ISTANBUL PROPERTY REPORT

Preparetions

library(readxl)
property_data<-read_xlsx("/home/idil/İndirilenler/EVDS_istanbul_property_data.xlsx",n_max = 130)
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.2     ✓ purrr   0.3.4
## ✓ tibble  3.0.4     ✓ dplyr   1.0.2
## ✓ tidyr   1.1.2     ✓ stringr 1.4.0
## ✓ readr   1.4.0     ✓ forcats 0.5.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
new_data<-property_data%>%
  select(date='Tarih',total_sales='TP AKONUTSAT1 T40',mortgage_sales='TP AKONUTSAT2 T40',first_hand_sales='TP AKONUTSAT3 T40',
         second_hand_sales='TP AKONUTSAT4 T40',foreign_sales='TP DISKONSAT ISTANBUL',
         price_index='TP HEDONIKYKFE IST',tr10='TP HKFE02',unit_price='TP TCBF02 ISTANBUL')

Finding the maximum sales after November 2020

new_data%>%
  filter(date>2019-12)%>%
arrange(desc(new_data))%>%
  top_n(1,total_sales)
## # A tibble: 1 x 9
##   date  total_sales mortgage_sales first_hand_sales second_hand_sal…
##   <chr>       <dbl>          <dbl>            <dbl>            <dbl>
## 1 2019…       40317          10137            14772            25545
## # … with 4 more variables: foreign_sales <dbl>, price_index <dbl>, tr10 <dbl>,
## #   unit_price <dbl>

Price index throught time

new_data%>%
filter(date>2018-12)%>%
group_by(price_index)%>%
arrange(desc(date))
## # A tibble: 129 x 9
## # Groups:   price_index [118]
##    date  total_sales mortgage_sales first_hand_sales second_hand_sal…
##    <chr>       <dbl>          <dbl>            <dbl>            <dbl>
##  1 2020…       25399           7527             7099            18300
##  2 2020…       30292          15367             8103            22189
##  3 2020…       39432          24000            10429            29003
##  4 2020…       28799          14767             8253            20546
##  5 2020…        7640           2570             2546             5094
##  6 2020…        6113           2451             2022             4091
##  7 2020…       19846           7843             6439            13407
##  8 2020…       22662           8281             7326            15336
##  9 2020…       21251           7615             6599            14652
## 10 2019…       40317          10137            14772            25545
## # … with 119 more rows, and 4 more variables: foreign_sales <dbl>,
## #   price_index <dbl>, tr10 <dbl>, unit_price <dbl>
library("ggplot2")
plot1<-ggplot(new_data, aes(x=foreign_sales,y=total_sales))
print(plot1)