Split delimited 2nd and 3rd column data into new rows - vba

Question

I have the following table
ID. ID2. String
123. 567, 986 ABC;BCD;ACD
142. 134, 654,1134 AA;BB
I want it to be displayed
ID ID2 String
123 567 ABC
123 986 BCD
123 ACD
142 134 AA
142 654 bb
142 1134
The values in the ID column are unique.
Is there an efficient macro solution to this? I have a very huge set of data.

Answer 1

Try this.
Sub FlattenData()
Dim rng As Range, arr() As Variant, i As Long, rw As Long, j As Long
Set rng = Range("A1:C2") //Update for your range
arr() = rng
rng.ClearContents
rw = 0
For i = 1 To UBound(arr, 1)
colBTemp = VBA.Split(arr(i, 2), ",")
colCTemp = VBA.Split(arr(i, 3), ";")
colBTempLength = UBound(colBTemp, 1) + 1
colCTempLength = UBound(colCTemp, 1) + 1
requiredRows = WorksheetFunction.Max(colBTempLength, colCTempLength)
For j = 1 To requiredRows
Range("A" & rw + j) = arr(i, 1)
If j <= colBTempLength Then
Range("B" & rw + j) = colBTemp(j - 1)
Else
Range("B" & rw + j) = vbNullString
End If
If j <= colCTempLength Then
Range("C" & rw + j) = colCTemp(j - 1)
Else
Range("C" & rw + j) = vbNullString
End If
Next j
rw = rw + requiredRows
Next i
End Sub

Answer 2

With only the starting, concatenated data in the active sheet and ID is in A1, run this macro.
Sub split_out()
Dim v As Long, vVALs As Variant, vID2s As Variant, vSTRs As Variant
Dim rw As Long, lr As Long, mx As Long
With ActiveSheet
lr = .Cells(Rows.Count, 1).End(xlUp).Row
.Cells(1, 1).CurrentRegion.Rows(1).Copy Destination:=.Cells(lr + 2, 1)
For rw = 2 To lr
vVALs = Application.Index(.Cells(rw, 1).Resize(1, 3).Value, 1, 0)
vID2s = Split(vVALs(2), Chr(44))
vSTRs = Split(vVALs(3), Chr(59))
mx = Application.Max(UBound(vID2s), UBound(vSTRs))
For v = LBound(vID2s) To mx
.Cells(Rows.Count, 1).End(xlUp).Offset(1, 0) = vVALs(1)
If UBound(vID2s) >= v Then _
.Cells(Rows.Count, 1).End(xlUp).Offset(0, 1) = vID2s(v)
If UBound(vSTRs) >= v Then _
.Cells(Rows.Count, 1).End(xlUp).Offset(0, 2) = vSTRs(v)
Next v
Next rw
End With
End Sub
The flattened data will be populated below the existing data. Your results should be similar to the following.