library(ggplot2)
library(GGally)
## Registered S3 method overwritten by 'GGally':
##   method from   
##   +.gg   ggplot2
library(gridExtra)
library(relaimpo)
## Loading required package: MASS
## Loading required package: boot
## Loading required package: survey
## Loading required package: grid
## Loading required package: Matrix
## Loading required package: survival
## 
## Attaching package: 'survival'
## The following object is masked from 'package:boot':
## 
##     aml
## 
## Attaching package: 'survey'
## The following object is masked from 'package:graphics':
## 
##     dotchart
## Loading required package: mitools
## This is the global version of package relaimpo.
## If you are a non-US user, a version with the interesting additional metric pmvd is available
## from Ulrike Groempings web site at prof.beuth-hochschule.de/groemping.

Reading data

# data from 
# https://ceoworld.biz/2019/08/05/revealed-countries-with-the-best-health-care-systems-2019/
# Data include
# Health care infrastructure; health care professionals (doctors, nursing staff, and other health workers) competencies; cost (USD p.a.per capita); quality medicine availability, and government readiness

hci = read.csv("~/Dropbox/Temp Files/Health Care Index 2019.csv")
head(hci)
##   Rank     Country Region   HCI Infra Professional  Cost MedAvail
## 1    1      Taiwan   Asia 78.72 87.16        14.23 83.59    82.30
## 2    2 South Korea   Asia 77.70 79.05        13.06 78.39    78.99
## 3    3       Japan   Asia 74.11 90.75        30.01 82.59    92.06
## 4    4     Austria Europe 71.32 86.18        20.25 78.99    88.23
## 5    5     Denmark Europe 70.73 78.77        21.60 74.88    74.18
## 6    6    Thailand   Asia 67.99 92.58        17.37 96.22    67.51
##   GovtReady
## 1     87.89
## 2     65.09
## 3     96.30
## 4     91.80
## 5     93.20
## 6     89.91

Correlational plot

ggpairs(data=hci, columns=4:9, ggplot2::aes(colour=Region))