---
title: "Plotting interactions among categorical variables in regression models"
author: "Jacob Long"
date: "`r Sys.Date()`"
output: 
  html_document:
      toc: true
      toc_float: true
      theme: "spacelab"
vignette: >
  %\VignetteIndexEntry{Plotting interactions among categorical variables in regression models}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r echo=FALSE}
knitr::opts_chunk$set(message = F, warning = F, fig.width = 6, fig.height = 5,
                      dpi = 150, fig.options = list(type = "cairo"))
library(jtools)
library(interactions)
```

When trying to understand interactions between categorical predictors, 
the types of visualizations called for tend to differ from those for continuous
predictors. For that (and some other) reasons, `interactions` offers support for 
these in `cat_plot` while continuous predictors (perhaps in interactions with
categorical predictors) are dealt with in `interact_plot`, which has a separate
vignette.

To be clear...

If all the predictors involved in the interaction are categorical, use
`cat_plot`. You can also use `cat_plot` to explore the effect of a single
categorical predictor. 

If one or more are continuous, use `interact_plot`.

## Simple two-way interaction

First, let's prep some data. I'm going to make some slight changes to the
`mpg` dataset from `ggplot2` for didactic purposes to drop a few factor
levels that have almost no values (e.g., there are 5 cylinder engines?).

```{r}
library(ggplot2)
mpg2 <- mpg
mpg2$cyl <- factor(mpg2$cyl)
mpg2["auto"] <- "auto"
mpg2$auto[mpg2$trans %in% c("manual(m5)", "manual(m6)")] <- "manual"
mpg2$auto <- factor(mpg2$auto)
mpg2["fwd"] <- "2wd"
mpg2$fwd[mpg2$drv == "4"] <- "4wd"
mpg2$fwd <- factor(mpg2$fwd)
## Drop the two cars with 5 cylinders (rest are 4, 6, or 8)
mpg2 <- mpg2[mpg2$cyl != "5",]
## Fit the model
fit3 <- lm(cty ~ cyl * fwd * auto, data = mpg2)
```

So basically what we're looking at here is an interaction between number of 
cylinders in the engine of some cars and whether the car has all-wheel drive or
two-wheel drive. The DV is fuel mileage in the city.

Here's summary output for our model:

```{r}
library(jtools) # for summ()
summ(fit3)
```

Let's see what happens using all the default arguments:

```{r}
cat_plot(fit3, pred = cyl, modx = fwd)
```

This is with `geom = "point"`. We can see a main effect of `cyl` and maybe
something is going on with the interaction as well, since the different 
between `2wd` and `4wd` seems to decrease as `cyl` gets higher.

You can also plot the observed data on the plot:

```{r}
cat_plot(fit3, pred = cyl, modx = fwd, plot.points = TRUE)
```

## Line plots

And since `cyl` does have a clear order, it might make more sense to connect
those dots. Let's try `geom = "line"`:

```{r}
cat_plot(fit3, pred = cyl, modx = fwd, geom = "line")
```

Okay, that makes the trend quite a bit clearer.

You have some other options, too. Suppose you will need this plot to look 
good in black and white. Let's change the shape of those points for different
values of the moderator.

```{r}
cat_plot(fit3, pred = cyl, modx = fwd, geom = "line", point.shape = TRUE)
```

You can change the line patterns as well for more clarity.

```{r}
cat_plot(fit3, pred = cyl, modx = fwd, geom = "line", point.shape = TRUE,
         vary.lty = TRUE)
```

You may also choose any color palette from `RColorBrewer` as well as several
preset palettes available in `jtools`:

```{r}
cat_plot(fit3, pred = cyl, modx = fwd, geom = "line", point.shape = TRUE,
         colors = "Set2")
```

Use `?jtools_colors` for more on your color options.

## Bar/dynamite plots

Last but not least, you can also make bar charts, AKA dynamite plots. For 
many situations, these are not the best way to show your data, but I know it's
what a lot of people are looking for.

```{r}
cat_plot(fit3, pred = cyl, modx = fwd, geom = "bar")
```

The transparency of the fill color depends on the presence of the error bars 
and observed data points.

```{r}
cat_plot(fit3, pred = cyl, modx = fwd, geom = "bar", interval = FALSE)
```

Now let's look with observed data:

```{r}
cat_plot(fit3, pred = cyl, modx = fwd, geom = "bar", interval = FALSE,
         plot.points = TRUE)
```
