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R for a geographic map

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ggplot2maprspatial

R-for-geographic-map-Session-1

This is a tutorial for rendering geographic map using R

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.

Note:

The powerpoint file, script, and datafiles are freely available. Just click on the corresponding hyperlink.

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:

A tutorial for package installation in RStudio is here.

It is more easier to install CRAN packages at once by typing

install.packages(c("rgdal", "mapdata", "mapproj" ,"maps" ,"ggplot2", "ggrepel", "dplyr", "scales", "ggmap", "gpclib", "rgeos"))

For installation of the legendMap package, you need to install the remotes package first.

install.packages("remotes")

Then install legendMap

remotes::install_github("3wen/legendMap", force = TRUE)

Please skip this part if you do not encounter package installation troubles. Continue here

Based on our recent experience on the online session, some participants have some troubles regarding this warning message:

Error in maptools::unionSpatialPolygons(cp, attr[, region]) : 
  isTRUE(gpclibPermitStatus()) is not TRUE

Don't worry at all!

It is easy to solve this issue.

Just run the following codes:

  1. You need to install gpclib package by doing
install.packages("gpclib", dependencies=TRUE)
  1. In addition you need to install an other package call rgeos
install.packages("rgeos", dependencies=TRUE)
  1. After you've run the previous codes, please check if the libraries are well installed:
library(gpclib)
library(rgeos)
  1. Then run your R code for a nice map.
  • Download the data for exercise here

Course outline

# Section Duration
1. Working directory setting, data preparation and shapefile importation 5 min
2. Rendering a basic map in R using ggplot2 10 min
3. Rendering a choropleth map 20 min
4. Add a scale bar and North Arrow 5 min
5. Tips 5 mn
6. Q & A 15 min
Total 60 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)

This result should appear

OGR data source with driver: ESRI Shapefile 
Source: "C:\Users\ANGE\Documents\R MAP", layer: "SEN_adm1"
with 14 features
It has 9 fields
Integer64 fields read as strings:  ID_0 ID_1

1.5. Check the class of the shapefile

class(myFile)

The class should display as follows

[1] "SpatialPolygonsDataFrame"
attr(,"package")
[1] "sp"

1.6. Check the variables names

names(myFile)

The name will display as follows:

[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)

The regions names should appear as follows:

[1] "Dakar"       "Diourbel"    "Fatick"      "Kédougou"   "Kaffrine"   
 [6] "Kaolack"     "Kolda"       "Louga"       "Matam"       "Sédhiou"   
[11] "Saint-Louis" "Tambacounda" "Thiès"      "Ziguinchor"

1.8. Library loading

You can import the packages like this:

library(rgdal)
library(mapdata)
library(mapproj)
library(maps)
library(ggplot2)
library(ggrepel)
library(legendMap)
library(dplyr)
library(scales)
library(ggmap)
library(gpclib)
library(rgeos)

Or, to load multiple packages at once, type:

Packages = "rgdal", "mapdata", "mapproj" ,"maps" ,"ggplot2", "ggrepel", "legendMap", "dplyr", "scales", "ggmap", "gpclib","rgeos")

lapply(Packages, library, character.only = TRUE)

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.

p

You should get this output:

Fig.1

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

3.4. Overview 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)

You will get this table

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() +
  

# English: Plot the shapefile geographic information
# French: Projeter les contours geographiques du shapefile
  
  geom_polygon(data = plotData, 
               aes(x = Longitude, 
                   y = Latitude, 
                   group = group,
                   fill = Production), 
               color = "black", size = 0.25) +
  
  #-------------------------------------------------------------------
  
  #English: Map projection type. Default is "mercator". 
  #See help(coord_map() for other choice)
  
  
  coord_map()+
  
    
  #------------------------------------------------------------------
  
  
  # English: Color choice, direction = 1 means colored from lowest to highest value
  # French: Choix de couleur. direction=1 veut dire colorer par ordre croissant de valeur  
    
  scale_fill_distiller(palette = "Greens",direction=1) +
  
  
  #------------------------------------------------------------------


  # English: Plot the localization points corresponding to each region
  # French: Projeter les points geographiques de chaque region
  
  
  
  geom_point(data=mydata1, 
             aes(x=long, y=lat), 
             shape = 21,
             fill = "white",
             size = 3, 
             color = "black") +
  
  #---------------------------------------------------------------------
  
  # English: Avoid overlapping text for the points annotation
  # French: Eviter la superposition des noms de regions
  
  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')+
  
  
 # English: Theme option
  # French: Mise forme de l'arriere plan
  
  theme_minimal() + # Simple background
  
  
  theme(panel.grid.major = 
          element_line(
            colour = "black", 
            size = 0.5, 
            linetype = "dotted")) + # Customize the grid line type, size and color
  
  theme(plot.background = 
          element_rect(
            colour = "white", 
            size = 1)) + # Customize the background line type, color and size
  
  # Add a title
  ggtitle("Map of Pearl Millet Production in Senegal (Rainy season 2017)")

Map rendering

Just call the map variable p

p

Fig.2

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')+
  
       
           
  # English: Scale bar and north arrow
  # French: Ajouter le north geographique et l'echelle
  
  scale_bar(lon = -12, lat = 16, #--longitude and latitude of the scale bar position 
            
            distance_lon = 40, distance_lat = 10, #--legnth and width of each rectangle
            
            distance_legend = 25, #--distance between legend rectangles and legend texts
            
            dist_unit = "km", #-- Unit
            
            arrow_length = 10, #-- Arrow length
            
            arrow_distance = 50, #-- Arrow distance to the scale bar
            
            arrow_north_size = 6) + #-- Arrow size
  
  
  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

p

Fig.3

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")

You will get this output

Fig.4

and then type:

locator(n=2)

2 is just an example. You can define many number as much as possible. Using your mouse, click on the position you want. You will get the coordinates.

References

6. Q & A

Scripts in this repository are licensed under General Public License v3.

For information inquiry please email Yedomon Ange Bovys Zoclanclounon PhD Candidate (angez9914@gmail.com)

About

R for a geographic map

ggplot2maprspatial

License:GNU General Public License v3.0