How to plot filled points and confidence ellipses with the same color using ggplot in R?

Question

I would like to plot a graph from a Discriminant Function Analysis in which points must have a black border and be filled with specific colors and confidence ellipses must be the same color as the points are filled. Using the following code, I get almost the graph I want, except that points do not have a black border:

library(ggplot2)
library(ggord)
library(MASS)

data("iris")

set.seed(123)
linear <- lda(Species~., iris)
linear

dfaplot <- ggord(linear, iris$Species, labcol = "transparent", arrow = NULL, poly = FALSE, ylim = c(-11, 11), xlim = c(-11, 11))
dfaplot +
  scale_shape_manual(values = c(16,15,17)) +
  scale_color_manual(values = c("#00FF00","#FF00FF","#0000FF")) +
  theme(legend.position = "none")

PLOT 1

I could put a black border on the points by using the following code, but then confidence ellipses turn black.

dfaplot +
  scale_shape_manual(values = c(21,22,24)) +
  scale_color_manual(values = c("black","black","black")) +
  scale_fill_manual(values = c("#00FF00","#FF00FF","#0000FF")) +
  theme(legend.position = "none")

PLOT 2

I would like to keep the ellipses as in the first graph, but the points as in the second one. However, I am being unable to figure out how I could do this. If anyone has suggestions on how to do this, I would be very grateful. I am using the "ggord" package because I learned how to run the analysis using it, but if anyone has suggestions on how to do the same with only ggplot, it would be fine.

Answer 1

This roughly replicates what is going on in ggord . Looking at the source for the package, the ellipses are implemented differently in ggord than below, hence the small differences. If that is a big deal you can review the source and make changes. By default, geom_point doesn't have a fill attribute. So we set the shapes to a character type that does, and then specify color = 'black' in geom_point() . The full code (including projecting the original data) is below.

set.seed(123)
linear <- lda(Species~., iris)
linear

# Get point x, y coordinates
df <- data.frame(predict(linear, iris[, 1:4]))
df$species <- iris$Species

# Get explained variance for each axis
var_exp <- 100 * linear$svd ^ 2 / sum(linear$svd ^ 2)

ggplot(data = df,
       aes(x = x.LD1,
           y = x.LD2)) +
  geom_point(aes(fill = species,
                 shape = species),
             size = 4) +
  stat_ellipse(aes(color = species),
               level = 0.95) +
  ylim(c(-11, 11)) +
  xlim(c(-11, 11)) +
  ylab(paste("LD2 (",
             round(var_exp[2], 2),
             "%)")) +
  xlab(paste("LD1 (",
             round(var_exp[1], 2),
             "%)")) +
  scale_color_manual(values = c("#00FF00","#FF00FF","#0000FF")) +
  scale_fill_manual(values = c("#00FF00","#FF00FF","#0000FF")) +
  scale_shape_manual(values = c(21, 22, 24)) +
  coord_fixed(1) +
  theme_bw() +
  theme(
    legend.position = "none"
  )