ggplot2: Independent Continuous Fill for Summary Row & Column

Question

I am using a strategy to plot summary (totals) rows in a heatmap using geom_tile, which involves creating extra rows in the data_frame for row and column totals:

library(dplyr)
library(ggplot2)    

entitites = LETTERS[1:10]
# create some sample data
df_foo = bind_cols(
  data_frame(Group1 = rep(c("A", "B"), each = 100)),
  bind_rows(
    expand.grid(
      Left = entitites, Right = entitites,
      stringsAsFactors = FALSE
    ),
    expand.grid(
      Left = entitites, Right = entitites,
      stringsAsFactors = FALSE
    )
  ),
  data_frame(Value = rpois(200, 15))
)

# create the summary row & column
df_foo_aug = bind_rows(
  df_foo,
  df_foo %>% 
    group_by(Left, Group1) %>% 
    summarize(
      Value = sum(Value),
      Right = "Total"
    ),
  df_foo %>% 
    group_by(Right, Group1) %>% 
    summarize(
      Value = sum(Value),
      Left = "Total"
    )
)

# create the plot
df_foo_aug %>% 
  ggplot(aes(x = Right, y = Left, fill = Value)) + 
  geom_tile() + 
  facet_wrap(~ Group1) + 
  theme_bw()

This yields:

Obviously, the totals row/column need their own fill gradient, but it is not clear how (if) I can add a second continuous/gradient fill.

Any other way to achieve the same intended outcome would be acceptable as a solution to this question as well.

Answer 1

The problem here is that in ggplot, in principle, an aesthetic can only have one scale. So fill can only have one scale. However, there are some ways to avoid this, for example by using color for a second scale. Alternatively, you could mess around with grobs to get the job done, as per shayaa's comment.

Here are some possible examples, using geom_point to display the totals:

base_plot <-  
  ggplot(df_foo_aug, aes(x = Right, y = Left)) + 
  geom_tile(data = filter(df_foo_aug, Right != 'Total', Left != 'Total'), 
            aes(fill = Value)) +
  coord_equal() +
  facet_wrap(~ Group1) + 
  scale_y_discrete(limits = rev(sort(unique(df_foo_aug$Left)))) +
  theme_classic() + theme(strip.background = element_blank())

A fairly standard approach:

base_plot +
  geom_point(data = filter(df_foo_aug, Right == 'Total' | Left == 'Total'), 
             aes(col = Value), size = 9.2, shape = 15) +
  scale_color_gradient('Total', low = 'black', high = 'red')

Using color scales with a wider perceptual range:

base_plot +
  geom_point(data = filter(df_foo_aug, Right == 'Total' | Left == 'Total'), 
            aes(col = Value), size = 9.2, shape = 15) +
  viridis::scale_fill_viridis(option = 'B') +
  viridis::scale_color_viridis('Total', option = 'D')

Also mapping size to the total Value:

base_plot +
  geom_point(data = filter(df_foo_aug, Right == 'Total' | Left == 'Total'), 
             aes(col = Value, size = Value)) +
  scale_size_area(max_size = 8, guide = 'none') +
  viridis::scale_fill_viridis(option = 'B') +
  viridis::scale_color_viridis('Total', option = 'D')

Personally, I quite like the last one.

One final improvement would be to move the y-axis up, for which I would recommend the cowplot package.