Creating maps in R

• Spatial visualization with ggplot2
• Add data to a map
• Easy, consistent and modular framework for spatial graphics and data analysis

Grammar of graphics (gg)

2 principles:

• distinct layers of graphical elements
• meaningful plots using aesthetic mapping

Graph elements

ggplot2 Cheatsheet: https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf

3 essentials:

• Data (what you collected, in the right format)
• Aesthetics (aes)
• Geometries (geom)

4 optionals:

• Facets
• Coordinates
• Themes
• Statistics

Have geo-referenced data

Coral bleaching data from ReefBase http://www.reefbase.org

# Upload data
datCoral <- read.csv("./Data/CoralBleachingData.csv", row.names = 1)
# head (datCoral) #sanity check
# str (datCoral)

Subset data based on bleaching events

levels (datCoral$bleaching_severity)

## [1] "High"             "Low"              "Medium"          
## [4] "No Bleaching"     "Severity Unknown"

# Remove the category "No Bleaching" and "Severity Unknown"
datCoralSub <- datCoral[
  datCoral$bleaching_severity %in% c("Low","Medium","High"), ]
# Replace the factors in a different order than in alphabetic order
datCoralSub$bleaching_severity <- factor(
  datCoralSub$bleaching_severity,
  levels = levels(datCoralSub$bleaching_severity)[c(2,3,1)]
  )

Add the data to the map

mapCoral <- mapWorld + 
  geom_point (data = datCoralSub,
             aes(x = longitude,
                 y = latitude,
                 colour = bleaching_severity),
             alpha = 0.5)
print (mapCoral)

Fine tuning: Adjusting colours

mapColour <- mapCoral +
  scale_colour_manual (values = c("Low" = "yellow",
                                  "Medium" = "orange",
                                  "High" = "red")) 
print(mapColour)

Fine tuning: Facets

mapFacets <- mapColour +
  facet_wrap (~ year)
print (mapFacets)

Steps to create a map

1. Have an idea: know what you want to visualize
2. Get the map you need
3. Get data that is geo-referenced (coordinates of places you want to show)
4. Plot map and data together
5. Customize: add extra visuals as you want
   • Colors
   • Facets
   • Name of places
   • Path
   • etc

Functions

• get_map()
  • get a map from somewhere
  • arguments:
    • source (Google, Stamen, OpenStreetMap and CloudMade)
    • maptype
    • zoom

Functions

• get_map()
  • get a map from somewhere
  • arguments:
    • source (Google, Stamen, OpenStreetMap and CloudMade)
    • maptype
    • zoom

• ggmap()
  • plot the map

Functions

• get_map()
  • get a map from somewhere
  • arguments:
    • source (Google, Stamen, OpenStreetMap and CloudMade)
    • maptype
    • zoom

• ggmap()
  • plot the map
• geocode()
  • finds latitude and longitude of places

Functions

• get_map()
  • get a map from somewhere
  • arguments:
    • source (Google, Stamen, OpenStreetMap and CloudMade)
    • maptype
    • zoom

• ggmap()
  • plot the map
• geocode()
  • finds latitude and longitude of places
• trek()
  • finds coordinates for path between places

Resources

Kahle, D., & Wickham, H. (2013). ggmap: Spatial Visualization with ggplot2. R Journal, 5(1).

Resources

Kahle, D., & Wickham, H. (2013). ggmap: Spatial Visualization with ggplot2. R Journal, 5(1).

Chang, W. (2012). R graphics cookbook: practical recipes for visualizing data. " O'Reilly Media, Inc.".

Resources

Kahle, D., & Wickham, H. (2013). ggmap: Spatial Visualization with ggplot2. R Journal, 5(1).

Chang, W. (2012). R graphics cookbook: practical recipes for visualizing data. " O'Reilly Media, Inc.".

