R - 使用多个标识符匹配值(当查找 ID 的顺序是随机的时)

R - Match values using multiple identifiers (when the order of lookup IDs are random)

我的问题是此 的后续问题。我在这里提出一个新问题 - 因为这与上一个问题非常不同。

假设我有以下两个数据集:

df1 = data.frame(PersonId1=c(1,2,3,4,5,6,7,8,9,10,1),PersonId2=c(11,12,13,14,15,16,17,18,19,20,11),
         Played_together = c(1,0,0,1,1,0,0,0,1,0,1),
         Event=c(1,1,1,1,2,2,2,2,2,2,2),
         Utility=c(20,-2,-5,10,30,2,1,.5,50,-1,60))

这看起来像:

   PersonId1 PersonId2 Played_together Event Utility
1          1        11               1     1    20.0
2          2        12               0     1    -2.0
3          3        13               0     1    -5.0
4          4        14               1     1    10.0
5          5        15               1     2    30.0
6          6        16               0     2     2.0
7          7        17               0     2     1.0
8          8        18               0     2     0.5
9          9        19               1     2    50.0
10        10        20               0     2    -1.0
11         1        11               1     2    60.0

.

df2 = data.frame(PersonId1=c(11,15,9,1),PersonId2=c(1,5,19,11),
         Played_together = c(1,1,1,1),
         Event=c(1,2,2,2),Utility=c(25,36,51,64))

这看起来像:

PersonId1 PersonId2 Played_together Event Utility
1        11         1               1     1      25
2        15         5               1     2      36
3         9        19               1     2      51
4         1        11               1     2      64

我想执行以下操作:在 df2[ 中查找每一对(每个事件中的 和 played_together == 1) =37=] 并将其与 df1 中的观察结果相匹配。如果匹配,则在 df1 中创建一个名为 'Utility from df2' 的新列。不是,填0。

对我来说挑战来自于人的顺序在 df1 和 df2 中不一致。例如,在 df1 第 1 行中,对于 event== 1 和 played_together == 1,我们看到:personid1 = 1 和 personid2 = 11 而在 df2 中,在第 1 行中我有 personid1=11 和 personid2 =1,对于事件== 1 和 played_together==1。因此两者是相同的。我想从 df2 中获取实用程序的值并将其放入 df1 中的新列中。如果没有匹配,则输入 0.

最终数据框应如下所示:

    PersonId1 PersonId2 Played_together Event Utility Utility_from_df2
1          1        11               1     1    20.0               25
2          2        12               0     1    -2.0                0
3          3        13               0     1    -5.0                0
4          4        14               1     1    10.0                0
5          5        15               1     2    30.0               36
6          6        16               0     2     2.0                0
7          7        17               0     2     1.0                0
8          8        18               0     2     0.5                0
9          9        19               1     2    50.0               51
10        10        20               0     2    -1.0                0
11         1        11               1     2    60.0               64

非常感谢。

使用 dplyrdata.table

df2 = data.frame(PersonId1=c(11,15,9,1),PersonId2=c(1,5,19,11),
                 Played_together = c(1,1,1,1),
                 Event=c(1,2,2,2),
                 Utility=c(25,36,51,64)) # you had missed adding Utility in your ques


library(data.table)
library(dplyr)
df3 <- copy(df2)
colnames(df2) <- c("PersonId2", "PersonId1", "Played_together", "Event", "Utility")
setDT(df2)
df2 <- df2[, c("PersonId2", "PersonId1", "Utility", "Event")]
df3 <- df3[, c("PersonId2", "PersonId1", "Utility", "Event")]
df <- left_join(df1, df2, c("PersonId2", "PersonId1", "Event"))
df <- left_join(df, df3, by = c("PersonId2", "PersonId1", "Event"))
setDT(df)
df[, Utility_from_df2 := ifelse(is.na(Utility), Utility.y, ifelse(is.na(Utility.y), Utility, 0))]
df[is.na(df)] <- 0
df[, c("Utility.y", "Utility") := NULL]
setnames(df, "Utility.x", "Utility")

期望的输出:

     PersonId1 PersonId2 Played_together Event Utility Utility_from_df2
 1:         1        11               1     1    20.0               25
 2:         2        12               0     1    -2.0                0
 3:         3        13               0     1    -5.0                0
 4:         4        14               1     1    10.0                0
 5:         5        15               1     2    30.0               36
 6:         6        16               0     2     2.0                0
 7:         7        17               0     2     1.0                0
 8:         8        18               0     2     0.5                0
 9:         9        19               1     2    50.0               51
10:        10        20               0     2    -1.0                0
11:         1        11               1     2    60.0               64