Changing the legend appearance without changing the plot appearance can be done using the override.aes argument in guide_legend(). I go through four plotting examples to demonstrate how it can be used.

This post demonstrates one way to add a background color gradient in ggplot2 based on a continuous variable and geom_segment().

Following up on a previous post, I show how I add conditional elements to automated ggplot2 plots through the use of if() statements within my plotting function.

The task of fitting many similar models can be automated by looping through variables. I show an example of fitting the same model for multiple different response variables and then making residual plots for all models prior to extracting model results.

There are a variety of ways to combine ggplot2 plots with a single shared axis, but things can get tricky if you want a lot of control over all plot elements. I show four approaches to make such a plot: using facets and with packages cowplot, egg and patchwork.

I first learned about embedding many small subplots into a larger plot as a way to visualize large datasets with package ggsubplot. Embedding subplots is still possible in ggplot2 today with the annotation_custom() function. I demonstrate one approach to do this, making many subplots in a loop and then adding them to the larger plot.

I show a general approach for plotting fitted lines with ggplot2 that works across many different model types.

In this post I show an example of how to automate the process of making many exploratory plots in ggplot2 with multiple continuous response and explanatory variables. To loop through both x and y variables involves nested looping. In the latter section of the post I go over options for saving the resulting plots, either together in a single document, separately, or by creating combined plots prior to saving.

Getting a legend in ggplot2 when the aesthetic value is set to be constant instead of a variable can be tricky. I go through an example of a situation where this might be useful and show how to first get a legend and then how to "prettify" that legend.

In this post I show one approach for making added variable plots from a model with many continuous explanatory variables. Since this is done for every variable in the model, I show how to automate the process via functions from package purrr.

A quick example of reversing the legend order in a ggplot2 plot, which is done via guide_legend() instead of by changing the order of the levels in the dataset.