Cheatsheet: https://www.nceas.ucsb.edu/~frazier/RSpatialGuides/ggmap/ggmapCheatsheet.pdf

Libraries needed

Get the updated version of ggmap:

• https://github.com/dkahle/ggmap
• Author: David Kahle
  • Baylor University, Waco, Texas

#if(!requireNamespace("devtools")) install.packages("devtools")
#devtools::install_github("dkahle/ggmap", ref = "tidyup")
library (ggmap)
library (tidyverse)

Exercise 1

Enter your API key in R with ggmap::register_google (key = "number")

Exercise 1

Get the desired map

# Get the map form source 
# Stockholm University recentered
su <- get_map (
  location = c(lon = 18.0590,
               lat = 59.3644),  
  source = "stamen",
  maptype = "terrain",
  zoom = 16, 
  crop = TRUE) 
# make the map into a 
# ggmap object to plot it
suMap <- ggmap(su,
   extent = "device")
# Visualise the map
print(suMap)

Note: zoom between 3 (continent) to 21 (building), default value 10 (city)

Exercise 1

Get the locations

(enable Geocoding API in the Google Console)

# Create a tibble of SU's important locations
suLocations <- tibble(
  location = c("DEEP, Stockholm University",
               "Stockholm University Library",
               "Universitetet, Stockholm"))
# Get the geocode (lat/lon) of the locations
suCoord <- geocode(suLocations$location)
# Create a data frame of the data
# for easier plotting
suDat <- cbind(suLocations, suCoord)
suDat

##                       location      lon      lat
## 1   DEEP, Stockholm University 18.06013 59.36604
## 2 Stockholm University Library 18.06097 59.36327
## 3     Universitetet, Stockholm 18.05460 59.36519

Exercise 1

Plot the locations

# Plot important locations
suLocationsMap <- suMap +
  geom_point(data = suDat,
             aes(x = lon, y = lat),
             color = 'red',
             size = 5)
print (suLocationsMap)

Exercise 1

Add names of locations

# Add name of places
suLocMapNames <- suLocationsMap +
  geom_text(data = suDat, 
            aes(label = location),
            size = 5,
            hjust = 0, 
            vjust = -1)
print (suLocMapNames)

Exercise 1

Get the route (enable Directions API in the Google Console)

# Create the route with trek()
# Deep to library
goto_library <- trek(
  from = "DEEP, Stockholm University",
  to = "Stockholm University Library",
  structure = "route",
  mode = "walking")
# Add route to map
deepToLibraryMap <- suLocMapNames +
  geom_path(data = goto_library,
            aes(x = lon, y = lat),
            colour = "blue",
            size = 1.5,
            alpha = .5,
            lineend = "round")
print (deepToLibraryMap)

Exercise 1

Get the route (enable Directions API in the Google Console)

# Library to t-bana station
goto_tbana <- trek(
  from = "Stockholm University Library",
  to = "Universitetet, Stockholm",
  structure = "route", 
  mode = "walking")
# Add route to map
LibraryToTbanaMap <- suLocMapNames +
  geom_path(data = goto_library,
            aes(x = lon, y = lat),
            colour = "blue",
            size = 1.5,
            alpha = .5,
            lineend = "round") +
  geom_path(data = goto_tbana,
            aes(x = lon, y = lat),
            colour = "blue",
            size = 1.5,
            alpha = .5,
            lineend = "round")
print (LibraryToTbanaMap)

Exercise 2

Option local: Create a map of where you live, the closest metro station and your favorite coffee shop/restaurant/bar in town.

Option UK: Create a map of the pubs and bars in the town “Oldham”, UK

• info: https://nbisweden.github.io/RaukR-2018/ggmap_Sebastian/lab/ggmap_Sebastian.html#33_pubs
• data: https://github.com/deepskillsr/Data_for_Students

For more

RaukR course:

• https://nbisweden.github.io/workshop-RaukR-1806/programme/ (see ggmap by Sebastian DiLorenzo)

library (leaflet): open-source JavaScript libraries for interactive maps