Lapsed Emails use-case using Einstein Compute Relative Transformation

At first glance, this seems like a trivial use-case. First, contacts that were sent emails in 2018 and 2019 need to be counted.Afterwards all those contacts will be considered 'lasped' if their emails bounced in 2020 or emails not opened in 2020. I had to extract the year because the date field containing email opened and email bounced were in string form so I had to use the len() function since dates can be in xx/xx/2020 or x/x/2020 or x/xx/2020 form.Here is the function substr('Date_Email_Opened',len('Date_Email_Opened') -3,4). Two fields were created-openedYr and bouncedYr. The left part of the snip below represents the raw data with derived fields from above added on right. In addition, 2 compute relative transformations were added to create 'bouncedIn2020' and 'openedIn2020' fields. After dataset creation, it becomes a simple cogroup in SAQL to bring out the different totals 1)# of contacts with emails sent in 2018-19   2)from that list, number of contacts where no emails were sent in 2020 or bounced in 2020.  3) % of lapsed = list2 / list 1

q = load "zds_processedYears";
q_B = filter q by 'OpenedYear' in ["2018", "2019"];
q_C = filter q by 'OpenedYear' in ["2018", "2019"] && ('openedIn2020' == "false" or 'bouncedIn2020' =="true");
result = group q by all full, q_B by all full, q_C by all;
result = foreach result generate count(q) as 'RawData', unique(q_B.'Contact_Id') as 'EmailSent2yrs', unique(q_C.'Contact_Id') as 'SentButNo2020OrBounced2020', unique(q_C.'Contact_Id') / unique(q_B.'Contact_Id')  as 'lapsedPercentage';