Automate tick max and min in faceted ggplot

Question

I am trying to just mark the max and min of each x-axis in a faceted ggplot. I have several facets with different x scales and the same y scale, and the x axis tick labels overlap each other. Rather than having to manually determine the limits and breaks for each facet x axis, I am looking for a way to just label the min and max values for each.

Code using example data of the CO2 dataset (see ?CO2):

CO2$num <- 1:nrow(CO2)
library(reshape2)
CO2.melt <- melt(CO2,
                 id.var=c("Type",
                          "Plant",
                          "Treatment",
                          "num"))
CO2.melt <- CO2.melt[order(CO2.melt$num),]

library(ggplot2)
ggplot(CO2.melt, 
       aes(x = value, 
           y = num)) +
  geom_path(aes(color = Treatment)) +
  facet_wrap( ~ variable, scales = "free_x",nrow=1)

Purpose is to replicate well log displays such as this one.

Answer 1

When you want to implemented this for the tick-labels, the use of scales = "free_x" in a faceted plot makes this hard to automate this. However, with a bit of tinkering and the help of several other packages, you could also use the following approach:

1) Summarise the data in order to get an idea which tick-labels / breaks you need on the x-axis:

library(data.table)
minmax <- melt(setDT(CO2.melt)[, .(min.val = min(value), max.val = max(value),
                                   floor.end = 10*ceiling(min(value)/10),
                                   ceil.end = 10*floor((max(value)-1)/10)),
                               variable][],
               measure.vars = patterns('.val','.end'),
               variable.name = 'var',
               value.name = c('minmax','ends'))

which gives:

> minmax
   variable var minmax ends
1:     conc   1   95.0  100
2:   uptake   1    7.7   10
3:     conc   2 1000.0  990
4:   uptake   2   45.5   40

2) Create break vecors for each facet:

brks1 <- c(95,250,500,750,1000)
brks2 <- c(7.7,10,20,30,40,45.5)

3) Create the facets:

p1 <- ggplot(CO2.melt[CO2.melt$variable=="conc",], 
             aes(x = value, y = num, colour = Treatment)) +
  geom_path() +
  scale_x_continuous(breaks = brks1) +
  theme_minimal(base_size = 14) +
  theme(axis.text.x = element_text(colour = c('red','black')[c(1,2,2,2,1)],
                                   face = c('bold','plain')[c(1,2,2,2,1)]),
        axis.title = element_blank(),
        panel.grid.major = element_line(colour = "grey60"),
        panel.grid.minor = element_blank())

p2 <- ggplot(CO2.melt[CO2.melt$variable=="uptake",], 
             aes(x = value, y = num, colour = Treatment)) +
  geom_path() +
  scale_x_continuous(breaks = brks2) +
  theme_minimal(base_size = 14) +
  theme(axis.text.x = element_text(colour = c('red','black')[c(1,2,2,2,2,1)],
                                   face = c('bold','plain')[c(1,2,2,2,2,1)]),
        axis.title = element_blank(),
        panel.grid.major = element_line(colour = "grey60"),
        panel.grid.minor = element_blank())

4) Extract the legend into a separate object:

library(grid)
library(gtable)
fill.legend <- gtable_filter(ggplot_gtable(ggplot_build(p2)), "guide-box")
legGrob <- grobTree(fill.legend)

5) Create the final plot:

library(gridExtra)
grid.arrange(p1 + theme(legend.position="none"), 
             p2 + theme(legend.position="none"), 
             legGrob, ncol=3, widths = c(4,4,1))

which results in:

---

A possible alternative solution to do this automatically, is either use geom_text or geom_label. An example to show how you can achieve this:

# create a summary
library(dplyr)
library(tidyr)
minmax <- CO2.melt %>% 
  group_by(variable) %>% 
  summarise(minx = min(value), maxx = max(value)) %>%
  gather(lbl, val, -1)

# create the plot
ggplot(CO2.melt, aes(x = value, y = num, color = Treatment)) +
  geom_path() +
  geom_text(data = minmax, 
            aes(x = val, y = -3, label = val), 
            colour = "red", fontface = "bold", size = 5) +
  facet_wrap( ~ variable, scales = "free_x", nrow=1) +
  theme_minimal()

which gives:

You can also get the minimum and maximum values on the fly inside ggplot (credit to @eipi10). Another example using geom_label:

ggplot(CO2.melt, aes(x = value, y = num, color = Treatment)) +
  geom_path() +
  geom_label(data = CO2.melt %>% 
               group_by(variable) %>% 
               summarise(minx = min(value), maxx = max(value)) %>%
               gather(lbl, val, -1), 
             aes(x = val, y = -3, label = val), 
             colour = "red", fontface = "bold", size = 5) +
  facet_wrap( ~ variable, scales = "free_x", nrow=1) +
  theme_minimal()

which gives: