---
title: "Mosaic plots with `ggplot2`"
author: "Haley Jeppson and Heike Hofmann"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Mosaic plots with `ggplot2`}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---


```{r setup, echo=FALSE}
knitr::opts_chunk$set(fig.width = 6, fig.height = 4, fig.align='center',
                      dev = "png", message = FALSE)
```

```{r pkgs, echo=FALSE}
library(ggmosaic)
library(gridExtra)
library(grid)
library(patchwork)
library(dplyr)
```

```{r data, echo=FALSE}
data(fly)
okabe <- c("#E69F00", "#56B4E9", "#009E73", "#F0E442","#0072B2", "#D55E00", "#CC79A7")
mypal <- okabe #source: the okabe color palette
scale_fill_discrete <- function(...) scale_fill_manual(..., values = mypal)

# set theme
theme_set(theme_mosaic()+theme(legend.position = "none"))

# A few modifications to data
flights <- fly %>%
  select(do_you_recline, rude_to_recline, eliminate_reclining, region) %>%
  filter(!is.na(do_you_recline), !is.na(rude_to_recline))
```

Designed to create visualizations of categorical data, `geom_mosaic()` has the capability to produce bar charts, stacked bar charts, mosaic plots, and double decker plots and therefore offers a wide range of potential plots. The plots below highlight the package's versatility.


```{r variety, fig.height=10.5, fig.cap = "An assortment of plots made with the ggmosaic package."}
# A few modifications to data
flights <- fly  %>%
  filter(!is.na(do_you_recline), !is.na(rude_to_recline))

bar_examp <- ggplot(data = flights) + 
  geom_mosaic(aes(x=product(do_you_recline), fill = do_you_recline), divider="vbar") +
  theme(axis.text.x = element_blank(),
        axis.ticks.x = element_blank()) +
  labs(y="Do you recline?", x = "", title = "Bar Chart")

spine_examp <- ggplot(data = flights) + 
  geom_mosaic(aes(x=product(do_you_recline), fill = do_you_recline), divider = "vspine") +  
  theme(axis.text.x = element_blank(),
        axis.ticks.x = element_blank()) +
  labs(y="Do you recline?", x = "", title = "Spine Plot")

stackbar_examp <- ggplot(data = flights) + 
  geom_mosaic(aes(x=product(do_you_recline, rude_to_recline), fill = do_you_recline),
              divider=c("vspine", "hbar")) +   
  labs(x="Is it rude to recline?", y = "Do you recline?", title = "Stacked Bar Chart")

mosaic_examp <- ggplot(data = flights) +
  geom_mosaic(aes(x = product(do_you_recline, rude_to_recline), fill = do_you_recline)) +   
  labs(y="Do you recline?", x="Is it rude to recline?", title = "Mosaic Plot (2 variables)") 

mosaic2_examp <- ggplot(data = flights) +
  geom_mosaic(aes(x=product(eliminate_reclining, do_you_recline, rude_to_recline), fill = do_you_recline, alpha = eliminate_reclining)) + 
  scale_alpha_manual(values =c(.7,.9)) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = .5)) + 
  labs(y="Do you recline?", x="Eliminate reclining?:Is it rude to recline?", title = "Mosaic Plot (3 variables)")


ddecker_examp <- ggplot(data = flights) +
  geom_mosaic(aes(x=product(do_you_recline, eliminate_reclining, rude_to_recline), fill = do_you_recline, alpha = eliminate_reclining), divider = ddecker()) + 
  scale_alpha_manual(values =c(.7,.9)) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = .5)) + 
  labs(y="Do you recline?", x="Eliminate reclining?: Is it rude to recline?", title = "Double Decker Plot")

spine_examp + bar_examp + mosaic_examp + stackbar_examp + mosaic2_examp + ddecker_examp + plot_layout(ncol = 2)
```

<br/>
Furthermore, **ggmosaic** allows various features to be customized:

-   the **order of the variables**,

-   the **formula setup** of the plot,

-   **faceting**,

-   the **type of partition**, and

-   the **space between the categories**.


## How to use ggmosaic

To fit **ggmosaic** within the **ggplot2** framework, we must be able to create the formula from the aesthetics defined in a call. That is, the aesthetics set up the formula which determines how to break down the joint distribution. The main hurdle **ggmosaic** faced is that mosaic plots do not have a one-to-one mapping between a variable and the `x` or `y` axis. To accommodate the variable number of variables, the mapping to `x` is created by the `product()` function. For example, the variables `var1` and `var2` are read in as `x = product(var2, var1)`. The `product()` function alludes to **ggmosaic**'s predecessor **productplots** and to the joint distribution as the product of the conditional and marginal distributions. `product()` creates a list of the variables and allows for to pass `check_aesthetics()`, a **ggplot2** internal function, and then splits the variables back into a data frame for the calculations.

