Welcome to the training module designed for postdocs and alumni fellows in the Climap Africa
programme. We will work today with the open data science tool R.
Course objective: Enhance R users for informative geographic maps rendering using R
Notice: Please visit the hyperlink (in blue) for installation and settings.
Pre-requisites:
Install R and RStudio on Windows 7, 8 or 10. A tutorial for a beginner is here.
Install the following packages before the course: rgdal, mapdata, mapproj, maps, ggplot2, ggrepel, legendMap, dplyr, scales, and ggmap. A tutorial for package installation in RStudio is here.
Download the data for exercise here
Course outline
Working directory setting, data preparation and shapefile importation (5 mn)
Rendering a basic map in R using ggplot2 (10 min)
Rendering a choropleth map (20 min)
Add a scale bar and North Arrow (5 min)
Tips (5 min)
Q & A (15 min)
1. Working directory setting, data preparation and shapefile importation
1.1. Set the working directory
The working directory is the folder named R MAP. Please put all the shapefiles and data in your working disrectory. To set your working directory, type:
setwd("C:/Users/ANGE/Documents/R MAP")1.2. Clean the R environment workspace
rm(list = ls())1.3. Set shapefile path
mySHP = "C:/Users/ANGE/Documents/R MAP"1.4. Import the shapefile
myFile = readOGR(mySHP, layer = "SEN_adm1", stringsAsFactors = FALSE)1.5. Check the class of the shapefile
class(myFile)[1] "SpatialPolygonsDataFrame"
attr(,"package")
[1] "sp"1.6. Check the variables names
names(myFile)[1] "ID_0" "ISO" "NAME_0" "ID_1" "NAME_1" "TYPE_1"
[7] "ENGTYPE_1" "NL_NAME_1" "VARNAME_1"1.7. Check the regions names
print(myFile$NAME_1) [1] "Dakar" "Diourbel" "Fatick" "Kédougou" "Kaffrine"
[6] "Kaolack" "Kolda" "Louga" "Matam" "Sédhiou"
[11] "Saint-Louis" "Tambacounda" "Thiès" "Ziguinchor" 1.5. Library loading
library(rgdal)
library(mapdata)
library(mapproj)
library(maps)
library(ggplot2)
library(ggrepel)
library(legendMap)
library(dplyr)
library(scales)
library(ggmap)2. Rendering a basic map in R using ggplot2
2.1. Change in dataframe format for ggplot2
myDF = fortify(myFile, region = "NAME_1")2.2. Overview of the data myDF
Type: head(myDF, 4
| long | lat | order | hole | piece | id | group |
|---|---|---|---|---|---|---|
| -17.16056 | 14.89375 | 1 | FALSE | 1 | Dakar | Dakar.1 |
| -17.16004 | 14.89333 | 2 | FALSE | 1 | Dakar | Dakar.1 |
| -17.16000 | 14.89335 | 3 | FALSE | 1 | Dakar | Dakar.1 |
| -17.15683 | 14.89042 | 4 | FALSE | 1 | Dakar | Dakar.1 |
2.3. Change long to Longitude and lat to Latitude
myDF = rename(myDF, Longitude = long, Latitude = lat)2.4.Overview of the myDF
Type: head(myDF, 4)
| Longitude | Latitude | order | hole | piece | id | group |
|---|---|---|---|---|---|---|
| -17.16056 | 14.89375 | 1 | FALSE | 1 | Dakar | Dakar.1 |
| -17.16004 | 14.89333 | 2 | FALSE | 1 | Dakar | Dakar.1 |
| -17.16000 | 14.89335 | 3 | FALSE | 1 | Dakar | Dakar.1 |
| -17.15683 | 14.89042 | 4 | FALSE | 1 | Dakar | Dakar.1 |
2.5. Make the basic plot
p <- ggplot() + geom_polygon(data = myDF, aes(x = Longitude, y = Latitude, group = group),
color = "black", size = 0.25) + coord_map() + theme_minimal() + ggtitle("Basic map with ggplot2")The map is in p
3. Rendering a choropleth map
3.1. Import the data we want to plot on the map.Here that is the production of pearl millet per region
mydata = read.csv("production_data.csv", header = TRUE, sep = ";")3.2. Import the the regions names for annotation step
mydata1 = read.csv("region_names.csv", header = TRUE, sep = ";")3.3. Overview of the data mydata
