04_maps.Rmd

# Maps

```{r}

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

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

```{r}
# Plotting simple features (sf) with plot 
plot(df_simplified$geometry.x)
plot(df_simplified$geometry.y)
library(sp)
library(leaflet)
```



# Using ggplot2

Start by building the map area:

```{r}
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

```{r}

# 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


```{r}
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
```{r}

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

```{r}
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
```



# Old work 

```{r}
multi_polygon_sf <- st_as_sf(df_simplified)
AA_plot <-ggplot() +
    geom_sf(data = multi_polygon_sf, aes(color = Majority_AA)) +
    theme_minimal() +
   labs(title = "Neighborhood by Demographics")
AA_plot

#+ facet_grid(~multi_polygon_sf$Majority_AA)+

```


```{r}
outline_plot + 
    geom_sf(data = multi_polygon_sf, aes(color = Majority_AA)) +
    theme_void() 


```


```{r}
outline_plot + 
    geom_sf(data = multi_polygon_sf, aes(color = Majority_AA))+
    facet_grid(~ASSET_High_text)+
    theme_void() 


```




```{r}

points_plot <-outline_plot + 
    geom_sf(data = multi_polygon_sf$geometry.x, fill = "lightblue", color = "blue", alpha = 0.5) +
    theme_minimal() +
   labs(title = "plot example")
points_plot

```






```{r}
coords <- st_coordinates(df_simplified$geometry.x)
coords_df <- as.data.frame(coords)
colnames(coords_df) <- c("longitude", "latitude")
df_simplified <-bind_cols(coords_df, df_simplified)
#leaflet(coords_df) %>% addMarkers() %>% addTiles()
```

```{r}
df_simplified %>% filter(Majority_AA == "Yes") %>%
ggplot( aes(x = longitude, y = latitude, color = ASSET_High)) + geom_point()+
    theme_minimal() 
```

```{r}
df_simplified %>% 
ggplot( aes(x = longitude, y = latitude)) +facet_grid(ASSET_High ~ Majority_AA) + geom_point()+ 
    theme_minimal()
```

- keep high asset overall for online 
- 
#############old stuff:
```{r, include = FALSE} 
#old
# geo_clean <- df_simplified %>% dplyr::select(ein, lon, lat) %>% drop_na(lon) # one row - will need to remove form cbos
# coordinates(geo_clean) <- ~lon+lat
# leaflet(geo_clean) %>% addMarkers() %>% addTiles()

```

```{r, eval = FALSE}
df_simplified <-bind_cols(coords_df, df_simplified)

library(ggmap)
library(osmdata) 
library(devtools)

mad_map <- get_map(getbb("Limete, Kinshasa"), maptype = "terrain", source = "osm")
ggmap(mad_map)
```

```{r, eval = FALSE}



for_plot <-df_simplified %>% dplyr::select(Name, ORG_NAME_CURRENT, longitude, latitude)
# getting the map
mapplotarea <- get_map(location = c(lon = mean(for_plot$longitude), lat = mean(for_plot$latitude)), zoom = 11,maptype = "satellite", scale = 2)
# plotting the map with some points on it
plot1 <- ggmap(mapplotarea) +
  geom_point(data =for_plot, aes(x = lon, y = lat, fill = "red", alpha = 0.8), size = 2, shape = 21) +
  guides(fill=FALSE, alpha=FALSE, size=FALSE)


```

```{r, eval = FALSE}
neighborhoods <-df_simplified$geometry.x
library(rnaturalearthdata)
library(rnaturalearth)
world <- ne_countries(scale = "medium", returnclass = "sf")
glimpse(world)
outline_plot<-ggplot(data = world) +
    geom_sf() +  geom_sf(data = neighborhoods)+
    coord_sf(xlim = c(-76.74, -76.5), ylim = c(39.19, 39.4), 
             expand = FALSE)


outline <-plot(df_simplified$geometry.y)



 multi_polygon_sf <- st_as_sf(df_simplified)



outline +geom_point(data = df_simplified, aes(x = longitude, y = latitude, fill = BMF_ASSET_CODE), size = 2, 
               shape = 23)

test <-outline + geom_point(data = df_simplified, aes(x = longitude, y = latitude))
```