[英]Plot hline at mean with geom_bar and stat=“identity”
I have a barplot where the exact bar heights are in the dataframe. 我有一个条形图,其中确切的条形高度在数据框中。
df <- data.frame(x=LETTERS[1:6], y=c(1:6, 1:6 + 1), g=rep(x = c("a", "b"), each=6))
ggplot(df, aes(x=x, y=y, fill=g, group=g)) +
geom_bar(stat="identity", position="dodge")
Now I want to add two hlines displaying the mean of all bars per group. 现在,我要添加两个 hline,以显示每组所有条形的平均值。 All I get with 我所拥有的
ggplot(df, aes(x=x, y=y, fill=g, group=g)) +
geom_bar(stat="identity", position="dodge") +
stat_summary(fun.y=mean, aes(yintercept=..y.., group=g), geom="hline")
is 是
As I want to do this for a arbitrary number of groups as well, I would appreciate a solution with ggplot only. 因为我也想对任意数量的组执行此操作,所以我只希望使用ggplot解决方案。
I want to avoid a solution like this, because it does not rely purely on the dataset passed to ggplot, has redundant code and is not flexible in the number of groups: 我想避免这样的解决方案,因为它不完全依赖传递给ggplot的数据集,具有冗余代码并且在组数方面不灵活:
ggplot(df, aes(x=x, y=y, fill=g, group=g)) +
geom_bar(stat="identity", position="dodge") +
geom_hline(yintercept=mean(df$y[df$g=="a"]), col="red") +
geom_hline(yintercept=mean(df$y[df$g=="b"]), col="green")
Thanks in advance! 提前致谢!
Edits: 编辑:
If I understand your question correctly, your first approach is almost there: 如果我正确理解您的问题,那么您的第一种方法就差不多了:
ggplot(df, aes(x = x, y = y, fill = g, group = g)) +
geom_col(position="dodge") + # geom_col is equivalent to geom_bar(stat = "identity")
stat_summary(fun.y = mean, aes(x = 1, yintercept = ..y.., group = g), geom = "hline")
According to the help file for stat_summary
: 根据stat_summary
的帮助文件:
stat_summary
operates on unique x;stat_summary
对唯一的x进行操作; ... ...
In this case, stat_summary
has inherited the top level aesthetic mappings of x = x
and group = g
by default, so it would calculate the mean y value at each x for each value of g, resulting in a lot of horizontal lines. 在这种情况下, stat_summary
继承了x = x
和group = g
的顶级美学映射,因此它将为g的每个值计算每个x的平均y值,从而导致许多水平线。 Adding x = 1
to stat_summary
's mapping overrides x = x
(while retaining group = g
), so we get a single mean y value for each value of g instead. 在stat_summary
的映射中添加x = 1
会覆盖x = x
(同时保留group = g
),因此对于g的每个值,我们得到一个均值y值。
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