Adding group-specific text/data to faceted plot in R/ggplot2

If, for whatever reason, you want to build this yourself instead of using the ggpubr functions, you can create your summary data, format labels, and place the labels with geom_text.

I'm formatting the stats so that R has a fixed 3 significant digits and p has 3 digits, falling back on scientific notation. I changed the names of those columns in summarise to R and p to make the labels below. Reshaping to long data and creating a new column with unite gets this:

library(tidyverse)
...

group_by(df, exp) %>% 
  summarize(R = cor.test(a, b)$estimate, p = cor.test(a, b)$p.value) %>%
  mutate(R = formatC(R, format = "fg", digits = 3),
         p = formatC(p, format = "g", digits = 3)) %>%
  gather(key = measure, value = value, -exp) %>%
  unite("stat", measure, value, sep = " = ")
#> # A tibble: 4 x 2
#>   exp   stat        
#>   <chr> <chr>       
#> 1 exp1  R = 0.965   
#> 2 exp2  R = 0.0438  
#> 3 exp1  p = 1.14e-58
#> 4 exp2  p = 0.665

Then for each of the groups, I want to collapse both labels, separated by a newline \n. This is a place that will scale well—you might have more summary stats to display, but this should still work.

summ <- group_by(df, exp) %>% 
  summarize(R = cor.test(a, b)$estimate, p = cor.test(a, b)$p.value) %>%
  mutate(R = formatC(R, format = "fg", digits = 3),
         p = formatC(p, format = "g", digits = 3)) %>%
  gather(key = measure, value = value, -exp) %>%
  unite("stat", measure, value, sep = " = ") %>%
  group_by(exp) %>%
  summarise(both_stats = paste(stat, collapse = "\n"))

summ
#> # A tibble: 2 x 2
#>   exp   both_stats               
#>   <chr> <chr>                    
#> 1 exp1  "R = 0.965\np = 1.14e-58"
#> 2 exp2  "R = 0.0438\np = 0.665"

In geom_text, I'm setting the x coordinate to -Inf, which gets the minimum of all x values, and the y coordinate as Inf for the maximum of all y values. That puts the label in the top-left corner, regardless of the values in the data.

The one thing I don't like here is then hacking the hjust and vjust outside their intended ranges of 0 to 1. But nudge_x/nudge_y won't do anything because of the values being set to infinity.

df %>% 
  ggplot(aes(x = a, y = b)) +
  geom_point() +
  geom_text(aes(x = -Inf, y = Inf, label = both_stats), data = summ, 
            hjust = -0.1, vjust = 1.1, lineheight = 1) +
  facet_wrap(~ exp)

^{Created on 2018-11-14 by the reprex package (v0.2.1)}

Similar to the answer of camille, but you can do all in one run

library(tidyverse)
set.seed(123)
df %>% 
  group_by(exp) %>% 
  mutate(p = cor.test(a, b)$p.value,
         rho = cor.test(a, b)$estimate) %>% 
  mutate_at(vars(p, rho), signif, 2) %>% 
  ggplot(aes(x=a, y=b)) +
  geom_point() + 
  geom_text(data = . %>% distinct(p, rho, exp), 
            aes(x = -Inf, y = Inf,label = paste("p=",p,"\nrho=",rho)),
            hjust = -0.1, vjust = 1.1, lineheight = 1) +
  facet_wrap(~exp) 

We can use the stat_cor function from the ggpubr package.

set.seed(123)

library(dplyr)
library(ggplot2)
library(ggpubr)

df <- data.frame(exp=c(rep('exp1', 100), rep('exp2', 100)), a=rnorm(200, 1000, 200))
df <- mutate(df, b=ifelse(exp=='exp1', a*rnorm(100,1,0.05), rnorm(100, 1000, 200)))

ggplot(df, aes(x=a, y=b))+
  geom_point() +
  facet_wrap(~exp) +
  stat_cor(method = "pearson")