leaflet.Rmd

# Interactive Geographic Data {#leaflet}

![](images/banners/banner_leaflet.png)
*This chapter originated as a community contribution created by [	AkhilPunia](https://github.com/AkhilPunia){target="_blank"}*

*This page is a work in progress. We appreciate any input you may have. If you would like to help improve this page, consider [contributing to our repo](contribute.html).*

## Overview

You would have already seen different libraries that can help one in beautifully displaying geographic data like `ggmap` and `choroplethr`. Even though these libraries provide lots of interesting features to better express information through 2-dimensional graphs, they still lack one feature: interactivity. Here comes `leaflet`---a library written in javascript to handle interactive maps. Fun Fact: It's actively used by a lot of leading newspapers like The New York Times and The Washington Post. 

Let's dive in.

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

## Brief Description about Dataset
For our analysis, we are using NYC Open Data about schools in New York City in 2016. [You can find more about it on the Kaggle page.](https://www.kaggle.com/passnyc/data-science-for-good/home){target="_blank"} We will be focusing on the the distribution of different variables as a factor of geographical positions.

```{r load library, eval=TRUE, message=FALSE}
library(tidyverse)
library(leaflet)
library(htmltools)
library(leaflet.extras)
library(viridis)
schools <- read_csv('data/2016_school_explorer.csv')
```

## Plotting Markers
Here we can see that all the Private schools in NYC have been plotted on a map that allows one to zoom in and out. The markers are used to denote the location of each individual school. If we hover over a marker, it displays the name of the school. Isn't that cool! 

```{r fig1-plot, echo=FALSE, warning=FALSE, message=FALSE, fig.height=6, fig.width=9}
lat<-median(schools$Latitude)
lon<-median(schools$Longitude)

schools %>% 
  filter(`Community School?`=="Yes") %>%
  leaflet(options = leafletOptions(dragging = TRUE))  %>%
  addTiles() %>%
  addMarkers(label=~`School Name`) %>%
  setView(lng=lon,lat=lat, zoom = 10)
```

Here's the code for it:

```{r fig1, eval=FALSE, message=FALSE}
lat<-median(schools$Latitude)
lon<-median(schools$Longitude)

schools %>% 
  filter(`Community School?`=="Yes") %>%
  leaflet(options = leafletOptions(dragging = TRUE))  %>%
  addTiles() %>%
  addMarkers(label=~`School Name`) %>%
  setView(lng=lon,lat=lat, zoom = 10)
```

## Dynamic Heatmaps
Heatmaps are really useful tools for visualizing the distribution of a particular variable over a certain region (they are so useful, [we got a page on 'em](heatmap.html)). In this example, we see how `leaflet` is able to dynamically calculate the number of schools in a given region from just latitude and longitude data. You can experience this by zooming in and out of the graph.

```{r fig2-plot, echo=FALSE, warning=FALSE, message=FALSE, fig.height=6, fig.width=9}
lat<-mean(schools$Latitude)
lon<-mean(schools$Longitude)

leaflet(schools) %>% 
  addProviderTiles(providers$CartoDB.DarkMatterNoLabels) %>%
  addWebGLHeatmap(size=15,units="px")  %>%
  setView(lng=lon,lat=lat, zoom = 10)
```

Here's the code for it:

```{r fig2, eval=FALSE, message=FALSE}
lat<-mean(schools$Latitude)
lon<-mean(schools$Longitude)

leaflet(schools) %>% 
  addProviderTiles(providers$CartoDB.DarkMatterNoLabels) %>%
  addWebGLHeatmap(size=15,units="px")  %>%
  setView(lng=lon,lat=lat, zoom = 10)
```

## Dynamic Clustering
Here we can see how `leaflet` allows one to dynamically cluster data based on its geographic distance at a given zoom level.

```{r fig3-plot, echo=FALSE, warning=FALSE, message=FALSE, fig.height=6, fig.width=9}
schools %>% 
  leaflet() %>% 
  addTiles() %>%
  addCircleMarkers(radius = 2, label = ~htmlEscape(`School Name`),
                         clusterOptions = markerClusterOptions()) 
```

