Take a moment to familiarize yourself with the various elements and tools available on the graph.
Drag the year slider (at the bottom of the chart) back to 1800 and then press play. Let it run until it gets to 2022 and then leave the slider there.
Click on the down arrow beside GDP per capita. Read the description of the variable, and then toggle between the log option and the linear option. What differences do you notice?
Find the USA. What is the GDP per capita and the life expectancy for the USA in 2022?
Drag the slider back to 1800. What was the GDP per capita and the life expectancy for the USA in 1800?
Please answer these questions
What variable is on the x-axis?
What variable is on the y-axis?
What does each bubble represent?
What do the colors of the bubbles represent?
What do the sizes of the bubbles represent?
Let’s replicate the 2022 data ourselves
The World Bank DataBank is an online platform that allows users to access and explore a vast collection of economic, financial, and social data frames. The DataBank offers free access to data from various sources within the World Bank, including the World Development Indicators (WDI) which we’ll explore today.
The WDI package for R
This package for R is a tool that provides an interface to the World Bank’s World Development Indicators (WDI) database directly from R using the World Bank’s Application Programming Interface (API).
Let’s import data using the WDI package
Please press Run Code on the code chunk in order to create our desired data frame.
Consider a generic case
Get help when needed (but, not in webR 😕 )
Take a glimpse() of our data
Press Run Code on the code chunk to display the data using the glimpse() function.
If you want to explore, you can add the width argument to the code below. Put your cursor inside the parentheses after glimpse then type width = 100. Remember that R is case sensitive. Press Run Code again to see the result. If you want to reset the code chunk to the original state, click on the start over button on the right.
Variable descriptions
country: The names of the countries included in the data frame.
region: The regional grouping of each country.
life_expectancy: The average life expectancy at birth, measured in years, for each country in 2022. It reflects the average number of years a newborn is expected to live under current mortality rates.
gdp_per_capita: The Gross Domestic Product (GDP) per capita for each country, measured in current US dollars. It represents the average economic output per person and is an indicator of the country’s economic health.
population: The total number of people living in each country in 2022.
Here’s the full data frame
Mapping of variables to elements of the graph
The Grammar of Graphics
Layer
Description
Data
The data to be plotted.
Aesthetics
The mapping of variables to elements of the plot.
Geometrics
The visual elements to display the data -- e.g., bar graph, scatterplot.
Facets
Plotting of small groupings within the plot area -- i.e., small multiples.
Statistics and Transformations
Additional statistics imposed onto plot or numerical transformations of variables to aid interpretation.
Coordinates
The space on which the data are plotted -- e.g., Cartesian coordinate system (x- and y-axis), polar coordinates (pie chart).
Themes
All non-data elements -- e.g., background color, labels.
Let’s focus on the first 3 layers to begin! These are ALWAYS required.
The basic syntax
A basic chart
Using the wdi_2022 data frame, aesthetically map gdp_per_capita to the x-axis and life_expectancy to the y-axis. Draw a scatter plot. Replace the three underscores with the correct code to accomplish this task
You might notice that we’re missing two aesthetics from the gapminder chart. Let’s now map population to the size of the points, and region to the color of the points.
The visual elements to display the data -- e.g., bar graph, scatterplot.
Facets
Plotting of small groupings within the plot area -- i.e., small multiples.
Statistics and Transformations
Additional statistics imposed onto plot or numerical transformations of variables to aid interpretation.
Coordinates
The space on which the data are plotted -- e.g., Cartesian coordinate system (x- and y-axis), polar coordinates (pie chart).
Themes
All non-data elements -- e.g., background color, labels.
Add a facet
Perhaps rather than putting all countries on one plot, we’d prefer to have a separate plot for each region. We can use the facet_wrap() function to accomplish this. This is an example of a Facet layer. To use the function, be sure to add a + sign at the end of the last line of code, return to a new line, and then list the function name. Inside the parentheses, list a tilde (~) and then the variable you want to facet by (region). Additionally, use the ncol and nrow arguments within the facet_wrap() function to request 2 columns and 4 rows.
GDP has a vast range ($259 for Burundi to $187,267 for Lichtenstein), with most countries stacked toward the lower end of GDP. As you’ll learn about in Module 15, it’s often useful to apply a non-linear transformation to these types of heavily skewed variables. This is a form of a Statistics/Transformation layer.
Apply the transformation layer
The scale_x_log10() function in ggplot2 is used to transform the x-axis of a plot to a logarithmic scale with base 10. Add a layer (remember to put the + sign at the end of the last line of existing code), then apply the transformation function.
If we wish, we could zoom into a certain part of the graph — for example, to focus on countries with a GDP between 1 and 25,000 USD. We can use the coord_cartesian() function for this — a Coordinates layer.
The argument to the function needed here is xlim for x-axis limits. To restrict the graph to x scores between 1 and 25,000 we use:
