The goal of r4kc is to simplify reading and using data posted by Kitsap County in R.
You can install the development version of r4kc from GitHub with:
# install.packages("devtools")
devtools::install_github("monkeywithacupcake/r4kc")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 errorslibrary(sf)
#> Linking to GEOS 3.11.0, GDAL 3.5.3, PROJ 9.1.0; sf_use_s2() is TRUEoutline <- 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 geometriesThis 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.