How to build a family of functions in ggplot2

Question

I need to build a family of functions that vary according to a set of parameters, for example, a family of normal distribution curves depending on the mean and standard deviation. I found here snipet code that almost performs the task:

p9 <- ggplot(data.frame(x = c(0, 1)), aes(x = x)) +
    stat_function(fun = dnorm, args = list(0.2, 0.1),
                  aes(colour = "Group 1")) +
    stat_function(fun = dnorm, args = list(0.7, 0.05),
                  aes(colour = "Group 2")) +
    scale_x_continuous(name = "Probability",
                          breaks = seq(0, 1, 0.2),
                          limits=c(0, 1)) +
    scale_y_continuous(name = "Frequency") +
    ggtitle("Normal function curves of probabilities") +
    scale_colour_brewer(palette="Accent") +
    labs(colour = "Groups")
p9 

In this case, the code displays exactly two curves, as shown below:

My problem is that the number of curves in the family can be any , so I tried to adapt the code as follows:

aa <- list(list(0.2, 0.1), list(0.7, 0.05), list(0.45, 0.2))
p9 <- ggplot(data.frame(x = c(0, 1)), aes(x = x))
for (i in 1:3) {
    p9 <- p9 + stat_function(fun = dnorm, args = aa[[i]],
                         aes(colour = paste("Group", i))
}
p9 <- p9 + 
  scale_x_continuous(name = "Probability",
                   breaks = seq(0, 1, 0.2),
                   limits=c(0, 1)) +
  scale_y_continuous(name = "Frequency") +
  ggtitle("Normal function curves of probabilities") +
  scale_colour_brewer(palette="Accent") +
  labs(colour = "Groups")
p9

The result is almost successful because it displays three curves, except that it does not distinguish them by color and legend , as shown below:

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Answer 1

ggplot

: , @BrodieG, : ggplot, Reduce lapply Map, , . @JulioSergio aes_ , :

ggplot(data.frame(x = 0:1), aes(x)) + 
    Map(function(params, name){stat_function(mapping = aes_(color = name), 
                                             fun = dnorm, args = params)}, 
        params = aa, 
        name = paste('Group', seq_along(aa)))

---

Reduce

Reduce init, ggplot. :

Reduce(function(x, y){
    x + stat_function(fun = dnorm, args = y, 
                      colour = scales::brewer_pal('qual', 'Set1')(length(aa))[length(x$layers) + 1])}, 
    aa, 
    init = ggplot(data.frame(x = c(0, 1)), aes(x = x)))

, , .

---

, , :

library(tidyverse)

aa <- list(list(0.2, 0.1), list(0.7, 0.05), list(0.45, 0.2))

aa %>% set_names(paste('Group', 1:3)) %>% 
    map_df(~dnorm(seq(0, 1, length = 100), .x[[1]], .x[[2]])) %>% 
    mutate(x = seq(0, 1, length = 100)) %>% 
    gather(Group, y, -x) %>% 
    ggplot(aes(x, y, color = Group)) + 
    geom_line()

Answer 2

, aes_(),

aes _() aes(), , , , . , alistaire.

library(ggplot2)
aa <- list(list(0.2, 0.1), list(0.7, 0.05), list(0.45, 0.2))
p9 <- Reduce(function(x, y){
  x + stat_function(fun = dnorm, args = y, 
                  aes_(colour = paste("Group", length(x$layers)+1)))}, 
  aa, 
  init = ggplot(data.frame(x = c(0, 1)), aes(x = x)))
p9 <- p9 + 
  scale_x_continuous(name = "Probability",
                   breaks = seq(0, 1, 0.2),
                   limits=c(0, 1)) +
  scale_y_continuous(name = "Frequency") +
  ggtitle("Normal function curves of probabilities") +
  scale_colour_brewer(palette="Accent") +
  labs(colour = "Groups")
p9

:

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