Home

cbal324

Hi all - I have a specific report format that I am trying to achieve using PROC REPORT, and I'm getting pretty close, but there's a column subtotal element that I cannot figure out. I have sales data, summarized by Client, Quarter, and Week. Each quarter contains an arbitrary number of weeks, up to around 13. I would like to list Client information down the side, with: Quarters listed across the top, each Quarter's corresponding Weeks nested underneath, the table populated with Sales for each Client-Week, and Grand Totals on the far right and bottom. I've gotten this far on my own just fine. Where I'm getting stuck is that I'd also like to show Quarterly subtotal columns, at the end of each Quarter. I have tried different variations of "break after" and the like, but I'm just not getting there. Does anyone in the community have any suggestions? Below is some sample code that represents the progress I've made so far. I've also attached an image showing what I currently "have" versus what I "want." I greatly appreciate any and all help - cheers! data one; input client_rank client $9. client_id quarter $ weekending sales; datalines; 1 Apple 12345 Q1 20240106 1000 1 Apple 12345 Q1 20240113 2000 1 Apple 12345 Q1 20240127 5000 1 Apple 12345 Q2 20240413 3000 1 Apple 12345 Q2 20240420 4000 1 Apple 12345 Q2 20240427 2000 2 Microsoft 67890 Q1 20240106 3000 2 Microsoft 67890 Q1 20240113 1000 2 Microsoft 67890 Q1 20240127 2500 2 Microsoft 67890 Q2 20240413 4000 2 Microsoft 67890 Q2 20240420 500 2 Microsoft 67890 Q2 20240427 1500 ; run; proc report data = one nowd; column client_rank client client_id quarter, weekending, sales ("Total" sales=tot); define client_rank / noprint group; define client / "Client" group; define client_id / "Client ID" group; define quarter / " " across; define weekending / " " across nozero; define sales / "Sales" analysis sum format=comma12.0; define tot / "Sales" analysis sum format=comma12.0; rbreak after / summarize; compute after; client = "Total"; endcomp; define client_rank / order group; run;

