Join us for Joins (The Joy in Joins!!)

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2 # T C 1 8 Join us for Joins (The Joy in Joins!!) Terrence Maas Software Engineer tmaas@tableau.com Joanna Chen Software Engineer jochen@tableau.com

3 Agenda Joins Why the hype? Intro to Tableau Prep Practical Join Cases: 1. Cleaning and Filtering 2. Outer joins Capture ALL the Values! 3. Range joins Visualizing Occupancy 4. Self Joins Correlations and Compositions Q&A

4 What s so great about joins?

5 Why do we use joins? Connect Data Fundamental purpose of joins. Data derives most of its value by connecting to other data. Reshape Data Different kinds of joins enable different kinds of analysis. Select Data Join Conditions provide a bar for your data to meet. Tableau Prep uses this to help you clean your data.

6 Terrence s Restaurant

7 Terrence s Restaurant Tables: Employees Schedules Orders Menu Items Questions: Am I collecting good quality data? When is my restaurant busiest? What menu items are most popular? What about pairings?

8 Tableau Prep!

9 Tableau Prep Tableau s newest product offering Released in April 2018 Mission Statement Tableau Prep empowers more people to get to analysis faster by helping them quickly and confidently combine, shape, and clean their data.

10 Tableau Prep

11 Tableau Prep

12 Connection Pane

13 Flow Pane

14 Steps in the Flow

15 Profile Pane

16 Changes Pane

17 Data Grid

18 Joins in Tableau Prep

19 Configuring Your Join in Prep Join Clause(s) are used to describe the relation of column(s) between two tables. Join types are used to control how to exclude or include rows from two tables, depending on the join clauses defined above. The Summary of Join Results shows you the distribution of values that are included and excluded from the tables in the join.

20 Joins in Tableau Prep

21 Join Clauses Pane Displays the columns from your join clause(s) side by side in 1 view Unmatched values are highlighted in red

22 Joins: Getting the Most Out of Your Data

23 Case 1: Cleaning and Filtering

24 Case 1: Cleaning and Filtering In every join Rows are matched according to the join condition. In Tableau Prep We pay equal attention to both matched and unmatched data. In general If a table is a source of truth, it can be a powerful tool for cleaning your data.

25 Case 1: Cleaning and Filtering Scenario: Terrence s employees manually record restaurant orders. Terrence has a table of Employees, which he knows is accurate and error-free. He has a table of Orders that may contain errors.

26 DEMO!

27 Summary Case 1: Cleaning and Filtering A table with reliable data can be used in a join to verify the quality of another table. In a join, mismatched values are accented in red. Clean values directly in the join pane.

28 Case 2: Outer Joins

29 Case 2: Outer Joins Inner Joins Default join type Only rows that match our join clause(s) is kept in the join result Inner Join

30 Case 2: Outer Joins Outer Joins Unmatched values can be important. Outer joins allow us to include unmatched data from one or more tables. Left Join Right Join Full Outer Join

31 Case 2: Outer Joins Filling out dimensions/axes All possible values might not show up in the data. Some data is best represented on a complete axis / dimension. Including unmatched values Outer joins allow us to include unmatched values in our result.

32 Case 2: Outer Joins Back to Terrence s Restaurant How is the menu doing? What about unordered items?

33 Case 2: Outer Joins Join Orders Table with Menu Table Orders Menu The Orders table alone only shows items ordered at least once. Use the rows from the Menu table to fill in the missing items not present in the Orders table.

34 DEMO!

35 Case 2: Outer Joins Summary Not every item on the menu has been ordered. Join Orders with Menu to fill in the complete set of orders when we visualize in Tableau. Change the join type from inner join to right join to include the unmatched values. Takeaway: use outer joins to fill out a dimension

36 Case 2: Outer Joins Full Outer Joins Not commonly used. Includes unmatched data from both tables in join result. Full Outer Join

37 Case 2: Outer Joins Back at Terrence s Restaurant Some employees are not assigned a shift Some shifts do not have employees assigned

38 DEMO!

39 Case 2: Outer Joins Full Outer Joins Every employee who has a shift assigned and every shift that has an employee assigned Every employee who does not have a shift assigned Every shift that does not have an employee assigned

40 Case 2: Outer Joins Summary Full Outer Join Employees with Schedules Change join type to unmatched only to see employees without shifts and shifts without employees

41 Case 3: Range Join Conditions

42 Case 3: Range Join Conditions Slowly Changing Dimensions Data that is best represented with start and end points. Examples: Job term lengths Assignment durations Subscription times

43 Case 3: Range Join Conditions Managers Manager Start Term End Term Linda 1/1/2018 3/31/2018 Faisal 4/1/2018 7/31/2018 Ruben 8/1/2018 9/31/2018 Sales Sales Date /5/ /28/ /29/ /6/ /5/2018

44 Case 3: Range Join Conditions Join Conditions: managers.start_term < sales.date managers.end_term > sales.date Manager Start Term End Term Sales Date Linda 1/1/2018 3/31/ /5/2018 Linda 1/1/2018 3/31/ /28/2018 Faisal 4/1/2018 7/31/ /29/2018 Faisal 4/1/2018 7/31/ /6/2018 Ruben 8/1/2018 9/31/ /5/2018

45 Case 3: Range Join Conditions Scenario: My employees tell me they are usually either overwhelmed or not busy at all. Questions I want to explore: Am I allocating my resources in a way that accurately reflects the restaurant s needs?

46 DEMO!

47 Case 4: Self Joins

48 Case 4: Self Joins Terrence s favorite case! Self Joins are seldom used. purely a reshaping operation no external data is connected. can bring wonderful insight on how values of a column relate to other values from the same column.

49 Case 4: Self Joins Scenario: I m still managing my restaurant (never give up!). This time, I want to analyze my menu. Questions I want to explore: What menu items are commonly ordered together? What do meals tend to look like compositionally?

50 The Recipe What we need: 1. Values we want to explore (column of interest) 2. A way they are grouped (grouping column) 3. A unique ID for each row Examples: Medications or conditions, grouped into patients Menu items, grouped into meals Etc.

51 Join Conditions Group_Column_1 = Group_Column_2 Column_of_Interest_1!= Column_of_Interest_2

52 Viz: Most Common Pairings Rows CNTD(Row_ID_1) Columns Column_of_Interest_1 Filter Column_of_Interest_2

53 Viz 2: Group Compositions Rows CNTD(Row_ID_1) Columns Group_Column_1 Color Column_of_Interest_1

54 Please complete the session survey from the My Evaluations menu in your TC18 app

55 Questions?