Here's the code for it:

```{r fig3, eval=FALSE, message=FALSE}
schools %>% 
  leaflet() %>% 
  addTiles() %>%
  addCircleMarkers(radius = 2, label = ~htmlEscape(`School Name`),
                         clusterOptions = markerClusterOptions()) 
```

## Plotting Groups

```{r fig4-plot, echo=FALSE, warning=FALSE, message=FALSE, fig.height=6, fig.width=9}
top<- schools %>%
  group_by(District)%>%
  summarise(top=length(unique(`School Name`)),lon=mean(Longitude),lat=mean(Latitude))%>%
  arrange(desc(top))%>%
  head(10)

pal <- colorFactor(viridis(100),levels=top$District )

top %>%
  leaflet(options = leafletOptions(dragging = TRUE))  %>%
  addProviderTiles(providers$CartoDB.DarkMatterNoLabels) %>%
  addCircleMarkers(radius=~top/10,label=~paste0("District ", District," - ", top," Schools"),color=~pal(District),opacity = 1) %>%
  setView(lng=lon,lat=lat, zoom = 10) %>%
  addLegend("topright", pal = pal, 
            values = ~District,
            title = "District",
            opacity = 0.8)
```

Here's the code for it:

```{r fig4, eval=FALSE, message=FALSE}
top<- schools %>%
  group_by(District)%>%
  summarise(top=length(unique(`School Name`)),lon=mean(Longitude),lat=mean(Latitude))%>%
  arrange(desc(top))%>%
  head(10)

pal <- colorFactor(viridis(100),levels=top$District )

top %>%
  leaflet(options = leafletOptions(dragging = TRUE))  %>%
  addProviderTiles(providers$CartoDB.DarkMatterNoLabels) %>%
  addCircleMarkers(radius=~top/10,label=~paste0("District ", District," - ", top," Schools"),color=~pal(District),opacity = 1) %>%
  setView(lng=lon,lat=lat, zoom = 10) %>%
  addLegend("topright", pal = pal, 
            values = ~District,
            title = "District",
            opacity = 0.8)
```

## Plotting Categorical Data
We can visualize the distribution of a particular class over the common map. This is achieved through an interactive widget provided on the top right that allows one to choose a particular category or multiple categories. The example below explores how schools in different neighborhoods are racially segregated.

```{r fig5-plot, echo=FALSE, warning=FALSE, message=FALSE, fig.height=6, fig.width=9}
ss <- schools %>% dplyr::select(`School Name`,Latitude, Longitude,`Percent White`, `Percent Black`, `Percent Asian`, `Percent Hispanic`)

segregation <- function(x){
 majority = c()
 w <- gsub("%","",x$`Percent White`)
 b <- gsub("%","",x$`Percent Black`)
 a <- gsub("%","",x$`Percent Asian`)
 h <- gsub("%","",x$`Percent Hispanic`)
 
 for (i in seq(1,nrow(ss))){
   if (max(w[i],b[i],a[i],h[i]) == w[i])
     {majority <- c(majority,'White')}
   else if (max(w[i],b[i],a[i],h[i]) == b[i])
     {majority <- c(majority,'Black')}
   else if (max(w[i],b[i],a[i],h[i]) == a[i])
     {majority <- c(majority,'Asian')}
   else if (max(w[i],b[i],a[i],h[i]) == h[i])
     {majority <- c(majority,'Hispanic')}  
  }
 return(majority)
}
 
ss$race <- segregation(ss)
  
white <- ss %>% filter(race == "White")
black <- ss %>% filter(race == "Black")
hispanic <- ss %>% filter(race == "Hispanic")
asian <- ss %>% filter(race =="Asian")

lng <- median(ss$Longitude)
lat <- median(ss$Latitude)

pal_sector <- colorFactor( viridis(4), levels = ss$race)

m3 <- leaflet() %>% addProviderTiles("CartoDB") %>% 
        addCircleMarkers(data = white, radius = 2, label = ~htmlEscape(`School Name`),
                         color = ~pal_sector(race), group = "White")

m3 %>% addCircleMarkers(data = black, radius = 2, label = ~htmlEscape(`School Name`),
                         color = ~pal_sector(race), group = "Black")  %>% 
        addCircleMarkers(data = hispanic, radius = 2, label = ~htmlEscape(`School Name`),
                         color = ~pal_sector(race), group = "Hispanic") %>% 
        addCircleMarkers(data = asian, radius = 2, label = ~htmlEscape(`School Name`),
                        color = ~pal_sector(race), group = "Asian") %>% 
        addLayersControl(overlayGroups = c("White", "Black","Hispanic","Asian")) %>%
        setView(lng=lng,lat=lat,zoom=10)

