如何在R中一起绘制两个直方图？

Question

我使用的是R,我有两个数据框:胡萝卜和黄瓜.每个数据框都有一个数字列,列出了所有测量胡萝卜(总计:100k胡萝卜)和黄瓜(总计:50k黄瓜)的长度.

我希望在同一块情节上绘制两个直方图 - 胡萝卜长度和黄瓜长度.它们重叠,所以我想我也需要一些透明度.我还需要使用相对频率而不是绝对数字,因为每组中的实例数不同.

像这样的东西会很好,但我不明白如何从我的两个表创建它:

Answer 1

这是一个更简单的解决方案,使用基本图形和alpha混合(它不适用于所有图形设备):

set.seed(42)
p1 <- hist(rnorm(500,4))                     # centered at 4
p2 <- hist(rnorm(500,6))                     # centered at 6
plot( p1, col=rgb(0,0,1,1/4), xlim=c(0,10))  # first histogram
plot( p2, col=rgb(1,0,0,1/4), xlim=c(0,10), add=T)  # second

关键是颜色是半透明的.

编辑,超过两年后:由于这只是一个upvote,我想我也可以添加一个代码生成的视觉,因为alpha混合是如此有用:

Answer 2

您链接的图像是密度曲线,而不是直方图.

如果您一直在阅读ggplot,那么您可能唯一缺少的是将两个数据帧合并为一个长数据帧.

所以,让我们从你拥有的东西开始,两个独立的数据集并将它们组合起来.

carrots <- data.frame(length = rnorm(100000, 6, 2))
cukes <- data.frame(length = rnorm(50000, 7, 2.5))

# Now, combine your two dataframes into one.  
# First make a new column in each that will be 
# a variable to identify where they came from later.
carrots$veg <- 'carrot'
cukes$veg <- 'cuke'

# and combine into your new data frame vegLengths
vegLengths <- rbind(carrots, cukes)

在那之后,如果你的数据已经很长,那么这是不必要的,你只需要一行来制作你的情节.

ggplot(vegLengths, aes(length, fill = veg)) + geom_density(alpha = 0.2)

现在,如果你真的想要直方图,下面的内容将起作用.请注意,您必须从默认的"堆栈"参数更改位置.如果你真的不知道你的数据应该是什么样子,你可能会错过.更高的alpha看起来更好.另请注意,我制作了密度直方图.删除它很容易y = ..density..让它重新计算.

ggplot(vegLengths, aes(length, fill = veg)) + 
   geom_histogram(alpha = 0.5, aes(y = ..density..), position = 'identity')

Answer 3

这是我写的一个函数,它使用伪透明来表示重叠的直方图

plotOverlappingHist <- function(a, b, colors=c("white","gray20","gray50"),
                                breaks=NULL, xlim=NULL, ylim=NULL){

  ahist=NULL
  bhist=NULL

  if(!(is.null(breaks))){
    ahist=hist(a,breaks=breaks,plot=F)
    bhist=hist(b,breaks=breaks,plot=F)
  } else {
    ahist=hist(a,plot=F)
    bhist=hist(b,plot=F)

    dist = ahist$breaks[2]-ahist$breaks[1]
    breaks = seq(min(ahist$breaks,bhist$breaks),max(ahist$breaks,bhist$breaks),dist)

    ahist=hist(a,breaks=breaks,plot=F)
    bhist=hist(b,breaks=breaks,plot=F)
  }

  if(is.null(xlim)){
    xlim = c(min(ahist$breaks,bhist$breaks),max(ahist$breaks,bhist$breaks))
  }

  if(is.null(ylim)){
    ylim = c(0,max(ahist$counts,bhist$counts))
  }

  overlap = ahist
  for(i in 1:length(overlap$counts)){
    if(ahist$counts[i] > 0 & bhist$counts[i] > 0){
      overlap$counts[i] = min(ahist$counts[i],bhist$counts[i])
    } else {
      overlap$counts[i] = 0
    }
  }

  plot(ahist, xlim=xlim, ylim=ylim, col=colors[1])
  plot(bhist, xlim=xlim, ylim=ylim, col=colors[2], add=T)
  plot(overlap, xlim=xlim, ylim=ylim, col=colors[3], add=T)
}

这是使用R支持透明色彩的另一种方法

a=rnorm(1000, 3, 1)
b=rnorm(1000, 6, 1)
hist(a, xlim=c(0,10), col="red")
hist(b, add=T, col=rgb(0, 1, 0, 0.5) )

结果最终看起来像这样:

Answer 4

已有漂亮的答案,但我想添加这个.在我看来很好.(从@Dirk复制随机数).library(scales)是需要的

set.seed(42)
hist(rnorm(500,4),xlim=c(0,10),col='skyblue',border=F)
hist(rnorm(500,6),add=T,col=scales::alpha('red',.5),border=F)

结果是......

更新:此重叠功能也可能对某些人有用.

hist0 <- function(...,col='skyblue',border=T) hist(...,col=col,border=border) 

