---
title: "Facts on Toronto Neighbourhood"
author: "Muralidhar, M.A."
date: "January 30, 2019"
output:
flexdashboard::flex_dashboard:
social: menu
orientation: columns
vertical_layout: scroll
source_code: embed
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(grid)
library(rgdal)
library(tmap)
library(ggplot2)
library(dplyr)
# read in the shapefile
TorontoMap = readOGR(dsn = "data", layer = "NEIGHBORHOODS_WGS84")
# Retain map for leaflet legend
toronto_leaflet <- TorontoMap
# remove the column AREA_S_CD
toronto_leaflet@data$AREA_S_CD <- NULL
# load related data on health and wellbeing in the City
chronicDiseases <- read.csv("data/Toronto/ChronicDiseases.csv")
WellbeingToronto <- read.csv("data/wellbeing_toronto_2011.csv")
safety <- readRDS("safety.rds")
counties <- readRDS("counties.rds")
secondarySchoolcounties <- readRDS("secondarySchoolcounties.rds")
# AREA_S_CD represents the neighbourhood ID,
# but this variable was read in as character string
# because of the way it was coded in Excel.
# The variable was then converted to a FACTOR variable when imported in R.
class(TorontoMap$AREA_S_CD) # factor
TorontoMap$AREA_S_CD<-as.numeric(TorontoMap$AREA_S_CD)
#perform the join
TorontoMap@data <- left_join(TorontoMap@data, chronicDiseases, by = c('AREA_S_CD' = 'Neighb_ID'))
TorontoMap@data <- left_join(TorontoMap@data, WellbeingToronto, by = c('AREA_S_CD' = 'Neighbourhood.Id'))
# use the tmap package to view some of the data we just joined
#qtm(TorontoMap, "PopPerSqKm") # plot the basic map
TorontoMap@data$SeniorsPerSqKm <- round(TorontoMap@data$Seniors.55.and.over / TorontoMap@data$Total.Area, 0)
TorontoMap@data$VisbMinorityPerSqKm <- round(TorontoMap@data$Visible.Minority.Category / TorontoMap@data$Total.Area, 0)
TorontoMap@data$NotVisbMinorityPerSqKm <- round(TorontoMap@data$Not.a.Visible.Minority / TorontoMap@data$Total.Area, 0)
# load related data on health and wellbeing in the City
TorontoHealth <- read.csv("data/City_Tor_WB-Health_2011.csv")
library(sf)
d <- st_read('data\\NEIGHBORHOODS_WGS84.shp',stringsAsFactors = F)
d$centroid <- st_centroid(d$geometry)
d$AREA_S_CD<-as.numeric(d$AREA_S_CD)
#perform the join
d <- left_join(d, TorontoHealth, by = c('AREA_S_CD' = 'NID'))
safety_sf <- left_join(d, safety, by = c('AREA_S_CD' = 'Neighbourhood Id'))
# get the street tree count for neighborhood
treeCount <- st_read('data\\GTATreeCount.shp',stringsAsFactors = F)
# get the fast food restaurant
fastFood <- st_read('data\\fastFoodCount.shp',stringsAsFactors = F)
```
# Health
## Column 1
### Diagonesed High Blood Pressure Patients Per 100 Residents
```{r echo = FALSE}
tm_shape(TorontoMap) + tm_polygons("HighBloodPressure",style=c("kmeans"),
palette="PuBu",
stretch.palette = TRUE,
title=c("High Blood Pressure Patients Per 100"),
position = c("left","top")) +
tm_credits("Source: http://ontariohealthprofiles.ca/ \n File: 3_ahd_db_ast_hbp_mhv_copd_neighb_2015_LHIN_8.xls \n Analysis:Murali", position=c("right", "bottom"))
```
### Diagonozed Diabetes Patients Per 100 Residents
```{r echo = FALSE}
tm_shape(TorontoMap) + tm_polygons(c("Diabetes"),
style=c("kmeans"),
palette="PuBu",
stretch.palette = TRUE,
title=c("Diabetes Patients Per 100 ")) +
tm_legend(position = c("right","bottom"))+
tm_credits("Source: http://ontariohealthprofiles.ca/ \n File: 3_ahd_db_ast_hbp_mhv_copd_neighb_2015_LHIN_8.xls \n Analysis:Murali", position=c("right", "bottom"))
```
