Split plot into individual plots

We build a function along these steps :

1. We go through the structure of the object to get the names of the variables used for faceting (here 'z').
2. We overwrite the facet element of our plot object with the one from the empty ggplot object (so if we print it at this stage facets are gone).
3. We extract the data and split it along the variables we identified in 1st step.
4. We overwrite the original data with each subset (12 times here) and store all outputs in a list.

code

splitFacet <- function(x){  facet_vars <- names(x$facet$params$facets)         # 1  x$facet    <- ggplot2::ggplot()$facet              # 2  datasets   <- split(x$data, x$data[facet_vars])    # 3  new_plots  <- lapply(datasets,function(new_data) { # 4    x$data <- new_data    x})}    new_plots <- splitFacet(myplot)length(new_plots) # [1] 12new_plots[[3]]    # 3rd plot

Split plot into faceted plots of n subplots max

If we want to keep the facets but have less plots by facet we can skip step 2, and rework our split instead so it includes several values of the variables used for faceting.

Rather than making a separate function we'll generalize the 1st, n is the number of facets you get by plot.

n = NULL means you get the previous output, which is slightly different from n = 1 (one facet by plot).

splitFacet <- function(x, n = NULL){  facet_vars <- names(x$facet$params$facets)               # 1  if(is.null(n)){    x$facet  <- ggplot2::ggplot()$facet                    # 2a    datasets <- split(x$data, x$data[facet_vars])          # 3a  } else {    inter0 <- interaction(x$data[facet_vars], drop = TRUE) # 2b    inter  <- ceiling(as.numeric(inter0)/n)    datasets <- split(x$data, inter)                       # 3b  }  new_plots  <- lapply(datasets,function(new_data) {       # 4    x$data <- new_data    x})} new_plots2 <- splitFacet(myplot,4)length(new_plots2) # [1] 3new_plots2[[2]]    

This might come in handy too :

unfacet <- function(x){  x$facet <- ggplot2::ggplot()$facet  x}

The tidy way

If the code is available, no need to go through all this trouble, we can split the data before feeding it to ggplot :

library(tidyverse)myplots3 <-  df %>%   split(ceiling(group_indices(.,z)/n_facets)) %>%   map(~ggplot(.,aes(x =x, y=y))+geom_point()+facet_wrap(~z))myplots3[[3]]

While I was looking for a solution for this I can across ggplus. Specifically the function facet_multiple:

https://github.com/guiastrennec/ggplus

It lets you split a facet over a number of pages by specifying the amount of plots you want per page. In your example it would be:

library(ggplus)df <- data.frame(x=seq(1,24,1), y=seq(1,24,1), z=rep(seq(1,12),each=2))myplot <- ggplot(df,aes(x=x, y=y))+geom_point()facet_multiple(plot = myplot, facets = 'z', ncol = 2, nrow = 2)

Is this the sort of thing you need? It worked a treat for me.

This is similar to Moody_Muddskipper's answer, but works with any type of faceting (facet_grid or facet_wrap), handles arbitrary expressions in facets, and doesn't draw facet strip bars.

library(rlang)library(ggplot2)split_facets <- function(x) {  facet_expr <- unlist(x[["facet"]][["params"]][c("cols", "rows", "facets")])  facet_levels <- lapply(facet_expr, rlang::eval_tidy, data = x[["data"]])  facet_id <- do.call(interaction, facet_levels)  panel_data <- split(x[["data"]], facet_id)  plots <- vector("list", length(panel_data))  for (ii in seq_along(plots)) {    plots[[ii]] <- x    plots[[ii]][["data"]] <- panel_data[[ii]]    plots[[ii]][["facet"]] <- facet_null()  }  plots}split_facets(ggplot(df,aes(x=x, y=y))+geom_point()+facet_wrap(~z))split_facets(ggplot(df,aes(x=x, y=y))+geom_point()+facet_grid(z %% 2 ~ z %% 5))

It uses rlang::eval_tidy to evaluate the facet expressions, combines them into a single categorical factor, then uses that to split the data. It also "suppresses" each subplot's faceting part by replacing it with facet_null().