Rプログラミング：plyr ddplyで列の値をカウントする方法

Question

私のデータの合否ステータスを以下に要約したいと思います。言い換えれば、各製品/タイプの合格と不合格のケースの数を伝えたいと思います。

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

望ましい結果は次のようになります。

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) 

次cmdはパス+の総数は例を失敗したが、私はパスのために個別の列をしたいと

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

結果は失敗を返します

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

私が試みた気にいらないよう：

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

が、結果は失敗し、合格のための壮大なtotatlているので、明らかにそれは間違っているのです。

お気軽にご連絡ください！ よろしく、 Riad。

Answer 1

試してみてください。

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

私は結果与える：

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

説明：あなたは、データ・セット、ddply機能へdfを与えている

1. 。
2. ddply変数に分割され、「製品」と二つの変数のすべての組み合わせの「タイプ」これはlength(unique(product)) * length(unique(type))片もたらす
   • （データのサブセットすなわちdf）スプリット。
3. 各部分では、ddplyが提供する機能がいくつか適用されます。この場合、result=="pass"とresult=="fail"の数を数えます。
4. 今すぐddplyには、分割した変数（製品とタイプ）とリクエストした結果（合格と失敗）のそれぞれの結果が表示されます。
5. それは一緒に作品のすべてを組み合わせて、あなたはまた、reshape2::dcastを使用することができ

Answer 2

それを返します。

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