Type:head(mydata, 4)
| long | lat | id | Production |
|---|---|---|---|
| -17.33 | 14.75 | Dakar | 0 |
| -16.25 | 14.75 | Diourbel | 46231 |
| -16.53 | 14.36 | Fatick | 80000 |
| -12.18 | 12.80 | Kédougou | 152 |
3.4Overview of the data mydata1
Type: head(mydata1, 4)
| Region | long | lat |
|---|---|---|
| DAKAR | -17.33 | 14.75 |
| DIOURBEL | -16.25 | 14.75 |
| FATICK | -16.53 | 14.36 |
| KAOLACK | -16.00 | 14.00 |
3.4. Join the data and the shapefle
plotData <- left_join(myDF, mydata)Key point: Note that myDF and mydata has id as a common variable
3.5. Overview of plotData
Type: head(plotData
| Longitude | Latitude | order | hole | piece | id | group | long | lat | Production |
|---|---|---|---|---|---|---|---|---|---|
| -17.16056 | 14.89375 | 1 | FALSE | 1 | Dakar | Dakar.1 | -17.33 | 14.75 | 0 |
| -17.16004 | 14.89333 | 2 | FALSE | 1 | Dakar | Dakar.1 | -17.33 | 14.75 | 0 |
| -17.16000 | 14.89335 | 3 | FALSE | 1 | Dakar | Dakar.1 | -17.33 | 14.75 | 0 |
| -17.15683 | 14.89042 | 4 | FALSE | 1 | Dakar | Dakar.1 | -17.33 | 14.75 | 0 |
3.6 Make the plot
p <- ggplot() +
geom_polygon(data = plotData, aes(x = Longitude, y = Latitude, group = group,
fill = Production), color = "black", size = 0.25) +
coord_map() +
scale_fill_distiller(palette = "Greens", direction = 1) +
geom_point(data = mydata1, aes(x = long, y = lat), shape = 21, fill = "white",
size = 3, color = "black") +
geom_label_repel(data = mydata1, aes(x = long, y = lat, label = Region), fontface = "bold",
color = "black", box.padding = 0.35, point.padding = 0.5, segment.color = "grey10") +
theme_minimal() +
theme(panel.grid.major = element_line(colour = "black", size = 0.5, linetype = "dotted")) +
theme(plot.background = element_rect(colour = "white", size = 1)) +
ggtitle("Map of Pearl Millet Production in Senegal (Rainy season 2017)")Map rendering
Just call the map variable p
That is it!
4. Add scale bar and north arrow
p <- ggplot() +
geom_polygon(data = plotData, aes(x = Longitude, y = Latitude, group = group,
fill = Production), color = "black", size = 0.25) +
coord_map() +
scale_fill_distiller(palette = "Greens", direction = 1) +
geom_point(data = mydata1, aes(x = long, y = lat), shape = 21, fill = "white",
size = 3, color = "black") +
geom_label_repel(data = mydata1, aes(x = long, y = lat, label = Region), fontface = "bold",
color = "black", box.padding = 0.35, point.padding = 0.5, segment.color = "grey10") +
scale_bar(lon = -12, lat = 16, distance_lon = 40, distance_lat = 10, distance_legend = 25,
dist_unit = "km", arrow_length = 10, arrow_distance = 50, arrow_north_size = 6) +
theme_minimal() +
theme(panel.grid.major = element_line(colour = "black", size = 0.5, linetype = "dotted")) +
theme(plot.background = element_rect(colour = "white", size = 1)) +
ggtitle("Map of Pearl Millet Production in Senegal (Rainy season 2017)")Map rendering
Just call the map variable p
Export a high quality map by typing:
PDF format
ggsave(p, file = "carte.pdf", limitsize = FALSE, width = 12, height = 10.5, dpi=500 )
PNG format
ggsave(p, file = "carte.png", limitsize = FALSE, width = 10, height = 6.5, type = "cairo-png", dpi=500)
5. Tips
To find out a desirable position for scale bar or any adjustment, it is possible to plot the map with the basic R by typing:
plot(myFile, axes = T, col = "aliceblue")
and then typing:
locator(n=2) # 2 is just an example. You can define many number as much as possible
and using your mouse, click on the position you want. You will get the coordinates.