Week 4: Apply 3

Import data

# excel file
data <- read_excel("../00_data/myData.xlsx")
data

## # A tibble: 854 × 7
##    geoid name            year geometry total_pop plumbing percent_lacking_plum…¹
##    <dbl> <chr>          <dbl> <chr>        <dbl> <chr>    <chr>                 
##  1  1003 Baldwin Count…  2023 "list(l…    253507 271      0.10690040117235422   
##  2  1069 Houston Count…  2023 "list(l…    108462 30       0.02765945676826907   
##  3  6037 Los Angeles C…  2023 "list(l…   9663345 5248     0.05430831663362946   
##  4  6087 Santa Cruz Co…  2023 "list(l…    261547 187      0.07149766581149851   
##  5  6097 Sonoma County…  2023 "list(l…    481812 308      0.06392534847616912   
##  6  6013 Contra Costa …  2023 "list(l…   1155025 517      0.04476093591047813   
##  7 10001 Kent County, …  2023 "list(l…    189789 4        0.002107603707274921  
##  8 12017 Citrus County…  2023 "list(l…    166696 198      0.11877909487930124   
##  9 12071 Lee County, F…  2023 "list(l…    834573 1269     0.15205380476003896   
## 10 12101 Pasco County,…  2023 "list(l…    632996 813      0.12843683056449015   
## # ℹ 844 more rows
## # ℹ abbreviated name: ¹​percent_lacking_plumbing

Plot prices

data %>%
    
    ggplot(aes(plumbing)) +
    geom_bar()