R ggplot：如何使用分面 ggplots 定义与组相关的 y 轴中断？

Question

I have 40 groups (defined by short_ID) and would like to produce 40 different plots that use different y-scale breaks for each short_ID. I want the breaks for the y-scale to be (1) mean-2SD, (2) mean and (3) mean+2SD.

I have a dataset called Dataplots containing my X and Y variables and the grouping variable "short_ID". I have created additional vectors M$SD11 (=mean-2SD), M$mean and M$SD22 (=mean+2SD) to define the breaks and M$short_ID as grouping variable. The code below partly works but the problem is that I do not know how to make the breaks group-dependent (ie, dependent on short_ID). When I run the code below I get the same y axis breaks for all plots, namely for example the max of the vector M$SD22 instead of a different M$SD22 value for each plot. So I think I need to add something to

"scale_y_continuous(breaks=c(M$SD11, M$mean, M$SD22)", for example "scale_y_continuous(group=M$short_ID, breaks=c(M$SD11, M$mean, M$SD22)" but this does not work.         

Does anybody know what I can do to define different breaks for my different groups (ie, short_IDs)? How can I change the code below to do this? Many thanks!

Dataplot <- ggplot(data = Dataplots, aes(x = Measure, y = Amylase_u, group = short_ID)) + geom_line() + facet_wrap(~ short_ID) +  scale_y_continuous(breaks=c(M$SD11, M$mean, M$SD22))

I have added an example of 'Dataplots' and 'M'. For the purpose of the example I included only two groups (ie, short_IDs) instead of the 40 I actually have. Thus this example would need to produce 2 plots, one for each short_ID with different y-axis breaks for each of the groups.

Example of Dataplots:

dput(Dataplots) structure(list(short_ID = c(1111, 1111, 1111, 1111, 2222, 2222, 2222, 2222), Measure = c(1, 2, 3, 4, 1, 2, 3, 4), Amylase_u = c(81.561, 75.648, 145.25, 85.246, 311.69, 261.74, 600.93, 291.39)), .Names = c("short_ID", "Measure", "Amylase_u"), row.names = c(NA, -8L), class = "data.frame", codepage = 65001L)

Example of M:

dput(M) structure(list(SD11 = c(162, 682), mean = c(97, 366), SD22 = c(32, 51), short_ID = c(1111, 2222)), .Names = c("SD11", "mean", "SD22", "short_ID"), row.names = 1:2, class = "data.frame")

@Mark I have been trying to apply your suggestions to my complete dataset but cannot seem to get it right. I have in total 61 plots. I started with:

myPlots <-
lapply(unique(Dataplots$short_ID), function(thisID){
Dataplots %>%
  filter(short_ID == thisID) %>%
  ggplot(aes(x = Measure, y = Amylase_u)) +
  geom_line() +
  scale_y_continuous(breaks= M %>%
                       filter(short_ID == thisID) %>%
                       select(mean) %>%
                       as.numeric()
  ) +
  ggtitle(thisID)
 })

(As you can see I decided to go for the subject-mean on the y-axis only and decided to drop the SDs.) I then continued with your final cowplot sugestion:

plot_grid(ggdraw() + draw_label("Amylase_u", angle = 90), plot_grid(
plot_grid(plotlist = lapply(myPlots, function(x){x + theme(axis.title = element_blank())}))
, ggdraw() + draw_label("Measurement")
, ncol = 1
, rel_heights = c(0.9, .1))
, nrow = 1, rel_widths =  c(0.05, 0.95))

This, however, results in 61 plots with the subject-mean on the y-axis but without the Measurements depecited in it (so the graph itself is missing). I figured there may be a ')' misplaced so I tried:

plot_grid(
ggdraw() + draw_label("Amylase_u", angle = 90)
, plot_grid(
plot_grid(plotlist = lapply(myPlots, function(x){x +theme(axis.title = element_blank())}))
, ggdraw() + draw_label("Measurement")
, ncol = 1
, rel_heights = c(0.9, .1)
, nrow = 1
, rel_widths =  c(0.05, 0.95)))

This does give me graphs but they are tiny and the layout is terrible (Rplot2). I tried adapting the rel-heights and widths too but even after reading the help-file don't quite get how I should adapt them.

Thanks again!

Rplot2

Finally, I removed the IDnumbers on top of each plot because they are not really necessary and this already greatly improves the plot (Rplot3), but still the layout needs to be adjusted.

Rplot3

Answer 1

My understanding is that this still remains impossible in the facet functions. However, you can accomplish it yourself using the cowplot package.

First, loop over your ideas (in lapply ) and generate each of the sub-plots you wanted. Note that I am using dplyr for the pipe and filtering.

myPlots <-
  lapply(unique(Dataplots$short_ID), function(thisID){
    Dataplots %>%
      filter(short_ID == thisID) %>%
      ggplot(aes(x = Measure, y = Amylase_u)) +
      geom_line() +
      scale_y_continuous(breaks= M %>%
                           filter(short_ID == thisID) %>%
                           select(SD11, mean, SD22) %>%
                           as.numeric()
                         ) +
      ggtitle(thisID)
  })

Then, call the function plot_grid from cowplot with the list of plots:

plot_grid(plotlist = myPlots)

gives:

A few notes:

• cowplot autoloads its own default style, so use theme_set to return to your preferred style
• Your included data appear to not actually span all of the thresholds you gave for the y-axis breaks
• This should work for an arbitrarily large number of subplots, though you may want/ need to adjust labels and alignment to make them readable.

Since I am not sure what your goal is, here is another alternative. If you just want to plot deviation from mean (in standard deviations) to make the changes comparable, you could just calculate the z-score of the column within the groups and plot the results. Using dplyr again:

Dataplots %>%
  group_by(short_ID) %>%
  mutate(scaledAmylase = as.numeric(scale(Amylase_u)) ) %>%
  ggplot(aes(x = Measure
             , y = scaledAmylase)) +
  geom_line() +
  facet_wrap(~short_ID)

gives

Or, if the mean/SD are calculated/defined somewhere else (and stored in M ) rather than coming directly from the data, you can scale using M instead of the data:

Dataplots %>%
  left_join(M) %>%
  mutate(scaledAmylase = (Amylase_u - mean) / ((SD22 - mean) / 2) ) %>%
  ggplot(aes(x = Measure
             , y = scaledAmylase)) +
  geom_line() +
  facet_wrap(~short_ID)

gives

And, because I can't leave well enough alone, here is a version of the plot_grid approach that removes the duplicated axis titles and includes them just once instead (like facet_wrap would). As above, increasing the number of subplots or the aspect ratio will force you to tweak the relative values here:

plot_grid(
  ggdraw() + draw_label("Amylase_u", angle = 90)
  , plot_grid(
    plot_grid(plotlist = lapply(myPlots, function(x){x + theme(axis.title = element_blank())}))
    , ggdraw() + draw_label("Measurement")
    , ncol = 1
    , rel_heights = c(0.9, .1))
  , nrow = 1
  , rel_widths =  c(0.05, 0.95)
 )

gives