Tunisia Map :

For the tunisian map, the dabase used is extracted from ths INS, Tunisia website in order to maintain an information about the Tunisian Population per Governorate ( 24Governorate in total)[Find it attached in my Github]

poprg <- read_excel("files/pop per region-Tunisie.xlsx")
poprg

## # A tibble: 24 x 3
##    Région                   `Valeur (En millier)` HASC 
##    <chr>                                    <dbl> <chr>
##  1 Gouvernorat de Tunis                      47.7 TN.TU
##  2 Gouvernorat de L'Ariana                   43.6 TN.AN
##  3 Gouvernorat de Ben Arous                  40.1 TN.BA
##  4 Gouvernorat de Manouba                    38.1 TN.MN
##  5 Gouvernorat de Zaghouan                   34.9 TN.ZA
##  6 Gouvernorat de Nabeul                     34.8 TN.NB
##  7 Gouvernorat de Bizerte                    33.4 TN.BZ
##  8 Gouvernorat de Bèja                       29.8 TN.BJ
##  9 Gouvernorat de Jendouba                   25.7 TN.JE
## 10 Gouvernorat de Kairouan                   24.7 TN.KR
## # ... with 14 more rows

GDP Per Country :

For the GDP, I extracted a database from Google Dataset Search with different worldwide Sources to build an interactive map for the whole world explained in “Projects” section

GDPperSource <- read_excel("files/data.xlsx")
GDPperSource

## # A tibble: 591 x 3
##    Country              GDP Source                   
##    <chr>              <dbl> <chr>                    
##  1 United States   19390600 InternationalMonetaryFund
##  2  China          12014610 InternationalMonetaryFund
##  3  Japan           4872135 InternationalMonetaryFund
##  4  Germany         3684816 InternationalMonetaryFund
##  5  United Kingdom  2624529 InternationalMonetaryFund
##  6  India           2611012 InternationalMonetaryFund
##  7  France          2583560 InternationalMonetaryFund
##  8  Brazil          2054969 InternationalMonetaryFund
##  9  Italy           1937894 InternationalMonetaryFund
## 10  Canada          1652412 InternationalMonetaryFund
## # ... with 581 more rows

For the average GDP, I created a new database to calculate GDP from each source to each country using SQL

avGDP <- read_excel("files/base.xlsx")
avGDP

## # A tibble: 226 x 2
##    Country              avGDP    
##    <chr>                <chr>    
##  1  Afghanistan         20942    
##  2  Albania             13151    
##  3  Algeria             187392   
##  4  Andorra             3145.5   
##  5  Angola              131698   
##  6  Anguilla            311      
##  7  Antigua and Barbuda 1438.33  
##  8  Argentina           607057.67
##  9  Armenia             11154.33 
## 10  Aruba               2664     
## # ... with 216 more rows