August 2017
Intermediate to advanced
500 pages
12h 3m
Japanese
Content preview from PythonとJavaScriptではじめるデータビジュアライゼーション
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384
17 章 各賞の可視化
...
]
},
...
];
このネストした配列を反復処理して年グループをバインディングし、それぞれの年グループが時
系列グラフの丸印の列で表示できます。
D3
は、データセットを目的のネスト形式に変換する便利なネスト演算子を提供しています。例
17-4
は、ノーベル賞データセットに必要な変換を実現しています。
例
17-4
受賞者配列の年ごとでのネスト
var nestDataByYear = function(entries) { #
❶
return nbviz.data.years = d3.nest()
.key(function(w) { #
❷
return w.year;
})
.entries(entries);
};
❶ エントリは、現在選択された受賞者全員を含むフラットなデータセット。
❷ ネスト配列の最初のキーを作成する。
key
メソッドを何度も使えばさらに深くネストしたデータを作
成できる。この例とチュートリアルはこの有益な
bl.ocks.org
（
http://bl.ocks.org/phoebebright/
raw/3176159/
）を参照。
「
15.4.2
基本的なデータの流れ」で説明したように、
onDataChange
関数は
nestDataByYear
関
数を使って選択した受賞者の配列をこのコンポーネントの
updateTimeChartMethod
で必要なデー
タに変換します。
データのネストは、多くの
D3
ネットワーク例や
D3
の非常に高度な階層レイアウト（ ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.