Chapter 6 Maps

6.1 Choropleth Maps

6.1.1 Choropleth Map Using GeoJSON

library(plotly)
library(rjson)

url <- 'https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json'
counties <- rjson::fromJSON(file=url)
url2<- "https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv"
df <- read.csv(url2, colClasses=c(fips="character"))
g <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showlakes = TRUE,
  lakecolor = toRGB('white')
)
fig <- plot_ly()
fig <- fig %>% add_trace(
    type="choropleth",
    geojson=counties,
    locations=df$fips,
    z=df$unemp,
    colorscale="Viridis",
    zmin=0,
    zmax=12,
    marker=list(line=list(
      width=0)
    )
  )
fig <- fig %>% colorbar(title = "Unemployment Rate (%)")
fig <- fig %>% layout(
    title = "2016 US Unemployment by County"
)

fig <- fig %>% layout(
    geo = g
  )

fig

6.1.2 Indexing by GeoJSON Properties

library(plotly)
library(rjson)

url <- 'https://raw.githubusercontent.com/plotly/datasets/master/election.geojson'
geojson <- rjson::fromJSON(file=url)
url2<- "https://raw.githubusercontent.com/plotly/datasets/master/election.csv"
df <- read.csv(url2)
g <- list(
  fitbounds = "locations",
  visible = FALSE
)
fig <- plot_ly() 
fig <- fig %>% add_trace(
    type="choropleth",
    geojson=geojson,
    locations=df$district,
    z=df$Bergeron,
    colorscale="Viridis",
    featureidkey="properties.district"
  )
fig <- fig %>% layout(
    geo = g
  )
fig <- fig %>% colorbar(title = "Bergeron Votes")
fig <- fig %>% layout(
    title = "2013 Montreal Election"
)
fig

6.1.3 Customize choropleth chart

library(plotly)
df <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/2011_us_ag_exports.csv")
df$hover <- with(df, paste(state, '<br>', "Beef", beef, "Dairy", dairy, "<br>",
                           "Fruits", total.fruits, "Veggies", total.veggies,
                           "<br>", "Wheat", wheat, "Corn", corn))
# give state boundaries a white border
l <- list(color = toRGB("white"), width = 2)
# specify some map projection/options
g <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showlakes = TRUE,
  lakecolor = toRGB('white')
)

fig <- plot_geo(df, locationmode = 'USA-states')
fig <- fig %>% add_trace(
    z = ~total.exports, text = ~hover, locations = ~code,
    color = ~total.exports, colors = 'Purples'
  )
fig <- fig %>% colorbar(title = "Millions USD")
fig <- fig %>% layout(
    title = '2011 US Agriculture Exports by State<br>(Hover for breakdown)',
    geo = g
  )

fig

6.1.4 World Choropleth Map

df <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_world_gdp_with_codes.csv')

# light grey boundaries
l <- list(color = toRGB("grey"), width = 0.5)

# specify map projection/options
g <- list(
  showframe = FALSE,
  showcoastlines = FALSE,
  projection = list(type = 'Mercator')
)

fig <- plot_geo(df)
fig <- fig %>% add_trace(
    z = ~GDP..BILLIONS., color = ~GDP..BILLIONS., colors = 'Blues',
    text = ~COUNTRY, locations = ~CODE, marker = list(line = l)
  )
fig <- fig %>% colorbar(title = 'GDP Billions US$', tickprefix = '$')
fig <- fig %>% layout(
    title = '2014 Global GDP<br>Source:<a href="https://www.cia.gov/library/publications/the-world-factbook/fields/2195.html">CIA World Factbook</a>',
    geo = g
  )

fig

6.2 Scatter Plots on Maps

6.2.1 Basic Scatter on Map

library(plotly)
df <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_us_airport_traffic.csv')

# geo styling
g <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showland = TRUE,
  landcolor = toRGB("gray95"),
  subunitcolor = toRGB("gray85"),
  countrycolor = toRGB("gray85"),
  countrywidth = 0.5,
  subunitwidth = 0.5
)

fig <- plot_geo(df, lat = ~lat, lon = ~long)
fig <- fig %>% add_markers(
    text = ~paste(airport, city, state, paste("Arrivals:", cnt), sep = "<br />"),
    color = ~cnt, symbol = I("square"), size = I(8), hoverinfo = "text"
  )
fig <- fig %>% colorbar(title = "Incoming flights<br />February 2011")
fig <- fig %>% layout(
    title = 'Most trafficked US airports<br />(Hover for airport)', geo = g
  )

fig

6.2.2 Style Scatter Map Layout

library(plotly)
df <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/2015_06_30_precipitation.csv')

