同一图上多个直方图上的法线密度曲线 [英] Normal density curves on multiple histograms on a same plot

问题描述

例如，我有一个数据框:

I have a dataframe, for example, as this:

sample1 <- seq(120,197, length.out =  60)
sample2 <- seq(113, 167, length.out = 60)
sample3 <- seq(90,180, length.out = 60)
sample4 <-seq(100, 160, length.out = 60)

df <- as.data.frame(cbind(sample1, sample2, sample3, sample4))

我现在需要为这四个变量创建直方图，以使它们全部共享相同的y轴，并且还需要在每个变量上覆盖正常密度曲线这些直方图.只要y轴相同，facet_wrap()就可以了.

I now need to create histograms for these four variables such that all of them share the same y-axis, and also need to overlay normal density curves on each of these histograms. facet_wrap() will be fine as long as the y-axis is same.

今天早些时候，我以为我已经在论坛的一位专家的指导下解决了这个问题，但后来意识到该解决方案只是覆盖了一条密度曲线，而不是正态分布的曲线.我已经尝试过使用ggplot以及基本绘图功能使用数字选项，但是当拥有多个变量时，对于单个变量来说似乎简单的任务却是无法实现的?

Earlier today, I thought I had this issue resolved with the guidance of an expert in the forum but realised later that the solution just overlaid a density curve, not one with a normal distribution. I have tried a number options with ggplot as well as base plotting functions but what seems to be a simple task for a single variable isn't quite achievable when having multiple variables??

关于如何解决这个问题的任何想法?

Any thoughts about how to go tackle this?

谢谢

推荐答案

这是使用 tidyverse

library(tidyverse)

# example data
sample1 <- seq(120, 197, length.out =  60)
sample2 <- seq(113, 167, length.out = 60)
sample3 <- seq(90, 180, length.out = 60)
sample4 <- seq(100, 160, length.out = 60)

df <- data.frame(sample1, sample2, sample3, sample4)

# update your original dataframe to a nested dataframe by adding simulated values from normal distribution 
df2 = df %>%
  gather() %>%                                                           # reshape data  
  group_nest(key) %>%                                                    # for each key (i.e. sample)
  mutate(norm = map(data, ~rnorm(10000, mean(.x$value), sd(.x$value))))  # simulate 10K observations from the corresponding normal distribution

ggplot()+
  # plot histogram using info from nested column data (i.e. original observations)
  geom_histogram(data = df2 %>% unnest(data), aes(value, fill=key, ..density..), alpha=0.3)+
  # plot density using info from nested column norm (i.e. simulated normal observations)
  geom_density(data = df2 %>% unnest(norm), aes(norm, col=key))+
  # separate plots by key (i.e. sample)
  facet_wrap(~key)

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