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..))) 

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..)))