The San Mateo County geography functions are designed to help clean and categorize geographic variables consistently.
smc_city_clean()
Basic usage
This function uses regular expressions to clean up San Mateo County
city names. Non-San Mateo city names will return an NA
value. By default, the function assumes the variable you want to clean
is called city and will save the cleaned results in a
variable called city_clean.
data <- data.frame(
city = c("Burligame", "Fost City", "San Mato", "Daily Cit", "S S Francisco", "South San Fransico", "SoSan Franc", "San Francisco")
)
data %>%
smc_city_clean()Additional options
If your input column is not called city you should pass
the name of your city variable in the city_col argument.
You also have the option of specifying the name of the variable for the
cleaned cities. By default it is city_clean.
data <- data.frame(
city_dirty = c("Burligame", "Fost City", "San Mato", "Daily Cit", "S S Francisco", "South San Fransico", "SoSan Franc", "San Francisco")
)
data %>%
smc_city_clean(city_col = "city_dirty",
new_col = "smc_city")
smc_zip_region_sort()
Basic usage
This function categorizes zip codes into county regions. The expected input is a variable in a data frame and it will return a second variable with the zip region. The region options are: North, Mid, South, Coastside and “Not a residential zip” (for PO Boxes).
data <- data.frame(
zip = c("94015", "94403", "94303", "94019", "94128", "94110")
)
data %>%
smc_zip_region_sort()Additional options
By default, the function expects the variable of zip codes to be
called zip and it will save the zip regions in a variable
called zip_region. However, if your input column is not
called zip you can override the default with the
zip_col argument. You can also change the outputted
variable name with the region_col argument.
data <- data.frame(
smc_zip = c("94015", "94403", "94303", "94019", "94128", "94110")
)
data %>%
smc_zip_region_sort(zip_col = "smc_zip",
region_col = "smc_zip_region")