To add a regression line equation and value of R^2 on your graph, add the following to your plot:
geom_text(x = 25, y = 300, label = lm_eq(df), parse = TRUE)
Where the following function finds the line equation and value of r^2.
lm_eq <- function(df){
m <- lm(y ~ x, df);
eq <- substitute((y) == a + b %.% (x)*","~~(r)^2~"="~r2,
list(a = format(unname(coef(m)[1]), digits = 2),
b = format(unname(coef(m)[2]), digits = 2),
r2 = format(summary(m)$r.squared, digits = 3)))
as.character(as.expression(eq));
}
In your case:
df <- data.frame(x = c(1:100))
df$y <- 2 + 3 * df$x + rnorm(100, sd = 40)
p <- ggplot(data = df, aes(x = x, y = y)) +
geom_smooth(method = "lm", se=FALSE, color="black", formula = y ~ x) +
geom_point()
lm_eq <- function(df){
m <- lm(y ~ x, df);
eq <- substitute((y) == a + b %.% (x)*","~~(r)^2~"="~r2,
list(a = format(unname(coef(m)[1]), digits = 2),
b = format(unname(coef(m)[2]), digits = 2),
r2 = format(summary(m)$r.squared, digits = 3)))
as.character(as.expression(eq));
}
p1 <- p + geom_text(x = 25, y = 300, label = lm_eq(df), parse = TRUE)
p1
Output: