Multiple pages, with several ggplot2 graphs and tables per page, coming from several data frames, each page being the level of a common factor

Question

I have different data frames providing information on the same levels of the same factor. Eventually I'd like to have a pdf containing, for each page, ggplot2 graphs, a gridExtra tableGrob and text as title, coming from the different data frames. Each page would display the information for 1 level of the factor. Using cowplot I managed to organise 1 page nicely, but I couldn't find a way to make a for loop or anything else do the ~1000 pages automatically.

Here is a reproducible example of my data:

# Loading necessary packages
library(dplyr)
library(ggplot2)
library(grid)
library(gridExtra)
library(gtable)
library(cowplot)

# Creating first data frame df1
fact <- c("level1", "level1", "level1", "level1", "level1", "level1", "level2", "level2", "level2", "level2", "level2", "level2")
fact <- as.factor(fact)
x1 <- c("1A", "1A", "1A", "1B", "1B", "1B", "1A", "1A", "1A", "1B", "1B", "1B")
x2 <- c(0, 1, 5, 0, 1, 5, 0, 1, 5, 0, 1, 5)
x3 <- c(5, 2, 4, 5, 6, 3, 2, 5, 6, 4, 6, 8)
df1 <- cbind.data.frame(fact, x1, x2, x3)

# Creating second data frame df2
fact <- c("level1", "level1", "level2", "level2")
fact <- as.factor(fact)
x4 <- c("1A", "1B", "1A", "1B")
x5 <- c("good", "bad", "good", "good")
df2 <- cbind.data.frame(fact, x4, x5)

# filtering the data frames to keep 1 level of factor fact
i <- "level1"
df1_plot <- df1 %>% filter(fact == i)
df2_table <- df2 %>% filter(fact == i)

# defining ggplot graphs and gridExtra table
plot1 <- ggplot(data = df1_plot, aes(x = x2, y = x3, color = x1)) + geom_line()
plot2 <- ggplot(data = df1_plot, aes(x = x2, y = x3, color = x1)) + geom_point()
table1 <- tableGrob(df2_table, theme = ttheme_minimal(), rows = NULL)

# Plotting everything in place and adding the level (i) as title of the page
pdf(file = sprintf("%s.pdf", i), width = 9, height = 12, onefile = TRUE)
table_drawn <- ggdraw() + draw_grob(table1)
right_column <- plot_grid(table_drawn, plot1, labels = c("B", "C"), ncol = 1, rel_heights = c(1, 3), scale = 0.9)
bottom_row <- plot_grid(plot2, right_column, labels = c("A", ""), nrow = 1, rel_widths = c(1.5, 2))
title1 <- ggdraw() + draw_label(i, fontface='bold', x = 0, y = 0.5, hjust = 0, vjust = 1, size = 14)
upper_row <- plot_grid(title1, hjust = 0, ncol = 1) 
plot_grid(upper_row, bottom_row, ncol=1, rel_heights=c(0.1, 1))
dev.off()

The result of this minimal example is ugly, I apologise for this! In my real data it reaches the "publication figure quality" that is so wonderful with cowplot.

So ideally, I'd be able to do that for i taking in turns all the levels of factor "fact" (here 2 levels, in the real data ~ 1000)... This is where my (very limited) R knowledge and internet exploration of already answered questions reach an end. Should I use a for loop, make a list, a list of lists, use a dplyr group_by?

Any help very much appreciated!

Answer 1

Here's a simpler approach. First, we bind the two data frames into a single data frame. Then within lapply we split the data frame by fact and create the whole layout for each level of fact in succession:

dat = bind_rows(df1, df2 %>% rename(x1=x4), .id="df")

lapply(split(dat, dat$fact), function(d) {
  pdf(paste0(unique(d$fact),".pdf"),9,12)
  p = ggplot(d %>% filter(df==1), aes(x2, x3, colour=x1))
  grid.draw(plot_grid(p + geom_point(),
                      plot_grid(tableGrob(d %>% filter(df==2) %>% select(fact,x1,x5), 
                                          theme=ttheme_minimal(), rows=NULL),
                                p + geom_line(),
                                ncol=1, rel_heights=c(0.1,1)),
                      ncol=2))
  dev.off()
})

Instead of lapply, you can also use map from the purrr package:

library(purrr)

split(dat, dat$fact) %>% map(function(d) {
[...same as above...]
})

Here's what the first of the two graphs looks like: