In this page I explain how to make maps of the delegations of Tunisian governorate with R Studio. I used Professor Malouche’s guide on “Map of Tunisia with R” as an initial reference for the project. I start by loading the required packages:
library(readxl) library(tigris) library(dplyr) library(leaflet) library(sp) library(ggmap) library(maptools) library(broom) library(httr) library(scales) library(rgdal) library(tidyverse)
From this address I choose Tunisia – R sp- level2 option to download the Database of Global Administrative Areas (GADM) for Tunisia for delegations. I save the file name as “tunisia”, and install it to my workflow. Then I use the “fortify” command to extract the latitude and longitude coordinates.
tunisia<- readRDS("tunisia.rds") tun_for <- fortify(tunisia) ggplot(tun_for) + geom_polygon(aes(x = long, y = lat,group = group),color = "white",fill = "red") + theme_void() + coord_fixed()

I match the coordinates [long, lat] in “tun_for” file with the delegations names in “tunisia” file [NAME_2].
tunisia@data$id<-rownames(tunisia@data) tunisia@data$id<-as.numeric(tunisia@data$id) tun_for$id<-as.numeric(tun_for$id) tun_for$id<-tun_for$id-1 match <- left_join(tun_for, tunisia@data, by = "id") unique(match$NAME_1)

As I conduct the analysis on Sidi Bouzid, I filter the coordinates of delegations within Sidi Bouzid:
sidi_for<-filter(match, grepl("Sidi Bou Zid", NAME_1)) head(sidi_for)

For this project I focus on the distribution of deep weels within Sidi Bouzid. I collected the data from this source (page: 20). A copy of the values can be downloaded below:
I merge the weel data with the “sidi_for” data that includes coordinates of delegations.
sidi_weels <- read_excel("sidi_weels.xlsx") names(sidi_weels )[names(sidi_weels) == "Delegation"] <- "NAME_2" final_map <- left_join(sidi_weels, sidi_for, by = "NAME_2")
Then I plot the graph:
p<-ggplot(final_map, aes(x=long, y=lat, group=group)) + geom_polygon(aes(fill=Total),color = "black")+ labs(x="",y="")+ theme_bw()+ coord_fixed() p<-p+scale_fill_gradientn(colours=c("white","yellow","green"), values=rescale(c(0,15,30))) p<-p+theme(axis.line=element_blank(), axis.text.x=element_blank(), axis.text.y=element_blank(), axis.ticks=element_blank(), axis.title.x=element_blank(), axis.title.y=element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.border = element_blank(), panel.background = element_blank()) p<-p+ theme(legend.position="right") p<-p+labs(fill = "%",title = "Distribution of Deep Weels in Sidi Bouzid", subtitle = "Source: Ministry of Equipment Housing and Infrastructure", caption = "Salih Yasun") p<-p+ theme(plot.title = element_text(hjust = 0.5),plot.subtitle = element_text(hjust=0.5)) p

For a comparison, here are the actual numbers:

If you have any questions or suggestions, please e-mail me at syasun@indiana.edu