R 编程 : plyr how to count values from a column with ddply

Question

这个问题在这里已经有了答案:





Faster ways to calculate frequencies and cast from long to wide

(4 个回答)


2年前关闭。




我想总结一下我的数据的通过/失败状态如下。换句话说，我想告诉每个产品/类型的通过和失败案例的数量。

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)

以下 cmd 返回通过 + 失败案例的总数，但我想要通过和失败的单独列

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

结果是:

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

想要的结果是

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

我尝试过类似的事情:

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

但显然这是错误的，因为结果是失败和通过的总和。

预先感谢您的建议!
问候，
里亚德。

Answer 1

尝试:

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

这给了我结果:

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

解释:

您正在提供数据集，df到 ddply功能。

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进行拆分。

每件作品，ddply应用您提供的某些功能。在这种情况下，您计算 result=="pass" 的数量和 result=="fail"有。

现在 ddply每个部分都留下了一些结果，即您拆分的变量(产品和类型)和您请求的结果(通过和失败)。

它将所有部分组合在一起并返回它