Analyze document collaboration patterns

You are given two CSV files and asked to analyze collaboration behavior on documents. ### File 1: `document_activity.csv` Each row is a document view event. Schema: - `page_id` STRING — unique document ID - `user_id` STRING — user who viewed the document - `viewed_at` TIMESTAMP — event timestamp in UTC - `is_creator` BOOLEAN — `true` if the user is the document creator, `false` otherwise - `collaborator_source` STRING NULL — for non-creators, how the user arrived at the document (for example: invite link, email invite, search, direct). This field may be `NULL` for creators. ### File 2: `users.csv` Schema: - `user_id` STRING — unique user ID - `country` STRING — user country ### Assumptions - Join key: `document_activity.user_id = users.user_id`. - Use the full dataset; do not filter by date. - A user may view the same document multiple times, so for collaboration questions you should deduplicate to distinct `(page_id, user_id)` pairs unless a task explicitly asks for event counts. - A document may have one creator and multiple collaborators. ### Tasks 1. **Compute the distribution of collaborator acquisition sources.** - Consider only non-creators (`is_creator = false`). - Define the metric on distinct collaborator-document pairs `(page_id, user_id)`. - Required output columns: - `collaborator_source` - `collaborator_count` - `collaborator_share` 2. **Given an input `user_id`, find which other users collaborated with that user the most.** - Two users are considered to have collaborated if they both appeared on the same `page_id` at least once. - Count distinct shared documents, not view events. - Exclude the target user themself. - Required output columns: - `target_user_id` - `co_user_id` - `shared_page_count` - Return the user or users with the maximum `shared_page_count`. 3. **Find which country's users are most likely to collaborate with others.** - To make the metric precise, define a user as having "collaborated with others" if they appear on at least one document with at least one other distinct user (that is, the document has 2 or more distinct users total). - For each country, compute: - `collaborating_users` = number of distinct users in that country who collaborated with at least one other user - `total_users` = number of distinct users in that country from `users.csv` - `collaboration_rate = collaborating_users / total_users` - Required output columns: - `country` - `collaborating_users` - `total_users` - `collaboration_rate` - Rank countries from highest to lowest collaboration rate.