我正在尝试找出以累积方式按组和总计对列进行求和的最佳方法。我想要做的是将group.1和group.2的最高值作为新变量total_top_1。获得此值后，我想创建第二个变量total_top_2并将其添加到第一个值。从这些数据开始。
df <- data.frame(stringsAsFactors=FALSE,
group.1 = c("A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",
"A", "A", "B", "B", "B", "B", "B", "B", "B", "B", "B",
"B", "B", "B", "B", "B", "B", "B"),
group.2 = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
2L),
value = c(56L, 74L, 39L, 86L, 7L, 100L, 18L, 43L, 68L, 32L, 4L, 17L,
18L, 24L, 94L, 11L, 67L, 87L, 96L, 93L, 31L, 29L, 31L,
25L, 64L, 23L, 44L, 92L, 36L, 93L)
)
我希望最终结果看起来像这样。
df_output <- data.frame(stringsAsFactors=FALSE,
group.1 = c("A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",
"A", "A", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B",
"B", "B", "B", "B", "B", "B"),
group.2 = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L),
value = c(56L, 74L, 39L, 86L, 7L, 100L, 18L, 43L, 68L, 32L, 4L, 17L,
18L, 24L, 94L, 11L, 67L, 87L, 96L, 93L, 31L, 29L, 31L, 25L,
64L, 23L, 44L, 92L, 36L, 93L),
total_top_1 = c(100L, 100L, 100L, 100L, 100L, 100L, 100L, 100L, 100L, 32L,
32L, 32L, 32L, 32L, 96L, 96L, 96L, 96L, 96L, 96L, 96L, 96L,
96L, 93L, 93L, 93L, 93L, 93L, 93L, 93L),
total_top_2 = c(186L, 186L, 186L, 186L, 186L, 186L, 186L, 186L, 186L, 56L,
56L, 56L, 56L, 56L, 190L, 190L, 190L, 190L, 190L, 190L,
190L, 190L, 190L, 185L, 185L, 185L, 185L, 185L, 185L, 185L)
）
理想情况下，我希望能够为我指定的许多变量执行此操作。因此，我可能需要创建total_top_1到total_top_5。
要获得前1和2的总数，我可以这样做：
df %>%
arrange(group.1, group.2, desc(value)) %>%
group_by(group.1, group.2) %>%
top_n(
n = 2, wt = value
) %>%
mutate(
total_top_1 = value,
total_top_2 = sum(value)
)
但是，如果我想要获得前三名，我需要重复此步骤，请修改top_n，现在sum正在计算total_top_3我真正想要的是total_top_1 }，total_top_2和total_top_3。我认为有更好的方法来做到这一点。
答案 0 :(得分：1)
我认为这就是你想要的。
library(reshape2)
df=df%>%group_by(group.1,group.2)%>%dplyr:: mutate(top_n=rank(-value,ties.method = "first"))
df$Group=paste(df$group.1,df$group.2)
df1=as.data.frame((acast(df, Group~top_n, value.var="value")))
df1[is.na(df1)]=0
df1=as.data.frame(t(apply(df1, 1, cumsum)))
names(df1)=paste("total_top_", colnames(df1))
df1$Group=rownames(df1)
df=merge(df,df1,by.x='Group',by.y='Group',all.x=T)
这是输出：
> df
Group group.1 group.2 value top_n total_top_ 1 total_top_ 2 total_top_ 3 total_top_ 4 total_top_ 5 total_top_ 6 total_top_ 7 total_top_ 8 total_top_ 9
1 A 1 A 1 56 5 100 186 260 328 384 427 466 484 491
2 A 1 A 1 74 3 100 186 260 328 384 427 466 484 491
3 A 1 A 1 39 7 100 186 260 328 384 427 466 484 491
4 A 1 A 1 86 2 100 186 260 328 384 427 466 484 491
5 A 1 A 1 7 9 100 186 260 328 384 427 466 484 491
6 A 1 A 1 100 1 100 186 260 328 384 427 466 484 491
7 A 1 A 1 18 8 100 186 260 328 384 427 466 484 491
8 A 1 A 1 43 6 100 186 260 328 384 427 466 484 491
9 A 1 A 1 68 4 100 186 260 328 384 427 466 484 491
10 A 2 A 2 32 1 32 56 74 91 95 95 95 95 95
11 A 2 A 2 4 5 32 56 74 91 95 95 95 95 95
12 A 2 A 2 17 4 32 56 74 91 95 95 95 95 95
13 A 2 A 2 18 3 32 56 74 91 95 95 95 95 95
14 A 2 A 2 24 2 32 56 74 91 95 95 95 95 95
15 B 1 B 1 94 2 96 190 283 370 437 468 499 528 539
16 B 1 B 1 11 9 96 190 283 370 437 468 499 528 539
17 B 1 B 1 67 5 96 190 283 370 437 468 499 528 539
18 B 1 B 1 87 4 96 190 283 370 437 468 499 528 539
19 B 1 B 1 96 1 96 190 283 370 437 468 499 528 539
20 B 1 B 1 93 3 96 190 283 370 437 468 499 528 539
21 B 1 B 1 31 6 96 190 283 370 437 468 499 528 539
22 B 1 B 1 29 8 96 190 283 370 437 468 499 528 539
23 B 1 B 1 31 7 96 190 283 370 437 468 499 528 539
24 B 2 B 2 25 6 93 185 249 293 329 354 377 377 377
25 B 2 B 2 64 3 93 185 249 293 329 354 377 377 377
26 B 2 B 2 23 7 93 185 249 293 329 354 377 377 377
27 B 2 B 2 44 4 93 185 249 293 329 354 377 377 377
28 B 2 B 2 92 2 93 185 249 293 329 354 377 377 377
29 B 2 B 2 36 5 93 185 249 293 329 354 377 377 377
30 B 2 B 2 93 1 93 185 249 293 329 354 377 377 377