rick_b

When a gambler decides how much to bet, their decision is influenced by their prior wager outcomes (perhaps they start to double down if they have sustained losses, for example), and I am trying to examine how NFL gamblers' prior betting profits/losses influence their next-round-of-betting behavior. I need to sum just the profits/losses from last round of completed NFL events. NFL events occur in distinct blocks (e.g., Sunday at 1:00, 4:00, and 8:00, Monday at 8:00, Thursday at 9:00) and last three hours, but bets can be placed any time from a week before the block up until game time. The dataset I am attaching contains only one gambler but there are many gamblers (each uniquely identified by product_account_id), so the sum needs to begin anew when a new product_account_id is reached. As you can see, things get especially messy with rows 88-103. I would also like to set up a structure where I can sum the profits from the last 2, 3, 4, and 5 rounds of betting. Any help would be greatly appreciated. data WORK.bets; infile datalines; input product_account_id:$10. bet_id:$15. eleven_digit_account_id:$11. net_stake:3. bet_date:mmddyy10. bet_time:time8. bet_day_of_week:$10. event_date:mmddyy10. event_time:time8. event_day_of_week:$10. bet_profit:5. desired_output:5.; format bet_date mmddyy10. bet_time time8. event_date mmddyy10. event_time time8.; datalines; 12101170 81758848.516 00012101170 2 10/9/2023 20:29 Monday 10/9/2023 20:16 Monday 2 . 12101170 81758939.516 00012101170 3 10/9/2023 20:30 Monday 10/9/2023 20:16 Monday 2.4 . 12101170 81759255.516 00012101170 2 10/9/2023 20:34 Monday 10/9/2023 20:16 Monday 1.68 . 12101170 81760925.516 00012101170 3 10/9/2023 20:48 Monday 10/9/2023 20:16 Monday 2.61 . 12101170 81764937.516 00012101170 3 10/9/2023 21:26 Monday 10/9/2023 20:16 Monday -3 . 12101170 81768542.516 00012101170 3 10/9/2023 22:03 Monday 10/9/2023 20:16 Monday 2.64 . 12101170 82231252.516 00012101170 3 10/14/2023 14:38 Saturday 10/15/2023 13:01 Sunday 2.73 8.33 12101170 82231251.516 00012101170 3 10/14/2023 14:38 Saturday 10/15/2023 13:05 Sunday 2.73 8.33 12101170 82231250.516 00012101170 3 10/14/2023 14:38 Saturday 10/15/2023 13:05 Sunday 2.46 8.33 12101170 82231253.516 00012101170 3 10/14/2023 14:38 Saturday 10/15/2023 16:26 Sunday 2.85 8.33 12101170 82231254.516 00012101170 3 10/14/2023 14:38 Saturday 10/15/2023 16:27 Sunday -3 8.33 12101170 82231345.516 00012101170 3 10/14/2023 14:39 Saturday 10/15/2023 13:05 Sunday 2.46 8.33 12101170 82231346.516 00012101170 3 10/14/2023 14:39 Saturday 10/15/2023 13:05 Sunday 2.73 8.33 12101170 82231347.516 00012101170 3 10/14/2023 14:39 Saturday 10/15/2023 16:26 Sunday 2.85 8.33 12101170 82231348.516 00012101170 3 10/14/2023 14:39 Saturday 10/15/2023 16:27 Sunday -3 8.33 12101170 82231466.516 00012101170 3 10/14/2023 14:40 Saturday 10/15/2023 13:05 Sunday 2.73 8.33 12101170 82231465.516 00012101170 3 10/14/2023 14:40 Saturday 10/15/2023 13:05 Sunday 2.46 8.33 12101170 82231467.516 00012101170 3 10/14/2023 14:40 Saturday 10/15/2023 16:26 Sunday 2.85 8.33 12101170 82348229.516 00012101170 3 10/15/2023 7:49 Sunday 10/15/2023 9:35 Sunday -3 8.33 12101170 82348234.516 00012101170 3 10/15/2023 7:49 Sunday 10/15/2023 13:01 Sunday -3 8.33 12101170 82348231.516 00012101170 3 10/15/2023 7:49 Sunday 10/15/2023 13:01 Sunday 2.61 8.33 12101170 82348230.516 00012101170 3 10/15/2023 7:49 Sunday 10/15/2023 13:03 Sunday -3 8.33 12101170 82348233.516 00012101170 3 10/15/2023 7:49 Sunday 10/15/2023 13:04 Sunday 2.55 8.33 12101170 82348235.516 00012101170 3 10/15/2023 7:49 Sunday 10/15/2023 13:05 Sunday 2.94 8.33 12101170 82348232.516 00012101170 3 10/15/2023 7:49 Sunday 10/15/2023 13:05 Sunday 2.73 8.33 12101170 82348267.516 00012101170 3 10/15/2023 7:50 Sunday 10/15/2023 13:01 Sunday 2.61 8.33 12101170 82348270.516 00012101170 3 10/15/2023 7:50 Sunday 10/15/2023 13:01 Sunday -3 8.33 12101170 82348266.516 00012101170 3 10/15/2023 7:50 Sunday 10/15/2023 13:03 Sunday -3 8.33 12101170 82348269.516 00012101170 3 10/15/2023 7:50 Sunday 10/15/2023 13:04 Sunday 2.55 8.33 12101170 82348271.516 00012101170 3 10/15/2023 7:50 Sunday 10/15/2023 13:05 Sunday 2.94 8.33 12101170 82348268.516 00012101170 3 10/15/2023 7:50 Sunday 10/15/2023 13:05 Sunday 2.73 8.33 12101170 82348305.516 00012101170 3 10/15/2023 7:51 Sunday 10/15/2023 13:01 Sunday -3 8.33 12101170 82348307.516 00012101170 3 10/15/2023 7:51 Sunday 10/15/2023 13:01 Sunday 2.85 8.33 12101170 82348308.516 00012101170 3 10/15/2023 7:51 Sunday 10/15/2023 13:03 Sunday -3 8.33 12101170 82348306.516 00012101170 3 10/15/2023 7:51 Sunday 10/15/2023 13:05 Sunday 2.94 8.33 12101170 82348304.516 00012101170 3 10/15/2023 7:51 Sunday 10/15/2023 13:05 Sunday 2.73 8.33 12101170 82439386.516 00012101170 3 10/15/2023 13:14 Sunday 10/15/2023 13:04 Sunday -3 8.33 12101170 82439455.516 00012101170 3 10/15/2023 13:14 Sunday 10/15/2023 13:01 Sunday 2.73 8.33 12101170 82439531.516 00012101170 3 10/15/2023 13:14 Sunday 10/15/2023 13:05 Sunday 2.73 8.33 12101170 82478787.516 00012101170 3 10/15/2023 16:02 Sunday 10/15/2023 16:06 Sunday 2.46 32.49 12101170 82478786.516 00012101170 3 10/15/2023 16:02 Sunday 10/15/2023 16:26 Sunday 2.73 32.49 12101170 82478788.516 00012101170 3 10/15/2023 16:02 Sunday 10/15/2023 16:26 Sunday 2.73 32.49 12101170 82478789.516 00012101170 3 10/15/2023 16:02 Sunday 10/15/2023 16:27 Sunday -3 32.49 12101170 82478974.516 00012101170 3 10/15/2023 16:02 Sunday 10/15/2023 16:06 Sunday 2.46 32.49 12101170 82478973.516 00012101170 3 10/15/2023 16:02 Sunday 10/15/2023 16:26 Sunday 2.73 32.49 12101170 82478975.516 00012101170 3 10/15/2023 16:02 Sunday 10/15/2023 16:27 Sunday -3 32.49 12101170 82479827.516 00012101170 6 10/15/2023 16:03 Sunday 10/15/2023 16:26 Sunday 5.46 32.49 12101170 82479826.516 00012101170 6 10/15/2023 16:03 Sunday 10/15/2023 16:27 Sunday -6 32.49 12101170 82482465.516 00012101170 3 10/15/2023 16:07 Sunday 10/15/2023 16:26 Sunday 2.73 32.49 12101170 82493073.516 00012101170 2 10/15/2023 16:34 Sunday 10/15/2023 16:26 Sunday 1.92 32.49 12101170 82550551.516 00012101170 2 10/15/2023 20:57 Sunday 10/15/2023 20:23 Sunday -2 11.22 12101170 82553228.516 00012101170 2 10/15/2023 21:23 Sunday 10/15/2023 20:23 Sunday -2 11.22 12101170 82555259.516 00012101170 2 10/15/2023 21:44 Sunday 10/15/2023 20:23 Sunday -2 11.22 12101170 82646184.516 00012101170 3 10/16/2023 20:25 Monday 10/16/2023 20:16 Monday -3 -6 12101170 82647533.516 00012101170 3 10/16/2023 20:34 Monday 10/16/2023 20:16 Monday -3 -6 12101170 82647545.516 00012101170 6 10/16/2023 20:34 Monday 10/16/2023 20:16 Monday -6 -6 12101170 82899789.516 00012101170 2 10/19/2023 20:23 Thursday 10/19/2023 20:16 Thursday 1.96 -12 12101170 82904693.516 00012101170 3 10/19/2023 20:59 Thursday 10/19/2023 20:16 Thursday 2.64 -12 12101170 82906147.516 00012101170 3 10/19/2023 21:08 Thursday 10/19/2023 20:16 Thursday -3 -12 12101170 83235976.516 00012101170 3 10/22/2023 9:23 Sunday 10/22/2023 13:02 Sunday -3 1.6 12101170 83235974.516 00012101170 3 10/22/2023 9:23 Sunday 10/22/2023 13:03 Sunday -3 1.6 12101170 83236035.516 00012101170 3 10/22/2023 9:24 Sunday 10/22/2023 13:02 Sunday -3 1.6 12101170 83236033.516 00012101170 3 10/22/2023 9:24 Sunday 10/22/2023 13:03 Sunday -3 1.6 12101170 83236190.516 00012101170 3 10/22/2023 9:25 Sunday 10/22/2023 13:02 Sunday -3 1.6 12101170 83237824.516 00012101170 3 10/22/2023 9:36 Sunday 10/22/2023 13:01 Sunday -3 1.6 12101170 83237827.516 00012101170 3 10/22/2023 9:36 Sunday 10/22/2023 13:02 Sunday -3 1.6 12101170 83237823.516 00012101170 3 10/22/2023 9:36 Sunday 10/22/2023 13:03 Sunday -3 1.6 12101170 83237825.516 00012101170 3 10/22/2023 9:36 Sunday 10/22/2023 13:05 Sunday 2.64 1.6 12101170 83237829.516 00012101170 3 10/22/2023 9:36 Sunday 10/22/2023 16:06 Sunday -3 1.6 12101170 83237922.516 00012101170 3 10/22/2023 9:36 Sunday 10/22/2023 13:01 Sunday -3 1.6 12101170 83237924.516 00012101170 3 10/22/2023 9:36 Sunday 10/22/2023 13:02 Sunday -3 1.6 12101170 83237921.516 00012101170 3 10/22/2023 9:36 Sunday 10/22/2023 13:03 Sunday -3 1.6 12101170 83237926.516 00012101170 3 10/22/2023 9:36 Sunday 10/22/2023 16:06 Sunday -3 1.6 12101170 83238000.516 00012101170 3 10/22/2023 9:37 Sunday 10/22/2023 13:01 Sunday -3 1.6 12101170 83238002.516 00012101170 3 10/22/2023 9:37 Sunday 10/22/2023 13:02 Sunday -3 1.6 12101170 83237999.516 00012101170 3 10/22/2023 9:37 Sunday 10/22/2023 13:03 Sunday -3 1.6 12101170 83238004.516 00012101170 3 10/22/2023 9:37 Sunday 10/22/2023 16:06 Sunday -3 1.6 12101170 83238165.516 00012101170 3 10/22/2023 9:38 Sunday 10/22/2023 13:01 Sunday -3 1.6 12101170 83238164.516 00012101170 3 10/22/2023 9:38 Sunday 10/22/2023 13:03 Sunday -3 1.6 12101170 83238170.516 00012101170 3 10/22/2023 9:38 Sunday 10/22/2023 13:03 Sunday -3 1.6 12101170 83238167.516 00012101170 3 10/22/2023 9:38 Sunday 10/22/2023 16:06 Sunday -3 1.6 12101170 83238244.516 00012101170 3 10/22/2023 9:38 Sunday 10/22/2023 13:01 Sunday -3 1.6 12101170 83238249.516 00012101170 3 10/22/2023 9:38 Sunday 10/22/2023 13:03 Sunday -3 1.6 12101170 83238246.516 00012101170 3 10/22/2023 9:38 Sunday 10/22/2023 16:06 Sunday -3 1.6 12101170 83238318.516 00012101170 3 10/22/2023 9:39 Sunday 10/22/2023 13:03 Sunday -3 1.6 12101170 83238315.516 00012101170 3 10/22/2023 9:39 Sunday 10/22/2023 16:06 Sunday -3 1.6 12101170 83263216.516 00012101170 6 10/22/2023 11:14 Sunday 10/22/2023 13:02 Sunday -6 1.6 12101170 83395974.516 00012101170 3 10/22/2023 16:35 Sunday 10/22/2023 16:06 Sunday -3 -63.36 12101170 83395975.516 00012101170 3 10/22/2023 16:35 Sunday 10/22/2023 16:06 Sunday -3 -63.36 12101170 83395972.516 00012101170 3 10/22/2023 16:35 Sunday 10/22/2023 16:26 Sunday 2.55 -63.36 12101170 83395973.516 00012101170 3 10/22/2023 16:35 Sunday 10/22/2023 16:26 Sunday 2.55 -63.36 12101170 83398125.516 00012101170 3 10/22/2023 16:44 Sunday 10/22/2023 16:06 Sunday -3 -63.36 12101170 83398126.516 00012101170 3 10/22/2023 16:44 Sunday 10/22/2023 16:06 Sunday -3 -63.36 12101170 83398127.516 00012101170 3 10/22/2023 16:44 Sunday 10/22/2023 16:26 Sunday 2.64 -63.36 12101170 83398128.516 00012101170 3 10/22/2023 16:44 Sunday 10/22/2023 16:26 Sunday 2.82 -63.36 12101170 83403470.516 00012101170 3 10/22/2023 17:13 Sunday 10/22/2023 16:06 Sunday -3 -63.36 12101170 83403472.516 00012101170 3 10/22/2023 17:13 Sunday 10/22/2023 16:26 Sunday -3 -63.36 12101170 83408418.516 00012101170 3 10/22/2023 17:44 Sunday 10/22/2023 16:26 Sunday -3 -63.36 12101170 83417137.516 00012101170 3 10/22/2023 18:35 Sunday 10/22/2023 16:06 Sunday -3 -63.36 12101170 83417136.516 00012101170 3 10/22/2023 18:35 Sunday 10/22/2023 16:06 Sunday 1.71 -63.36 12101170 83417135.516 00012101170 3 10/22/2023 18:35 Sunday 10/22/2023 16:26 Sunday -3 -63.36 12101170 83417134.516 00012101170 3 10/22/2023 18:35 Sunday 10/22/2023 16:26 Sunday 1.89 -63.36 12101170 83417192.516 00012101170 3 10/22/2023 18:35 Sunday 10/22/2023 16:26 Sunday -3 -63.36 12101170 83540873.516 00012101170 3 10/23/2023 19:09 Monday 10/23/2023 20:16 Monday -3 -24 12101170 83575341.516 00012101170 3 10/23/2023 21:21 Monday 10/23/2023 20:16 Monday -3 -24 12101170 83576079.516 00012101170 3 10/23/2023 21:28 Monday 10/23/2023 20:16 Monday -3 -24 proc print; run;

osmelbrito

Does anyone have compared Dataflux vs Profisee? I think that Dataflux has more features than Profisee.

lindamtl

when use proc logistic procedure to set up default model in bank risk analysis, for the current customer, there are 4 kinds of results after application for the loan, accept, refuse , cancel or nouse. when set up model, to analysis if new customer will default or could approve their application of loan, we will use the current customer loan history. refused=1, accept=0 but how to deal with cancel and nouse, i think nouse looked as accepted, but for cancel, how to deal with it?

stellapersis7

Hi all, Please help me to code this. This is the dataset below- I have 2 drugs being used for each ID i.e., SGA and DM. I have multiple start date and end dates for each ID and drug. I want to see if the DM_start falls into any of the SGA_start and SGA_end under each id. That is DM_start of period 3 can fall in the SGA_start and SGA_end of period1. And also calculate the number of overlapping days for each time period. I want to know the no. of days for each orange period. Thank you all in advance, Any help would be appreciated.