Week 4: Apply 3

Import data

# excel file
data <- read_excel("Raw data Roundabout.xlsx")
data

## # A tibble: 27,887 × 18
##    name    address town_city county_area state_region country    lat  long type 
##    <chr>   <chr>   <chr>     <chr>       <chr>        <chr>    <dbl> <dbl> <chr>
##  1 Arapah… Boulde… Boulder   Boulder Co. CO           United… -105.   40.0 Traf…
##  2 Arapah… Boulde… Boulder   Boulder Co. CO           United… -105.   40.0 Traf…
##  3 10th A… Belmar… Belmar    Monmouth C… NJ           United…  -74.0  40.2 Traf…
##  4 10th A… Belmar… Belmar    Monmouth C… NJ           United…  -74.0  40.2 Traf…
##  5 Bagley… Seattl… Seattle   King Co.    WA           United… -122.   47.7 Traf…
##  6 8th Av… Seattl… Seattle   King Co.    WA           United… -122.   47.7 Traf…
##  7 8th Av… Seattl… Seattle   King Co.    WA           United… -122.   47.7 Traf…
##  8 8th Av… Seattl… Seattle   King Co.    WA           United… -122.   47.7 Traf…
##  9 Kendal… Madiso… Madison   Dane Co.    WI           United…  -89.4  43.1 Traf…
## 10 Bagley… Seattl… Seattle   King Co.    WA           United… -122.   47.7 Traf…
## # ℹ 27,877 more rows
## # ℹ 9 more variables: status <chr>, year_completed <dbl>, approaches <dbl>,
## #   driveways <dbl>, lane_type <chr>, functional_class <chr>,
## #   control_type <chr>, other_control_type <chr>, previous_control_type <chr>

Plot prices

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