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07_​Data_​Aggregation

Data Aggregation

Exercise 7 for the KNIME Analytics Platform for Data Wranglers course
- Calculate multiple aggregations using the Pivoting and GroupBy node
- Join grouped data
- Extract date fields from a DateTime value
- Handle missing values
- Manipulate strings
- Calculate new features using the Math Formula node





Exercise 7: Data AggregationIn this exercise you generate new columns by aggregating data by groups and create pivot tables that showaggregated values by two or more groups 4) (optional) Convert the dates ofbirths of the customers to Date&Timeand extract the birth year into aseparate column 2a) Calculate the total purchase amount by quarter and transaction type (Pivoting)2b) Set missing numeric values to zero2c) Combine the year and quarter information to a single column. Use "/" as separator.(String Manipulation node) 3a) (optional) Calculate the numbers of orders by a basket size and a transaction type 3.1 Calculate the number of purchased products by an order number. Retain theinformation on the transaction type. 3.2 Create a pivot table that shows the numbers of orders by a basket size and atransaction type3b) (optional) Set missing integers to zero3c) (optional) Calculate the proportions of the different basket sizes among eachtransaction type 1a) Calculate the total purchase amount by a customer ID both in 2019 and earlier 1.1 Calculate the sum of product prices by a customer ID in the data for purchasesin 2019 and retain the information whether or not a customer has signed up for thenewsletter (GroupBy node) 1.2 Rename the aggregated column to "Basket Sum 2019" 1.3 Calculate the sum of product prices by a customer ID concerning thepurchases earlier than 2019. Rename the aggregated column to "Basket Sum Overall" 1.4 Use an inner join to join the aggregated columns on the CustomerID. (for nowwe are only interested in the customers who bought something in 2019 AND before 2019) Note - we didn't cover Pivoting slides yet! Extract QuarterAnd YearTransform Transaction Date into Date&Time FomatTop: 2019Bottom: Earlier than 2019Node 314Extract Date&TimeFields (deprecated) String to Date&Time Read joined andpreprocessed data Partition by time GroupBy Exercise 7: Data AggregationIn this exercise you generate new columns by aggregating data by groups and create pivot tables that showaggregated values by two or more groups 4) (optional) Convert the dates ofbirths of the customers to Date&Timeand extract the birth year into aseparate column 2a) Calculate the total purchase amount by quarter and transaction type (Pivoting)2b) Set missing numeric values to zero2c) Combine the year and quarter information to a single column. Use "/" as separator.(String Manipulation node) 3a) (optional) Calculate the numbers of orders by a basket size and a transaction type 3.1 Calculate the number of purchased products by an order number. Retain theinformation on the transaction type. 3.2 Create a pivot table that shows the numbers of orders by a basket size and atransaction type3b) (optional) Set missing integers to zero3c) (optional) Calculate the proportions of the different basket sizes among eachtransaction type 1a) Calculate the total purchase amount by a customer ID both in 2019 and earlier 1.1 Calculate the sum of product prices by a customer ID in the data for purchasesin 2019 and retain the information whether or not a customer has signed up for thenewsletter (GroupBy node) 1.2 Rename the aggregated column to "Basket Sum 2019" 1.3 Calculate the sum of product prices by a customer ID concerning thepurchases earlier than 2019. Rename the aggregated column to "Basket Sum Overall" 1.4 Use an inner join to join the aggregated columns on the CustomerID. (for nowwe are only interested in the customers who bought something in 2019 AND before 2019) Note - we didn't cover Pivoting slides yet! Extract QuarterAnd YearTransform Transaction Date into Date&Time FomatTop: 2019Bottom: Earlier than 2019Node 314Extract Date&TimeFields (deprecated) String to Date&Time Read joined andpreprocessed data Partition by time GroupBy

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