R编程：plyr如何使用ddply计算列中的值

我想将我的数据的通过/失败状态总结如下。换句话说，我想告诉您每种产品/类型的合格和不合格案例的数量

library(ggplot2)
library(plyr)
product=c("p1","p1","p1","p1","p1","p1","p1","p1","p1","p1","p1","p1","p2","p2","p2","p2","p2","p2","p2","p2","p2","p2","p2","p2")
type=c("t1","t1","t1","t1","t1","t1","t2","t2","t2","t2","t2","t2","t1","t1","t1","t1","t1","t1","t2","t2","t2","t2","t2","t2")
skew=c("s1","s1","s1","s2","s2","s2","s1","s1","s1","s2","s2","s2","s1","s1","s1","s2","s2","s2","s1","s1","s1","s2","s2","s2")
color=c("c1","c2","c3","c1","c2","c3","c1","c2","c3","c1","c2","c3","c1","c2","c3","c1","c2","c3","c1","c2","c3","c1","c2","c3")
result=c("pass","pass","fail","pass","pass","pass","fail","pass","fail","pass","fail","pass","fail","pass","fail","pass","pass","pass","pass","fail","fail","pass","pass","fail")
df = data.frame(product, type, skew, color, result)

下面的cmd返回pass+fail案例的总数，但我希望pass和fail有单独的列

dfSummary <- ddply(df, c("product", "type"), summarise, N=length(result))

理想的结果是

         product type Pass Fail
 1       p1      t1   5    1
 2       p1      t2   3    3
 3       p2      t1   4    2
 4       p2      t2   3    3

我尝试过这样的事情：

 dfSummary <- ddply(df, c("product", "type"), summarise, Pass=length(df$product[df$result=="pass"]), Fail=length(df$product[df$result=="fail"]) )

dfSummary试试：

说明：

您正在将数据集
df
提供给
ddply
函数
ddply
正在拆分变量“产品”和“类型”

这导致
length（unique（product））*length（unique（type））
片段（即数据的子集
df
）在两个变量的每个组合上分割

对于每个片段，
ddply
应用您提供的一些功能。在本例中，您计算有多少个
result==“pass”
和
result==“fail”
现在，
ddply
为每个工件留下了一些结果，即您拆分的变量（产品和类型）和您请求的结果（通过和失败）
它将所有片段组合在一起并返回
您还可以使用
重塑2:：dcast

library(reshape2)
dcast(product + type~result,data=df, fun.aggregate= length,value.var = 'result')
##   product type fail pass
## 1      p1   t1    1    5
## 2      p1   t2    3    3
## 3      p2   t1    2    4
## 4      p2   t2    3    3

太好了，这正是我需要的！谢谢你的及时答复！太多了！这也行。比ddply快得多。Thanx:）
dfSummary <- ddply(df, c("product", "type"), summarise, 
                   Pass=sum(result=="pass"), Fail=sum(result=="fail") )

  product type Pass Fail
1      p1   t1    5    1
2      p1   t2    3    3
3      p2   t1    4    2
4      p2   t2    3    3

library(reshape2)
dcast(product + type~result,data=df, fun.aggregate= length,value.var = 'result')
##   product type fail pass
## 1      p1   t1    1    5
## 2      p1   t2    3    3
## 3      p2   t1    2    4
## 4      p2   t2    3    3