set x/y limits in facet_wrap with scales = 'free'

Question

I've seen similar questions asked, and this discussion about adding functionality to ggplot Setting x/y lim in facet_grid . In my research I often want to produce several panels plots, say for different simulation trials, where the axes limits remain the same to highlight differences between the trials. This is especially useful when showing the plot panels in a presentation. In each panel plot I produce, the individual plots require independent y axes as they're often weather variables, temperature, relative humidity, windspeed, etc. Using

    ggplot() + ... + facet_wrap(~ ..., scales = 'free_y')

works great as I can easily produce plot panels of different weather variables. When I compare between different plot panels, its nice to have consistent axes. Unfortunately ggplot provides no way of setting the individual limits of each plot within a panel plots. It defaults to using the range of given data. The Google Group discussion linked above discusses this shortcoming, but I was unable to find any updates as to whether this could be added. Is there a way to trick ggplot to set the individual limits?

Answer 1

A first suggestion that somewhat sidesteps the solution I'm looking for is to combine all my data into one data table and use facet_grid on my variable and simulation

    ggplot() + ... + facet_grid(variable~simulation, scales = 'free_y')

This produces a fine looking plot that displays the data in one figure, but can become unwieldy when considering many simulations.

To 'hack' the plotting into producing what I want, I first determined which limits I desired for each weather variable. These limits were found by looking at the greatest extents for all simulations of interest. Once determined I created a small data table with the same columns as my simulation data and appended it to the end. My simulation data had the structure

    'year' 'month' 'variable' 'run' 'mean'
     1973     1     'rhmax'     1    65.44
     1973     2     'rhmax'     1    67.44
     ...     ...      ...      ...    ...
     2011    12    'windmin'   200    0.4 

So I created a new data table with the same columns

    ylims.sims <- data.table(year = 1, month = 13, 
    variable =  rep(c('rhmax','rhmin','sradmean','tmax','tmin','windmax','windmin'), each = 2),
    run = 201, mean = c(20, 100, 0, 80, 100, 350, 25, 40, 12, 32, 0, 8, 0, 2))

Which gives

    'year' 'month' 'variable' 'run' 'mean'
       1     13     'rhmax'    201    20
       1     13     'rhmax'    201    100
       1     13     'rhmin'    201    0
       1     13     'rhmin'    201    80
       1     13    'sradmean'  201    100
       1     13    'sradmean'  201    350
       1     13     'tmax'     201    25
       1     13     'tmax'     201    40
       1     13     'tmin'     201    12
       1     13     'tmin'     201    32
       1     13    'windmax'   201    0
       1     13    'windmax'   201    8
       1     13    'windmin'   201    0
       1     13    'windmin'   201    2

While the choice of year and run is aribtrary, the choice of month need to be anything outside 1:12. I then appended this to my simulation data

    sim1data.ylims <- rbind(sim1data, ylims)
    ggplot() + geom_boxplot(data = sim1data.ylims, aes(x = factor(month), y = mean)) +
    facet_wrap(~variable, scale = 'free_y') + xlab('month') +
    xlim('1','2','3','4','5','6','7','8','9','10','11','12')

When I plot these data with the y limits, I limit the x-axis values to those in the original data. The appended data table with y limits has month values of 13. As ggplot still scales axes to the entire dataset, even when the axes are limited, this gives me the y limits I desire. Important to note that if there are data values greater than the limits you specify, this will not work.

Before: Notice the differences in the y limits for each weather variable between the panels.

After: Now the y limits remain consistent for each weather variable between the panels.

I hope to edit this post in the coming days and add a reproducible example for better explanation. Please comment if you've heard anything about adding this functionality to ggplot.