---
title: "Customising Plots"
author: "ssdtools Team"
date: '`r format(Sys.time(), "%Y-%m-%d", tz = "UTC")`'
bibliography: references.bib
mathfont: Courier
latex_engine: MathJax
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Customising Plots}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 6,
  fig.height = 4
)
```

## Plotting the cumulative distributions

The `ssdtools` package plots the cumulative distribution functions using `ssd_plot_cdf()`. 

For example, consider the CCME boron data from the [`ssddata`](https://github.com/open-AIMS/ssddata) package. 
We can fit, and then plot the cdfs as follows.

```{r warning=FALSE, message=FALSE, fig.alt="A plot of the CCME boron data with the six default distributions."}
library(ssdtools)
library(ggplot2)

fits <- ssd_fit_dists(ssddata::ccme_boron)
ssd_plot_cdf(fits)
```

This graphic is a [ggplot](https://ggplot2.tidyverse.org) object and so can be customized in the usual way.

For example, we can add the model-averaged cdf by setting `average = NA`, change the string used to separate thousands using `big.mark`, customize the color scale with `scale_color_manual()` and change the theme.

```{r, fig.alt="A plot of the CCME boron data with the model average distribution and the six default distributions."}
ssd_plot_cdf(fits, average = NA, big.mark = " ") + 
  scale_color_manual(name = "Distribution", breaks = c("average", names(fits)), values = 1:7) +
  theme_bw()
```

## ggplot Geoms

The `ssdtools` package provides four ggplot geoms to allow you construct your own plots.

### `geom_ssdpoint()`

The first is `geom_ssdpoint()` which plots species sensitivity data

```{r, fig.alt = "A plot of the CCME boron data."}
ggplot(ssddata::ccme_boron) +
  geom_ssdpoint(aes(x = Conc)) +
  ylab("Probability density") +
  xlab("Concentration")
``` 

### `geom_ssdsegments()`

The second is `geom_ssdsegments()` which plots the ranges of censored species sensitivity data

```{r, fig.alt = "A plot of CCME boron data with the ranges of the censored data indicated by horizontal lines."}
ggplot(ssddata::ccme_boron) +
  geom_ssdsegment(aes(x = Conc, xend = Conc * 4)) +
  ylab("Probability density") +
  xlab("Concenration")
``` 

### `geom_xribbon()`

The third is `geom_xribbon()` which plots species sensitivity confidence intervals

```{r, fig.alt="A plot of the confidence intervals for the CCME boron data."}
ggplot(boron_pred) +
  geom_xribbon(aes(xmin = lcl, xmax = ucl, y = proportion)) +
  ylab("Probability density") +
  xlab("Concenration")
```  

### `geom_hcintersect()`

And the fourth is `geom_hcintersect()` which plots hazard concentrations

```{r, fig.alt="A plot of hazard concentrations as dotted lines."}
ggplot() +
  geom_hcintersect(xintercept = c(1, 2, 3), yintercept = c(0.05, 0.1, 0.2)) +
  ylab("Probability density") +
  xlab("Concenration")
```    

### Putting it together

Geoms can be combined as follows

```{r, fig.alt="A plot of censored CCME boron data with confidence intervals"}
gp <- ggplot(boron_pred, aes(x = est)) +
  geom_xribbon(aes(xmin = lcl, xmax = ucl, y = proportion), alpha = 0.2) +
  geom_line(aes(y = proportion)) +
  geom_ssdsegment(data = ssddata::ccme_boron, aes(x = Conc / 2, xend = Conc * 2)) +
  geom_ssdpoint(data = ssddata::ccme_boron, aes(x = Conc / 2)) +
  geom_ssdpoint(data = ssddata::ccme_boron, aes(x = Conc * 2)) +
  scale_y_continuous("Species Affected (%)", labels = scales::percent) +
  xlab("Concentration (mg/L)") +
  expand_limits(y = c(0, 1))

gp
```

To log the x-axis and include mathematical notation and add the HC5 value use the following code.

```{r, fig.alt="A plot of censored CCME boron data on log scale with confidence intervals, mathematical and the 5% hazard concentration."}
gp + 
  scale_x_log10(
    latex2exp::TeX("Boron $(\\mu g$/L)$")
  ) +
  geom_hcintersect(xintercept = ssd_hc(fits)$est, yintercept = 0.05)
```

## Saving plots

The most recent plot can be saved as a file using `ggsave()`, which also allows the user to set the resolution.

```{r, eval = FALSE}
ggsave("file_name.png", dpi = 300)
```

<div id="refs"></div>

```{r, results = "asis", echo = FALSE}
cat(ssdtools::ssd_licensing_md())
```