在R中如何调用dbeta的函数

#emp bayes
num_trials <- 10e6

simulations <- data_frame(
  true_average = rbeta(num_trials, 81, 219),
  hits = rbinom(num_trials, 300, true_average)
)

hit_100 <- simulations %>%
  filter(hits == 100)

dens <- function(z) dbeta(z, 81 + 100, 219 + 200)

ggplot(hit_100, aes(true_average)) +
  geom_histogram(aes(y = ..density..),bins = 100) +
  stat_function(color = "red", fun = dens) +
  labs(x = "Batting average of players who got 100 H / 300 AB")

我了解R函数的基本知识,例如

square.it <- function(x) {
    square <- x * x
    return(square)
}
# square a number
square.it(5)

## [1] 25

但密度函数的不同之处在于,没有输入值 z

dens <- function(z) dbeta(z, 81 + 100, 219 + 200)

或线路

stat_function(color = "red", fun = dens)

所以我的问题是,当密度函数没有提供值时,R如何在ggplot中创建平滑函数?