'Second_Hand_Sales','Foreign_Sales','New_Price_Index',
                                'Price_Index','Unit_Price'))

housedf_xx <- housedf %>% select(Month,Unit_Price) %>% filter(Month<"2020-09" & Month>="2020-01")

sc_plot <- ggplot(housedf_xx, aes(x=Month, y=Unit_Price)) + geom_point()

sc_plot2 <- sc_plot + labs(x="Months in 2020", y="Istanbul Housing Unit Prices(TL/m^2)", title = "Change of Istanbul Housing Unit Prices in 2020 by Months") 

sc_plot2

Conclusion

I used the İstanbul Property Data to analyze whether the House Unit Prices in İstanbul icreased starting from this year or not. It can be observed from the gragh that there is steady increase in House Unit Prices from 2020-01 to 2020-08. In conclusion, I can say that every month in 2020, there is steady incrase in House Unit Prices.