We need to install and then load the following packages

library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✔ ggplot2 3.3.6     ✔ purrr   0.3.4
## ✔ tibble  3.1.7     ✔ dplyr   1.0.9
## ✔ tidyr   1.2.0     ✔ stringr 1.4.0
## ✔ readr   2.1.2     ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
library(gapminder)
library(datasets)

Basic Plotting (slide 32)

hist(gapminder$lifeExp, main = "Life Expectancy", xlab = "years")

Plotting variables against each other (slide 35)

plot(lifeExp ~ gdpPercap, gapminder, main = "Life Expectancy vs. GDP",
     xlab = "GDP per capita", ylab = "Life Expectancy")

Instead of specifying the second argument, we could plot the vectors directly:

plot(gapminder$lifeExp ~ gapminder$gdpPercap) # no second argument

The above plot is clearer on the log-scale. This is because countries tend to be exponentially wealthier than each other in terms of GDP per capita. Note for instance that the x-axis in the above plot contains large values.

plot(lifeExp ~ log(gdpPercap), gapminder,
     main = "Life Expectancy and GDP on log scale")

The relationship is much clearer after transforming one of the variables!