# change default color scale title
m <- list(colorbar = list(title = "Total Inches"))

# geo styling
g <- list(
  scope = 'north america',
  showland = TRUE,
  landcolor = toRGB("grey83"),
  subunitcolor = toRGB("white"),
  countrycolor = toRGB("white"),
  showlakes = TRUE,
  lakecolor = toRGB("white"),
  showsubunits = TRUE,
  showcountries = TRUE,
  resolution = 50,
  projection = list(
    type = 'conic conformal',
    rotation = list(lon = -100)
  ),
  lonaxis = list(
    showgrid = TRUE,
    gridwidth = 0.5,
    range = c(-140, -55),
    dtick = 5
  ),
  lataxis = list(
    showgrid = TRUE,
    gridwidth = 0.5,
    range = c(20, 60),
    dtick = 5
  )
)

fig <- plot_geo(df, lat = ~Lat, lon = ~Lon, color = ~Globvalue)
fig <- fig %>% add_markers(
    text = ~paste(df$Globvalue, "inches"), hoverinfo = "text"
  )
fig <- fig %>% layout(title = 'US Precipitation 06-30-2015<br>Source: NOAA', geo = g)

fig

6.3 Mapbox Density

6.3.1 Mapbox Access Token

library(plotly)

quakes = read.csv('https://raw.githubusercontent.com/plotly/datasets/master/earthquakes-23k.csv')

fig <- quakes 
fig <- fig %>%
  plot_ly(
    type = 'densitymapbox',
    lat = ~Latitude,
    lon = ~Longitude,
    coloraxis = 'coloraxis',
    radius = 10) 
fig <- fig %>%
  layout(
    mapbox = list(
      style="stamen-terrain",
      center= list(lon=180)), coloraxis = list(colorscale = "Viridis"))

fig

6.4 Mapbox Layers

6.4.1 OpenStreetMap Tiles, no Token Needed

library(plotly)

us_cities = read.csv("https://raw.githubusercontent.com/plotly/datasets/master/us-cities-top-1k.csv")

fig <- us_cities 
fig <- fig %>%
  plot_ly(
    lat = ~lat,
    lon = ~lon,
    marker = list(color = "fuchsia"),
    type = 'scattermapbox',
    hovertext = us_cities[,"City"]) 
fig <- fig %>%
  layout(
    mapbox = list(
      style = 'open-street-map',
      zoom =2.5,
      center = list(lon = -88, lat = 34))) 

fig

6.4.2 Base Map Below A Trace: No Token Needed

library(plotly)

us_cities = read.csv("https://raw.githubusercontent.com/plotly/datasets/master/us-cities-top-1k.csv")

fig <- us_cities 
fig <- fig %>%
  plot_ly(
    lat = ~lat,
    lon = ~lon,
    type = "scattermapbox",
    hovertext = us_cities[,"City"],
    marker = list(color = "fuchsia")) 
fig <- fig %>%
  layout(mapbox= list(
    style = "white-bg",
    zoom = 3,
    center = list(lon = -93 ,lat= 41),
    layers = list(list(
      below = 'traces',
      sourcetype = "raster",
      source = list("https://basemap.nationalmap.gov/arcgis/rest/services/USGSImageryOnly/MapServer/tile/{z}/{y}/{x}")))))

fig

6.5 Bubble Maps

6.5.1 United States Bubble Map

library(plotly)
df <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_us_cities.csv')

df$q <- with(df, cut(pop, quantile(pop)))
levels(df$q) <- paste(c("1st", "2nd", "3rd", "4th", "5th"), "Quantile")
df$q <- as.ordered(df$q)

g <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showland = TRUE,
  landcolor = toRGB("gray85"),
  subunitwidth = 1,
  countrywidth = 1,
  subunitcolor = toRGB("white"),
  countrycolor = toRGB("white")
)

fig <- plot_geo(df, locationmode = 'USA-states', sizes = c(1, 250))
fig <- fig %>% add_markers(
    x = ~lon, y = ~lat, size = ~pop, color = ~q, hoverinfo = "text",
    text = ~paste(df$name, "<br />", df$pop/1e6, " million")
  )
fig <- fig %>% layout(title = '2014 US city populations<br>(Click legend to toggle)', geo = g)

fig