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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.

Usage

The following examples show how the step can be used in a recipe.

Examples

Aggregate tweets by author using standard Twitter column names

Inputs & Outputs

The following are the inputs expected by the step and the outputs it produces. These are generally columns (ds.first_name), datasets (ds or ds[["first_name", "last_name"]]) or models (referenced by name e.g. "churn-clf").
dataset
required
A dataset where each row is a tweet.
dataset
required
Result of the aggregation, where each row is a twitter account. It will include for each author up to the following columns, depending on information present on the original dataset:
  • author_id: Official Twitter ID
  • tweet_count: Number of tweets by this author
  • handler: Official Twitter handle
  • name: User name
  • pic: Link to user’s profile picture
  • links: A list of links mentioned by the user
  • dates: A list of dates of published tweets by this author
  • tweet_ids: The official Twitter IDs of the tweets published by the author
  • retweets: The number of retweets received
  • favorites: The number of favorites received
  • mention_ids: List of other accounts (IDs) the author has mentioned
  • mention_names: List of other accounts (names) the author has mentioned
  • rp_user_ids: List of other accounts (IDs) the author has replied to
  • rp_user_names: List of other accounts (names) the author has replied to
  • mentions: The count of mentions received
  • replies: The count of replies received
  • tweet_text: The text of the author’s tweets, concatenated.

Configuration

The following parameters can be used to configure the behaviour of the step by including them in a json object as the last “input” to the step, i.e. step(..., {"param": "value", ...}) -> (output).

Parameters

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.
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].