### Diagonozed COPD Patients Per 100 Residents
```{r echo = FALSE}
tm_shape(TorontoMap) +
tm_polygons(c("COPD"),
style=c("kmeans"),
palette="PuBu",
stretch.palette = TRUE,
title=c("COPD Patients Per 100 ")) +
tm_legend(position = c("right","bottom"))+
tm_credits("Source: http://ontariohealthprofiles.ca/ \n File: 3_ahd_db_ast_hbp_mhv_copd_neighb_2015_LHIN_8.xls \n Analysis:Murali", position=c("right", "bottom"))
```
### Diagonozed Asthma Patients Per 100 Residents
```{r echo = FALSE}
tm_shape(TorontoMap) +
tm_polygons(c("Asthma"),
style=c("kmeans"),
palette="PuBu",
stretch.palette = TRUE,
title=c("Asthma Patients Per 100 ")) +
tm_legend(position = c("right","bottom"))+
tm_credits("Source: http://ontariohealthprofiles.ca/ \n File: 3_ahd_db_ast_hbp_mhv_copd_neighb_2015_LHIN_8.xls \n Analysis:Murali", position=c("right", "bottom"))
```
### Mental Health Visits Per 100 Residents
```{r echo = FALSE}
tm_shape(TorontoMap) +
tm_polygons( c("MentalHealthVisits"),
style=c("kmeans"),
palette="PuBu",
stretch.palette = TRUE,
title=c("Mental Health Visits Per 100 ")) +
tm_legend(position = c("right","bottom")) +
tm_credits("Source: http://ontariohealthprofiles.ca/ \n File: 3_ahd_db_ast_hbp_mhv_copd_neighb_2015_LHIN_8.xls \n Analysis:Murali", position=c("right", "bottom"))
```
### Number of Healthcare Providers Per Neighbourhood
```{r echo = FALSE}
tm_shape(d) +
tm_polygons(c("HealthProviders"),
style=c("kmeans"),
palette="PuRd",
stretch.palette = TRUE,
title=c("Healthcare Providers Count")) +
tm_legend(position = c("right","bottom"))+
tm_credits("Source: https://www.toronto.ca/city-government/data-research-maps/open-data/open-data-catalogue/ \n City_Tor_WB-Health_2011.csv \n Analysis:Murali", position=c("right", "bottom"))
```
## Column 2
### Average Family Income In the Neighbourhood
```{r echo = FALSE}
tm_shape(TorontoMap) +
tm_polygons("Average.Family.Income",style=c("kmeans"),
palette="Greens",
stretch.palette = TRUE,
title=c("Average Family Income $")) +
tm_legend(position = c("right","bottom"))+
tm_credits("Source: https://www.toronto.ca/city-government/data-research-maps/open-data/open-data-catalogue/ \n wellbeing_toronto_2011.csv \n Analysis:Murali", position=c("right", "bottom"))
```
### Number of Visible Minority Residents Per Square Kilometer
```{r echo = FALSE}
tm_shape(TorontoMap) +
tm_polygons(c("VisbMinorityPerSqKm"),
style=c("kmeans"),
palette= "Purples",
stretch.palette = TRUE,
title=c("Visible Minorities Per Sq.Km.")) +
tm_legend(position = c("right","bottom"))+
tm_credits("Source: https://www.toronto.ca/city-government/data-research-maps/open-data/open-data-catalogue/ \n wellbeing_toronto_2011.csv \n Analysis:Murali", position=c("right", "bottom"))
```
### Number of Senior Citizen Residents Per Square Kilometer
```{r echo = FALSE}
tm_shape(TorontoMap) +
tm_polygons(c("SeniorsPerSqKm"),
style=c("kmeans"),
palette="Purples",
stretch.palette = TRUE,
title=c("Seniors Per Sq.Km.")) +
tm_legend(position = c("right","bottom")) +
tm_credits("Source: https://www.toronto.ca/city-government/data-research-maps/open-data/open-data-catalogue/ \n wellbeing_toronto_2011.csv \n Analysis:Murali", position=c("right", "bottom"))
```
### Tree Counts Each Neighbourhood
```{r echo = FALSE}
tm_shape(treeCount) +
tm_polygons( c("NUMPOINTS"),
style=c("kmeans"),
palette="YlGn",
stretch.palette = TRUE,
title=c("Tree counts in Neighborhood ")) +
tm_legend(position = c("right","bottom"))+
