Classwork 13

Time Trend Plots

R Packages

For Classwork 13, please load the tidyverse package:

library(tidyverse)

Question 1. Trend in GDP per capita

For Question 1, please load the R package gapminder before starting:

# install.packages("gapminder")
library(gapminder)
??gapminder

The gapminder package provides a built-in dataset named gapminder, which contains country-level data on life expectancy, GDP per capita, and population across time.

Let’s assign it to a new object called df_gapminder:

df_gapminder <- gapminder::gapminder

Part A

• 🤖 Task 1: Fill in the blanks in the provided ggplot() code chunks.

Visualization 1

• Something has gone wrong in this given plot. What happened?

ggplot(data = df_gapminder,
       mapping = aes(__BLANK_1__,
                     y = __BLANK_2__)) +
  geom_point(size = .5) +
  geom___BLANK_3__()

Visualization 2

ggplot(data = df_gapminder,
       mapping = aes(__BLANK_1__,
                     y = __BLANK_2__,
                     __BLANK_3__ = country)) +
  geom_point(size = .5,
             color = "black") +
  geom___BLANK_4__()

Visualization 3

ggplot(data = df_gapminder,
       mapping = aes(__BLANK_1__,
                     y = __BLANK_2__,
                     __BLANK_3__ = country)) +
  geom_point(size = .5,
             color = "black") +
  geom___BLANK_4__(show.legend = FALSE)

Part B

• 🤖 Task 1: Fill in the blanks in the provided ggplot() code chunk.
  
• 💬 Task 2: Add a brief comment describing how the overall time trend of GDP per capita (gdpPercap) varies by continent (continent).

Visualization 1

ggplot(data = df_gapminder,
       mapping = aes(__BLANK_1__,
                     y = __BLANK_2__,
                     __BLANK_3__ = country)) +
  geom_point(size = .5,
             color = "black") +
  geom___BLANK_4__(show.legend = FALSE) +
  facet___BLANK_5__( ~ __BLANK_6__)

Visualization 2

• Because we have only five continents it might be worth seeing if we can fit them on a single row (which means we’ll have five columns).

ggplot(data = df_gapminder,
       mapping = aes(__BLANK_1__,
                     y = __BLANK_2__,
                     __BLANK_3__ = country)) +
  geom_point(size = .5,
             color = "black") +
  geom___BLANK_4__(show.legend = FALSE) +
  facet___BLANK_5__( ~ __BLANK_6__, 
             __BLANK_7__ = 1)

Visualization 3

• Since GDP per capita is highly skewed, let’s take a log transformation on it:

ggplot(data = df_gapminder,
       mapping = aes(__BLANK_1__,
                     y = __BLANK_2__,
                     __BLANK_3__ = country)) +
  geom_point(size = .5,
             color = "black") +
  geom___BLANK_4__(show.legend = FALSE) +
  facet___BLANK_5__( ~ __BLANK_6__, 
             __BLANK_7__ = 1)

Visualization 4

ggplot(data = df_gapminder,
       mapping = aes(__BLANK_1__,
                     y = __BLANK_2__)) +
  geom___BLANK_3__(show.legend = FALSE,
                  color = 'grey',
                  mapping = aes(group = country)) + # Advanced ggplot: we can add a specific aes() to a specific geom.
  geom___BLANK_4__() +
  facet_wrap(~ __BLANK_5__, 
             __BLANK_6__ = 1)