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Join

As explained in Everyday Data Visualization

A join is an operation that combines rows from tables according to matching or related fields.

A join combines rows from separate tables by matching related fields. Before joining, check whether those fields use compatible values or need a linking key. section 9.3.1

Weekly productivity reporting

One table connects team names to product areas. Another connects team names to assigned people and weekly completed work. Joining them enables a report of weekly work by product area. If one system stores TEAM ALPHA as uppercase text without spaces while the other uses a different structure, simple cleaning may not be enough; a mapping table must link the names. section 9.3.1

Check these conditions before relying on a join:

  • Inspect how each system stores the proposed matching field. section 9.3.1
  • Use light data cleaning when differences are only formatting. section 9.3.1
  • Create a mapping key when the values are structured differently and cannot be fixed programmatically. section 9.3.1
Figure 9.9 System A stores team names in the format on the left, uppercase with no spaces, while system B on the right doesn’t follow any particular format. If you try to join these tables on System A’s team name equals system B’s team name, you might, at best, get a few results, but certainly not all of them. You’ll need to find some way to link them.
09-09

Compare the two team-name formats to see why an apparent match can return incomplete results. section 9.3.1

How Everyday Data Visualization covers it

How deep it goes
Explains table linkage and mapping keys, but stops short of implementation details.
Examples
Uses team names, assigned people, weekly work, and product areas across two systems.
What it assumes
Assumes awareness of tables and fields while teaching that existing data must also be usable.
What you can do after
Evaluate whether available fields can support a join and identify when a mapping key is required.
How it connects
Connects data cleaning, keys, requirements gathering, and reporting.

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This explainer was generated by Manning's knowledge graph from the text of Everyday Data Visualization. The section links point to the parts of the book it draws on.