我觉得结果hist0比看起来更漂亮hist

hist2 <- function(var1, var2,name1='',name2='',
              breaks = min(max(length(var1), length(var2)),20), 
              main0 = "", alpha0 = 0.5,grey=0,border=F,...) {    

library(scales)
  colh <- c(rgb(0, 1, 0, alpha0), rgb(1, 0, 0, alpha0))
  if(grey) colh <- c(alpha(grey(0.1,alpha0)), alpha(grey(0.9,alpha0)))

  max0 = max(var1, var2)
  min0 = min(var1, var2)

  den1_max <- hist(var1, breaks = breaks, plot = F)$density %>% max
  den2_max <- hist(var2, breaks = breaks, plot = F)$density %>% max
  den_max <- max(den2_max, den1_max)*1.2
  var1 %>% hist0(xlim = c(min0 , max0) , breaks = breaks,
                 freq = F, col = colh[1], ylim = c(0, den_max), main = main0,border=border,...)
  var2 %>% hist0(xlim = c(min0 , max0),  breaks = breaks,
                 freq = F, col = colh[2], ylim = c(0, den_max), add = T,border=border,...)
  legend(min0,den_max, legend = c(
    ifelse(nchar(name1)==0,substitute(var1) %>% deparse,name1),
    ifelse(nchar(name2)==0,substitute(var2) %>% deparse,name2),
    "Overlap"), fill = c('white','white', colh[1]), bty = "n", cex=1,ncol=3)

  legend(min0,den_max, legend = c(
    ifelse(nchar(name1)==0,substitute(var1) %>% deparse,name1),
    ifelse(nchar(name2)==0,substitute(var2) %>% deparse,name2),
    "Overlap"), fill = c(colh, colh[2]), bty = "n", cex=1,ncol=3) }

的结果

par(mar=c(3, 4, 3, 2) + 0.1) 
set.seed(100) 
hist2(rnorm(10000,2),rnorm(10000,3),breaks = 50)

是

Answer 5

这是一个如何在"经典"R图形中执行此操作的示例:

## generate some random data
carrotLengths <- rnorm(1000,15,5)
cucumberLengths <- rnorm(200,20,7)
## calculate the histograms - don't plot yet
histCarrot <- hist(carrotLengths,plot = FALSE)
histCucumber <- hist(cucumberLengths,plot = FALSE)
## calculate the range of the graph
xlim <- range(histCucumber$breaks,histCarrot$breaks)
ylim <- range(0,histCucumber$density,
              histCarrot$density)
## plot the first graph
plot(histCarrot,xlim = xlim, ylim = ylim,
     col = rgb(1,0,0,0.4),xlab = 'Lengths',
     freq = FALSE, ## relative, not absolute frequency
     main = 'Distribution of carrots and cucumbers')
## plot the second graph on top of this
opar <- par(new = FALSE)
plot(histCucumber,xlim = xlim, ylim = ylim,
     xaxt = 'n', yaxt = 'n', ## don't add axes
     col = rgb(0,0,1,0.4), add = TRUE,
     freq = FALSE) ## relative, not absolute frequency
## add a legend in the corner
legend('topleft',c('Carrots','Cucumbers'),
       fill = rgb(1:0,0,0:1,0.4), bty = 'n',
       border = NA)
par(opar)

唯一的问题是,如果直方图中断是对齐的,它看起来好多了,这可能必须手动完成(在传递给的参数中hist).

Answer 6

这里的版本就像ggplot2一样,我只在基础R中给出了.我从@nullglob复制了一些.

生成数据

carrots <- rnorm(100000,5,2)
cukes <- rnorm(50000,7,2.5)

您不需要将其放入与ggplot2类似的数据框中.这种方法的缺点是你必须写出更多的情节细节.优点是您可以控制绘图的更多细节.

## calculate the density - don't plot yet
densCarrot <- density(carrots)
densCuke <- density(cukes)
## calculate the range of the graph
xlim <- range(densCuke$x,densCarrot$x)
ylim <- range(0,densCuke$y, densCarrot$y)
#pick the colours
carrotCol <- rgb(1,0,0,0.2)
cukeCol <- rgb(0,0,1,0.2)
## plot the carrots and set up most of the plot parameters
plot(densCarrot, xlim = xlim, ylim = ylim, xlab = 'Lengths',
     main = 'Distribution of carrots and cucumbers', 
     panel.first = grid())
#put our density plots in
polygon(densCarrot, density = -1, col = carrotCol)
polygon(densCuke, density = -1, col = cukeCol)
## add a legend in the corner
legend('topleft',c('Carrots','Cucumbers'),
       fill = c(carrotCol, cukeCol), bty = 'n',
       border = NA)

Answer 7

@Dirk Eddelbuettel:基本的想法非常好,但显示的代码可以改进.[需要很长时间来解释,因此是单独的答案,而不是评论.]

hist()默认情况下,该功能绘制绘图,因此您需要添加该plot=FALSE选项.此外,通过plot(0,0,type="n",...)调用建立绘图区域更清晰,您可以在其中添加轴标签,绘图标题等.最后,我想提一下,也可以使用阴影来区分两个直方图.这是代码:

set.seed(42)
p1 <- hist(rnorm(500,4),plot=FALSE)
p2 <- hist(rnorm(500,6),plot=FALSE)
plot(0,0,type="n",xlim=c(0,10),ylim=c(0,100),xlab="x",ylab="freq",main="Two histograms")
plot(p1,col="green",density=10,angle=135,add=TRUE)
plot(p2,col="blue",density=10,angle=45,add=TRUE)

这是结果(由于RStudio有点太宽:-)):

Answer 8

Plotly的R API可能对您有用.下图是在这里.

library(plotly)
#add username and key
p <- plotly(username="Username", key="API_KEY")
#generate data
x0 = rnorm(500)
x1 = rnorm(500)+1
#arrange your graph
data0 = list(x=x0,
         name = "Carrots",
         type='histogramx',
         opacity = 0.8)

data1 = list(x=x1,
         name = "Cukes",
         type='histogramx',
         opacity = 0.8)
#specify type as 'overlay'
layout <- list(barmode='overlay',
               plot_bgcolor = 'rgba(249,249,251,.85)')  
#format response, and use 'browseURL' to open graph tab in your browser.
response = p$plotly(data0, data1, kwargs=list(layout=layout))

url = response$url
filename = response$filename

browseURL(response$url)

完全披露:我在团队中.