1. Load the gapminder and tidyverse packages. Is the gapminder dataset a data frame? Use the str() function to find out. What class of data is each column (Factor, vector etc)?

  2. What is the range of life expectancies observed in the Asian continent? Use filter, select and summary(). The summary function displays summary statistics (mean, median, IQR) for a given variable.

  3. What countries are part of the Asian continent? Use filter, select and unique(). Unique() identifies all unique entries, including those that are included twice or more.

  4. Explore how life expectancy in Asia has been changing been 1952 and 2007.

  • Explore the range of life expectancy values in each year using summary().
  • Say we want to represent this data in units of 10^3 people. Use mutate() to create a new column, pop_k, in which the population is represented in units of 1000 people. For the year 1952, plot this against lifeExp for all countries within Asia. Label both axes.
  • Use ggplot2() to plot life expectancy for each country in Asia within this time period. Label both axes.
  1. Using a scatterplot, examine the relationshp between GDP per capita and life expectancy in each country in Asia across all recorded years.

  2. You want to isolate only rows with data describing Rwanda or Afghanistan. What is wrong with the following piece of code? How would you fix it?** **gapminder %>% filter(country == c(“Rwanda”, “Afghanistan”))