ggplot2 - a custom histogram with a rug plot - r

Question

I am trying to create a custom histogram with a rug plot showing the original values on the X axis.
I am going to use the mtcars dataset to illustrate. Its not be best dataset for this question...but hopefully the reader will understand what I am trying to achieve...
Below shows the basic histogram, without any rug plot attempt.
I want to create the histogram using geom_bar as this allows for more flexibility with custom bins.
I also want a small gap between the histgram bars (i.e width = 0.95) .... which adds to this
problem's complexity.
library(dplyr)
library(ggplot2)
# create custom bins
vct_seq <- c(seq(from = 10, to = 25, by = 5), 34)
mtcars$bin <- cut(mtcars$mpg, breaks = vct_seq)
# create data.frame for the ggplot graph..using bins above
df_mtcars_count <- mtcars %>% group_by(bin) %>% summarise(count = n())
# indicative labels
vct_labels <- c("bin 1", "bin 2", "bin 3", "bin 4")
# attempt 1 - basic plot -- no rug plot
p <- ggplot(data = df_mtcars_count, aes(x = bin, y = count))
p <- p + geom_bar(stat = "identity", width = 0.95)
p <- p + geom_text(aes(label = count), vjust = -0.5)
p <- p + scale_x_discrete("x title to go here", labels = df_mtcars_count$bin, breaks = df_mtcars_count$bin)
p
Next, try and add a basic rug plot on the X axis. This obviously doesn't work as the geom_bar and geom_rug have completely different scales.
# attempt 2 with no scaling.... doesn't work as x scale for ordinal (bins) and
# x scale for continuous (mpg) do not match
p <- ggplot(data = df_mtcars_count, aes(x = bin, y = count))
p <- p + geom_bar(stat = "identity", width = 0.95)
p <- p + geom_text(aes(label = count), vjust = -0.5)
p <- p + scale_x_discrete("x title to go here", labels = df_mtcars_count$bin, breaks = df_mtcars_count$bin)
p <- p + geom_rug(data = mtcars, aes(x = mpg), inherit.aes = F, alpha = 0.3)
p
Now, try and rescale the mpg column to match with the ordinal scale....
First define a linear mapping function...
fn_linear_map <- function(vct_existing_val, vct_new_range) {
# example....converts 1:20 into the range 1 to 10 like this:
# fn_linear_map(1:20, c(1, 10))
fn_r_diff <- function(x) x %>% range() %>% diff()
flt_ratio <- fn_r_diff(vct_new_range) / fn_r_diff(vct_existing_val)
vct_old_min_offset <- vct_existing_val - min(vct_existing_val)
vct_new_range_val <- (vct_old_min_offset * flt_ratio) + min(vct_new_range)
return(vct_new_range_val)
}
Now apply the function...we try and map mpg to the range 1 to 4 (which is an attempt to match
the ordinal scale)
mtcars$mpg_remap <- fn_linear_map(mtcars$mpg, c(1, 4))
Try the plot again.... getting closer ... but not really accurate...
# attempt 3: getting closer but doesn't really match the ordinal scale
p <- ggplot(data = df_mtcars_count, aes(x = bin, y = count))
p <- p + geom_bar(stat = "identity", width = 0.95)
p <- p + geom_text(aes(label = count), vjust = -0.5)
p <- p + scale_x_discrete("x title to go here", labels = df_mtcars_count$bin, breaks = df_mtcars_count$bin)
p <- p + geom_rug(data = mtcars, aes(x = mpg_remap), inherit.aes = F, alpha = 0.3)
p
The graph above is getting close to what I want....but rug plot does not line up
with the actual data ... example the max observation (33.9) should be displayed
almost aligning with the right hand side of the bar.. see below:
mtcars %>% filter(bin == "(25,34]") %>% arrange(mpg) %>% dplyr::select(mpg, mpg_remap)

Answer 1

Your scale makes no sense to me, as you are showing a bin that is twice as wide using the same bar width. Doing that in combination with a rug strikes me as confusing as best and misleading at worst. I suggest you plot the bars with their correct widths, after which the rug is trivial.
I think the best solution is to just use geom_histogram:
ggplot(mtcars, aes(mpg)) +
geom_histogram(breaks = vct_seq, col = 'grey80') +
geom_rug(aes(mpg, y = NULL))
If you really want the gaps between the bars you'll have to do more work:
library(tidyr)
d <- mtcars %>%
count(bin) %>%
separate(bin, c('min', 'max'), sep = ',', remove = FALSE) %>%
mutate_at(vars('min', 'max'), readr::parse_number) %>%
mutate(
middle = min + (max - min) / 2,
width = 0.9 * (max - min)
)
ggplot(d, aes(middle, n)) +
geom_col(width = d$width) +
geom_rug(aes(mpg, y = NULL), mtcars)