Converting Basic D3 Charts into Reusable Style Templates
Converting Basic D3 Charts into Reusable Style Templates
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DOI：
10.1109/tvcg.2017.2659744
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发表时间：
2016-09
影响因子：
5.2
通讯作者：
Jonathan Harper;Maneesh Agrawala
中科院分区：
文献类型：
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作者：
Jonathan Harper;Maneesh Agrawala
我们提出了一种将基本D3图表转换为可重复使用的样式模板的技术。然后，给定一个新的数据源，我们可以应用样式模板来生成描绘新数据但模板样式的图表。要构建样式模板，我们首先解构输入D3图表以恢复其基础结构：数据，标记和映射描述标记如何编码数据。然后，我们对解构映射的感知有效性进行排名。要将结果样式模板应用于新数据源，我们首先获得每个新数据字段的重要性等级。然后，我们通过将最重要的数据字段与最有效的映射匹配来调整模板映射以描述源数据。我们展示了如何以数据表或其他D3图表的形式将样式模板应用于源数据。尽管我们的实现着重于生成基本图表类型的模板（例如，条形图，线图，点图，散点图等的变体），但这些是当今最常用的图表类型。用户可以轻松地在网络上找到此类基本的D3图表，将它们变成模板，并立即以模板的视觉样式（例如颜色，形状，字体等）查看其自己的数据的外观。我们通过将各种样式模板应用于各种来源数据集来证明我们的方法的有效性。
We present a technique for converting a basic D3 chart into a reusable style template. Then, given a new data source we can apply the style template to generate a chart that depicts the new data, but in the style of the template. To construct the style template we first deconstruct the input D3 chart to recover its underlying structure: the data, the marks and the mappings that describe how the marks encode the data. We then rank the perceptual effectiveness of the deconstructed mappings. To apply the resulting style template to a new data source we first obtain importance ranks for each new data field. We then adjust the template mappings to depict the source data by matching the most important data fields to the most perceptually effective mappings. We show how the style templates can be applied to source data in the form of either a data table or another D3 chart. While our implementation focuses on generating templates for basic chart types (e.g., variants of bar charts, line charts, dot plots, scatterplots, etc.), these are the most commonly used chart types today. Users can easily find such basic D3 charts on the Web, turn them into templates, and immediately see how their own data would look in the visual style (e.g., colors, shapes, fonts, etc.) of the templates. We demonstrate the effectiveness of our approach by applying a diverse set of style templates to a variety of source datasets.