1. Gapminder

The goal of the gapminder dataset is to explore the relationship between GDP, population, and life expectancy.

1. Load the tidyverse package and the gapminder dataset from the gapminder package.

2. What are the mean life expectancies and populations seen by year for each continent?

3. Using across(), can you get the min, mean, median, and max life expectancy for each country?

4. Building off of your last answer, can you use rowwise() and mutate() to calculate the range for each country, subtracting lifeExp_min from lifeExp_max? Arrange descending by your new lifeExp_range variable.

   • Yes, there is a range() function you could have used in your across() function call. While you can validate your max-min subtraction with that, I’d like you to practice rowwise() here!

5. How many countries are there on each continent, excluding duplicates?

2. Diamonds

The goal of the diamonds dataset is to see which characteristics are most influential on price.

1. Use data() to load the diamonds dataset from ggplot2.

2. According to the diamond documentation, the variable depth is calculated using the formula \(total depth percentage = 100 * z / mean(x, y)\). Implement this formula using rowwise() and mutate(). Name your new variable depth_check.

3. Using case_when(), create an indicator variable perfect of all diamonds that meet the following conditions:

   • cut is premium or ideal & color is D or E & carat is bigger than 1 & clarity is VVS1 or IF. create a plot with this new variable of carat (x) by price (y), colored by “perfect”. Here’s some code to get your started…

diamonds %>% 
  mutate(perfect = case_when((cut conditions) & 
                               (color conditions) & 
                               (carat conditions) &
                               (clarity conditions) ~ "Perfect",",
                             TRUE ~ "Not Perfect")) %>% 
  ggplot()