Create combined dataset

[oreHGA]

From the existing data sources, we will take the following columns:

• activity_watch_summarized = summary_day, total_time_surfing, what_app_did_user_spend_x_percent_of_time_on (one column per percent) , events_usage (tuple: event_classification (encoded), sum_total_time)

• google_calendar = summary_day, total_time_in_meetings, average_meeting_duration, mode_meeting_duration, count_of_meetings_initiated, count_of_meetings_invited, avg_number_of_people_per_meeting

• oura = (drop some rows) - remove combination identifiers

• tweets = summary_day, count_of_tweets, count_of_total_positive, total_neutral, total_negative, sum_likes_positive, sum_likes_negative, sum_likes_neutral, sum_retweets_positive, sum_retweets_negative, sum_retweets_neutral

these datasets will then be joined by summary_day for a single day's entry

Things to keep in mind during combination:

activity watch

• cluster events into groups (max 5 & then make them columns)
• encode all events, consider magnitude = 0: 70, 1: 30

google calendar

• considering how many meetings were created impromptu
• meetings initiated vs invited ( giving / taking time )
• how start_time vs end_time fit into daily activities

twitter

• original tweets vs retweets
• how are people interacting with tweets
• people interacting with

[oreHGA]

we now have daily summaries for

• oura --- daily summaries of activity, sleep and readiness
• spotify - average of the number of features happening daily
• activitywatch - day, classification of event, number of times it happened