Problem Set 1: Data Visualization

Background

In this problem set, you will get your bearings on how to produce an Rmarkdown report and how to produce data visualizations using ggplot. The data we will use is the Gapminder dataset, which gives some economic and demographic information about countries over time. The variables in this data are described below.

Name Description
country name of the country
continent name of the country’s continent
year year of the measurement, ranging from 1952 to 2007 in 5-year increments
lifeExp life expectancy at birth, in years
pop population
gdpPercap GDP per capita (US dollars, inflation-adjusted)

NOTE: In this template, the default settings for chunks is to show both R codes and the output of the chunks (echo = TRUE); if you do not want to show the R codes, you can change it to echo = FALSE, which can generate a clean look and highlight the output rather than the source code. However, for the purpose of grading (or evaluation), let’s keep the current option as it is.

Question 1

Make sure that you load the gapminder and tidyverse packages in the setup chunk (right after the header). For this question, use the glimpse function to show basic information about the gapminder dataset. In the main text (that is, outside of a code chunk), tell us how many rows and columns there are in the data set and which of the variables are factors.

Question 2

Let’s investigate how life expectancy varies across the continents. Using ggplot, we want you to recreate the following figure:

These are boxplots of the distribution of life expectancy in each continent. Please make sure that you include the labels as shown in this figure. Your code should look like this:

plot_q2 <- ggplot(<arguments>) +
  geom_<type>(<arguments>) +
  ...

plot_q2

Question 3

Looking at the previous plot, which continent has the highest median life expectancy? Which part of the boxplot can we determine this from?

Question 4

The previous boxplot groups all the years together into one boxplot, but what if we want to understand how life expectancy is changing over time? Next, we will recreate the following plot:

The plot shows each country’s life expectancy trajectory over time, broken out by continent with smoothed average lines overlayed for each continent. To get started, we’ll give you a few clues about what we’ve done here:

  • The lines for each country use the color "gray70".
  • The size of the smoothed line is 1.1 and the method used is the loess smoother. We also have turned off the standard errors.
  • Make sure that the facets are all on one row. Look at the facet_wrap documentation if need help with this.
  • Make sure that the labels are correctly specified.
  • Use the chunk options fig.width = 11 and fig.height = 4 to shrink the font size so the year labels will not overlap (this is for Rmarkdown).

Finally, assign the output of your ggplot call to plot_q4 and then evaluate plot_q4 (similarly to what you did in Question 2).