Advanced ggplot - using facets

The video tutorial introduces the concept of facets in GG plot, a tool for disaggregating data by categorical variables. It demonstrates the use of Gapminder data to create plots, explaining the aesthetics and geometry involved. The tutorial covers the facet wrap function, detailing its arguments for customizing plots, and the facet grid function, which allows for visualization using two categorical variables. The video also provides resources for replicating the examples and encourages viewer interaction.

What is the primary purpose of using facets in GGPlot?

• To increase the size of plot points

• To disaggregate data by categorical variables

• To aggregate data into a single plot

• To change the color scheme of a plot

Which dataset is used in the example to demonstrate plot creation in GGPlot?

• Gapminder

• Iris

• GSS cat

• mtcars

In the Gapminder plot example, what is used as the x-axis aesthetic?

• Year

• Continent

• Life expectancy

• GDP per capita

What does the facet_wrap function primarily do?

• Changes the plot theme

• Separates data into multiple plots based on a categorical variable

• Wraps data points into a single plot

• Creates a single plot with multiple colors

Which argument in facet_wrap allows you to specify the number of columns?

• facet.by

• strip.position

• nrow

• ncol

What is the main difference between facet_wrap and facet_grid?

• facet_wrap is for continuous data, facet_grid is for categorical data

• facet_wrap changes plot colors, facet_grid changes plot size

• facet_wrap uses one categorical variable, facet_grid uses two

• facet_wrap is faster than facet_grid

In facet_grid, what determines the row headings?

• The first categorical variable

• The second categorical variable

• The plot theme

• The plot geometry