tm_credits("Source: https://www.toronto.ca/city-government/data-research-maps/open-data/open-data-catalogue/ \n wellbeing_toronto_2011.csv \n Analysis:Murali", position=c("right", "bottom"))
```
### Tree Counts Each Neighbourhood
```{r echo = FALSE}
tm_shape(treeCount) +
tm_polygons( c("NUMPOINTS"),
style=c("kmeans"),
palette="YlGn",
stretch.palette = TRUE,
title=c("Tree counts in Neighborhood ")) +
tm_legend(position = c("right","bottom"))+
tm_credits("Source: https://www.toronto.ca/city-government/data-research-maps/open-data/open-data-catalogue/ \n wellbeing_toronto_2011.csv \n Analysis:Murali", position=c("right", "bottom"))
```
### Average Family Income In the Neighbourhood
```{r echo = FALSE}
tm_shape(TorontoMap) +
tm_polygons("Average.Family.Income",style=c("kmeans"),
palette="Greens",
stretch.palette = TRUE,
title=c("Average Family Income $")) +
tm_legend(position = c("right","bottom"))+
tm_credits("Source: https://www.toronto.ca/city-government/data-research-maps/open-data/open-data-catalogue/ \n wellbeing_toronto_2011.csv \n Analysis:Murali", position=c("right", "bottom"))
```
# Education
## Column 1
### Learing Opportunity Index - Elementary Schools
```{r echo = FALSE}
tm_shape(counties) +
tm_polygons(c("Score_2017"),
style=c("kmeans"),
palette="YlOrRd",
stretch.palette = TRUE,
title=c("Learning Opportunities Index - 2017")) +
tm_legend(position = c("right","bottom")) +
tm_credits("Source: https://www.tdsb.on.ca \n LOI2017.pdf \n Analysis:Murali", position=c("right", "bottom"))
```
### Learing Opportunity Index - Secondary Schools
```{r echo = FALSE}
tm_shape(secondarySchoolcounties) +
tm_polygons(c("Sc_2017"),
style=c("kmeans"),
palette="YlOrRd",
stretch.palette = TRUE,
title=c("Learning Opportunities Index - 2017")) +
tm_legend(position = c("right","bottom")) +
tm_credits("Source: https://www.tdsb.on.ca \n LOI2017.pdf \n Analysis:Murali", position=c("right", "bottom"))
```
## Column 2
### Average Family Income In the Neighbourhood
```{r echo = FALSE}
tm_shape(TorontoMap) +
tm_polygons("Average.Family.Income",style=c("kmeans"),
palette="Greens",
stretch.palette = TRUE,
title=c("Average Family Income $")) +
tm_legend(position = c("right","bottom"))+
tm_credits("Source: https://www.toronto.ca/city-government/data-research-maps/open-data/open-data-catalogue/ \n wellbeing_toronto_2011.csv \n Analysis:Murali", position=c("right", "bottom"))
```
### Number of Visible Minority Residents per Square Kilometer
```{r echo = FALSE}
tm_shape(TorontoMap) +
tm_polygons(c("VisbMinorityPerSqKm"),
style=c("kmeans"),
palette="Purples",
stretch.palette = TRUE,
title=c("Visible Minorities Per Sq.Km.")) +
tm_legend(position = c("right","bottom"))+
tm_credits("Source: https://www.toronto.ca/city-government/data-research-maps/open-data/open-data-catalogue/ \n wellbeing_toronto_2011.csv \n Analysis:Murali", position=c("right", "bottom"))
```
# Safety
## Column 1
### Drug Arrests Per 1000 Residents
```{r echo = FALSE}
tm_shape(safety_sf) +
tm_polygons(c("Drug Arrests"),
style=c("kmeans"),
palette="YlOrRd",
stretch.palette = TRUE,
title=c("Drug Arrests Per 1000")) +
tm_legend(position = c("right","bottom")) +
tm_credits("Source: https://www.toronto.ca/city-government/data-research-maps/open-data/open-data-catalogue/ \n WB-Safety.xlsx \n Analysis:Murali", position=c("right", "bottom"))
```
### Sexual Assaults Per 1000 Residents
```{r echo = FALSE}
tm_shape(safety_sf) +
tm_polygons(c("Sexual Assaults"),
style=c("kmeans"),
palette="YlOrRd",
stretch.palette = TRUE,
title=c("Sexual Assaults Per 1000")) +
tm_legend(position = c("right","bottom")) +
tm_credits("Source: https://www.toronto.ca/city-government/data-research-maps/open-data/open-data-catalogue/ \n WB-Safety.xlsx \n Analysis:Murali", position=c("right", "bottom"))
```
### Vehicle Thefts Per 1000 Residents
```{r echo = FALSE}
tm_shape(safety_sf) +
tm_polygons(c("Vehicle Thefts"),
style=c("kmeans"),
palette="YlOrRd",
stretch.palette = TRUE,
title=c("Vehicle Thefts Per 1000")) +
tm_legend(position = c("right","bottom")) +
tm_credits("Source: https://www.toronto.ca/city-government/data-research-maps/open-data/open-data-catalogue/ \n WB-Safety.xlsx \n Analysis:Murali", position=c("right", "bottom"))
```
### Break & Enters Per 1000 Residents
```{r echo = FALSE}
tm_shape(safety_sf) +
tm_polygons(c("Break & Enters"),
style=c("kmeans"),
palette="YlOrRd",
stretch.palette = TRUE,
title=c("Break & Enters Per 1000")) +
tm_legend(position = c("right","bottom")) +
tm_credits("Source: https://www.toronto.ca/city-government/data-research-maps/open-data/open-data-catalogue/ \n WB-Safety.xlsx \n Analysis:Murali", position=c("right", "bottom"))
```
## Column 2
### Number of Visible Minority Residents per Square Kilometer
```{r echo = FALSE}
tm_shape(TorontoMap) +
tm_polygons(c("VisbMinorityPerSqKm"),
style=c("kmeans"),
palette="YlOrBr",
stretch.palette = TRUE,
title=c("Visible Minorities Per Sq.Km.")) +
tm_legend(position = c("right","bottom"))+
tm_credits("Source: https://www.toronto.ca/city-government/data-research-maps/open-data/open-data-catalogue/ \n wellbeing_toronto_2011.csv \n Analysis:Murali", position=c("right", "bottom"))
```
### Number of Visible Minority Residents per Square Kilometer
```{r echo = FALSE}
tm_shape(TorontoMap) +
tm_polygons(c("VisbMinorityPerSqKm"),
style=c("kmeans"),
palette="YlOrBr",
stretch.palette = TRUE,
title=c("Visible Minorities Per Sq.Km.")) +
tm_legend(position = c("right","bottom"))+
tm_credits("Source: https://www.toronto.ca/city-government/data-research-maps/open-data/open-data-catalogue/ \n wellbeing_toronto_2011.csv \n Analysis:Murali", position=c("right", "bottom"))
```
### Number of Tree Counts Each Neighbourhood
```{r echo = FALSE}
tm_shape(treeCount) +
tm_polygons( c("NUMPOINTS"),
style=c("kmeans"),
palette="YlGn",
stretch.palette = TRUE,
title=c("Tree counts in Neighborhood ")) +
tm_legend(position = c("right","bottom"))+
tm_credits("Source: https://www.toronto.ca/city-government/data-research-maps/open-data/open-data-catalogue/ \n wellbeing_toronto_2011.csv \n Analysis:Murali", position=c("right", "bottom"))
```
### Number of Visible Minority Residents per Square Kilometer
```{r echo = FALSE}
tm_shape(TorontoMap) +
tm_polygons(c("VisbMinorityPerSqKm"),
style=c("kmeans"),
palette="YlOrBr",
stretch.palette = TRUE,
title=c("Visible Minorities Per Sq.Km.")) +
tm_legend(position = c("right","bottom"))+
tm_credits("Source: https://www.toronto.ca/city-government/data-research-maps/open-data/open-data-catalogue/ \n wellbeing_toronto_2011.csv \n Analysis:Murali", position=c("right", "bottom"))
```
# Know Your Neighbourhood
### Mouse Over To Get Neighbourhood
````{r echo =FALSE}
map <- tm_shape(toronto_leaflet) +
tm_polygons("MAP_COLORS", palette="Purples", alpha = .25) +
tm_borders(lwd=2, alpha = .25) +
tm_layout("Neighbourhoods in Greater Toronto Area")
lf <- tmap_leaflet(map)
lf
```
