Works like the generic aggregate step, but with a predefined set of aggregation functions. See the ds_out argument below for the columns generated in the resulting dataset.

add_referenced_accounts
boolean
default: "true"

Whether to add rows for accounts only “mentioned” in original tweets. If mentions or replies are recorded in the dataset (in columns mention_ids, mention_names and/or rp_user_id, rp_user_name) will add the corresponding accounts as rows in the result, even if they didn’t have a tweet in the original dataset.

Will add mentions and replies columns recording how many times the accounts were mentioned or replied to.

column_map
object

Column Map. If the names of any of your dataset’s columns don’t correspond to those we expect to find in a tweet dataset (e.g. originating in Twitter’s own API), you can provide a mapping of of the sort {"your_column": "author_id"}.

The expected column names are [author_id, author_handler, author_name, author_avatar, links, date, id, retweets, favorites, mention_ids, mention_names, rp_user_id, rp_user_name , text].

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