How to add R2 for each facet of ggplot in R? - r

Question

Is there a way to first change the facet label from 1:3 to something like c(good, bad, ugly). Also, i would like to add R2 value to each of the facet. Below is my code- i tried a few things but didn't succeed.
DF = data.frame(SUB = rep(1:3, each = 100), Ob = runif(300, 50,100), S1 = runif(300, 75,95), S2 = runif(300, 40,90),
S3 = runif(300, 35,80),S4 = runif(300, 55,100))
FakeData = gather(DF, key = "Variable", value = "Value", -c(SUB,Ob))
ggplot(FakeData, aes(x = Ob, y = Value))+
geom_point()+ geom_smooth(method="lm") + facet_grid(Variable ~ SUB, scales = "free_y")+
theme_bw()
Here is the figure that i am getting using above code.
I tried below code to change the facet_label but it didn't work
ggplot(FakeData, SUB = factor(SUB, levels = c("Good", "Bad","Ugly")), aes(x = Ob, y = Value))+
geom_point()+ geom_smooth(method="lm") + facet_grid(Variable ~ SUB, scales = "free_y")+
theme_bw()
I do not have any idea how to add R2 to the facets. Is there any efficient way of computing and R2 to the facets?

Answer 1

You can use ggpubr::stat_cor() to easily add correlation coefficients to your plot.
library(dplyr)
library(ggplot2)
library(ggpubr)
FakeData %>%
mutate(SUB = factor(SUB, labels = c("good", "bad", "ugly"))) %>%
ggplot(aes(x = Ob, y = Value)) +
geom_point() +
geom_smooth(method = "lm") +
facet_grid(Variable ~ SUB, scales = "free_y") +
theme_bw() +
stat_cor(aes(label = after_stat(rr.label)), color = "red", geom = "label")

Answer 2

If you don't want to use functions from other packages and only want to use ggplot2, you will need to compute the R2 for each SUB and Variable combination, and then add to your plot with geom_text or geom_label. Here is one way to do it.
library(tidyverse)
set.seed(1)
DF = data.frame(SUB = rep(1:3, each = 100), Ob = runif(300, 50,100), S1 = runif(300, 75,95), S2 = runif(300, 40,90),
S3 = runif(300, 35,80),S4 = runif(300, 55,100))
FakeData = gather(DF, key = "Variable", value = "Value", -c(SUB,Ob))
FakeData_lm <- FakeData %>%
group_by(SUB, Variable) %>%
nest() %>%
# Fit linear model
mutate(Mod = map(data, ~lm(Value ~ Ob, data = .x))) %>%
# Get the R2
mutate(R2 = map_dbl(Mod, ~round(summary(.x)$r.squared, 3)))
ggplot(FakeData, aes(x = Ob, y = Value))+
geom_point()+
geom_smooth(method="lm") +
# Add label
geom_label(data = FakeData_lm,
aes(x = Inf, y = Inf,
label = paste("R2 = ", R2, sep = " ")),
hjust = 1, vjust = 1) +
facet_grid(Variable ~ SUB, scales = "free_y") +
theme_bw()

Answer 3

The following answer makes use of package 'ggpmisc' (version >= 0.5.0 for the second example). In addition, I simply used a call to factor() within aes() to set the labels.
library(tidyverse)
library(ggpmisc)
DF = data.frame(SUB = rep(1:3, each = 100), Ob = runif(300, 50,100), S1 = runif(300, 75,95), S2 = runif(300, 40,90),
S3 = runif(300, 35,80),S4 = runif(300, 55,100))
FakeData = gather(DF, key = "Variable", value = "Value", -c(SUB,Ob))
# As asked in the question
# Ensuring that the R^2 label does not overlap the observations
ggplot(FakeData, aes(x = Ob, y = Value)) +
geom_point()+
geom_smooth(method = "lm") +
stat_poly_eq() +
scale_y_continuous(expand = expansion(mult = c(0.1, 0.33))) +
facet_grid(Variable ~ factor(SUB,
levels = 1:3,
labels = c("good", "bad", "ugly")),
scales = "free_y") +
theme_bw()
# As asked in a comment, adding P-value
ggplot(FakeData, aes(x = Ob, y = Value))+
geom_point()+
geom_smooth(method = "lm") +
stat_poly_eq(mapping = use_label(c("R2", "P")), p.digits = 2) +
scale_y_continuous(expand = expansion(mult = c(0.1, 0.33))) +
facet_grid(Variable ~ factor(SUB,
levels = 1:3,
labels = c("good", "bad", "ugly")),
scales = "free_y")+
theme_bw()
And the plot from the second example adding P to the label.
Note: With older versions of 'ggpmisc' which lack function use_label() the mapping can be written as aes(label = paste(after_stat(rr.label), after_stat(p.label), sep = "*\", \"*") in the same way as when using 'ggpubr'.
Package 'ggpubr' includes code copied from 'ggpmisc' without acknowledgenment, which explains why some statistics are so similar between the two packages. 'ggpmisc' is more narrowly focused but the statistics in it have been much improved after they were taken into 'ggpubr'. 'ggpmisc' is actively maintained while maintenance of 'ggpubr' seems currently stalled.