Maps

#library(RColorBrewer) 
#library(leaflet)
#library(terra)
# for metadata/attributes- vectors or rasters
#library(raster)
library(sf)

## Linking to GEOS 3.13.0, GDAL 3.8.5, PROJ 9.5.1; sf_use_s2() is TRUE

#library(rnaturalearthdata)
#library(rnaturalearth)
library(tidyverse)

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## ✔ forcats   1.0.0     ✔ stringr   1.5.2
## ✔ ggplot2   4.0.0     ✔ tibble    3.3.0
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.1.0

## ── 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(sp)
#library(leaflet)

df_simplified <- read_rds(here::here("data/processed/formaps.rds")) # org and shape data
neighborhoods <- read_rds(here::here("data/processed/neighborhood_shape_data.rds"))#neighborhood stat data (read into R)

Plot using plot() function

# Plotting simple features (sf) with plot 
plot(df_simplified$geometry.x)

plot(df_simplified$geometry.y)

Using ggplot2

Start by building the map area:

multi_polygon_sf <- st_as_sf(df_simplified) #converting to sf object
outline_plot <-ggplot() +
    geom_sf(data = multi_polygon_sf$geometry.y, fill = "lightblue", color = "blue") +
    theme_minimal() +
   labs(title = "Nonprofits and Neighborhood Demographics")
outline_plot

Neighborhood Demographics

# make data at neighborhood level to plot maps 
neighborhoods_sf <- st_as_sf(neighborhoods) # convert to sf object
neighborhoods_sf <- neighborhoods_sf %>% mutate(Percent_AA
  = round(Blk_AfAm/Population*100, digits = 2))  %>% # make our variables like before but this time at neighborhood level only

  # create new Majority_AA variable that indicates if Percent_AA is greater than 50% or not %>%
  mutate(Majority_AA = case_when(
    Percent_AA > 50 ~ "Yes", 
    Percent_AA <  50 ~ "No")) %>%
  # create a new variable about this in text
   mutate(Neighborhood_type = case_when(
    Percent_AA > 50 ~ "Majority\nBlack", 
    Percent_AA <  50 ~ "Majority\nNon-Black")) %>% 
  # make this a factor and order by level appearance in the data
  mutate(Neighborhood_type = as_factor(Neighborhood_type),
         Neighborhood_type = forcats::fct_inorder(Neighborhood_type))


 neighborhood_dem <- ggplot() +geom_sf(data = neighborhoods_sf, aes(fill = Neighborhood_type))+
   scale_fill_manual(values = c("Majority\nBlack" = "#482677FF", "Majority\nNon-Black" = "#20A387FF"))+
    theme_minimal()
 neighborhood_dem

Map of High vs Low Asset Orgs

asset_plot_high_Vs_low <- neighborhood_dem + 
    geom_sf(data = multi_polygon_sf, alpha = 0.8, shape = 21, color ="black", fill = "#DCE319FF")+ facet_grid(~ASSET_High_text)+
    theme_void() +
     labs(title = "High vs Low Asset Non-Profits in Baltimore") 


asset_plot_high_Vs_low

Map of different types of orgs by asset

asset_plot_all_faceted <- neighborhood_dem + 
    geom_sf(data = multi_polygon_sf, alpha = 0.8, shape = 21, color ="black", fill = "#DCE319FF")+ facet_wrap(~NTEE_text, ncol = 3 )+
    theme_void() +
  labs(title = "Non-Profits in Baltimore by Type") 
asset_plot_all_faceted

High Asset Non-Profits in Baltimore by Type

data_to_map <-multi_polygon_sf %>% filter(ASSET_High_text == "High Asset") 

asset_plot <- neighborhood_dem + 
    geom_sf(data = data_to_map, alpha = 0.8, shape = 21, color ="black", fill = "#DCE319FF")+ facet_wrap(~NTEE_text, ncol = 3 )+
    theme_void() +
  labs(title = "High Asset Non-Profits in Baltimore by Type") 
asset_plot