Most tabular data visualization techniques focus on overviews, yet many practical analysis tasks are concerned with investigating individual items of interest. At the same time, relating an item to the rest of a potentially large table is important. In this work, we present Taggle, a tabular visualization technique for exploring and presenting large and complex tables. Taggle takes an item-centric, spreadsheet-like approach, visualizing each row in the source data individually using visual encodings for the cells. At the same time, Taggle introduces data-driven aggregation of data subsets. The aggregation strategy is complemented by interaction methods tailored to answer specific analysis questions, such as sorting based on multiple columns and rich data selection and filtering capabilities. We demonstrate Taggle by a case study conducted by a domain expert on complex genomics data analysis for the purpose of drug discovery.

中文翻译：

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Taggle：在表格数据可视化中结合概览和细节

大多数表格数据可视化技术侧重于概览，但许多实际分析任务都与调查感兴趣的单个项目有关。同时，将项目与潜在大表的其余部分相关联也很重要。在这项工作中，我们展示了 Taggle，一种用于探索和呈现大型复杂表格的表格可视化技术。Taggle 采用以项目为中心、类似于电子表格的方法，使用单元格的可视化编码分别可视化源数据中的每一行。同时，Taggle 引入了数据驱动的数据子集聚合。聚合策略由为回答特定分析问题而量身定制的交互方法补充，例如基于多列的排序和丰富的数据选择和过滤功能。