Customising Plots

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 package. We can fit, and then plot the cdfs as follows.

library(ssdtools)
library(ggplot2)

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

A plot of the CCME boron data with the six default distributions.

This graphic is a ggplot 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.

ssd_plot_cdf(fits, average = NA, big.mark = " ") + 
  scale_color_manual(name = "Distribution", breaks = c("average", names(fits)), values = 1:7) +
  theme_bw()

A plot of the CCME boron data with the model average distribution and the six default distributions.

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

ggplot(ssddata::ccme_boron) +
  geom_ssdpoint(aes(x = Conc)) +
  ylab("Probability density") +
  xlab("Concentration")

A plot of the CCME boron data.

`geom_ssdsegments()`

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

ggplot(ssddata::ccme_boron) +
  geom_ssdsegment(aes(x = Conc, xend = Conc * 4)) +
  ylab("Probability density") +
  xlab("Concenration")

A plot of CCME boron data with the ranges of the censored data indicated by horizontal lines.

`geom_xribbon()`

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

ggplot(boron_pred) +
  geom_xribbon(aes(xmin = lcl, xmax = ucl, y = proportion)) +
  ylab("Probability density") +
  xlab("Concenration")

A plot of the confidence intervals for the CCME boron data.

`geom_hcintersect()`

And the fourth is geom_hcintersect() which plots hazard concentrations

ggplot() +
  geom_hcintersect(xintercept = c(1, 2, 3), yintercept = c(0.05, 0.1, 0.2)) +
  ylab("Probability density") +
  xlab("Concenration")

A plot of hazard concentrations as dotted lines.

Putting it together

Geoms can be combined as follows

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

A plot of censored CCME boron data with confidence intervals

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

gp + 
  scale_x_log10(
    latex2exp::TeX("Boron $(\\mu g$/L)$")
  ) +
  geom_hcintersect(xintercept = ssd_hc(fits)$est, yintercept = 0.05)

A plot of censored CCME boron data on log scale with confidence intervals, mathematical and the 5% hazard concentration.

Saving plots

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

ggsave("file_name.png", dpi = 300)

Licensing

Copyright 2015-2023 Province of British Columbia
Copyright 2021 Environment and Climate Change Canada
Copyright 2023-2024 Australian Government Department of Climate Change, Energy, the Environment and Water

The documentation is released under the CC BY 4.0 License

The code is released under the Apache License 2.0

ssdtools Team

2024-12-03