如何使用另一个数据框的列和行组合在 dataframe 中查找值？

Question

I want to used values of two columns in one dataframe and use these values as column * row combination in another dataframe. Sounds terrible, so I rather explain with example below.(simplified version, the actual dataset is much larger)

Data1
#   ID       Date
# 1  A 2022-02-01
# 2  B 2022-02-02
# 3  C 2022-02-03
# 4  D 2022-02-04
# 5  E 2022-02-05
# 6  F 2022-02-06
# 7  G 2022-02-07
# 8  H 2022-02-08

Data2
#   ID X2022.02.01 X2022.02.02 X2022.02.03 X2022.02.04 X2022.02.05 X2022.02.06 X2022.02.07 X2022.02.08
# 1  A           1           9          17          25          33          41          49          57
# 2  B           2          10          18          26          34          42          50          58
# 3  C           3          11          19          27          35          43          51          59
# 4  D           4          12          20          28          36          44          52          60
# 5  E           5          13          21          29          37          45          53          61
# 6  F           6          14          22          30          38          46          54          62
# 7  G           7          15          23          31          39          47          55          63
# 8  H           8          16          24          32          40          48          56          64

and I would like to use ID and Date combination in Data1 to find value in Data2 So I would like to have the following outcome:

#   ID       Date Value
# 1  A 2022-02-01     1
# 2  B 2022-02-02    10
# 3  C 2022-02-03    19
# 4  D 2022-02-04    28
# 5  E 2022-02-05    37
# 6  F 2022-02-06    46
# 7  G 2022-02-07    55
# 8  H 2022-02-08    64

so far, I used the following code, but it took too many time as the original dataset (both Data1 and Data2 ) is huge.

for (i in 1:nrow(Data1)) {
  a <- Data1[[1]][[i]]
  b <- Data1[[2]][[i]]
  c <- Data2[b, a]
  Data1$Value[i] <- c
}

Could someone kindly help my code?? :)

Data

Data1 <- data.frame(ID=c("A", "B", "C", "D", "E", "F", "G", "H"), 
                    Date=c("2022-02-01", "2022-02-02", "2022-02-03", "2022-02-04",
                           "2022-02-05", "2022-02-06","2022-02-07", "2022-02-08"))
Data2 <- data.frame(ID=c("A", "B", "C", "D", "E", "F", "G", "H"), 
                    "2022-02-01"=c(1:8),
                    '2022-02-02'=c(9:16),
                    '2022-02-03'=c(17:24),
                    '2022-02-04'=c(25:32),
                    '2022-02-05'=c(33:40),
                    '2022-02-06'=c(41:48),
                    '2022-02-07'=c(49:56),
                    '2022-02-08'=c(57:64))

Answer 1

Consider to match IDs together as well as dates with column names. To let the dates look like the (valid) column names we may use make.names .

transform(Data1, Value=mapply(\(i, j) Data2[i, j], match(Data1$ID, Data2$ID), 
                              match(make.names(Data1$Date), names(Data2))))
#   ID       Date Value
# 1  A 2022-02-01     1
# 2  B 2022-02-02    10
# 3  C 2022-02-03    19
# 4  D 2022-02-04    28
# 5  E 2022-02-05    37
# 6  F 2022-02-06    46
# 7  G 2022-02-07    55
# 8  H 2022-02-08    64

Column names should not start with a number, actually they are no valid names, so R internally changes them (using make.names ). This is also why you need to use quotes to get those "wrong" names. So I wonder if your for loop actually works. I only brought it to work with your data like so:

for (i in 1:nrow(Data1)) {
  a <- Data1[[1]][[i]]
  b <- make.names(Data1[[2]][[i]])
  c <- Data2[Data2$ID == a, b]
  Data1$Value[i] <- c
}

If the column names of your real data start with numbers, please convert them using

names(data) <- make.names(names(data))

---

Data:

Data1 <- structure(list(ID = c("A", "B", "C", "D", "E", "F", "G", "H"), 
    Date = c("2022-02-01", "2022-02-02", "2022-02-03", "2022-02-04", 
    "2022-02-05", "2022-02-06", "2022-02-07", "2022-02-08")), class = "data.frame", row.names = c(NA, 
-8L))

Data2 <- structure(list(ID = c("A", "B", "C", "D", "E", "F", "G", "H"), 
    X2022.02.01 = 1:8, X2022.02.02 = 9:16, X2022.02.03 = 17:24, 
    X2022.02.04 = 25:32, X2022.02.05 = 33:40, X2022.02.06 = 41:48, 
    X2022.02.07 = 49:56, X2022.02.08 = 57:64), class = "data.frame", row.names = c(NA, 
-8L))