---
title: "Exploration of Social Vulnerability and Child Opportunity in NYC in the context of COVID19 and Racial Injustice"
author: "by Jensen Hu"
output: 
  flexdashboard::flex_dashboard:
    storyboard: true
    source: embed
---

```{r setup, include=FALSE, cache=TRUE}
# Load packages
library(dplyr) # data manipulation
library(reshape) # data manipulation
library(tidyverse) # data manipulation
library(DescTools) # data manipulation
library(tigris) # geospatial data
library(sf) # geo spatial
library(sp) # geo spatial
library(htmlwidgets) # interactive widgets
library(DT) # interactive table
library(leaflet) # visualization
library(ggplot2) # visualization
library(flexdashboard) # visualization
library(ggpubr) # visualization

schools <- readRDS(file = "rds_files/schools.rds")
merge_opp <- readRDS(file = "rds_files/merge_opp.rds")
nyc_covid <- readRDS(file = "rds_files/nyc_covid.rds")
nyc_zips <- readRDS(file = "rds_files/nyc_zips.rds")
county_sp <- readRDS(file = "rds_files/county_sp.rds")
svi_tracts <- readRDS(file = "rds_files/svi_tracts.rds")
ds_svi <- readRDS(file = "rds_files/ds_svi.rds")
svi_opp <- readRDS(file = "rds_files/svi_opp.rds")
school_tracts <- readRDS(file = "rds_files/school_tracts.rds")
svi_nta_ranked <- readRDS(file = "rds_files/svi_nta_ranked.rds")
coi_nta_shp <- readRDS(file = "rds_files/coi_nta_shp.rds")
```


### Interactive Leaflet of Social Vulnerability, Child Opporunity, COVID19 Impact, and Geo boundaries in NYC

```{r, cache=TRUE}
# Layer 1 - COVID by ZIP options
popup1 <- paste0("Modified ZCTA: ", nyc_covid$MODZCTA, "
", "Level of COVID Impact: ", nyc_covid$impact_cat, "
", "Case Rate: ", nyc_covid$total_case_rate, "
", "Death Rate: ", nyc_covid$total_death_rate)
pal1 <- colorFactor(palette = "YlGnBu", domain= nyc_covid$impact_cat)
# Layer 2 - SVI by NTA
popup2 <- paste0("NTA: ", svi_nta_ranked$ntaname, "
","svi: ", svi_nta_ranked$cat_nta)
pal2 <- colorFactor(palette = "YlGnBu", domain = svi_nta_ranked$cat_nta)
# Layer 3 - COI by NTA
popup3 <- paste0("NTA: ", coi_nta_shp$ntaname, "
", "COI: ", coi_nta_shp$cat_coi_nta)
pal3 <- colorFactor(palette = "YlGnBu", domain = coi_nta_shp$cat_coi_nta, reverse = TRUE)
# Layer 4 - SMH Layer
popup4 <- paste0("School Name: ", schools$location_name, "
","DBN: ", schools$system_code, "
","Address: ", schools$primary_address_line_1)

# Label Text
labels1 <- sprintf( # label for covid by zip code
  "Modified ZCTA: %s
Level of COVID Impact: %s
Case Rate: %s
Death Rate: %s",
  nyc_covid$MODZCTA, nyc_covid$impact_cat, nyc_covid$total_case_rate, nyc_covid$total_death_rate) %>% lapply(htmltools::HTML)
labels2 <- sprintf( # label for zip codes
  "Zip code: %s",
  nyc_zips$MODZCTA) %>% lapply(htmltools::HTML)
labels3 <- sprintf( # label for county names
  "County: %s",
  county_sp$NAME) %>% lapply(htmltools::HTML)
labels4 <- sprintf( # label for school names
  "School Name: %s",
  schools$location_name) %>% lapply(htmltools::HTML)

# label parameters
label_Options <- labelOptions(style = list("font-weight" = "normal", padding = "3px 8px"), textsize = "15px", direction = "auto")
# highlight parameters
highlight_Options <- highlightOptions(color = "white", weight = 2, fillOpacity = 0.9, bringToFront = TRUE)
highlight_Options2 <- highlightOptions(color = "white", weight = 2, bringToFront = TRUE)

p <- leaflet() %>%
  # Base Groups 
  addProviderTiles("CartoDB.Positron", group = "Positron") %>%  
  addTiles(urlTemplate = "http://mt0.google.com/vt/lyrs=m&hl=en&x={x}&y={y}&z={z}&s=Ga",
           attribution = 'Google', group = "Google Map") %>%
  # Overlay Groups
    addCircleMarkers(data=schools, # school layer
                   lat = schools$latitude,
                   lng = schools$longitude,
                   weight = 2,
                   radius = 2,
                   stroke = FALSE,
                   color = "black",
                   fillOpacity = 1.0,
                   label = labels4,
                   popup = popup4,
                   group = "NYC Schools") %>%
  addPolygons(data = nyc_covid,  # NYC ZIPS + COVID19 % Positive
              fillColor = ~pal1(nyc_covid$impact_cat),
              weight =2,
              color = "black",
              fillOpacity = 0.7,
              popup = popup1,
              label = labels1,
              labelOptions = label_Options,
              highlightOptions = highlight_Options,
              group = "COVID19 Impact by MODZCTA") %>%
  addPolygons(data = svi_nta_ranked, # SVI by NTA
              fillColor = ~pal2(svi_nta_ranked$cat_nta),
              weight =2,
              color = "black",
              fillOpacity = 0.6,
              popup = popup2,
              highlightOptions = highlight_Options,
              group = "Social Vulnerability Index by NTA") %>% 
  addPolygons(data = coi_nta_shp, # COI by NTA
              fillColor = ~pal3(coi_nta_shp$cat_coi_nta),
              weight =2,
              color = "black",
              fillOpacity = 0.7,
              popup = popup3,
              highlightOptions = highlight_Options,
              group = "Child Opportunity Index by NTA") %>% 
  addPolygons(data = nyc_zips, # NYC Zip Codes
              weight =2,
              color= "blue",
              fillOpacity = 0, 
              label = labels2,
              labelOptions = label_Options,
              highlightOptions = highlight_Options2,
              group = "Zip Code") %>%
  addPolygons(data = county_sp, # NY County Boundaries
              color = "orange", 
              fillOpacity = 0, 
              weight = 3, 
              label = labels3,
              labelOptions = label_Options,
              highlightOptions = highlight_Options2,
              group = "County") %>%
  setView(lng = "-73.935242", lat = "40.730610", zoom = 10.3) %>% 
 # Layers control
  addLayersControl(
    baseGroups = c("Positron","Google Map"),
    overlayGroups = c("COVID19 Impact by MODZCTA", "Social Vulnerability Index by NTA", "Child Opportunity Index by NTA", "NYC Schools", "Zip Code", "County"),
    options = layersControlOptions(collapsed = FALSE)
  )  %>%
  addLegend(pal = pal1, 
            values = nyc_covid$impact_cat, 
            position = "bottomleft", 
            title = "Level of Impact",
            group = "COVID19 Impact by MODZCTA") %>% 
  addLegend(pal = pal2,
            values = svi_nta_ranked$cat_nta,
            position = "topleft",
            title = "Social Vulnerability Index",
            group = "Social Vulnerability Index by NTA") %>% 
  addLegend(pal = pal3,
            values = coi_nta_shp$cat_coi_nta,
            title = "Child Opportunity Index",
            group = "Child Opportunity Index by NTA") %>% 
  hideGroup(c("COVID19 Impact by MODZCTA","NYC Schools", "Zip Code", "County", "Child Opportunity Index by NTA"))


p

