Align discrete and continuous axes with ggplot2 and the grid

Question

I am trying to display a grid shape of summed weekly data from several variables. The two components of this graph that are most relevant are a summary graph of the distribution (so that the graph or the violin graph) of the values ​​that some variables took during the week, and the cumulative graph of the integer variable graph that accumulates over the weeks (so that is the step). I would like to plot these two graphs on a aligned x axis using grid. I will use it ggplot2to create separate graphs, because I heard Hadley Wickham (j / k, ggplot - it really is really, really nice).

The problem is that geom_boxplotit only accepts factors for the x axis, and geom_stepaccepts continuous data only for the x axis. They are not necessarily aligned, even if you apply the same x-limits with coord_cartesianor scale_x_....

I combined the hack with the help geom_rectthat will work for this particular application, but it will be a pain to adapt if, for example, I have another factor that leads to the appearance of several mailboxes within one week.

Mandatory reproducible:

library(ggplot2)
library(grid)

var1 <- data.frame(val = rnorm(300),
                   week = c(rep(25, 100), 
                        rep(26, 100), 
                        rep(27, 100))
                  )

var2 <- data.frame(cumul = cumsum(c(0, rpois(2, 15))),
                   week = c(25, 26, 27)
                  )


g1   <- ggplot(var1, aes(x = factor(week), y = val)) + 
  geom_boxplot()

g2   <- ggplot(var2, aes(x = week, y = cumul)) + 
  geom_step() + scale_x_continuous(breaks = 25:27)

grid.newpage()
grid.draw(rbind(ggplotGrob(g1),
                ggplotGrob(g2),
                size = "last"))

And kludge:

library(dplyr)

chiggity_check <- var1 %>% 
  group_by(week) %>% 
  summarise(week.avg = mean(val),
            week.25  = quantile(val)[2],
            week.75  = quantile(val)[4],
            week.05  = quantile(val)[1],
            week.95  = quantile(val)[5])

riggity_rect <- ggplot(chiggity_check) +
  geom_rect(aes(xmin = week - 0.25, xmax = week + 0.25,
                ymin = week.25,
                ymax = week.75)) +
  geom_segment(aes(x = week - 0.25, xend = week + 0.25,
                   y = week.avg, yend=week.avg),
               color = "white") +
  geom_segment(aes(x = week, xend = week ,
                   y = week.25, yend=week.05)) +
  geom_segment(aes(x = week, xend = week ,
                   y = week.75, yend=week.95)) +
  coord_cartesian(c(24.5,27.5)) +
  scale_x_continuous(breaks = 25:27)

grid.newpage()
grid.draw(rbind(ggplotGrob(riggity_rect),
                ggplotGrob(g2 + coord_cartesian(c(24.5,27.5))),
                size = "last"))

, /: geom_boxplot geom_step ? - , , stat_summary, , , , , , ?

Answer 1

, factor(week), g2 ( ) geom_blank(), . geom_step(), : as.numeric(factor(week))

library(ggplot2)
library(grid)

# Your data
var1 <- data.frame(val = rnorm(300),
                   week = c(rep(25, 100), 
                        rep(26, 100), 
                        rep(27, 100))
                  )

var2 <- data.frame(cumul = cumsum(c(0, rpois(2, 15))),
                   week = c(25, 26, 27)
                  )

# Your g1
g1   <- ggplot(var1, aes(x = factor(week), y = val)) + 
  geom_boxplot()

# Modified g2
g2   <- ggplot(var2) + geom_blank(aes(x = factor(week), y = cumul)) +
geom_step(aes(x = as.numeric(as.factor(week)), y = cumul)) 

grid.newpage()
grid.draw(gridExtra::rbind.gtable(ggplotGrob(g1),
                ggplotGrob(g2),
                size = "last"))