The Diamondback - PG Permits Data Dive

Reading in the data & filtering

Filtering and organizing

permits %>%
  arrange(desc(`Expected Construction Cost`)) %>% # Looking at the most expensive type Permit Type for this dataset, we see that is it RZ
  head(5)

## # A tibble: 5 × 12
##   `Permit Category` `County Agency` `Permit Case ID` `Permit Case Year`
##   <chr>             <chr>                      <dbl>              <dbl>
## 1 Building Permit   DPIE                     3137172               2021
## 2 Building Permit   DPIE                     3128016               2021
## 3 Building Permit   DPIE                     3082137               2020
## 4 Building Permit   DPIE                     3122165               2020
## 5 Building Permit   DPIE                     3112960               2020
## # ℹ 8 more variables: `Permit Type` <chr>, `Case Name` <chr>,
## #   `Street Address` <chr>, City <chr>, `Zip Code` <dbl>,
## #   `Permit Issuance Date` <chr>, `Expected Construction Cost` <dbl>,
## #   Location <chr>

rzpermits_summary <- permits %>%
  filter(`Permit Type` == "RZ") %>% # Filtering for RZ permits only
  group_by(`Permit Case Year`) %>% # Grouping RZ permits by year
  summarise(Total_Cost = sum(`Expected Construction Cost`, na.rm = TRUE)) # Adding the sum of all RZ permits per year  

Visualization

ggplot(rzpermits_summary, aes(x = `Permit Case Year`, y = Total_Cost)) + 
  geom_col(fill = "green") + 
  theme_minimal() + 
  labs(title = "Total Estimated Construction Cost of RZ Permits by Year",
       x = "Permit Case Year", 
       y = "Expected Construction Cost") + 
  geom_text(aes(label = paste0("(", `Permit Case Year`, ") ", scales::comma(round(Total_Cost)))), 
            vjust = -0.5, size = 2.5)

WOW, look at 2021!