Here We have dataset from countries

where top 10 countries with highest life expectancy and top 10 countries with highest infant mortality. # Comparing how the life expectancy will be better/higher if there is less infant mortality. There can be multiple #factors but we can assume that one of the them is the medical facilites provides better services to its citizen leaving in that country

library(rmarkdown)
library(tidyverse)

## Warning: package 'tidyverse' was built under R version 3.3.3

## Loading tidyverse: ggplot2
## Loading tidyverse: tibble
## Loading tidyverse: tidyr
## Loading tidyverse: readr
## Loading tidyverse: purrr
## Loading tidyverse: dplyr

## Warning: package 'ggplot2' was built under R version 3.3.3

## Warning: package 'purrr' was built under R version 3.3.3

## Conflicts with tidy packages ----------------------------------------------

## filter(): dplyr, stats
## lag():    dplyr, stats

countrytest <- read_csv("C:/Users/senet/Desktop/COLLEGE/St.Martins Classes/CSC 463 Data Visualization Tools/R/countrytest.csv")

## Parsed with column specification:
## cols(
##   country = col_character(),
##   life_exp = col_double(),
##   inf_mort = col_double()
## )

Install and load the necessary package needed to create the required graphical presentation.

a1 = ggplot(countrytest, aes(x=life_exp, y=inf_mort))+geom_point()

a1

Assigning the x and y axis to understand the relationship between the two variables where scatterplot gives

the relation between the two variables.It can be drawn using geom_point().

when the above statement was executed it proved the correlation between the life expectancy and infant mortality numbers.

a2=a1 + geom_point(aes(color=country))

a2

a3=a2 + geom_smooth(method="loess", se=F) + 
                      xlim(c(0, 130)) + 
                      ylim(c(0, 130)) + 
                      labs(subtitle="Life Expectancy Vs Infant Mortality", 
                           x="Life Expectancy", 
                           y="Infant Mortality", 
                           title="Scatterplot", 
                           caption = "Source: Country Dataset")
                           
a3

Additionally, geom_smooth which draws a smoothing line (based on loess) by default.

a4=a2 + geom_smooth(method="lm", se=F) + 
  xlim(c(0, 130)) + 
  ylim(c(0, 130)) + 
  labs(subtitle="Life Expectancy Vs Infant Mortality", 
       x="Life Expectancy", 
       y="Infant Mortality", 
       title="Scatterplot", 
       caption = "Source: Country Dataset")
a4

## Warning: Removed 13 rows containing missing values (geom_smooth).