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Map Clusters

Source: R/mapping_functions.R
map_clusters.Rd

Utility for mapping clusters. The function ingest an object `cl` as returned by find_clusters() and a shape file provisioned by the caller, along with a string name of a column in the shape file that uniquely defines the locations. Note that this column will be used to merge with the clusters and therefore must aligned with the labels in `cl` cluster locations. The function returns a basic plotly map object that can be further modified by the user

Usage

map_clusters(
  cl,
  s,
  s_id,
  label_id = NULL,
  label = c("none", "cluster_centers", "cluster_locations", "all"),
  engine = c("plotly", "ggplot"),
  point_crs = NULL
)

Arguments

cl

an object of class "clusters" as returned by find_clusters()

s

shape file; must be of class sf

s_id

string unique identifier of `s`

label_id

string column of `s` that indicates display label for the row in `s` (default is NULL)

label

for engine = "ggplot", indicates which locations should receive visible text labels. Valid choices are "none", "cluster_centers", "cluster_locations", and "all". The default is "none". This argument is ignored when engine = "plotly" because plotly maps always include hover labels for all locations.

engine

string label to indicate plotting engine; either "plotly" (default) or "ggplot"

point_crs

optional coordinate reference system used to compute representative points when `s` is in longitude/latitude coordinates. If `NULL`, EPSG:3857 is used as a general-purpose fallback. The resulting points are transformed back to the CRS of `s` before plotting

Examples

if (
  requireNamespace("tigris", quietly = TRUE) &&
    requireNamespace("ggplot2", quietly = TRUE)
) {
  # get some data
  dd <- example_count_data[, max(date)]
  # get a distance matrix
  dm <- create_dist_list("county", 50, st = "OH")
  # find the clusters
  cl <- find_clusters(
    cases = example_count_data,
    detect_date = dd,
    distance_matrix = dm
  )
  # get shape file
  ohio_shape <- tigris::counties("OH", cb = TRUE, class = "sf")

  # prepare map data
  md <- map_clusters(cl, ohio_shape, "GEOID")
}
#> Retrieving data for the year 2024
#> 
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