You’ve completed the Data Visualization in R lesson! You now know how to choose and implement different kinds of geoms in ggplot2, how to customize your plot axes, and how to visualize additional variables through facets.

Below is a summary of the key concepts you learned – great job!

How to create different geoms and when to use which type:

• Histograms can be created using geom_histogram() to show the distribution of a continuous variable.
• Heatmaps can be created using geom_bin2d() to show the distribution of the intersections of two continuous variables.
• Box-and-whisker plots can be created using geom_boxplot() to show the distribution of a continuous variable by quantiles, e.g. 25th, 50th, and 75th percentiles.
• Bar plots can be created using geom_bar(), which shows the count of observations for different values of a discrete variable by default. geom_col() will create bar plots showing the value of the variable on the y axis rather than counts.
• Using the position argument and a fill aesthetic mapping, we can create stacked bar plots (position = "stack"), stacked bar plots showing ratios (position = "fill"), and clustered bar plots (position = "dodge").

How to show different statistics in our data:

• The stat argument allows us to display different kinds of values.
• stat = "identity" will show the y axis variable values on a bar plot as is, rather than displaying the x axis value counts.
• stat = "summary" combined with a function supplied in fun will display bar heights based on the summary function. For example, stat = "summary", fun = "mean" will calculate and display means.

How to add error bars to bar plots to show variance around a mean:

• geom_error() creates error bars on bar plots when provided ymin and ymax variables representing the upper and lower bounds of error ranges.

How to customize discrete and continuous axes:

• We can customize discrete axes using scale_x_discrete() and scale_y_discrete().
• We can customize continuous axes using scale_x_continuous() and scale_y_continuous()`.
• We can zoom in on a region of our data using coord_cartesian().

How to show additional variables in panels of a grid using facets:

• By adding facet_grid(), we can map up to two additional variables along facet columns and rows.