Set x / y limits in facet_wrap using scales = 'free'

Question

I saw similar questions and this discussion is about adding functionality to ggplot Installing x / y lim in facet_grid . In my research, I often want to create several panels, say, for different simulation tests, where the axial constraints remain unchanged to identify differences between the tests. This is especially useful when displaying story panels in a presentation. In each chart of the panel that I create, separate charts require independent y axes, since they often are variables of weather, temperature, relative humidity, wind speed, etc. Using

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

works great, as I can easily create panels with varying weather conditions. When I compare between different story panels, its nice to have consistent axes. Unfortunately, ggplot does not allow you to set the individual limits of each chart in panel plots. The default is a data range. The discussion in the Google Group discussed above discusses this flaw, but I could not find any updates as to whether this could be added. Is there a way to trick ggplot into setting individual limits?

Answer 1

The first suggestion that wraps up the solution I'm looking for a bit is to combine all my data into one data table and use facet_grid for my variable and simulation

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

This creates a beautiful plot that displays the data in one drawing, but can become cumbersome when considering many simulations.

To “crack” the idea of ​​creating what I want, I first determined what limits I wanted for each weather variable. These limits were found by looking at the greatest extent for all simulations of interest. After defining, I created a small data table with the same columns as my simulation data, and added it to the end. My simulation data was structured

  '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)) 

What 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 

Although the choice of year and mileage is arbitrary, the choice of the month should be something outside 1:12. Then I added 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 draw this data with y constraints, I limit the x-axis values ​​to those specified in the source data. The enclosed data table with y constraints has values ​​of month 13. Since ggplot still scales the axes to the entire dataset, even if the axes are limited, this gives me the y constraints that I want. It is important to note that if the data values ​​are greater than the limits you specified, this will not work.

Before: Note the difference in y values ​​for each weather variable between panels.

After: now the y limits remain the same for each weather variable between panels.

I hope to edit this post in the coming days and add a reproducible example for a better explanation. Comment if you have heard anything about adding this feature to ggplot .