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 using a case study conducted by a domain expert on complex genomics data analysis for the purpose of drug discovery.
@cite_30: General purpose graphical interfaces for data exploration are typically based on manual visualization and interaction specifications. While designing manual specification can be very expressive, it demands high efforts to make effective decisions, therefore reducing exploratory speed. Instead, principled automated designs can increase exploratory speed, decrease learning efforts, help avoid ineffective decisions, and therefore better support data analytics novices. Towards these goals, we present Keshif, a new systematic design for tabular data exploration. To summarize a given dataset, Keshif aggregates records by value within attribute summaries, and visualizes aggregate characteristics using a consistent design based on data types. To reveal data distribution details, Keshif features three complementary linked selections: highlighting, filtering, and comparison. Keshif further increases expressiveness through aggregate metrics, absolute part-of scale modes, calculated attributes, and saved selections, all working in synchrony. Its automated design approach also simplifies authoring of dashboards composed of summaries and individual records from raw data using fluid interaction. We show examples selected from @math datasets from diverse domains. Our study with novices shows that after exploring raw data for 15 minutes, our participants reached close to 30 data insights on average, comparable to other studies with skilled users using more complex tools.
@cite_9: Improvise is a fully-implemented system in which users build and browse multiview visualizations interactively using a simple shared-object coordination mechanism coupled with a flexible, expression-based visual abstraction language. By coupling visual abstraction with coordination, users gain precise control over how navigation and selection in the visualization affects the appearance of data in individual views. As a result, it is practical to build visualizations with more views and richer coordination in Improvise than in other visualization systems. Building and browsing activities are integrated in a single, live user interface that lets users alter visualizations quickly and incrementally during data exploration
Complementary to the three types of tabular data visualization techniques introduced in the previous section, there is an option of representing (groups of) attributes of a tabular dataset independently of each other in a multiple coordinated view system. Representative systems in this category include @cite_9 and the recent @cite_30 . These systems allow users to choose representations that are suitable for the subset of data represented by a single view, and usually rely on linked highlighting to re-introduce connections. Common configurations of Keshif, for example, use a tabular view to identify specific items, but represent other attributes in other views using, for instance, histograms or bar charts.