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Supergroup performs single- or multi-level grouping on collections of records. It provides a host of utitily and conveniece methods on the returned array of group values as well as on each of these specific group values. If a multi-level grouping is performed, each value's children array also acts as a Supergroup list.

Supergroup is implemented as an Underscore or LoDash mixin, so just include one of those first:

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At first glance what Supergroup returns appears to be a list of String objects representing the top-level grouping. (Examples use a subset of these Olympic athlete records.)

sg = _.supergroup(data, ['Country','Sport','Year']) // ==> ["United States","Russia","Australia"]
sg[0]  // ==> "United States"

Original records in each group show up as a property of that group's String object:

sg[0].records.length // ==> 210
sg[0].records.slice(0,2) // ==> [
    {"Athlete":"Michael Phelps","Age":"23","Country":"United States","Year":"2008","Closing Ceremony Date":"8/24/08","Sport":"Swimming","Gold Medals":"8","Silver Medals":"0","Bronze Medals":"0","Total Medals":"8"},
    {"Athlete":"Michael Phelps","Age":"19","Country":"United States","Year":"2004","Closing Ceremony Date":"8/29/04","Sport":"Swimming","Gold Medals":"6","Silver Medals":"0","Bronze Medals":"2","Total Medals":"8"}

and subgroups appear in a children property:

sg[0].children // ==> ["Swimming","Gymnastics","Diving","Wrestling","Weightlifting"]


Unlike common data grouping/nesting utilities (D3.nest, Underscore.Nest) Supergroup gives you record subsets at every level, not just at the leaf level. No need to roll up subgroups for calculating aggregates at higher levels. Supergroup also provides convenience methods for aggregating:

sg[0].aggregate(d3.sum, "Total Medals") // ==> 352
sg[0].children[0].aggregate(d3.sum, "Total Medals") // ==> 267
sg.aggregates(d3.sum, "Total Medals") // ==> [352,157,180]
sg.aggregates(d3.sum, "Total Medals", "dict") // ==> {"United States":352,"Russia":157,"Australia":180}


Supergroup remembers the dimension names used to produce groupings. And individual nodes contain a reference to the level they’re on and to their parent values and lists:

sg[0].children[0].children[0] // ==> 2000
sg[0].children[0].children[0].depth // (top level is 0) ==> 2
sg[0].children[0].children[0].dim // ==> "Year"
sg[0].children[0].children[0].parent // ==> "Swimming"
sg[0].children[0].children[0].parentList // ==> [2000,2004,2008,2012]
sg[0].children[0].children[0].namePath() // ==> "United States/Swimming/2000"
sg[0].children[0].children[0].dimPath() // ==> "Country/Sport/Year"

lookup, descendants, leafNodes

Find nodes by looking up specific values. From any node, get all descendant or just leaf nodes:

sg.lookup(["Russia","Swimming"]) // ==> "Swimming"
sg.lookup("Russia").descendants() // ==> ["Gymnastics",2000,2004,2008,2012,"Diving",2000,2004,2008,2012,"Swimming",2000,2004,2008,2012,"Wrestling",2000,2004,2008,2012,"Weightlifting",2000,2004,2008,2012]
sg.lookup("Russia").leafNodes() // ==> [2000,2004,2008,2012,2000,2004,2008,2012,2000,2004,2008,2012,2000,2004,2008,2012,2000,2004,2008,2012]

All nodes in a single array:

sg.flattenTree() // ==> ["United States","Swimming",2000,2004,2008,2012,"Gymnastics",2000,2004,2008,2012,"Diving",2000,2012,"Wrestling",2000,2004,2008,2012,"Weightlifting",2000,"Russia","Gymnastics",2000,2004,2008,2012,"Diving",2000,2004,2008,2012,"Swimming",2000,2004,2008,2012,"Wrestling",2000,2004,2008,2012,"Weightlifting",2000,2004,2008,2012,"Australia","Swimming",2000,2004,2008,2012,"Diving",2000,2004,2008,2012]
_.invoke(sg.flattenTree(), "namePath") // ==> [
    "United States",
    "United States/Swimming",
    "United States/Swimming/2000",
    "United States/Swimming/2004",

Output in d3.nest formats

sg.d3map() // ==> {
    "United States":{
                {"Athlete":"Dara Torres","Age":"33", ...
                {"Athlete":"Gary Hall Jr.","Age":"25", ...
                {"Athlete":"Michael Phelps","Age":"19", ...

sg.d3entries() // ==> [
    {"key":"United States","values":[
                {"Athlete":"Dara Torres","Age":"33", ...

For use in D3 hierarchy layouts

// D3 hierarchies need a single root node
root = sg.asRootVal("Olympics") // ==> "Olympics"
root.children // ==> ["United States","Russia","Australia"]

// normally leaf nodes are the bottom level grouping:
_.invoke(root.leafNodes(),'namePath') // ==> ["United States/Swimming/2000", "United States/Swimming/2004", ...

// but D3 hierachies need to have actual records as leaf nodes
root.addRecordsAsChildrenToLeafNodes() // this adds a level to the grouping (changes sg also)
_.invoke(root.leafNodes(),'namePath')  //
  ==> ["Olympics/United States/Swimming/2000/[object Object]", "Olympics/United States/Swimming/2000/[object Object]"]
  // it's now a 5-level hierarchy with a root node at top and original records at bottom

Multivalued groups

_.supergroup([{A:[1,2]}, {A:[2,3]}], 'A').d3map() // normal operation
// ==> { 
        "1,2": [{"A":[1,2]}],
        "2,3": [{"A":[2,3]}]

_.supergroup([{A:[1,2]}, {A:[2,3]}], 'A',{multiValuedGroup:true}).d3map() // allow records to appear in more than one group
// ==> {