All functions
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add_journey_id()
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Adds column grouping applicable rides as numbered journeys |
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add_tagon_time()
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Add columns for tag on time, trip duration to tag off transactions |
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add_transfer_time()
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Adds column classifying transfers as given by the transfer rules dataframe |
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add_trip_cost()
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Add column for previous purse amount and actual trip cost for trips with subtypes 2/3 |
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as_bart_journeys()
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Spreads multiple transactions across columns into one-row-per-bart-trip (with a focus on transfers in and out) |
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as_rides()
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Drop rows with tag on subtypes after recording the relevant tag on information in the tag off transactions |
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bart_identify()
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Adds columns identifying bart transactions, including lags and leads |
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bart_lag_and_lead_metadata()
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Adds columns describing the route, time, and operator name of previous and later transactions |
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bart_to_bart()
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Filters transactions to BART intra-transfers only |
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bart_transactions_per_user()
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Adds a column with a counts of the number of bart transactions per user and diff with all transactions |
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combined_bart_transactions()
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Creates combined dataframe of multiple transactions per row |
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create_transfer_df()
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Creates a summary table of all combinations of transfers |
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day_of_transactions()
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Pull all transactions for a given number of users |
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descriptive_tables()
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Get descriptive tables for transactions |
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fares_for_day()
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Partition the transaction table by travel survey/model day (starting at 3 am) |
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get_product_description()
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Add human-readable product names to transaction entries |
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identify_transfer_for_time()
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Adds column to indicate a transfer under given time windows |
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sample_day_of_transactions()
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Sample a day of transactions for a given number of users |
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spread_time_column()
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get a nicely formatted time dataframe
(yday, hour, yday, row, etc) for a column
we do the date parsing this way because of the UTC/datetime crossover |
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transactions_per_user()
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Adds a column with a counts of the number of transactions per user |