Before mining frequent sequential patterns, you are required to create transactions with the temporal information. In this recipe, we introduce two methods to obtain transactions with temporal information. In the first method, we create a list of transactions and assign a transaction ID for each transaction. We use the as function to transform the list data into a transaction dataset. We then add eventID and sequenceID as temporal information; sequenceID is the sequence that the event belongs to, and eventID indicates when the event occurred. After generating transactions with temporal information, one can use this dataset for frequent sequential pattern mining.

In addition to creating your own transactions with temporal information, if you already have data stored in a text file, you can use the read_basket function from arulesSequences to read the transaction data into the basket format. We can also read the transaction dataset for further frequent sequential pattern mining.