d3 & Hierarchical Datasets

# Hierarchical Datasets
### When?  What?  How?

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.right-column[
<br> <br>
<iframe width="400" height="500" frameborder="0" src="../d3/tree.html"></iframe>
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By the end of this session,<br>
      you should have<br>
a solid understanding,<br>
      and a comfort with,<br>
hierarchical datasets<br>
      and d3.stratify.

* _When_ should I use them?
* _What_ does the code look like?
* _How_ do we work with them?

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<iframe style="display: block;" width="400" height="250" frameborder="0" src="../d3/sunburst.html"></iframe><br>
<iframe style="display: block;" width="400" height="250" frameborder="0" src="../d3/radialtree.html"></iframe>
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<iframe width="400" height="250" frameborder="0" src="../d3/pack.html"></iframe><br>
<iframe width="400" height="250" frameborder="0" src="../d3/treemap.html"></iframe>
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???

Radial tree is a little pathetic...

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# Defined

[TODO: include top snipped of definition png and make this look like a definition]

A hierarchical dataset is:
1. Pyramid-like organization of ideas, people, items...
2. With a _single_, top-level item that we call the "__root__".
3. Every level has _one higher_ and _zero or more lower_ neighbors.
4. Higher levels have greater inclusion, influence, or breadth.

[TODO: engineering picture]

???

Briefly explain the hierarchical dataset. Emphasize importance of root.

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# Hello World Example

>Continent ⇢ Country ⇢ State ⇢ City ⇢ Street ⇢ House

might look like this in json:

``` json
{
  "name": "North America",
  "children": [
    { "name": "Canada" },
    { "name": "Mexico" },
    { "name": "United States",
      "children": [
        { "name": "California" },
        { "name": "Indiana",
          "children": [
            { "name": "Fishers" },
            { "name": "Carmel" },
            { "name": "Indianapolis" }
          ]
        }
      ]
    }
  ]
}
```

???

* Note our simple example.
* "You can often go higher, but we'll start with just simply picking a beginning spot."
* Note simplified "name" and "children" keys.

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Some data is naturally hierarchical:

* Location: Continent ⇢ Country ⇢ State ⇢ City ⇢ Street ⇢ House
* Company: CEO ⇢ EVP ⇢ SVP ⇢ VP ⇢ DIR ⇢ MGR
* Filesystem: Folder ⇢ Subfolder ⇢ File
* Book: Dictionary ⇢ Letter ⇢ Word ⇢ Part-of-speech ⇢ Definitions ⇢ Examples
* Family Tree: Granparents ⇢ Parents ⇢ Kids (this one kinda' falls apart...)

Some fields use hiararchies to make decisions

* MECE: mutually exclusive and collectively exhaustive (_consulting_)
* Decision Tree illustration (_data science_)

[TODO: illustrations of MECE and/or Decision Tree]

???

* Not everything that seems hierarchical is: family tree
* MECE is actually a good way to divide up complex problems and a litmus test for true hierarchy.

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## Our Hierarchical Dataset in Action

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# Let's Create an Example Together

.exercise[PostIt Notes & Pencil: Draw a picture of a dataset that you want to work with that is hierarchical.]

[TODO: Add a short description of what's expected]

???

1. Explain the exercise
2. Give time to complete
3. Provide Ideas:
    * Bucket List items, divided by completed/not completed, terrifying/not terrifying
    * Places you've visited, divided by contintent or companions
    * Things you want to learn, divided by technology/not
    * Upcoming projects: work/personal
    * Cars you've owned, divided by how the relationship ended
4. Show my example

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# Thanks!