How do I add multiple linear regression equations to the bottom of a ggplot?

Question

I want to display three linear regressions, with the R2 value, at the bottom of my plot to show the correlation between O2 and bias for each concentration of C3H8.

  C3H8    O2  bias
1   85 20.90  0.01
2   50 20.90  0.10
3   25 20.94  0.32
4   85 10.00 -1.22
5   50 10.00 -1.05
6   25 10.00 -1.29
7   85  0.10 -3.13
8   50  0.10 -2.39
9   25  0.10 -2.55

Here is the code I am using for ggplot:

library(ggrepel)

ggplot(Data.Frame.1, aes(O2, bias)) +
  theme_bw() +
  theme(legend.position = 'bottom', plot.title = element_text(hjust=0.5)) + 
  geom_point(aes(colour = factor(C3H8))) +
  geom_line(aes(colour = factor(C3H8))) +
  geom_text_repel(aes(label=paste(bias),
                      hjust= 0.4,
                      vjust=-.8, colour = factor(C3H8)),
                  size = 3) +
  ggtitle(expression(O[2]~Bias)) + 
  labs(
    x = expression('O'[2]),
    y = "% bias",
    colour = expression('C'[3]*'H'[8]~(ppm))
  )

If it doesn't make sense to include the linear regressions in the plot, I would be fine with having them listed as a separate table or data frame. Or even something similar to this:

https://cran.r-project.org/web/packages/jtools/vignettes/summ.html

Answer 1

You could something similar to what you want with next code. For models you can use broom and for the final plot patchwork :

library(ggrepel)
library(broom)
library(gridExtra)
library(patchwork)
library(ggplot2)
#Data
df <- structure(list(C3H8 = c(85L, 50L, 25L, 85L, 50L, 25L, 85L, 50L, 
25L), O2 = c(20.9, 20.9, 20.94, 10, 10, 10, 0.1, 0.1, 0.1), bias = c(0.01, 
0.1, 0.32, -1.22, -1.05, -1.29, -3.13, -2.39, -2.55)), class = "data.frame", row.names = c("1", 
"2", "3", "4", "5", "6", "7", "8", "9"))

The code:

#First plot
G1 <- ggplot(Data.Frame.1, aes(O2, bias)) +
  theme_bw() +
  theme(legend.position = 'bottom', plot.title = element_text(hjust=0.5)) + 
  geom_point(aes(colour = factor(C3H8))) +
  geom_line(aes(colour = factor(C3H8))) +
  geom_text_repel(aes(label=paste(bias),
                      hjust= 0.4,
                      vjust=-.8, colour = factor(C3H8)),
                  size = 3) +
  ggtitle(expression(O[2]~Bias)) + 
  labs(
    x = expression('O'[2]),
    y = "% bias",
    colour = expression('C'[3]*'H'[8]~(ppm))
  )
#Models
regs <- df %>% group_by(C3H8) %>%
  do(fit = lm(bias ~ O2 , data = .))
coeffs <- tidy(regs,fit)
#Format
coeffs[,-c(1,2)] <- round(coeffs[,-c(1,2)],3)
#Arrange new plot
G2 <- G1 / tableGrob(coeffs)

The output:

I am not sure if the relationship you want in lm() is bias ~ O2 but you can change that.