Maps in R

class: center, middle, inverse, title-slide

# Maps in R
## Developing skills in R
### Malie Lessard-Therrien
### November 14, 2018

---

## Creating maps in R

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

***

.center[<img src="maps_pres_figures/europe_map.png" style="max-width:400px;">]

---
## Grammar of graphics (gg)

2 principles:
- distinct layers of graphical elements
- meaningful plots using aesthetic mapping

***

.center[![layers of a plot](./maps_pres_figures/separate_elements.png)]

---
## 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

.pull-right-70[<img src="maps_pres_figures/graph_layers.png" style="max-width:300px;">]

---
class: small-code
## Basic world map

```r
mapWorld <- ggplot () + 
 borders ("world",
 colour = "gray50",
 fill = "gray50")
print (mapWorld)
```

![](Maps_in_R_MLT_files/figure-html/basic_world_map-1.png)

---
## Have geo-referenced data

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

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

---
## Subset data based on bleaching events

```r
levels (datCoral$bleaching_severity)
```

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

```r
# 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

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

![](Maps_in_R_MLT_files/figure-html/coral_data_to_map-1.png)

---
## Fine tuning: Adjusting colours

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

print(mapColour)
```

![](Maps_in_R_MLT_files/figure-html/adjusting_colours-1.png)

---
## Fine tuning: Facets

```r
mapFacets <- mapColour +
 facet_wrap (~ year)

print (mapFacets)
```

![](Maps_in_R_MLT_files/figure-html/facets-1.png)

---
## 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

***

.center[<img src="maps_pres_figures/map_types.png" style="max-width:350px;">]
 
---
## Functions

---
## 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

---
## 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.".

.center[<img src="maps_pres_figures/rGraphicsCookbook.jpg" style="max-width:300px;">]

---
## 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
 
.center[<img src="maps_pres_figures/davidKahle.png" style="max-width:150px;">]

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

???
Devtools: (developper tools) providing R functions that simplify many common tasks for package developping

---
## Exercise 1

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

---
## Exercise 1

Get the desired map

.pull-left-50[

```r
# 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)
```
]

.pull-right-50[
<img src="Maps_in_R_MLT_files/figure-html/get_SU_map2-1.png" style="display: block; margin: auto 0 auto auto;" />
]

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

---
## Exercise 1

Get the locations

(enable Geocoding API in the Google Console)

```r
# 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

.pull-left-50[

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

print (suLocationsMap)
```
]

.pull-right-50[
<img src="Maps_in_R_MLT_files/figure-html/plot_our_locations2-1.png" style="display: block; margin: auto 0 auto auto;" />
]

---
## Exercise 1

Add names of locations

.pull-left-50[

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

print (suLocMapNames)
```
]

.pull-right-50[
<img src="Maps_in_R_MLT_files/figure-html/add_names2-1.png" style="display: block; margin: auto 0 auto auto;" />
]

---
##Exercise 1

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

.pull-left-50[

```r
# 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)
```
]

.pull-right-50[
<img src="Maps_in_R_MLT_files/figure-html/create route2-1.png" style="display: block; margin: auto 0 auto auto;" />
]

---
##Exercise 1

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

.pull-left-50[

```r
# 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)
```
]

.pull-right-50[
<img src="Maps_in_R_MLT_files/figure-html/route4-1.png" style="display: block; margin: auto 0 auto auto;" />
]

---
## 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

---
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class: end-slide

# Thank you