The fоllоwing SQL query creаtes а tаble named gym with the fоllowing columns and data: DROP TABLE IF EXISTS gym;CREATE TABLE gym (trans_id int PRIMARY KEY, userid text, workout_type text, calories_burned int, checkin timestamp, duration int);INSERT INTO gym VALUES(1,'user_1063','CrossFit',429,'2023-06-01 07:06:00',38),(2,'user_1104','Swimming',954,'2023-06-01 10:54:00',67),(3,'user_1014','CrossFit',1464,'2023-06-02 08:52:00',140),(4,'user_1010','CrossFit',1325,'2023-06-02 11:50:00',61),(5,'user_1010','Weightlifting',344,'2023-06-03 06:24:00',127),(6,'user_1098','Yoga',344,'2023-06-03 12:06:00',48),(7,'user_1071','Swimming',1102,'2023-06-03 14:29:00',112),(8,'user_1034','Yoga',849,'2023-06-03 17:14:00',133),(9,'user_1023','CrossFit',723,'2023-06-04 09:02:00',139),(10,'user_1063','Cardio',1028,'2023-06-04 16:27:00',122),(11,'user_1034','Pilates',698,'2023-06-04 19:15:00',128),(12,'user_1010','Yoga',672,'2023-06-05 08:58:00',168),(13,'user_1006','Weightlifting',291,'2023-06-05 09:13:00',122),(14,'user_1023','Weightlifting',1682,'2023-06-05 11:00:00',170),(15,'user_1028','Weightlifting',432,'2023-06-05 20:20:00',177),(16,'user_1071','CrossFit',948,'2023-06-06 06:48:00',55),(17,'user_1104','Yoga',805,'2023-06-06 15:09:00',158),(18,'user_1006','Yoga',998,'2023-06-07 08:12:00',151),(19,'user_1010','Swimming',502,'2023-06-07 08:56:00',171),(20,'user_1063','Cardio',1058,'2023-06-07 09:28:00',65),(21,'user_1097','Yoga',1169,'2023-06-07 14:26:00',84),(22,'user_1071','Weightlifting',1012,'2023-06-08 06:02:00',157),(23,'user_1104','Yoga',1602,'2023-06-08 16:29:00',155),(24,'user_1071','Weightlifting',1194,'2023-06-09 07:07:00',159),(25,'user_1023','Yoga',322,'2023-06-11 09:48:00',113),(26,'user_1063','CrossFit',1387,'2023-06-11 13:03:00',179),(27,'user_1063','CrossFit',637,'2023-06-14 13:45:00',146) ; Source: https://www.kaggle.com/datasets/mexwell/gym-check-ins-and-user-metadata Here are brief descriptions of the data fields: trans_id: unique identifier for the visit userid: ID of the user who checked in workout_type: Type of workout performed during the visit calories_burned: Estimated number of calories burned during the workout checkin: date and time user checked in duration: time from check in to completion of workout (minutes) Using pgAdmin, execute the table creation script provided above to initialize your dataset. Then, construct a SQL query that accomplishes the following tasks using a Common Table Expression (CTE) structure, organized into three logical parts: Part 1: Daily Aggregation by Workout Type Each user can only check in once per day. Your first step is to extract the date component from the checkin timestamp and alias it as checkin_date. Then, aggregate the data at the (workout_type, checkin_date) level to compute: cal_per_min: Represents the daily rate of calories burned per minute for each workout_type, computed by dividing the total daily calories_burned by the corresponding total daily duration. This metric captures workout intensity on a per-minute basis across individual exercise types. Your output should include the following three columns: workout_type, checkin_date, and cal_per_min. --> Example: On 2023-06-04, about 8.43 was burned per minute for Cardio; On 2023-06-01, about 11.29 was burned per minute for CrossFit ; Part 2: Moving and Overall Averages Create an additional CTE based on the results from Part 1 to compute the following metrics: Part 2.1: 3-Day Moving Average For each workout type and check-in date, calculate a 3-day moving average of cal_per_min, considering only the three most recent prior check-in dates, excluding the current date and any future dates (based on actual check-in history, not calendar days). Name this column cal_pm_3dma. Additionally, count the number of days included in each moving average window and store this as num_days. Part 2.2: Overall Average Within the same CTE as Part 2.1, use a different window frame to compute the overall average of cal_per_min for each workout type across all available check-in dates. Name this column cal_pm_avg. Part 3: Final Output Return the following columns: workout_type, checkin_date, cal_per_min, cal_pm_3dma, cal_pm_avg Filter the results to include only those rows where the 3-day moving average (cal_pm_3dma) is based on a full window of three prior days. The final result set should align with the structure and layout of the output shown below, with the exception of minor rounding differences. workout_type checkin_date cal_per_min cal_pm_3dma cal_pm_avg CrossFit 2023-06-06 17.24 10.12 9.95 CrossFit 2023-06-11 7.75 12.10 9.95 CrossFit 2023-06-14 4.36 10.06 9.95 Weightlifting 2023-06-09 7.51 4.76 5.45 Yoga 2023-06-07 9.22 5.23 6.35 Yoga 2023-06-08 10.34 6.11 6.35 Yoga 2023-06-11 2.85 8.22 6.35 Here is a template to follow for constructing the query:-- Use common table expression to write the query in three partsWITH daily_agg AS ( --Part 1 ),sec_agg AS ( --Part 2) -- Part 3SELECT Submit your complete query in the window below.
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