0.1 Data source

The data used for this tutorial are available via this link by opening it a new tab.

0.2 Note

For any country, the shapefile can be downloaded here

This tutorial is based on Zoclanclounon et al. (2019) paper.

Make sure you’ve installed all requiered packages.

0.3 Code

0.3.1 Clean the R environment workspace

rm(list = ls())

0.3.2 Library loading

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

0.3.3 Set working directory

setwd("C:/Users/ANGE/Documents/R MAP")

0.3.4 Set shapefile path

mySHP = "C:/Users/ANGE/Documents/R MAP"

0.3.5 Import the shapefile

myFile = readOGR(mySHP, layer = "SEN_adm1", stringsAsFactors = FALSE)
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

0.3.6 Change in dataframe format for ggplot2

myDF = fortify(myFile, region = "NAME_1")

0.3.7 Change long to Longitude and lat to Latitude

myDF = rename(myDF, Longitude = long, Latitude = lat)

0.3.8 Overview of the data myDF

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

0.3.9 Import the data we want to plot on the map

mydata = read.csv("dta2.csv", header = TRUE, sep = ";")
mydata1 = read.csv("data1.csv", header = TRUE, sep = ";")

0.3.10 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

0.3.11 Overview of the data mydata1

Type: head(mydata1, 4)

NAME_1 Production Unity Prod long lat Produc Prodd
DAKAR 0 - ( 0 ton) -17.33 14.75 DAKAR ( 0 ton) DAKAR ( 0 ton)
DIOURBEL 46231 tons 46231 tons -16.25 14.75 DIOURBEL 46231 tons DIOURBEL (46231 tons)
FATICK 80000 tons 80000 tons -16.53 14.36 FATICK 80000 tons FATICK (80000 tons)
KAOLACK 77613 tons 77613 tons -16.00 14.00 KAOLACK 77613 tons KAOLACK (77613 tons)

0.3.12 Join the data and the shapefle

plotData <- left_join(myDF, mydata)

0.3.13 Overview of 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

0.3.14 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 = NAME_1), 
        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)")

0.3.15 Map rendering

Just call the map variable p

That is it!