r4kc

The goal of r4kc is to simplify reading and using data posted by Kitsap County in R.

Installation

You can install the development version of r4kc from GitHub with:

# install.packages("devtools")
devtools::install_github("monkeywithacupcake/r4kc")

Example

This is a basic example which shows you how to solve a common problem:

library(r4kc)
# this will return the three commissioner districts as an sf
commiss <- get_kc_data(get_dataset_name("commission"))

You can pull these data down and make a quick map.

library(tidyverse, quietly)
#> ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
#> ✔ dplyr     1.1.4     ✔ readr     2.1.5
#> ✔ forcats   1.0.0     ✔ stringr   1.5.1
#> ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
#> ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
#> ✔ purrr     1.0.2     
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag()    masks stats::lag()
#> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(sf)
#> Linking to GEOS 3.11.0, GDAL 3.5.3, PROJ 9.1.0; sf_use_s2() is TRUE

outline <- get_kc_data("Kitsap_County_Outline")
uga <- get_kc_data(get_dataset_name("uga"))
cities_and_uga <- uga %>%
  mutate(type = ifelse(grepl("City ", GMA_JURISDICTION), "City", "UGA")) %>%
  group_by(type, jurisdiction = GMA_JURISDICTION) %>%
  summarize(geometry = st_union(geometry),
            .groups = "drop") %>%
  select(jurisdiction)

ggplot() +
  geom_sf(data = outline) +
  geom_sf(data = cities_and_uga, aes(fill = jurisdiction)) +
  labs(title = "Cities and UGA in Kitsap County") +
  theme_void()

You can do more extensive analysis - combining with census or transit or other kitsap county data. This is especially useful if you want to limit an analysis to a particular area in Kitsap County, for example.

One quick function is a geometric difference - that handles the transformation of removing an sf from another and appending a column with the percent that is remaining. It is called exclude_cities() based on its initial use. It is more generic than excluding cities, and it may be renamed in future.

kit_no_cities <- exclude_cities(sf_poly = outline,
                                sf_cities =
                                      filter(cities_and_uga,
                                             grepl("City", jurisdiction)),
                                match_col = 'OBJECTID'
                                      )
#> Warning: attribute variables are assumed to be spatially constant throughout
#> all geometries

This doesn’t seem super exciting when we are just throwing together made up maps, like so:

ggplot() +
  geom_sf(data = outline) +
  geom_sf(data = kit_no_cities,
          fill = "hotpink4") +
  theme_void()

However, it can be useful, and the portion of the remaining geometry can be passed to further calculations. For example, in this exclusion, the remaining objects have a mean of 0.7765957 of their initial size (when they still included cities). Note: this example is meaningless, as the 8 rows making the outline are meaningless.