ggplot2 is a powerful and widely-used data visualization package in R. It is part of the tidyverse collection of R packages and is based on the Grammar of Graphics, which provides a coherent system for describing and building graphs. This module will introduce you to the basics of ggplot2, including how to create simple plots and customize them.

1. Grammar of Graphics: The underlying theory of ggplot2 that describes how to build a plot by combining different components.
2. Aesthetics (aes): Mappings that describe how data variables are mapped to visual properties (e.g., x and y coordinates, color, size).
3. Geoms: Geometric objects that represent data points (e.g., points, lines, bars).
4. Layers: Different components of a plot that can be added incrementally.
5. Facets: Subplots that display subsets of the data.
6. Themes: Customizations for the non-data components of the plot (e.g., background, grid lines).

A ggplot2 plot is built up in layers, starting with the data and aesthetic mappings, followed by geometric objects, and then additional customizations. The basic structure is as follows:

Let's start with a simple scatter plot using the built-in mtcars dataset.

Explanation:

• ggplot(data = mtcars, mapping = aes(x = wt, y = mpg)): Initializes the plot with the mtcars dataset and maps the wt (weight) variable to the x-axis and the mpg (miles per gallon) variable to the y-axis.
• geom_point(): Adds a layer of points to the plot.

We can enhance the scatter plot by mapping additional variables to color and size.

Explanation:

• color = cyl: Maps the cyl (number of cylinders) variable to the color of the points.
• size = hp: Maps the hp (horsepower) variable to the size of the points.

We can add titles and labels to make the plot more informative.

Explanation:

• labs(): Adds titles and labels to the plot.

Faceting allows us to create multiple plots based on a categorical variable.

Explanation:

• facet_wrap(~ cyl): Creates separate plots for each level of the cyl variable.

Create a bar plot of the mtcars dataset showing the count of cars for each number of cylinders (cyl).

Solution:

Create a line plot of the pressure dataset showing the relationship between temperature and pressure.

Solution:

Create a box plot of the mtcars dataset showing the distribution of miles per gallon (mpg) for each number of cylinders (cyl).

Solution:

• Mistake: Forgetting to load the ggplot2 package.
  • Tip: Always start your script with library(ggplot2).
• Mistake: Incorrectly specifying the aesthetic mappings.
  • Tip: Ensure that the variables you map to aesthetics exist in your dataset.
• Mistake: Overcomplicating plots with too many layers.
  • Tip: Start simple and add layers incrementally.

In this section, we covered the basics of ggplot2, including how to create simple plots and customize them. We explored scatter plots, bar plots, line plots, and box plots, and learned how to add titles, labels, and facets. With these foundational skills, you are now ready to explore more advanced features of ggplot2 in the next section.