```

Here's the code for it:

```{r fig5, eval=FALSE, message=FALSE}
ss <- schools %>% dplyr::select(`School Name`,Latitude, Longitude,`Percent White`, `Percent Black`, `Percent Asian`, `Percent Hispanic`)

segregation <- function(x){
 majority = c()
 w <- gsub("%","",x$`Percent White`)
 b <- gsub("%","",x$`Percent Black`)
 a <- gsub("%","",x$`Percent Asian`)
 h <- gsub("%","",x$`Percent Hispanic`)
 
 for (i in seq(1,nrow(ss))){
   if (max(w[i],b[i],a[i],h[i]) == w[i])
     {majority <- c(majority,'White')}
   else if (max(w[i],b[i],a[i],h[i]) == b[i])
     {majority <- c(majority,'Black')}
   else if (max(w[i],b[i],a[i],h[i]) == a[i])
     {majority <- c(majority,'Asian')}
   else if (max(w[i],b[i],a[i],h[i]) == h[i])
     {majority <- c(majority,'Hispanic')}  
  }
 return(majority)
}

ss$race <- segregation(ss)

white <- ss %>% filter(race == "White")
black <- ss %>% filter(race == "Black")
hispanic <- ss %>% filter(race == "Hispanic")
asian <- ss %>% filter(race =="Asian")

lng <- median(ss$Longitude)
lat <- median(ss$Latitude)

pal_sector <- colorFactor( viridis(4), levels = ss$race)

m3 <- leaflet() %>% addProviderTiles("CartoDB") %>% 
        addCircleMarkers(data = white, radius = 2, label = ~htmlEscape(`School Name`),
                         color = ~pal_sector(race), group = "White")

m3 %>% addCircleMarkers(data = black, radius = 2, label = ~htmlEscape(`School Name`),
                         color = ~pal_sector(race), group = "Black")  %>% 
        addCircleMarkers(data = hispanic, radius = 2, label = ~htmlEscape(`School Name`),
                         color = ~pal_sector(race), group = "Hispanic") %>% 
        addCircleMarkers(data = asian, radius = 2, label = ~htmlEscape(`School Name`),
                        color = ~pal_sector(race), group = "Asian") %>% 
        addLayersControl(overlayGroups = c("White", "Black","Hispanic","Asian")) %>%
        setView(lng=lng,lat=lat,zoom=10)
``` 

These examples provide only a glimpse to what is truly possible with this library. If you want to explore more features and use-cases, check out the links listed below.
 
## External Resources 
- [Leaflet in R Documentation](https://rstudio.github.io/leaflet/){target="_blank"}: main documentation of the package.
- [Basic leaflet maps in R](https://rpubs.com/mattdray/basic-leaflet-maps){target="_blank"}: tutorial with examples.
- [Interesting Kaggle Kernel visualizing earthquake data using leaflet in R ](https://www.kaggle.com/verawe/earthquakes-leaflet-highcharter-visualizations){target="_blank"}: another use-case to explore.