R编程：plyr如何使用ddply从列中计算值

Question

我想总结下面我的数据的通过/失败状态。换句话说，我想告诉每种产品/类型的合格和不合格情况的数量。

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) 

以下CMD返回Pass总数+失败案例，但我要为通单独的列和失败

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

的结果是：

 product type N 
1  p1  t1 6 
2  p1  t2 6 
3  p2  t1 6 
4  p2  t2 6 

的desireable结果将被

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

我试图像财产以后：

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

但明显因为结果是失败，并通过盛大totatl是错误的。

在此先感谢您的建议！ Regards， Riad。

Answer 1

尝试：

dfSummary <- ddply(df, c("product", "type"), summarise, 
        Pass=sum(result=="pass"), Fail=sum(result=="fail")) 

这使我产生：

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

说明：

1. 你给的数据集，df到ddply功能。
2. ddply是关于变量分裂，“产品”和对两个变量的每一种组合的“类型”
   • 这导致length(unique(product)) * length(unique(type))件（即，数据的子集df）分裂。
3. 对于每一块，ddply应用您提供的一些功能。在这种情况下，您可以统计result=="pass"和result=="fail"的数量。
4. 现在ddply剩下一部分结果，即您分割的变量（产品和类型）和您请求的结果（通过和失败）。
5. 它结合了所有的作品一起，并返回其

Answer 2

你也可以使用reshape2::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