hospitalOverlays.Rmd

---
title: "MAPPINGjustAllegheny"
output: html_document
date: "2024-11-11"
---


```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

```{r}
library(tidycensus)
library(tidyverse)
library(tigris)
library(sf)
library(tidygeocoder)

options(tigris_use_cache = TRUE)

#load income data for Allegheny County
ac_income <- get_acs(
  geography = "tract", 
  variables = "B19013_001", # Median household income
  county = "Allegheny",
  state = "PA", 
  year = 2020,
  geometry = TRUE
)


# Step 3: Filter hospital data (assuming hospitals are geocoded with latitude and longitude columns)
filtered_hospitals <- hospitals %>%
  filter(!name %in% c("UPMC Horizon – Shenango Valley", "UPMC Horizon – Greenville"))

# Plot 1: Map of Median Household Income with Hospital Overlay
ggplot(ac_income) + 
  geom_sf(aes(fill = estimate), color = "black", size = 0.2) + 
  geom_point(data = filtered_hospitals, aes(x = longitude, y = latitude), color = "red", size = 2) +
  geom_text(data = filtered_hospitals, aes(x = longitude, y = latitude, label = name), 
            size = 2, nudge_y = 0.003, check_overlap = TRUE, color = "white") +  # Set text color to white
  scale_fill_viridis_c(option = "plasma", na.value = "grey90") + 
  labs(title = "Median Household Income in Allegheny County, 2020",
       caption = "Data source: 2020 ACS, US Census Bureau",
       fill = "Median Income") + 
  theme_void() +
  theme(panel.border = element_rect(color = "white", fill = NA))

```

```{r}
# Step 2: Load median age data for Allegheny County
ac_age <- get_acs(
  geography = "tract", 
  variables = "B01002_001", # Median age
  county = "Allegheny",
  state = "PA", 
  year = 2020,
  geometry = TRUE
)
# Plot 2: Map of Median Age with Hospital Overlay
ggplot(ac_age) + 
  geom_sf(aes(fill = estimate), color = "grey", size = 0.2) + 
  geom_point(data = filtered_hospitals, aes(x = longitude, y = latitude), color = "yellow", size = 2) +
  geom_text(data = filtered_hospitals, aes(x = longitude, y = latitude, label = name), 
            size = 2, nudge_y = 0.003, check_overlap = TRUE) + 
  scale_fill_distiller(palette = "RdPu", direction = 1, na.value = "grey90") + 
  labs(title = "Median Age in Allegheny County, 2020",
       caption = "Data source: 2020 ACS, US Census Bureau",
       fill = "Median Age") + 
  theme_void() +
  theme(panel.border = element_rect(color = "black", fill = NA))

```
# race data
#employ log transformation for a better race distribution
```{r}
library(tidycensus)
library(tidyverse)
library(tigris)
library(sf)
library(tidygeocoder)


options(tigris_use_cache = TRUE)

ac_black_population <- get_acs(
  geography = "tract", 
  variables = "B02001_003", # Black or African American alone
  county = "Allegheny",
  state = "PA", 
  year = 2020,
  geometry = TRUE
)

filtered_hospitals <- hospitals %>%
  filter(!name %in% c("UPMC Horizon – Shenango Valley", "UPMC Horizon – Greenville"))

ggplot(ac_black_population) + 
  geom_sf(aes(fill = estimate), color = "grey", size = 0.2) + 
  geom_point(data = filtered_hospitals, aes(x = longitude, y = latitude), color = "red", size = 2.5) +
  geom_text(data = filtered_hospitals, aes(x = longitude, y = latitude, label = name), 
            size = 3, nudge_y = 0.003, check_overlap = TRUE, color = "white") + 
  scale_fill_viridis_c(option = "magma", na.value = "grey90", name = "Population Count") + 
  labs(title = "Black or African American Population by Census Tract in Allegheny County, 2020",
       caption = "Data source: 2020 ACS, US Census Bureau") + 
  theme_void() +
  theme(
    plot.title = element_text(size = 16, hjust = 0.5),
    plot.caption = element_text(size = 10),
    panel.border = element_rect(color = "white", fill = NA)
  )
```