Control 4

library(kableExtra)
library(magrittr)
library(cluster)
library(factoextra)

## Loading required package: ggplot2

## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa

library(ggrepel)
library(ggplot2)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following object is masked from 'package:kableExtra':
## 
##     group_rows

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(BBmisc)

## 
## Attaching package: 'BBmisc'

## The following objects are masked from 'package:dplyr':
## 
##     coalesce, collapse

## The following object is masked from 'package:base':
## 
##     isFALSE

library(rio)

ML=import("militarisation.xlsx")
SS=import("safeandsecurity.xlsx")
OC=import("ongoinconflicts.xlsx")
ecologial=import("ecological.xlsx")

ML=select(ML, c(1,17))
names(ML)=c("Country","mili2022")

SS=select(SS, c(1,17))
names(SS)=c("Country","ss2022")

OC=select(OC,c(1,17))
names(OC)=c("Country","ongoing2022")

merge1=inner_join(ML,OC)

## Joining, by = "Country"

merge2=inner_join(merge1,SS)

## Joining, by = "Country"

db=inner_join(merge2,ecologial)

## Joining, by = "Country"

str(db)

## 'data.frame':    163 obs. of  8 variables:
##  $ Country                      : chr  "Afghanistan" "Albania" "Algeria" "Angola" ...
##  $ mili2022                     : num  2.47 1.67 2.04 1.71 1.61 ...
##  $ ongoing2022                  : num  3.65 1.4 2.07 1.67 1.2 ...
##  $ ss2022                       : num  4.13 2.12 2.3 2.41 2.66 ...
##  $ Food Risk Score              : num  4 2 2 5 2 2 1 1 2 2 ...
##  $ Natural Disasters Score      : num  5 4 5 3 4 2 4 3 1 1 ...
##  $ Rapid Population Growth Score: num  5 1 3 5 2 1 3 1 1 3 ...
##  $ Water Risk Score             : num  5 3 3 5 3 4 3 1 5 3 ...

dataClus=db[,c(2:8)]
row.names(dataClus)=db$Country

g.dist=daisy(dataClus,metric = "gower")

library(factoextra)
fviz_nbclust(dataClus, pam,diss=g.dist,method = "gap_stat",k.max = 10,verbose = F)

Tipificado gaaa

boxplot(normalize(db[,c(3:6)],method='standardize'))

Cluster tipificado

dataClus2=db[,c(2:8)]
row.names(dataClus2)=db$Country

g.dist2=daisy(dataClus,metric = "gower")

library(factoextra)
fviz_nbclust(dataClus2, pam,diss=g.dist,method = "gap_stat",k.max = 10,verbose = F)