Here is my problem:
I have 1 year of data, but one of the key criteria, rate per miles, is updated three times during the year. If you can imagine, from January to March we would charge $1.00, from March to September we would charge $1.25 and from September til year-end, we would charge $1.30. Updates were not made so uniformly however (such that the rate changes, and months of change differed). I created a formula which, based on some characteristics of the data, identifies which rows to keep, remove or overwrite.
My dataset would have data points for an April trip at $1.00, despite the rate having changed to $1.25 back in March. I had an identifier in the dataset which called out whether a rate was updated, and, if so, when the update happened. If the contract's start date lines us with the expected start date, the item is given a "Keep" identifier. If an update occurred before the month of accrual for the datapoint, the row should be dropped.
I was able to get rid of all the 'Remove' items with a filter tool, but am unsure how to get rid of the 'Keep' items which are obsolete due to the update.
Does anyone have any ideas?