我正在绘制一个类别变量,而不是显示每个类别值的计数。

我正在寻找一种方法来让ggplot显示该类别中值的百分比。当然,可以用计算出的百分比创建另一个变量并绘制该变量,但我必须这样做几十次,我希望在一个命令中实现这一点。

我在做一些实验,比如

qplot(mydataf) +
  stat_bin(aes(n = nrow(mydataf), y = ..count../n)) +
  scale_y_continuous(formatter = "percent")

但我一定是使用不正确,因为我得到了错误。

为了方便地重现设置,这里有一个简化的示例:

mydata <- c ("aa", "bb", NULL, "bb", "cc", "aa", "aa", "aa", "ee", NULL, "cc");
mydataf <- factor(mydata);
qplot (mydataf); #this shows the count, I'm looking to see % displayed.

在实际情况中,我可能会使用ggplot而不是qplot,但是使用stat_bin的正确方法仍然让我困惑。

我也尝试了以下四种方法:

ggplot(mydataf, aes(y = (..count..)/sum(..count..))) + 
  scale_y_continuous(formatter = 'percent');

ggplot(mydataf, aes(y = (..count..)/sum(..count..))) + 
  scale_y_continuous(formatter = 'percent') + geom_bar();

ggplot(mydataf, aes(x = levels(mydataf), y = (..count..)/sum(..count..))) + 
  scale_y_continuous(formatter = 'percent');

ggplot(mydataf, aes(x = levels(mydataf), y = (..count..)/sum(..count..))) + 
  scale_y_continuous(formatter = 'percent') + geom_bar();

但所有4个都给予:

错误:ggplot2不知道如何处理类因子的数据

的简单情况也会出现相同的错误

ggplot (data=mydataf, aes(levels(mydataf))) +
  geom_bar()

这显然是关于ggplot如何与单个向量交互的。我摸不着头脑,在谷歌上搜索这个错误只得到一个结果。


当前回答

注意,如果你的变量是连续的,你将不得不使用geom_histogram(),因为该函数将根据“bin”对变量进行分组。

df <- data.frame(V1 = rnorm(100))

ggplot(df, aes(x = V1)) +  
  geom_histogram(aes(y = 100*(..count..)/sum(..count..))) 

# if you use geom_bar(), with factor(V1), each value of V1 will be treated as a
# different category. In this case this does not make sense, as the variable is 
# really continuous. With the hp variable of the mtcars (see previous answer), it 
# worked well since hp was not really continuous (check unique(mtcars$hp)), and one 
# can want to see each value of this variable, and not to group it in bins.
ggplot(df, aes(x = factor(V1))) +  
  geom_bar(aes(y = (..count..)/sum(..count..))) 

其他回答

截至2017年3月,对于ggplot2 2.2.1,我认为最佳解决方案在Hadley Wickham的R for数据科学书籍中得到了解释:

ggplot(mydataf) + stat_count(mapping = aes(x=foo, y=..prop.., group=1))

Stat_count计算两个变量:默认使用count,但您可以选择使用显示比例的prop。

对于ggplot2 2.1.0版本,就是这样

+ scale_y_continuous(labels = scales::percent)

如果你想在y轴上显示百分比,并在条形图上标注:

library(ggplot2)
library(scales)
ggplot(mtcars, aes(x = as.factor(am))) +
  geom_bar(aes(y = (..count..)/sum(..count..))) +
  geom_text(aes(y = ((..count..)/sum(..count..)), label = scales::percent((..count..)/sum(..count..))), stat = "count", vjust = -0.25) +
  scale_y_continuous(labels = percent) +
  labs(title = "Manual vs. Automatic Frequency", y = "Percent", x = "Automatic Transmission")

当添加条形标签时,您可能希望省略y轴以使图表更清晰,只需在末尾添加:

  theme(
        axis.text.y=element_blank(), axis.ticks=element_blank(),
        axis.title.y=element_blank()
  )

从ggplot2 3.3版开始,我们可以访问方便的after_stat()函数。

我们可以做一些类似于@Andrew的回答,但是不用..语法:

# original example data
mydata <- c("aa", "bb", NULL, "bb", "cc", "aa", "aa", "aa", "ee", NULL, "cc")

# display percentages
library(ggplot2)
ggplot(mapping = aes(x = mydata,
                     y = after_stat(count/sum(count)))) +
  geom_bar() +
  scale_y_continuous(labels = scales::percent)

您可以在geom_和stat_函数的文档中找到所有可用的“计算变量”。例如,对于geom_bar(),可以访问count和prop变量。(请参阅计算变量的文档。)

关于你的NULL值的一个注释:当你创建向量时,它们被忽略(即你最终得到一个长度为9的向量,而不是11)。如果你真的想跟踪丢失的数据,你将不得不使用NA (ggplot2将把NA放在图的右端):

# use NA instead of NULL
mydata <- c("aa", "bb", NA, "bb", "cc", "aa", "aa", "aa", "ee", NA, "cc")
length(mydata)
#> [1] 11

# display percentages
library(ggplot2)
ggplot(mapping = aes(x = mydata,
                     y = after_stat(count/sum(count)))) +
  geom_bar() +
  scale_y_continuous(labels = scales::percent)

由reprex包于2021-02-09创建(v1.0.0)

(请注意,使用chr或fct数据不会对您的示例产生影响。)

注意,如果你的变量是连续的,你将不得不使用geom_histogram(),因为该函数将根据“bin”对变量进行分组。

df <- data.frame(V1 = rnorm(100))

ggplot(df, aes(x = V1)) +  
  geom_histogram(aes(y = 100*(..count..)/sum(..count..))) 

# if you use geom_bar(), with factor(V1), each value of V1 will be treated as a
# different category. In this case this does not make sense, as the variable is 
# really continuous. With the hp variable of the mtcars (see previous answer), it 
# worked well since hp was not really continuous (check unique(mtcars$hp)), and one 
# can want to see each value of this variable, and not to group it in bins.
ggplot(df, aes(x = factor(V1))) +  
  geom_bar(aes(y = (..count..)/sum(..count..)))