Motivation
Lets look at a very simple example set using dplyr to summarise the diamonds dataset.
summariseDiamond <- diamonds %>%
mutate(`Cut Clarity` = paste(cut, clarity)) %>%
group_by(`Cut Clarity`,cut, clarity, color) %>%
summarise(n = n(),
`mean Price` = mean(price),
sd = sd(price))
#> `summarise()` has grouped output by 'Cut Clarity', 'cut', 'clarity'. You can override using the `.groups` argument.
ggplot(summariseDiamond, aes(x = color, y = `Cut Clarity`)) +
geom_tile(aes(fill = `mean Price`))
p <-ggplot(summariseDiamond, aes(x = color, y = `Cut Clarity`)) +
geom_tile(aes(fill = `mean Price`)) +
geom_tile(aes(x=0, fill = cut))
tryCatch({
p
},
error = function(e) e
)
#> Error: Discrete value supplied to continuous scale
As you can see, trying to place a colorbar causes an error because the previous geom_tile call has already mapped mean Price to fill and has deemed the scale as continuous. Thus a categorical variable is unable to map to the fill aesthetic anymore.
However, you could map another continuous variable, but this will place these to the same guide, shifting the limits and washing out all color.
summariseDiamond <- summariseDiamond %>%
group_by(`Cut Clarity`) %>%
mutate(`sd of means` = sd(`mean Price`))
ggplot(summariseDiamond, aes(x = color, y = `Cut Clarity`)) +
geom_tile(aes(fill = `mean Price`)) +
geom_tile(aes(x=0, fill = `sd of means`))
Using ggside allows for aesthetics to be mapped to a separate scale, which can also be controlled with scale_*fill_gradient functions (more on this later).
ggplot(summariseDiamond, aes(x = color, y = `Cut Clarity`)) +
geom_tile(aes(fill = `mean Price`)) +
geom_ysidetile(aes(x = "sd of means", yfill = `sd of means`)) +
scale_yfill_gradient(low ="#FFFFFF", high = "#0000FF") 
ggplot(summariseDiamond, aes(x = color, y = `Cut Clarity`)) +
geom_tile(aes(fill = `mean Price`)) +
geom_ysidetile(aes(x = "max", yfill = after_stat(summarise),
domain = `mean Price`), stat = "summarise", fun = max) +
geom_ysidetile(aes(x = "mean",yfill = after_stat(summarise),
domain = `mean Price`), stat = "summarise", fun = mean) +
geom_ysidetile(aes(x = "median",yfill = after_stat(summarise),
domain = `mean Price`), stat = "summarise", fun = median) +
geom_ysidetile(aes(x = "min",yfill = after_stat(summarise),
domain = `mean Price`), stat = "summarise", fun = min) +
scale_yfill_gradient(low ="#FFFFFF", high = "#0000FF") 
.tmp <- summariseDiamond %>% group_by(`Cut Clarity`) %>%
summarise_at(vars(`mean Price`),.funs = list(max,median,mean,min)) %>%
tidyr::gather(key = key, value = value, -`Cut Clarity`)
ggplot(summariseDiamond, aes(x = color, y = `Cut Clarity`)) +
geom_tile(aes(fill = `mean Price`)) +
geom_ysidetile(data = .tmp, aes(x = key, yfill = value)) +
scale_yfill_gradient(low ="#FFFFFF", high = "#0000FF") 
Unfortunately using xfill or yfill with geom_xsidetile or geom_ysidetile respecitively will lock its associated scale with the first layer. So you cannot first assign yfill to a discrete scale and then add a layer with yfill maps to a continuous variable or vise a versa. For example, the following code still produces an error. This is largely due to the original motivation for making this package, but at least ggside can give some ease to plotting information to the sides of the main figure.
p <- ggplot(summariseDiamond, aes(x = color, y = `Cut Clarity`)) +
geom_tile(aes(fill = `mean Price`)) +
geom_ysidetile(aes(yfill = `sd of means`)) + #sets yfill to a continuous scale
geom_ysidetile(aes(yfill = cut)) #attempting to add discrete color values
tryCatch({
p
},
error = function(err){
print(paste(err))
})
#> Error: Discrete value supplied to continuous scale
