设置

Question

我一直试图用ggplot 2在我的直方图上叠加一条正常曲线。

我的公式：

data <- read.csv (path...)

ggplot(data, aes(V2)) + 
  geom_histogram(alpha=0.3, fill='white', colour='black', binwidth=.04)

我尝试了几件事：

+ stat_function(fun=dnorm)

....没有改变任何东西

+ stat_density(geom = "line", colour = "red")

...在x轴上给了我一条直线红线。

+ geom_density()

对我不起作用，因为我想将频率值保持在y轴上，并且不需要密度值。

有什么建议吗？

提前感谢任何提示！

找到解决方案！

+geom_density(aes(y=0.045*..count..), colour="black", adjust=4)

Answer 1

想想我明白了：

set.seed(1)
df <- data.frame(PF = 10*rnorm(1000))
ggplot(df, aes(x = PF)) + 
    geom_histogram(aes(y =..density..),
                   breaks = seq(-50, 50, by = 10), 
                   colour = "black", 
                   fill = "white") +
stat_function(fun = dnorm, args = list(mean = mean(df$PF), sd = sd(df$PF)))

Answer 2

已经回答here和部分here。

如果希望y轴具有频率计数，则需要根据观察数量和binwidth来缩放法线曲线。

# Simulate some data. Individuals' heights in cm.
n        <- 1000
mean     <- 165
sd       <- 6.6
binwidth <- 2
height <- rnorm(n, mean, sd)


qplot(height, geom = "histogram", breaks = seq(130, 200, binwidth), 
      colour = I("black"), fill = I("white"),
      xlab = "Height (cm)", ylab = "Count") +
  # Create normal curve, adjusting for number of observations and binwidth
  stat_function( 
    fun = function(x, mean, sd, n, bw){ 
      dnorm(x = x, mean = mean, sd = sd) * n * bw
    }, 
    args = c(mean = mean, sd = sd, n = n, bw = binwidth))

修改

或者，对于允许使用构面并使用列出的here方法的更灵活的方法，创建一个单独的数据集，其中包含正常曲线的数据并覆盖这些数据。

library(plyr)

dd <- data.frame(
  predicted = rnorm(720, mean = 2, sd = 2),
  state = rep(c("A", "B", "C"), each = 240)
) 

binwidth <- 0.5

grid <- with(dd, seq(min(predicted), max(predicted), length = 100))
normaldens <- ddply(dd, "state", function(df) {
  data.frame( 
    predicted = grid,
    normal_curve = dnorm(grid, mean(df$predicted), sd(df$predicted)) * length(df$predicted) * binwidth
  )
})

ggplot(dd, aes(predicted))  + 
  geom_histogram(breaks = seq(-3,10, binwidth), colour = "black", fill = "white") + 
  geom_line(aes(y = normal_curve), data = normaldens, colour = "red") +
  facet_wrap(~ state)

Answer 3

这是对JWilliman答案的延伸评论。我发现J的答案非常有用。在玩游戏时我发现了一种简化代码的方法。我不是说这是一种更好的方式，但我想我会提到它。

请注意，JWilliman的答案提供了y轴上的计数和“hack”来缩放相应的密度法线近似值（否则将覆盖总面积为1并因此具有更低的峰值）。

此评论的要点：stat_function内的更简单的语法，通过将所需的参数传递给美学功能，例如：

aes(x = x, mean = 0, sd = 1, binwidth = 0.3, n = 1000)

这样可以避免将args =传递给stat_function，因此更加方便用户使用。好吧，它没有什么不同，但希望有人会发现它很有趣。

# parameters that will be passed to ``stat_function``
n = 1000
mean = 0
sd = 1
binwidth = 0.3 # passed to geom_histogram and stat_function
set.seed(1)
df <- data.frame(x = rnorm(n, mean, sd))

ggplot(df, aes(x = x, mean = mean, sd = sd, binwidth = binwidth, n = n)) +
    theme_bw() +
    geom_histogram(binwidth = binwidth, 
        colour = "white", fill = "cornflowerblue", size = 0.1) +
stat_function(fun = function(x) dnorm(x, mean = mean, sd = sd) * n * binwidth,
    color = "darkred", size = 1)

Answer 4

此代码应该这样做：

set.seed(1)
z <- rnorm(1000)

qplot(z, geom = "blank") + 
geom_histogram(aes(y = ..density..)) + 
stat_density(geom = "line", aes(colour = "bla")) + 
stat_function(fun = dnorm, aes(x = z, colour = "blabla")) + 
scale_colour_manual(name = "", values = c("red", "green"), 
                               breaks = c("bla", "blabla"), 
                               labels = c("kernel_est", "norm_curv")) + 
theme(legend.position = "bottom", legend.direction = "horizontal")

注意：我使用了qplot，但你可以使用更多功能的ggplot。

Answer 5

这是一个 tidyverse 知情版本：

设置

library(tidyverse)

一些数据

d <- read_csv("https://vincentarelbundock.github.io/Rdatasets/csv/openintro/speed_gender_height.csv")

准备数据

我们将对整个样本使用“总”直方图，为此，我们需要从数据中删除分组信息。

d2 <-
  d |> 
  select(-gender)

这是一个包含汇总数据的数据集：

d_summary <-
  d %>% 
  group_by(gender) %>% 
  summarise(height_m = mean(height, na.rm = T),
            height_sd = sd(height, na.rm = T))

d_summary

绘制它

d %>% 
  ggplot() +
  aes() +
  geom_histogram(aes(y = ..density.., x = height, fill = gender)) +
  facet_wrap(~ gender) +
  geom_histogram(data = d2, aes(y = ..density.., x = height), 
                 alpha = .5) +
  stat_function(data = d_summary %>% filter(gender == "female"),
                fun = dnorm,
                #color = "red",
                args = list(mean = filter(d_summary, 
                                          gender == "female")$height_m,
                            sd = filter(d_summary, 
                                        gender == "female")$height_sd)) +
  stat_function(data = d_summary %>% filter(gender == "male"),
                fun = dnorm,
                #color = "red",
                args = list(mean = filter(d_summary, 
                                          gender == "male")$height_m,
                            sd = filter(d_summary, 
                                        gender == "male")$height_sd)) +
  theme(legend.position = "none",
        axis.title.y = element_blank(),
        axis.text.y = element_blank(),
        axis.ticks.y = element_blank()) +
  labs(title = "Facetted histograms with overlaid normal curves",
       caption = "The grey histograms shows the whole distribution (over) both groups, i.e. females and men") +
  scale_fill_brewer(type = "qual", palette = "Set1")

ggplot2：具有正常曲线的直方图

5 个答案:

设置

一些数据

准备数据

绘制它