Generating multiple plots containing functions in ggplot2

Question

I am trying to make a composite plot in R using the packages ggplot2 and ggpubr. I have no problem in making the composite plots except each plot has a normal distribution curve specific to that dataset. When I generate the composite plot, both plots have the same curve, that of the last dataset.

How can I generate the composite plot with each plot having its own specific normal distribution curve?

CODE AND OUTPUT PLOTS

## PLOT 1 ##

results_matrix_C <- data.frame(matrix(rnorm(20), nrow=20))
colnames(results_matrix_C) <- c("X")

m <- mean(results_matrix_C$X)
sd <- sd(results_matrix_C$X)
dnorm_C <- function(x){
  norm_C <- dnorm(x, m, sd)
  return(norm_C)
}

e = 1
dnorm_one_sd_C <- function(x){
  norm_one_sd_C <- dnorm(x, m, sd)
  # Have NA values outside interval x in [e]:
  norm_one_sd_C[x <= e] <- NA
  return(norm_one_sd_C)
}


C <- ggplot(results_matrix_C, aes(x = results_matrix_C$X)) +
  geom_histogram(aes(y=..density..), bins = 10, colour = "black", fill = "white") +
  stat_function(fun = dnorm_one_sd_C, geom = "area", fill = "#CE9A05", color = "#CE9A05", alpha = 0.25, size = 1) +
  stat_function(fun = dnorm_C, colour = "#CE0539", size = 1) +
  theme_classic()

## PLOT 2 ##

results_matrix_U <- data.frame(matrix(rnorm(20)+1, nrow=20))
colnames(results_matrix_U) <- c("X")

m <- mean(results_matrix_U$X)
sd <- sd(results_matrix_U$X)
dnorm_U <- function(x){
  norm_U <- dnorm(x, m, sd)
  return(norm_U)
}

e = 2
dnorm_one_sd_U <- function(x){
  norm_one_sd_U <- dnorm(x, m, sd)
  # Have NA values outside interval x in [e]:
  norm_one_sd_U[x <= e] <- NA
  return(norm_one_sd_U)
}


U <- ggplot(results_matrix_U, aes(x = results_matrix_U$X)) +
  geom_histogram(aes(y=..density..), bins = 10, colour = "black", fill = "white") +
  stat_function(fun = dnorm_one_sd_U, geom = "area", fill = "#CE9A05", color = "#CE9A05", alpha = 0.25, size = 1) +
  stat_function(fun = dnorm_U, colour = "#CE0539", size = 1) +
  theme_classic()

library(ggpubr)

ggarrange(C, U,
          nrow = 1, ncol = 2)

As you can see in the composite plot, the first one has taken the normal distribution curve of the second plot rather than its own one from my initial plot (Plot 1).

UPDATE

Variable "e" refers to the shaded area which is related to the distribution curve. m = mean of the dataset sd = standard deviation of the dataset m and sd are used to generate the normal distribution curves

Answer 1

SOLVED

By inserting the function in full into the stat_function section of the ggplot2 code, this has worked

i.e:

## PLOT 1 ##

results_matrix_C <- data.frame(matrix(rnorm(20), nrow=20))
colnames(results_matrix_C) <- c("X")

mean <- mean(results_matrix_C$X)
sd <- sd(results_matrix_C$X)
e = 1


C <- ggplot(results_matrix_C, aes(x = results_matrix_C$X)) +
  geom_histogram(aes(y=..density..), bins = 10, colour = "black", fill = "white") +
  stat_function( 
    fun = function(x, mean, sd, e){ 
      norm_one_sd_C <- dnorm(x, mean, sd)
      norm_one_sd_C[x <= e] <- NA
  return(norm_one_sd_C)}, 
    args = c(mean = mean, sd = sd, e = e), geom = "area", fill = "#CE9A05", color = "#CE9A05", alpha = 0.25, size = 1) +
  stat_function( 
    fun = function(x, mean, sd){ 
      dnorm(x = x, mean = mean, sd = sd)}, 
    args = c(mean = mean, sd = sd), colour = "#CE0539", size = 1) +
  theme_classic()

## PLOT 2 ##

results_matrix_U <- data.frame(matrix(rnorm(20)+1, nrow=20))
colnames(results_matrix_U) <- c("X")

mean <- mean(results_matrix_U$X)
sd <- sd(results_matrix_U$X)
e = 2


U <- ggplot(results_matrix_U, aes(x = results_matrix_U$X)) +
  geom_histogram(aes(y=..density..), bins = 10, colour = "black", fill = "white") +
  stat_function( 
    fun = function(x, mean, sd, e){ 
      norm_one_sd_U <- dnorm(x, mean, sd)
      norm_one_sd_U[x <= e] <- NA
  return(norm_one_sd_U)}, 
    args = c(mean = mean, sd = sd, e = e), geom = "area", fill = "#CE9A05", color = "#CE9A05", alpha = 0.25, size = 1) +
  stat_function( 
    fun = function(x, mean, sd){ 
      dnorm(x = x, mean = mean, sd = sd)}, 
    args = c(mean = mean, sd = sd), colour = "#CE0539", size = 1) +
  theme_classic()

library(ggpubr)

ggarrange(C, U,
          nrow = 1, ncol = 2)