Installing neccessary packages.

# install.packages("gapminder")

Load the required packages.

library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5     v purrr   0.3.4
## v tibble  3.1.6     v dplyr   1.0.7
## v tidyr   1.1.4     v stringr 1.4.0
## v readr   2.1.1     v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(gapminder)
library(ggplot2)
data(gapminder)

Plot the population vs time of Germany

# scatter plot of Germany
gapminder %>%
  filter(country=="Germany") %>%
  ggplot(aes(x = year, y = pop)) +
  ggtitle(label = "Scatter plot of Germany population vs Time") +
  geom_point() +
  theme(plot.title = element_text(size = 20, face = "bold"))

# line plot of germany population
gapminder %>%
  filter(country=="Germany") %>%
  ggplot(aes(x = year, y = pop)) +
  ggtitle(label = "Time plot for Germany population") +
  geom_line() +
  theme(plot.title = element_text(size = 20, face = "bold"))

The line graph is better than the scatter plot. The line graph shows the trend of population growth over time while a scatter plot shows the linear relationship between time and population.

Same plots for Switzerland

# scatter plot for Switzerland
gapminder %>%
  filter(country=="Switzerland") %>%
  ggplot(aes(x = year, y = pop)) +
  ggtitle(label = "Scatterplot for Switzerland population vs time") +
  geom_point() +
  theme(plot.title = element_text(size = 20, face = "bold"))

# line plot of Switzerland population
gapminder %>%
  filter(country=="Switzerland") %>%
  ggplot(aes(x = year, y = pop)) +
  ggtitle(label = "Time plot for Switzerland population") +
  geom_line() +
  theme(plot.title = element_text(size = 20, face = "bold"))

## Histogram of life expectancy in 2007

gapminder %>%
  subset(gapminder$year==2007) %>%
  select(country, continent, lifeExp) %>%
  ggplot(aes(x=lifeExp, fill = continent)) +
  geom_histogram(bins = 15) +
  ggtitle(label = "Histogram of life expectancy in 2007") +
  theme(plot.title = element_text(size = 20, face = "bold"))

gapminder %>%
  select(continent, year, lifeExp) %>%
  ggplot(aes(x=lifeExp, fill=continent)) +
  geom_histogram(bins = 15) + 
  facet_wrap(~year)

Line plot of life expectancy values for the countries.

# line plot for all the countries.
gapminder %>%
  ggplot(aes(x = year, y = lifeExp, by = country)) +
  ggtitle(label = "Time plot for Life Expectancy for all the Countries.") +
  geom_line() +
  theme(plot.title = element_text(size = 20, face = "bold"))

## Create a line plot of life expectancy values for the countries. Set the color for the continent values In the aesthetics, you need to set x,y, by and color.

gapminder %>%
  ggplot(aes(x = year, y = lifeExp, by = country, color = country)) +
  ggtitle(label = "Time plot for Life Expectancy for all the Countries.") +
  geom_line(show.legend = F) +
  theme(plot.title = element_text(size = 15, face = "bold"))

## Create a line plot of life expectancy values for the countries. create the facets for the continent values

gapminder %>%
  ggplot(aes(x = year, y = lifeExp, by = country, color = country)) +
  ggtitle(label = "Time plot for Life Expectancy for all the Countries.") +
  geom_line(show.legend = F) +
  theme(plot.title = element_text(size = 20, face = "bold")) +
  facet_wrap(~continent)