In `geom_mosaic()`, the following aesthetics can be specified:

-   `weight`: select a weighting variable

-   `x`: select variables to add to formula

    -   declared as `x = product(var2, var1, ...)`

-   `alpha`: add an alpha transparency to the selected variable

    -   unless the variable is called in `x`, it will be added to the formula in the first position

-   `fill`: select a variable to be filled

    -   unless the variable is called in `x`, it will be added to the formula in the first position after the optional `alpha` variable.

-   `conds` : select a variable to condition on

    -   declared as `conds = product(cond1, cond2, ...)`

These values are then sent through repurposed `productplots` functions to create the desired formula: `weight ~ alpha + fill + x | conds`. Because the plot is constructed hierarchically, the ordering of the variables in the formula is vital. 

## Examples 

### One variable

```{r formula-1b}
 ggplot(data = flights) +
  geom_mosaic(aes(x = product(rude_to_recline), fill=rude_to_recline)) + 
  labs(title='f(rude_to_recline)')
```


### Two variables

```{r formula-2b}
ggplot(data = flights) +
  geom_mosaic(aes(x = product(do_you_recline, rude_to_recline), fill=do_you_recline)) + 
  labs(title='f(do_you_recline | rude_to_recline) f(rude_to_recline)')
```

### Conditioning


```{r formula-4b}
ggplot(data = flights) +
  geom_mosaic(aes(x=product(do_you_recline), fill = do_you_recline, 
                  conds = product(rude_to_recline))) +
  labs(title='f(do_you_recline | rude_to_recline)')
```

###  Alternative to conditioning: faceting

```{r formula-5b, fig.width = 6, fig.height = 6}
ggplot(data = flights) +
  geom_mosaic(aes(x = product(do_you_recline), fill=do_you_recline), divider = "vspine") +
  labs(title='f(do_you_recline | rude_to_recline)') + 
  facet_grid(~rude_to_recline) +
  theme(aspect.ratio = 3,
        axis.text.x = element_blank(),
        axis.ticks.x = element_blank())
```


### The importance of ordering 

```{r order-b, fig.width = 7, fig.height = 2.4}
order1 <- ggplot(data = flights) +
  geom_mosaic(aes(x = product(do_you_recline, rude_to_recline), fill = do_you_recline)) 

order2 <- ggplot(data = flights) +
  geom_mosaic(aes(x=product(rude_to_recline, do_you_recline), fill = do_you_recline)) 
  
order1 + order2
```


## Other features of `geom_mosaic()`
  
Arguments unique to `geom_mosaic()`:

- `divider`: used to declare the type of partitions to be used  

- `offset`: sets the space between the first spine


### Divider function: Types of partitioning

Four options available for each partition:

- `vspine`:  <!-- (Vertical spine partition) --> width constant, height varies. 

- `hspine`:  <!-- Horizontal spine partition: --> height constant, width varies.

- `vbar`: <!--  Vertical bar partition: --> height constant, width varies.

- `hbar`:  <!--  Horizontal bar partition: --> width constant, height varies.
    

```{r partitions, fig.height = 2}
part1 <- ggplot(data = flights) + 
  geom_mosaic(aes(x=product(do_you_recline), fill = do_you_recline)) +  
  theme(axis.text = element_blank(),
        axis.ticks = element_blank()) +
  labs(x="", y = "", title = "hspine")

part2 <- ggplot(data = flights) + 
  geom_mosaic(aes(x=product(do_you_recline), fill = do_you_recline),
              divider = "vspine") +  
  theme(axis.text = element_blank(),
        axis.ticks = element_blank()) +
  labs(x="", y = "", title = "vspine")

part3 <- ggplot(data = flights) + 
  geom_mosaic(aes(x=product(do_you_recline), fill = do_you_recline),
              divider = "hbar") +  
  theme(axis.text = element_blank(),
        axis.ticks = element_blank()) +
  labs(x="", y = "", title = "hbar")

part4 <- ggplot(data = flights) + 
  geom_mosaic(aes(x=product(do_you_recline), fill = do_you_recline),
              divider = "vbar") +  
  theme(axis.text = element_blank(),
        axis.ticks = element_blank()) +
  labs(x="", y = "", title = "vbar")

part1 + part2 + part3 + part4 + plot_layout(nrow = 1)
```


