Keypup

Field ID: <code>author_username</code>

Description: The username of the person who created the comment. Note that usernames are app-specific.

The <code>Author</code> field from the Comments dataset is essential for understanding who is participating in discussions, identifying your team's most active communicators, and analyzing the flow of feedback. As a text field, it is primarily used for filtering and as a dimension for grouping.

Filtering for Specific Contributors: You can easily scope your reports to focus on comments from specific individuals or to exclude noise.

- Track Individual Activity: Create a report showing all comments made by a specific person, with a filter like <code>Author = "jane.doe"</code>.
- Exclude Bots: A common use case is to filter out automated comments from bots to focus on human interaction, using a filter like <code>Author !~ "bot"</code>.
- Personal View: Use the is me operator to create a widget that shows all the comments you have made.

Reporting on Engagement and Workload: Using this field as a dimension allows you to see who is most active in discussions.

- Top Commenters: A bar chart with <code>Author</code> as the dimension and <code>COUNT()</code> as the metric will instantly show you who your most active commenters are.
- Comment Distribution: By adding a second dimension like <code>Parent Type</code>, you can analyze who tends to comment more on "issues" versus "pull requests."

Custom Formulas for Advanced Analysis: You can use this field to create more nuanced metrics about your communication patterns.

- Peer vs. Self-Comments: You can distinguish between an author commenting on their own pull request and a peer providing feedback. The formula <code>COUNT_IF(author_username != parent_author_username)</code> will count only the comments made by someone other than the PR author.
- Number of Unique Commenters: A KPI with the formula <code>COUNT_DISTINCT(author_username)</code> can measure the breadth of participation in discussions over a given period.

- Filtering for Specific Contributors: You can easily scope your reports to focus on comments from specific individuals or to exclude noise.
 - Track Individual Activity: Create a report showing all comments made by a specific person, with a filter like <code>Author = "jane.doe"</code>.
 - Exclude Bots: A common use case is to filter out automated comments from bots to focus on human interaction, using a filter like <code>Author !~ "bot"</code>.
 - Personal View: Use the is me operator to create a widget that shows all the comments you have made.
- Reporting on Engagement and Workload: Using this field as a dimension allows you to see who is most active in discussions.
 - Top Commenters: A bar chart with <code>Author</code> as the dimension and <code>COUNT()</code> as the metric will instantly show you who your most active commenters are.
 - Comment Distribution: By adding a second dimension like <code>Parent Type</code>, you can analyze who tends to comment more on "issues" versus "pull requests."
- Custom Formulas for Advanced Analysis: You can use this field to create more nuanced metrics about your communication patterns.
 - Peer vs. Self-Comments: You can distinguish between an author commenting on their own pull request and a peer providing feedback. The formula <code>COUNT_IF(author_username != parent_author_username)</code> will count only the comments made by someone other than the PR author.
 - Number of Unique Commenters: A KPI with the formula <code>COUNT_DISTINCT(author_username)</code> can measure the breadth of participation in discussions over a given period.

Comments > Author

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From:
Github (Comments)	`author`
Gitlab (Comments)	`author`
Bitbucket (Comments)	`user`
Azure DevOps (Comments)	`author`
JIRA (Comments)	`author`
ClickUp (Comments)	`user`
Trello (Comments)	`idMemberCreator`

Comments > Author

Reporting Use Cases