Summarise the data

library(skimr)

## 
## Attaching package: 'skimr'

## The following object is masked from 'package:stats':
## 
##     filter

skim(gapminder)

## Skim summary statistics
##  n obs: 1704 
##  n variables: 6 
## 
## ── Variable type:factor ────────────────────────────
##   variable missing complete    n n_unique
##  continent       0     1704 1704        5
##    country       0     1704 1704      142
##                              top_counts ordered
##  Afr: 624, Asi: 396, Eur: 360, Ame: 300   FALSE
##      Afg: 12, Alb: 12, Alg: 12, Ang: 12   FALSE
## 
## ── Variable type:integer ───────────────────────────
##  variable missing complete    n    mean       sd    p0        p25     p50
##       pop       0     1704 1704 3e+07    1.1e+08 60011 2793664    7e+06  
##      year       0     1704 1704  1979.5 17.27     1952    1965.75  1979.5
##       p75       p100     hist
##  2e+07       1.3e+09 ▇▁▁▁▁▁▁▁
##   1993.25 2007       ▇▃▇▃▃▇▃▇
## 
## ── Variable type:numeric ───────────────────────────
##   variable missing complete    n    mean      sd     p0     p25     p50
##  gdpPercap       0     1704 1704 7215.33 9857.45 241.17 1202.06 3531.85
##    lifeExp       0     1704 1704   59.47   12.92  23.6    48.2    60.71
##      p75      p100     hist
##  9325.46 113523.13 ▇▁▁▁▁▁▁▁
##    70.85     82.6  ▁▂▅▅▅▅▇▃

Relationship between GDP per capita and Life Expectancy

lm <- lm(lifeExp ~ gdpPercap, data = gapminder) 
lm

## 
## Call:
## lm(formula = lifeExp ~ gdpPercap, data = gapminder)
## 
## Coefficients:
## (Intercept)    gdpPercap  
##   5.396e+01    7.649e-04

summary(lm)

## 
## Call:
## lm(formula = lifeExp ~ gdpPercap, data = gapminder)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -82.754  -7.758   2.176   8.225  18.426 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.396e+01  3.150e-01  171.29   <2e-16 ***
## gdpPercap   7.649e-04  2.579e-05   29.66   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10.49 on 1702 degrees of freedom
## Multiple R-squared:  0.3407, Adjusted R-squared:  0.3403 
## F-statistic: 879.6 on 1 and 1702 DF,  p-value: < 2.2e-16

Plot Life expectancy vs GDP per cap on a log scale

library(ggplot2)
library(plotly)

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
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##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

p <- ggplot(gapminder, 
       aes(x = gdpPercap, y = lifeExp, colour = continent)) +
  geom_point() + 
  scale_x_log10() 

ggplotly(p)