使用 R 从满足每个 vendor_id 多个条件的大型数据集中提取行

Question

Using R Programming. I have dataset with Vendor_id, Bank_account_no and Date with over 3 million. I want to get the rows for each vendor_id where the Bank_account_no changes, for example from X to X to X (at least three time, could be more than three) to Y (just once) to X again within a three months period. The dataset is such that the changes are all random so the window is not fixed with number of rows for each vendor_id. I using the rle function to get length for different Bank_account_no. Not sure how can I create a logic in R for these many rows considering I want to run this logic for each vendor_id. May be data.table can help in this. The input is as follows:

Vendor_ID   Bank_account_no   Date    

<!-- -->
dddd       X              24-12-2018
dddd       X              24-12-2018
dddd       X              26-12-2018
dddd       Y              27-12-2018
dddd       X              28-12-2018
dddd       X              29-12-2018
dddd       X              29-12-2018
dddd       X              31-12-2018
dddd       X              24-01-2019
dddd       Z              25-01-2019
dddd       X              28-01-2019
dddd       G              28-01-2019
dddd       G              28-01-2019
eeee       A              30-01-2019
eeee       A              31-01-2019
eeee       A              31-01-2019    
eeee       B              31-01-2019
eeee       A              31-01-2019

The output should be:

Vendor_ID   Bank_account_no   Date    Case

<!-- -->
dddd       X              24-12-2018  Case1
dddd       X              24-12-2018  Case1
dddd       X              26-12-2018  Case1
dddd       Y              27-12-2018  Case1
dddd       X              28-12-2018  Case1
dddd       X              29-12-2018  Case2
dddd       X              29-12-2018  Case2
dddd       X              31-12-2018  Case2
dddd       X              24-01-2019  Case2
dddd       Z              25-01-2019  Case2
dddd       X              28-01-2019  Case2
eeee       A              30-01-2019  Case3
eeee       A              31-01-2019  Case3
eeee       A              31-01-2019  Case3    
eeee       B              31-01-2019  Case3
eeee       A              31-01-2019  Case3

Answer 1

There might be edge cases not covered but this is an option to start you off:

#get run length encoding for ID
DT[, r := rleid(ID)]

#identify locations of desired changes in ID
DT[, w := ID!=shift(ID, -1L) & ID==shift(ID, -2L)]

#set to TRUE at the start of a sequence
DT[, sw := replace(shift(w, 3L, fill=FALSE), 1L, TRUE), Vendor_ID]

#filter out those not part of any sequence
ans <- DT[-DT[, {
        ix <- which(w)
        if (length(ix) > 0L) .I[!r %in% r[sapply(ix, `+`, 0L:2L)]]
    }, Vendor_ID]$V1]

#create the desired Case column
ans[, Case := paste0("Case", cumsum(sw))]

output:

    Vendor_ID ID       Date r     w    sw  Case
 1:      dddd  X 24-12-2018 1 FALSE  TRUE Case1
 2:      dddd  X 24-12-2018 1 FALSE FALSE Case1
 3:      dddd  X 26-12-2018 1  TRUE FALSE Case1
 4:      dddd  Y 27-12-2018 2 FALSE FALSE Case1
 5:      dddd  X 28-12-2018 3 FALSE FALSE Case1
 6:      dddd  X 29-12-2018 3 FALSE  TRUE Case2
 7:      dddd  X 29-12-2018 3 FALSE FALSE Case2
 8:      dddd  X 31-12-2018 3 FALSE FALSE Case2
 9:      dddd  X 24-01-2019 3  TRUE FALSE Case2
10:      dddd  Z 25-01-2019 4 FALSE FALSE Case2
11:      dddd  X 28-01-2019 5 FALSE FALSE Case2
12:      eeee  A 30-01-2019 7 FALSE  TRUE Case3
13:      eeee  A 31-01-2019 7 FALSE FALSE Case3
14:      eeee  A 31-01-2019 7  TRUE FALSE Case3
15:      eeee  B 31-01-2019 8    NA FALSE Case3
16:      eeee  A 31-01-2019 9    NA FALSE Case3

data:

library(data.table)
DT <- fread("Vendor_ID   ID   Date    
dddd       X              24-12-2018
dddd       X              24-12-2018
dddd       X              26-12-2018
dddd       Y              27-12-2018
dddd       X              28-12-2018
dddd       X              29-12-2018
dddd       X              29-12-2018
dddd       X              31-12-2018
dddd       X              24-01-2019
dddd       Z              25-01-2019
dddd       X              28-01-2019
dddd       G              28-01-2019
dddd       G              28-01-2019
eeee       A              30-01-2019
eeee       A              31-01-2019
eeee       A              31-01-2019    
eeee       B              31-01-2019
eeee       A              31-01-2019")