Learn

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.

### Instructions

The code included to the right creates the plot shown in the very first exercise, depicting how different animals spend the hours of their day. Feel free to experiment with this plot and modify it further!