R：計算利潤率或行和山坳資金用於數據幀

Question

我有一個數據幀，看起來像這樣：

  Flag1    Flag2 Type1 Type2 Type3 
1  A     FIRST  2 0  0 
2  A    SECOND  1 9  0 
3  A     THIRD  3 7  0 
4  A    FOURTH  9 18  0 
5  A     FIFTH  1 22  0 
6  A     SIXTH  1 13  0 
7  B     FIRST  0 0  0 
8  B    SECOND  3 9  0 
9  B     THIRD  5 85  0 
10  B    FOURTH  4 96  0 
11  B     FIFTH  3 40  0 
12  B     SIXTH  0 17  0 

我需要在我的數據幀終於看起來像這樣

這樣的方式來總結

  Flag1    Flag2 Type1 Type2 Type3 Sum 
1  A     FIRST  2 0  0  2 
2  A    SECOND  1 9  0  10 
3  A     THIRD  3 7  0  10 
4  A    FOURTH  9 18  0  27 
5  A     FIFTH  1 22  0  23 
6  A     SIXTH  1 13  0  14 
7  B     FIRST  0 0  0  0 
8  B    SECOND  3 9  0  12 
9  B     THIRD  5 85  0  90 
10  B    FOURTH  4 96  0 100 
11  B     FIFTH  3 40  0  43 
12  B     SIXTH  0 17  0  17 
13  (all)    FIRST  2 0  0  2 
14  (all)    SECOND  4 18  0  22 
15  (all)    THIRD  8 92  0 100 
16  (all)    FOURTH  13 114  0 127 
17  (all)    FIFTH  4 62  0  66 
18  (all)    SIXTH  1 30  0  31 
19  A     (all)  17 68  0  86 
20  B     (all)  15 247  0 262 
21  (all)    (all)  32 315  0 348 

我已經嘗試了reshape2包中的add_margins函數，沒有用，它不會計算像我想要的那樣的總和。我嘗試過聚合，rowSums & colSums - 沒有結果。

在這裏的任何幫助將是偉大的。

謝謝

求和功能也需要添加以前的Flag2的總和。像，

 Flag1    Flag2 Type1 Type2 Type3 Sum 
1  A     FIRST  2 0  0  2 
2  A    SECOND  1 9  0  12 
3  A     THIRD  3 7  0  22 
4  A    FOURTH  9 18  0  49 
5  A     FIFTH  1 22  0  72 
6  A     SIXTH  1 13  0  86 
7  B     FIRST  0 0  0  0 
8  B    SECOND  3 9  0  12 
9  B     THIRD  5 85  0 102 
10  B    FOURTH  4 96  0 202 
11  B     FIFTH  3 40  0 245 
12  B     SIXTH  0 17  0 262 
13  (all)    FIRST  2 0  0  2 
14  (all)    SECOND  4 18  0  24 
15  (all)    THIRD  8 92  0 124 
16  (all)    FOURTH  13 114  0 251 
17  (all)    FIFTH  4 62  0 317 
18  (all)    SIXTH  1 30  0 348 
19  A     (all)  17 68  0  85 
20  B     (all)  15 247  0 262 
21  (all)    (all)  32 315  0 347 

Answer 1

假設你有這樣的數據，框架和它的名字是dtable：

dt1 <- as.data.frame(addmargins(xtabs(Type1~Flag1+Flag2, data=dtable))) 
dt2 <- as.data.frame(addmargins(xtabs(Type2~Flag1+Flag2, data=dtable))) 
dt3 <- as.data.frame(addmargins(xtabs(Type3~Flag1+Flag2, data=dtable))) 
names(dt1)[3] <- "Type1" 
names(dt2)[3] <- "Type2" 
names(dt3)[3] <- "Type3" 

dt.all <- merge(merge(dt1,dt2), dt3) 
dt.all$Sum <- with(dt.all, Type1+Type2+Type3) 

我無法得到確切的排序順序，你想，但這是接近：

levels(dt.all$Flag2) <- c("FIRST", "SECOND", "THIRD", "FOURTH" ,"FIFTH", "SIXTH", "Sum") 
dt.all[order(dt.all$Flag1, dt.all$Flag2), ] 

    Flag1 Flag2 Type1 Type2 Type3 Sum 
1  A FIRST  1 22  0 23 
2  A SECOND  2  0  0 2 
3  A THIRD  9 18  0 27 
4  A FOURTH  1  9  0 10 
5  A FIFTH  1 13  0 14 
7  A SIXTH  3  7  0 10 
6  A Sum 17 69  0 86 
8  B FIRST  3 40  0 43 
9  B SECOND  0  0  0 0 
10  B THIRD  4 96  0 100 
11  B FOURTH  3  9  0 12 
12  B FIFTH  0 17  0 17 
14  B SIXTH  5 85  0 90 
13  B Sum 15 247  0 262 
15 Sum FIRST  4 62  0 66 
16 Sum SECOND  2  0  0 2 
17 Sum THIRD 13 114  0 127 
18 Sum FOURTH  4 18  0 22 
19 Sum FIFTH  1 30  0 31 
21 Sum SIXTH  8 92  0 100 
20 Sum Sum 32 316  0 348 

Answer 2

rowSums適合我（或者我錯過了什麼？）。

> my.df <- read.table(textConnection("   Flag1    Flag2 Type1 Type2 Type3 
+ 1  A     FIRST  2 0  0 
+ 2  A    SECOND  1 9  0 
+ 3  A     THIRD  3 7  0 
+ 4  A    FOURTH  9 18  0 
+ 5  A     FIFTH  1 22  0 
+ 6  A     SIXTH  1 13  0 
+ 7  B     FIRST  0 0  0 
+ 8  B    SECOND  3 9  0 
+ 9  B     THIRD  5 85  0 
+ 10  B    FOURTH  4 96  0 
+ 11  B     FIFTH  3 40  0 
+ 12  B     SIXTH  0 17  0 
+ ")) 
Browse[2]> my.df 
    Flag1 Flag2 Type1 Type2 Type3 
1  A FIRST  2  0  0 
2  A SECOND  1  9  0 
3  A THIRD  3  7  0 
4  A FOURTH  9 18  0 
5  A FIFTH  1 22  0 
6  A SIXTH  1 13  0 
7  B FIRST  0  0  0 
8  B SECOND  3  9  0 
9  B THIRD  5 85  0 
10  B FOURTH  4 96  0 
11  B FIFTH  3 40  0 
12  B SIXTH  0 17  0 
Browse[2]> rowSums(my.df[3:5]) 
    1 2 3 4 5 6 7 8 9 10 11 12 
    2 10 10 27 23 14 0 12 90 100 43 17 
Browse[2]> my.df$Sum <- rowSums(my.df[3:5]) 
Browse[2]> my.df 
    Flag1 Flag2 Type1 Type2 Type3 Sum 
1  A FIRST  2  0  0 2 
2  A SECOND  1  9  0 10 
3  A THIRD  3  7  0 10 
4  A FOURTH  9 18  0 27 
5  A FIFTH  1 22  0 23 
6  A SIXTH  1 13  0 14 
7  B FIRST  0  0  0 0 
8  B SECOND  3  9  0 12 
9  B THIRD  5 85  0 90 
10  B FOURTH  4 96  0 100 
11  B FIFTH  3 40  0 43 
12  B SIXTH  0 17  0 17