ggplot2：具有正常曲线的直方图 [英] ggplot2: histogram with normal curve

问题描述

我的公式：

我的直方图上覆盖了一条正常曲线

  data<  -  read.csv（path ...）
 
 ggplot（data，aes（V2））+ 
 geom_histogram（alpha = 0.3，fill ='white'，color ='black'，binwidth = .04）
  

I尝试了几件事：

  + stat_function（fun = dnorm）
  

没有改变任何东西

  + stat_density（ geom =line，color =red）
  

...给了我一条红色的直线

  + geom_density（）
  

不适合我，因为我想保留我的频率值在y轴上，并且不需要密度值。

有什么建议？

感谢您提供任何提示！

解决方案找到了！

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

解决方案

已回答

编辑

或者，为了更灵活的方法，使用方面并使用列出的方法此处，创建一个分隔符包含正常曲线数据的数据集并覆盖这些数据。

  library（plyr）
 
 dd <  -  data.frame（
 predict = rnorm（720，mean = 2，sd = 2），
 state = rep（c（A，B，C）） = 240）
）
 
 binwidth <-0.5 
 
 grid < -  with（dd，seq（min（预测），max（预测），length = $ 100））
 normaldens<  -  ddply（dd，state，function（df）{
 data.frame（
 predict = grid，
 normal_curve = dnorm（grid ，平均（df $预测），sd（df $预测））*长度（df $预测）* binwidth 
）
}）
 
 ggplot（dd，aes（预测的））+ 
 geom_histogram（breaks = seq（-3,10，binwidth），color =black，fill =white）+ 
 geom_line（aes（y = normal_curve），data = normaldens ，color =red）+ 
 facet_wrap（〜state）
  

I've been trying to superimpose a normal curve over my histogram with ggplot 2.

My formula:

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

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

I tried several things:

+ stat_function(fun=dnorm)  

....didn't change anything

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

...gave me a straight red line on the x-axis.

+ geom_density()  

doesn't work for me because I want to keep my frequency values on the y-axis, and want no density values.

Any suggestions?

Thanks in advance for any tips!

Solution found!

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

解决方案

This has been answered here and partially here.

If you want the y-axis to have frequency counts, then the normal curve needs to be scaled according to the number of observations and the binwidth.

# 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))

EDIT

Or, for a more flexible approach that allows for use of facets and draws upon an approach listed here, create a separate dataset containing the data for the normal curves and overlay these.

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)

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