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))
pAs 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 respectively
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
p
