Is there a way to compare the variables without splitting them into individual datasets for a time series analysis?
library(readr)
ClarksvilleRedFinData <- read_csv("ClarksvilleRedFinData.csv",
col_types = cols(EndDate = col_date(format = "%m/%d/%Y")))
View(ClarksvilleRedFinData)
print(ClarksvilleRedFinData)
## # A tibble: 442 x 11
## Region EndDate Sales PendingSales Med_Sale_Price Ppsf Off2w SAL
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
## 1 Clarksville 2012-07-01 266 151 151000 88.9 0.126 0.09
## 2 Clarksville 2012-08-01 245 149 151000 89.9 0.154 0.078
## 3 Clarksville 2012-09-01 202 108 156000 89.8 0.148 0.064
## 4 Clarksville 2012-10-01 243 140 152000 89.7 0.2 0.049
## 5 Clarksville 2012-11-01 202 112 154000 88.3 0.205 0.064
## 6 Clarksville 2012-12-01 225 84 150000 86.2 0.19 0.08
## 7 Clarksville 2013-01-01 144 83 146000 86.4 0.217 0.049
## 8 Clarksville 2013-02-01 196 119 149000 86.5 0.16 0.036
## 9 Clarksville 2013-03-01 222 134 148000 86.2 0.134 0.068
## 10 Clarksville 2013-04-01 254 190 150000 88.0 0.158 0.079
## # ... with 432 more rows, and 3 more variables: New_Listings <dbl>,
## # Active_Listings <dbl>, DOM <dbl>
tail(ClarksvilleRedFinData)
## # A tibble: 6 x 11
## Region EndDate Sales PendingSales Med_Sale_Price Ppsf Off2w SAL
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
## 1 ZipCode_37043 2021-05-01 404 95 326000 145. 66.30% 0.374
## 2 ZipCode_37043 2021-06-01 423 115 330000 147. 58.30% 0.442
## 3 ZipCode_37043 2021-07-01 443 134 330000 149. 51.50% 0.485
## 4 ZipCode_37043 2021-08-01 459 111 333000 153. 52.30% 0.479
## 5 ZipCode_37043 2021-09-01 450 108 338000 157. 37.00% 0.442
## 6 ZipCode_37043 2021-10-01 415 89 345000 160. 43.80% 0.39
## # ... with 3 more variables: New_Listings <dbl>, Active_Listings <dbl>,
## # DOM <dbl>