在Jenkins 2管道内执行SonarQube扫描仪(Execute SonarQube Scanner within Jenkins 2 Pipeline)

 在这个一般主题上有很多SO问题，根据个人偏好，可读性，紧凑性，灵活性和速度，推荐ave() ， aggregate() ， plyr() ， reshape2::cast或data.table 。 ..这是一个简单的解决方案，使用aggregate()似乎可以做你想要的： 
 
(aa <- aggregate(Sepal.Length~Species,data=iris,quantile))

##      Species Sepal.Length.0% Sepal.Length.25% Sepal.Length.50% Sepal.Length.75%
## 1     setosa           4.300            4.800            5.000            5.200
## 2 versicolor           4.900            5.600            5.900            6.300
## 3  virginica           4.900            6.225            6.500            6.900
##   Sepal.Length.100%
## 1             5.800
## 2             7.000
## 3             7.900
 
 编辑 ：重新阅读/基于评论，这不是您想要的：您需要为每一行复制的汇总值，而不是每组一次。 
 
 也许 
 
merge(iris,aa,by="Species")
 
 虽然这给出了一个奇怪的数据框（最后的“列”实际上是一个矩阵）。 
 
 这有点神奇，但是 
 
merge(iris,with(aa,data.frame(Species,Sepal.Length)))
 
 更好 - 它会将aggregate()返回的奇怪数据帧解压缩一点（名称仍然有点不稳定）。 

There are a lot of SO questions on this general topic, recommending various uses of ave(), aggregate(), plyr(), reshape2::cast, or data.table depending on personal preference, readability, compactness, flexibility, speed ... Here's a simple solution with aggregate() that seems to do what you want:
 
(aa <- aggregate(Sepal.Length~Species,data=iris,quantile))

##      Species Sepal.Length.0% Sepal.Length.25% Sepal.Length.50% Sepal.Length.75%
## 1     setosa           4.300            4.800            5.000            5.200
## 2 versicolor           4.900            5.600            5.900            6.300
## 3  virginica           4.900            6.225            6.500            6.900
##   Sepal.Length.100%
## 1             5.800
## 2             7.000
## 3             7.900
 
edit: re-reading/based on comment, this is not what you want: you need the summarized values replicated for each row, not just once per group.
 
Perhaps
 
merge(iris,aa,by="Species")
 
although that gives a slightly weird data frame (the last "column" is actually a matrix).
 
It's a little bit magical, but
 
merge(iris,with(aa,data.frame(Species,Sepal.Length)))
 
is better -- it unpacks the weird data frame returned by aggregate() a bit more (the names are still a bit wonky).