Funky ordination plots with ggvegan

Yesterday, I tweeted a photo of a ordination I plotted with ggvegan, and thought I should show how I made it.

Ordinations can be plotted with base R (see ?plot.cca). This is fine for simple plots, but it is a lot of effort to make a complex plot. The ggvegan package, written by Gavin Simpson, lets you use the power of ggplot2 and can make complex plots easier to make.

Start by loading some packages. ggvegan will need to be installed from GitHub if you don’t already have it.

library(tidyverse)
library(vegan)
#devtools::install_github("gavinsimpson/ggvegan")
library(ggvegan)

I’m going to use the pyrifos dataset, which is included in vegan. The data are the abundances of aquatic invertebrates from an experiment in which twelve ditches were studies several times before and after insecticide treatment. We need to make a tibble (or a data.frame) of the predictors (dose, time and ditch ID).

data("pyrifos")

env <- tibble(
week = rep(c(-4, -1, 0.1, 1, 2, 4, 8, 12, 15, 19, 24), each = 12),
dose = factor(rep(c(0.1, 0, 0, 0.9, 0, 44, 6, 0.1, 44, 0.9, 0, 6), 11)),
ditch = gl(12, 1, length=132))

We can run a principal components analysis with rda.

pca <- rda(pyrifos)

Simple ordination plots can be made with the autoplot function. This plot shows all the samples. This is fine, but we want to see dose and time information.

autoplot(pca, layers = "sites", arrows = FALSE) At the moment, it is difficult to do much to this plot. But we can use the fortify function from ggvegan to extract the scores from the ordination object and then combine these with the predictor data before plotting with ggplot. I’m going to colour the samples by dose, and draw a line through the samples in each ditch, with a point on the first value.

pca_fort <- fortify(pca, display = "sites") %>%
bind_cols(env)

ggplot(pca_fort, aes(x = PC1, y = PC2, colour = dose, group = ditch)) +
geom_path() + #use geom_path not geom_line
geom_point(aes(size = if_else(week == min(week), 1, NA_real_)), show.legend = FALSE) +
scale_color_viridis_d() +
scale_size(range = 2) +
coord_equal() Don’t forget to add coord_equal() to ensure that the plot is correctly scaled.

Happy plotting. 