```

***
This is an interactive leaflet containing NYC specific data on NYC public schools, Social Vulnerability, Child Opportunity, COVID19 and County/Zip geographic boundaries.  

-  Positron and Google Map are different choices of basemap.   
-  COVID19 Impact, SVI by NTA, and COI by NTA layers are best viewed one at a time. The overlay of multiple layers will not correspond to any defined color legends.
-  The Zip Codes or actually modified zip code tabulation areas, can be used with the SVI and COI layers to get a sense of "zip" boundaries.    
-  The County layer can be used with any layer to get a sense of county boundaries.  
-  NYC schools were added as a potential means of (1) identifying mental health need in children, (2) planning for resource distribution, and (3) establishing a starting point in assessing the surrounding community needs.

### Modified Zip Code Tabulation Areas (MODZCTAs) with the highest COVID-19 impact (as of 5/29)

```{r}
st_geometry(nyc_covid) <- NULL # remove geometry 
highest_impact <- nyc_covid %>% 
  filter(impact_cat == "Highest Impact") %>% 
  select(MODZCTA, total_case_rate, total_death_rate, rank_impact) %>% 
  arrange(desc(rank_impact))

datatable(highest_impact, extensions = 'Buttons', options = list(dom = 'Bfrtip',buttons = list(extend = 'collection', buttons = c('csv', 'excel', 'pdf'), text = 'Download')))
```

***
There are 45 modified zip code tabulation areas currently impacted the highest by COVID19, based on case and death rate only. 


### Neighborhood Tabulation Areas (NTAs) with the highest social vulnerability and lowest child opportunity in NYC

```{r}
# priority list of NTAs w/ very low opportunity, highest vulnerability
priority_list <- svi_opp %>% 
  filter(cat_nta == "Highest Vulnerability" & cat_coi_nta == "Very Low") %>% 
  select(boro_name.x, ntacode, ntaname.x) %>% 
  group_by(boro_name.x) %>% 
  summarize(count = n(), Neighborhoods = paste(unique(ntaname.x), collapse = ', '))
  
datatable(priority_list, extensions = 'Buttons', options = list(dom = 'Bfrtip',buttons = list(extend = 'collection', buttons = c('csv', 'excel', 'pdf'), text = 'Download')))
```

***
There are 34 neighborhoods in NYC identified as "highest social vulnerability" and "very low child opportunity." These neighborhoods overlap with areas most impacted by COVID-19.   

-  Please note this does NOT factor in current COVID19 impact data as data by NTA is not publicly available (and crosswalk between MODZCTA and NTA is tricky).   
-  This table can be output into CSV, Excel or PDF format.


### NYC Schools in neighborhoods with the highest vulnerability and lowest child opportunity
```{r}
priority_dbn <- svi_opp %>% 
  filter(cat_nta == "Highest Vulnerability" & cat_coi_nta == "Very Low") %>% 
  left_join(., schools, by = c("ntacode" = "nta")) %>% 
  group_by(ntaname.x) %>% 
  summarize(Schools = paste(unique(location_name), collapse = ', '))

datatable(priority_dbn, extensions = 'Buttons', options = list(dom = 'Bfrtip',buttons = list(extend = 'collection', buttons = c('csv', 'excel', 'pdf'), text = 'Download')))
```

***
There are 507 NYC schools in the 34 identified neighborhoods. As mentioned, schools were used here as a potential means of    

(1) identifying mental health need in children,  
(2) planning for resource distribution, and  
(3) establishing a starting point in assessing the surrounding community needs.     

This table can be output into CSV, Excel or PDF format.