Adding the regression line equation and R2 on the SEPARATE LINES column

Question

A few years ago, a poster asked how to add the regression line equation and R2 to ggplot plots from the link below.

Adding the regression line equation and R2 to the graph

The top solution was as follows:

lm_eqn <- function(df){
    m <- lm(y ~ x, df);
    eq <- substitute(italic(y) == a + b %.% italic(x)*","~~italic(r)^2~"="~r2, 
         list(a = format(coef(m)[1], digits = 2), 
              b = format(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_eqn(df), parse = TRUE)

I use this code and it works great. However, I was wondering if it is even possible for this code to have the R2 equation and the regression line equation on separate lines instead of being separated by a comma.

Instead of this

Something like that

Thanks in advance for your help!

Answer 1

EDIT:

. RNG set.seed(2L), . .

geom_text

set.seed(3L)
library(ggplot2)
df <- data.frame(x = c(1:100))
df$y <- 2 + 3 * df$x + rnorm(100, sd = 40)

lm_eqn <- function(df){
  # browser()
  m <- lm(y ~ x, df)
  a <- coef(m)[1]
  a <- ifelse(sign(a) >= 0, 
              paste0(" + ", format(a, digits = 4)), 
              paste0(" - ", format(-a, digits = 4))  )
  eq1 <- substitute( paste( italic(y) == b, italic(x), a ), 
                     list(a = a, 
                          b = format(coef(m)[2], digits = 4)))
  eq2 <- substitute( paste( italic(R)^2 == r2 ), 
                     list(r2 = format(summary(m)$r.squared, digits = 3)))
  c( as.character(as.expression(eq1)), as.character(as.expression(eq2)))
}

labels <- lm_eqn(df)


p <- ggplot(data = df, aes(x = x, y = y)) +
  geom_smooth(method = "lm", se=FALSE, color="red", formula = y ~ x) +
  geom_point() +
  geom_text(x = 75, y = 90, label = labels[1], parse = TRUE,  check_overlap = TRUE ) +
  geom_text(x = 75, y = 70, label = labels[2], parse = TRUE, check_overlap = TRUE )

print(p)

Answer 2

ggpmisc stat_poly_eq ( ). data @Sathish, R2 , label.y.npc . label.x.npc .

library(ggplot2)
library(ggpmisc)
#> For news about 'ggpmisc', please, see https://www.r4photobiology.info/

set.seed(21318)
df <- data.frame(x = c(1:100))
df$y <- 2 + 3*df$x + rnorm(100, sd = 40)

formula1 <- y ~ x

ggplot(data = df, aes(x = x, y = y)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE, formula = formula1) +
  stat_poly_eq(aes(label = paste(..eq.label.., sep = "~~~")), 
               label.x.npc = "right", label.y.npc = 0.15,
               eq.with.lhs = "italic(hat(y))~'='~",
               eq.x.rhs = "~italic(x)",
               formula = formula1, parse = TRUE, size = 5) +
  stat_poly_eq(aes(label = paste(..rr.label.., sep = "~~~")), 
               label.x.npc = "right", label.y.npc = "bottom",
               formula = formula1, parse = TRUE, size = 5) +
  theme_bw(base_size = 16)

# using 'atop'
ggplot(data = df, aes(x = x, y = y)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE, formula = formula1) +
  stat_poly_eq(aes(label = paste0("atop(", ..eq.label.., ",", ..rr.label.., ")")), 
               formula = formula1, 
               parse = TRUE) +
  theme_bw(base_size = 16)

### bonus: including result table
ggplot(data = df, aes(x = x, y = y)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE, formula = formula1) +
  stat_fit_tb(method = "lm",
              method.args = list(formula = formula1),
              tb.vars = c(Parameter = "term", 
                          Estimate = "estimate", 
                          "s.e." = "std.error", 
                          "italic(t)" = "statistic", 
                          "italic(P)" = "p.value"),
              label.y = "bottom", label.x = "right",
              parse = TRUE) +
  stat_poly_eq(aes(label = paste0("atop(", ..eq.label.., ",", ..rr.label.., ")")), 
               formula = formula1, 
               parse = TRUE) +
  theme_bw(base_size = 16)

^(v0.3.0)