Assign different transparency and point sizes in scatterplot

Question

Below is a code that works using the plot() function to run a 2D scatterplot of Height vs Weight where points are classed as “Good”, “Fair”, “Poor” based on whether the Class value is 1, 2 or 3, respectively. Points for "Good" are bright green, "Fair", olive green and Poor, red. All points are the same size (pch=19). Is it possible to have different sizes and transparency for each data point depending on what each point is assigned in the “Group” column: either an Opaque and Small size point, Semi-transparent and Medium sized, or 100% Transparent and Large size point. Thanks for your ideas!

  df
  #           Group        Class      Height      Weight
  #  1       Opaque small     1 0.831777874 0.859223152
  #  2 Semi-transprnt med     2 0.751019511 0.807521752
  #  3 Semi-transprnt med     1 0.751019511 0.807521752
  #  4    Transprnt large     3 0.527390539 0.599957241
  #  5    Transprnt large     3 0.527390539 0.599957241

    color <- c(rgb(0, 1, 0, 1), rgb(0.5, 0.5, 0), rgb(1, 0, 0))
    plot(x=c(0.0, 0.5, 0.5, 0.0, 0.0), y=c(0.0, 0.0, 0.5, 0.5, 0.0), 
    type='l', col='gray', lwd=2,xlab='Height', ylab='Weight', 
    xlim=c(1,0), ylim=c(1, 0))
    par(new=T)
    plot(x=c(0.0, 0.5, 0.5, 0.0, 0.0), y=c(0.5, 0.5, 1, 1, 0.5), 
    type='l', col='gray', lwd=2, xlab='', ylab='', 
    xlim=c(1, 0.0), ylim=c(1, 0.0), axes=F)
    par(new=T)
    plot(x=c(0.5, 1, 1, 0.5, 0.5), y=c(0.0, 0.0, 0.5, 0.5, 0.0), 
    type='l', col='gray', lwd=2,
    xlab='', ylab='', xlim=c(1, 0.0), ylim=c(1, 0.0), axes=F)
    par(new=T)
    plot(x=c(0.5, 1, 1, 0.5, 0.5), y=c(0.5, 0.5, 1, 1, 0.5), type='l', 
    col='gray', lwd=2, xlab='', ylab='', 
    xlim=c(1, 0.0), ylim=c(1, 0.0), axes=F)
    par(new=T)
    for (i in 1:3) {
    plot(Height[Class==i], Weight[Class==i], xlim=c(0, 1), ylim=c(0, 1), 
    col=color[i], pch=19, xlab='', ylab='', axes=F)
    par(new=T)
    }
    legend(0.8, 0.586,legend=c('Good', 'Fair', 'Poor'), pch=19, 
    col=color, title='Class')

Answer 1

Here's an approach that adds transparency using the method described in this answer (as suggested by @mischva11). First, add columns that contain the desired plotting properties:

library(dplyr)
library(tidyr)
library(scales)
df = df %>%
  separate(Group, into = c("Transparency", "Size"), sep = " ") %>%
  mutate(Color = case_when(Class == 1 ~ "Chartreuse",
                           Class == 2 ~ "Olive Drab",
                           Class == 3 ~ "Red"),
         Alpha = case_when(Transparency == "Opaque" ~ 0.9,
                           Transparency == "Semi-transprnt" ~ 0.9,
                           Transparency == "Transprnt" ~ 0.3),
         Size = case_when(Size == "small" ~ 0.8,
                          Size == "med" ~ 1,
                          Size == "large" ~ 1.2))

The replace the for loop in the original code with the following:

plot(df$Height, df$Weight, xlim = c(0, 1), ylim = c(0, 1),
     col = df$Color, pch = 21, xlab = "", ylab = "", axes = F,
     cex = df$Size, bg = alpha(df$Color, df$Alpha))

Edit: Using pch = 21 allows us to control the fill and border of the point separately (col for the border, bg for the fill). This example applied transparency to the fill but not the border.

On my machine, the semi-transparent point doesn't actually appear to be very transparent at all, so this might not quite accomplish what you're looking for. You could play around with the alpha values, or you could try ggplot instead. Here's how:

library(dplyr)
library(tidyr)
library(ggplot2)
df = df %>%
  separate(Group, into = c("Transparency", "Size"), sep = " ") %>%
  mutate(Color = case_when(Class == 1 ~ rgb(0, 1, 0),
                           Class == 2 ~ rgb(0.5, 0.5, 0),
                           Class == 3 ~ rgb(1, 0, 0)),
         Alpha = case_when(Transparency == "Opaque" ~ 1,
                           Transparency == "Semi-transprnt" ~ 0.4,
                           Transparency == "Transprnt" ~ 0.2),
         Size = case_when(Size == "small" ~ 1,
                          Size == "med" ~ 2,
                          Size == "large" ~ 4))
ggplot(df, aes(x = Height, y = Weight, col = Color, alpha = Alpha, size = Size)) +
  geom_point() +
  scale_color_identity() +
  scale_alpha_identity()

This is maybe a little better, although to me, as a naive viewer, it's difficult to distinguish between ordinary points (with expected colors) and overlapping points (with colors that are the sum of other colors). But this may be easier to see with the full dataset. Again, you could try playing around with the specific sizes and alpha values to see what works.