在ggplot2中分别规格化分面直方图 [英] Normalizing faceted histograms separately in ggplot2

问题描述

我的问题类似于
将R ggplot中的直方图中的y轴规格化为比例
，但我想补充一点。

通常，I在2x3方面设计中有6个直方图，并且我想分别标准化它们中的每一个。我会尝试在这里创建一个示例数据集来给出一个想法：

  hvalues = c（3,1,3， 2,2,5,1,1,12,1,4,3）
 season = c（fall，fall，fall，fall，winter，winter， 冬季，冬季，夏季，夏季，夏季，夏季）
年份= c（第1年，第1年，第2年 ，年1，年1，年2，年2，年1，年1，年2，年2）
 group = c 秋季1，秋季1，秋季2，秋季2，冬季1，冬季1，冬季2，冬季2，夏季年份1，夏季1，夏季2，夏季2）
 all = data.frame（hvalues，season，year）
  

使用

  ggplot（all，aes（x = hvalues，group = group））+ 
 geom_histogram（aes（y = .. count ../ sum（.. count ..）））+ 
 facet_grid（season〜year）
  

给出整体比例（即组合所有方面）。我希望每个组方面都归一化为1.在我的实际数据中，hvalues不是整数 - 它们是数字。

我是使用R的新手，真的很感谢一些帮助。

解决方案

解决方案是：

  ggplot（all，aes（x = hvalues））+ 
 facet_grid（season〜year，drop = T）+ 
 geom_histogram（aes（y =（.. count ..） ）/ tapply（.. count ..，.. PANEL .. sum）[.. PANEL ..]））
  

我从这个问题< a>

我觉得你的问题可能是那个问题的重复。

My questions is similar to Normalizing y-axis in histograms in R ggplot to proportion but I'd like to add to it a bit.

In general, I have 6 histograms in a 2x3 facet design, and I'd like to normalize each of them separately. I'll try to make a sample data set here to give an idea:

hvalues=c(3,1,3,2,2,5,1,1,12,1,4,3)
season=c("fall","fall","fall","fall","winter","winter","winter","winter","summer","summer","summer","summer")
year=c("year 1","year 1","year 2","year 2","year 1","year 1","year 2","year 2","year 1","year 1","year 2","year 2")
group=c("fall year 1","fall year 1","fall year 2","fall year 2","winter year 1","winter year 1","winter year 2","winter year 2","summer year 1","summer year 1","summer year 2","summer year 2")
all=data.frame(hvalues,season,year)

Using

ggplot(all, aes(x=hvalues,group=group)) + 
geom_histogram(aes(y=..count../sum(..count..))) + 
facet_grid(season ~ year)

gives the proportions overall (i.e. combining all the facets). I'd like each group facet to be normalized to 1. hvalues are not integers in my actual data - they are numerical.

I am a novice using R, and would really appreciate some help. Thanks in advance!

解决方案

The solution is:

ggplot(all, aes(x=hvalues)) +
    facet_grid(season ~ year,drop=T) +
    geom_histogram(aes(y=(..count..)/tapply(..count..,..PANEL..,sum)[..PANEL..]))

I stole this from this question

I feel your question might be a duplicate of that one by the way.

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