Create a ggplot Object — ordiggplot • ggvegan

Function ordiggplot sets up an ordination graph but draws no result. You can add new graphical elements to this plot with geom_ordi_* function of this package, or you can use standard ggplot2 geom_* functions and use ggscores as their data argument.

ordiggplot(model, axes = c(1, 2), arrowmul, ...)

ggscores(score)

Arguments

model: An ordination result object from vegan.
axes: Two axes to be plotted
arrowmul: Multiplier to arrow length. If missing, the arrow length are adjusted to fit to other scores, but if some score types are not displayed, the arrows may be badly scaled, and manual adjustment can be useful.
...: Parameters passed to underlying functions.
score: Ordination score to be added to the plot.

Details

The ggvegan package has two contrasting approaches to draw ordination plots. The autoplot functions (e.g. autoplot.rda(), autoplot.cca(), and autoplot.metaMDS) draw a complete plot with one command, but the design is hard-coded in the function. However, you can add new elements to the graph.

In contrast, function ordiggplot() only sets up an ordination plot, and does not draw anything. It allows you to add layers to the plot one by one with full flexibility of the ggplot2 functions. There are some specific functions geom_ordi_* functions that are similar as similarly named geom_* functions. For these you need to give the type of ordination scores to be added, and in addition, you can give any geom_* function arguments to modify the plot. Alternatively, you can use any ggplot2 function and in its data argument use ggscores() function to select the data elements for the function.

The ordiggplot() function extracts results using fortify() functions of this package, and it accepts the arguments of those functions. This allows setting, e.g., the scaling of ordination axes.

Examples

library("vegan")
data(dune, dune.env, varespec, varechem)
m <- cca(dune ~ Management + A1, dune.env)

## use geom_ordi_* functions
ordiggplot(m) + geom_ordi_axis() +
  geom_ordi_point("sites") +
  geom_ordi_text("species", col = "darkblue",
                 mapping = aes(fontface = "italic")) +
  geom_ordi_label("centroids") +
  geom_ordi_arrow("biplot")


## use ggscores + standard geom_* functions
ordiggplot(m, scaling = "sites") +
  geom_point(data = ggscores("sites")) +
  geom_text(data = ggscores("species"),
            mapping = aes(fontface = "italic")) +
  geom_label(data = ggscores("centroids"), fill = "yellow") +
  geom_ordi_arrow("biplot")


## Messy arrow biplot for PCA
m <- rda(dune)
ordiggplot(m) +
  geom_ordi_axis() +
  geom_ordi_point("sites") +
  geom_ordi_arrow("species")


## Fitted vectors, selecting variables with formula
set.seed(1) 
m <- metaMDS(varespec, trace = FALSE)
## plot
ordiggplot(m) +
  geom_ordi_point("sites") +
  geom_ordi_arrow("sites", stat = "vectorfit", edata = varechem,
                  formula = ~ N + Ca + Al + Humdepth + pH)
#> Warning: The following aesthetics were dropped during statistical transformation: N, P,
#> K, Ca, Mg, S, Al, Fe, Mn, Zn, Mo, Baresoil, Humdepth, pH
#> ℹ This can happen when ggplot fails to infer the correct grouping structure in
#>   the data.
#> ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
#>   variable into a factor?
#> Warning: The following aesthetics were dropped during statistical transformation: N, P,
#> K, Ca, Mg, S, Al, Fe, Mn, Zn, Mo, Baresoil, Humdepth, pH
#> ℹ This can happen when ggplot fails to infer the correct grouping structure in
#>   the data.
#> ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
#>   variable into a factor?