In sol-eng/accesstocare: Example content of Access to Care project

knitr::opts_chunk$set(echo = params$printcode)

library(dplyr)
library(readr)
library(purrr)
library(ggplot2)
library(leaflet)
library(DT)
library(usmap)
library(pins)
library(blastula)
library(connections)

under <- "#CC79A7"
over <- "#0072B2"
at_level <- "#008b00"
hospital_color <- "#F0E442"

if(Sys.getenv("R_CONFIG_ACTIVE") == "rsconnect") {
  boardname <- "rsconnect"
  board_register_rsconnect(
    server = Sys.getenv("CONNECT_SERVER"),
    key = Sys.getenv("CONNECT_API_KEY")
    )
} else {
  boardname <- "local"
  board_register(boardname)  
}

states <- pin_get("atc-states", board = boardname) %>% 
  filter(full == !! params$state) %>%
  collect()

curr_state <- pull(states, state_id)

counties <- pin_get("atc-county-map", boardname) %>%
  filter(state_id == !! curr_state) %>%
  collect()

state <- counties$state_id[1]

counties_table <- pin_get("atc-county-table", boardname) %>%
  filter(full %in% !! params$state) %>%
  collect()

base_map <- pin_get("atc-base-map", boardname) %>%
  filter(full %in% !! params$state) %>%
  collect()

hospital_locations <- pin_get("atc-hospitals", boardname) %>%
  filter(state_id == !! state) %>%
  select(-state_id) %>%
  collect()

r params$state

Background

We are trying to determine which counties in r params$state have comparatively more access or less access to hospital care.

A statistical model using data from across the country is used to identify those counties that have more or less than the model expects the specific county to have.

County Map

Breakdown of counties and their status based on county population compared to the number of hospitals

locations <- pin_get("atc-hospitals", board = boardname) %>%
  filter(state_id == !! curr_state) %>%
  select(longitude, latitude) %>%
  collect() %>%
  mutate_all(round, 1) %>%
  count(longitude, latitude) %>%
  mutate(popup = paste0("Hospitals: ", n))

initial_map <- leaflet() %>%
  addProviderTiles(providers$CartoDB) %>%
  addLegend(
    "bottomright", 
    color  = c(under,over ,at_level, hospital_color), 
    labels = c(
      "Less hopitals than expected",
      "More hospitals than expected", "Within Range", 
      "Hospital Location"), 
    title  = "Legend",
    opacity = 0.5
    )

full_map <- base_map %>%
  select(popup, color, county_id) %>%
  inner_join(counties, by = "county_id")

full_map %>%
  group_by(county_id, shape_id) %>%
  group_map(~ .x) %>%
  reduce(
    ~ addPolygons(
      .x, 
      lng = .y$long, 
      lat = .y$lat, 
      color = .y$color[[1]], 
      popup = .y$popup[[1]],
      weight = 1, 
      fillOpacity = 0.3
      ), 
    .init = initial_map
    ) %>%
  addCircleMarkers(
    lng = locations$longitude, 
    lat = locations$latitude,
    radius = 2 * (1 + log(locations$n)),
    popup = locations$popup,
    fillColor = hospital_color, color = "gray", 
    fillOpacity = 0.7,weight = 1
    )

County Data

Here is the county level data used in this report

display_table <- counties_table %>%
  select(-full)
datatable(display_table, class = "cell-border stripe")

sol-eng/accesstocare documentation built on Dec. 23, 2021, 3:32 a.m.