### Partitioning with one or more variables

- `mosaic()`
    - default
    - will use spines in alternating directions 
    - begins with a horizontal spine 
- `mosaic("v")`
    - begins with a vertical spine and then alternates
- `ddecker()`
    - selects n-1 horizontal spines and ends with a vertical spine
- Define each type of partition 
    -  e.g. `c("hspine", "vspine", "hbar")`


```{r mosaic-a, fig.height = 2}
h_mosaic <- ggplot(data = flights) +
  geom_mosaic(aes(x = product(do_you_recline, rude_to_recline, eliminate_reclining), fill=do_you_recline), divider=mosaic("h")) +
  theme(axis.text=element_blank(), axis.ticks=element_blank()) + 
  labs(x="", y="")

v_mosaic <- ggplot(data = flights) +
  geom_mosaic(aes(x = product(do_you_recline, rude_to_recline, eliminate_reclining), fill=do_you_recline), divider=mosaic("v"))  +
  theme(axis.text=element_blank(), axis.ticks=element_blank()) + 
  labs(x="", y="")

doubledecker <- ggplot(data = flights) +
  geom_mosaic(aes(x = product(rude_to_recline, eliminate_reclining), fill=do_you_recline), divider=ddecker()) +
  theme(axis.text=element_blank(), axis.ticks=element_blank()) + 
  labs(x="", y="")

h_mosaic + v_mosaic + doubledecker + plot_layout(nrow = 1)
```



```{r mosaic-d, fig.height = 2}
mosaic4 <-  ggplot(data = flights) +
  geom_mosaic(aes(x = product(do_you_recline, rude_to_recline, eliminate_reclining), fill=do_you_recline), divider=c("vspine", "vspine", "hbar"))  +
  theme(axis.text=element_blank(), axis.ticks=element_blank()) + 
  labs(x="", y="")

mosaic5 <-  ggplot(data = flights) +
  geom_mosaic(aes(x = product(do_you_recline, rude_to_recline, eliminate_reclining), fill=do_you_recline), divider=c("hbar", "vspine", "hbar"))  +
  theme(axis.text=element_blank(), axis.ticks=element_blank()) + 
  labs(x="", y="")

mosaic6 <-  ggplot(data = flights) +
  geom_mosaic(aes(x = product(do_you_recline, rude_to_recline, eliminate_reclining), fill=do_you_recline), divider=c("hspine", "hspine", "hspine"))  +
  theme(axis.text=element_blank(), axis.ticks=element_blank()) + 
  labs(x="", y="")

mosaic7 <-  ggplot(data = flights) +
  geom_mosaic(aes(x = product(do_you_recline, rude_to_recline, eliminate_reclining), fill=do_you_recline), divider=c("vspine", "vspine", "vspine"))  +
  theme(axis.text=element_blank(), axis.ticks=element_blank()) + 
  labs(x="", y="")

mosaic4 + mosaic5 + mosaic6 + mosaic7 + plot_layout(nrow = 1)
```

  
### Adjusting the offset


**ggmosaic** adopts the procedure followed by Hartigan and Kleiner, Friendly, Theus and Lauer, and Hofmann, where an amount of space is allocated for each of the splits, with subsequent divisions receiving a smaller amount of space. The created spaces preserve the impact of small counts. The effect becomes immediately apparent when an empty group is included. In this case, the gaps between the categories, which are empty, create an empty space.

`offset`: Set the space between the first spine

- default is 0.01

- space between partitions decreases as layers build


```{r offset-b, fig.width = 10, fig.height = 4}
offset1 <- ggplot(data = flights) +
  geom_mosaic(aes(x = product(do_you_recline, region), fill=do_you_recline)) + 
  labs(y = "", title=" offset = 0.01") +
  theme(axis.text.y=element_blank(),
        axis.ticks.y=element_blank(),
        axis.text.x = element_text(angle = 90))

offset0 <- ggplot(data = flights) +
  geom_mosaic(aes(x = product(do_you_recline, region), fill=do_you_recline), offset = 0) + 
  labs(y = "", title=" offset = 0") +
  theme(axis.text.y=element_blank(),
        axis.ticks.y=element_blank(),
        axis.text.x = element_text(angle = 90))

offset2 <- ggplot(data = flights) +
  geom_mosaic(aes(x = product(do_you_recline, region), fill=do_you_recline), offset = 0.02) + 
  labs(y="",  title=" offset = 0.02") +
  theme(axis.text.y = element_blank(),
        axis.ticks.y=element_blank(),
        axis.text.x = element_text(angle = 90),
        legend.position = "right")

offset0 + offset1 + offset2 + plot_layout(ncol = 3)
```


## Current Issues

- `geom_mosaic()` is no longer compatible with `ggplotly()`


## Version compatibility issues with ggplot2

Since the initial release of ggmosaic, ggplot2 has evolved considerably. And as ggplot2 continues to evolve, ggmosaic must continue to evolve alongside it. Although these changes affect the underlying code and not the general usage of ggmosaic, the general user may need to be aware of compatibility issues that can arise between versions. The table below summarizes the compatibility between versions. 


| ggmosaic | ggplot2 | Axis labels                                                                   | Tick marks    |
|----------|---------|-------------------------------------------------------------------------------|---------------|
| 0.3.3    | 3.3.3   | x                                                                             | x             |
| 0.3.0    | 3.3.0   | x                                                                             | x             |
| 0.2.2    | 3.3.0   | Default labels are okay, but must use <br>`scale_*_productlist()` to modify | No tick marks |
| 0.2.2    | 3.2.0   | Default labels okay, but must use <br>`scale_*_productlist()` to modify     | x             |
| 0.2.0    | 3.2.0   | Default labels are wrong, but can use <br>`labs()` to modify                  | x             |

