Tabular Visualization Tools

  • A tool for producing tables from the National Bureau of Economic Statistics in 1975 including options for displaying the table and features of the table producing language.

    The Table Producing Language of the Bureau of Labor Statistics, NBER 1975

  • A tool for interactive visualizations of tabular data including tables and matrix displays where data are shown with visual graphics arranged in tabular visualizations.

    W. H. Benson and B. Kitous, “Interactive analysis and display of tabular data,” ACM SIGGRAPH Computer Graphics, vol. 11, no. 2, pp. 48–53, Jul. 1977, doi: 10.1145/965141.563869.

  • The Table Lens is an interactive tool for exploring large tables by displaying some data values as rows in the table as the focus and aggregating the remaining data values not fully displayed as histograms to provide context.

    R. Rao and S. K. Card, “The table lens: Merging graphical and symbolic representations in an interactive focus + context visualization for tabular information,” in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, in CHI ’94. New York, NY, USA: Association for Computing Machinery, Apr. 1994, pp. 318–322. doi: 10.1145/191666.191776.

  • The Feature-Oriented Catalog USer (FOCUS) interface is an interactive table viewer with query support using the focus and context method presented in Table Lens with specific application to cases-by-attribute data.

    M. Spenke, C. Beilken, and T. Berlage, “FOCUS: The interactive table for product comparison and selection,” in Proceedings of the 9th annual ACM symposium on User interface software and technology, in UIST ’96. New York, NY, USA: Association for Computing Machinery, Nov. 1996, pp. 41–50. doi: 10.1145/237091.237097.

  • Visualization Spreadsheets is a spreadsheet approach for displaying and exploring visualizations particularly suited for large, abstract, and multidimensional datasets illustrating how spreadsheet methods can be powerful for visualization.

    E. H. Chi, J. Riedl, P. Barry, and J. A. Konstan, “Principles for information visualization spreadsheets,” IEEE Computer Graphics and Applications, vol. 18, no. 4, pp. 30–38, Jul. 1998, doi: 10.1109/38.689659.

    • Subsequent sense-making analysis of their Visualization Spreadsheets.

      E. H. Chi and S. K. Card, “Sensemaking of evolving Web sites using visualization spreadsheets,” in Proceedings 1999 IEEE Symposium on Information Visualization (InfoVis’99), Oct. 1999, pp. 18–25. doi: 10.1109/INFVIS.1999.801853.

  • ValueCharts is a set of visualizations and interactive techniques designed to facilitate a series of particular tasks in evaluation and decision making of linear models. The visualization is a tabular barchart or stacked barchart display.

    G. Carenini and J. Loyd, “ValueCharts: Analyzing linear models expressing preferences and evaluations,” in Proceedings of the working conference on Advanced visual interfaces, in AVI ’04. New York, NY, USA: Association for Computing Machinery, May 2004, pp. 150–157. doi: 10.1145/989863.989885.

  • Skimmer is a system for browsing relational data query results by presenting the user with a set of representative tuples instead of facilitating scrolling. The representative tuples are selected to reduce information loss according to their metric and the resulting Skimmer system is evaluated in a usability study with four tasks Skimmer:

    M. Singh, A. Nandi, and H. V. Jagadish, “Skimmer: Rapid scrolling of relational query results,” in Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, in SIGMOD ’12. New York, NY, USA: Association for Computing Machinery, May 2012, pp. 181–192. doi: 10.1145/2213836.2213858.

  • Lineup is a visualization tool using stacked barcharts displayed in tabular visualizations. The system is designed to visualize rankings and aid users in ranking tasks. The system is evaluated in a user study with 12 tasks.

    K. Furmanova et al., “Taggle: Combining overview and details in tabular data visualizations,” Information Visualization, vol. 19, no. 2, pp. 114–136, Apr. 2020, doi: 10.1177/1473871619878085.

  • Bertifier is an interactive web based tabular visualization tool for using spreadsheets to create and use Bertin matrices, visually encoded data in matrix form. The crosset widget and visual reordering interactive techniques build on the interactive capabilities of the Bertin matrix.

    C. Perin, P. Dragicevic, and J.-D. Fekete, “Revisiting Bertin Matrices: New Interactions for Crafting Tabular Visualizations,” IEEE Transactions on Visualization and Computer Graphics, vol. 20, no. 12, pp. 2082–2091, Dec. 2014, doi: 10.1109/TVCG.2014.2346279.

  • IF and FI (Item-Feature, Feature-Item) Tables display items and features vertically or horizontally for visualizing high dimensional data in tabular visualizations. Focus can be applied by the user through filtering by relevance and similarity.

    P. van der Corput and J. J. van Wijk, “Exploring Items and Features with IF, FI-Tables,” Computer Graphics Forum, vol. 35, no. 3, pp. 31–40, 2016, doi: 10.1111/cgf.12879.

  • TACO is a visualization for comparison of the differences between two tables particularly designed for large and high dimensional datasets. Based on an overview+details concept, the two versions of the table are displayed as tabular heatmaps and the differences are highlighted in a color encoded aggregated pairwise comparison visualization.

    C. Niederer, H. Stitz, R. Hourieh, F. Grassinger, W. Aigner, and M. Streit, “TACO: Visualizing Changes in Tables Over Time,” IEEE Transactions on Visualization and Computer Graphics, vol. 24, no. 1, pp. 677–686, Jan. 2018, doi: 10.1109/TVCG.2017.2745298.

  • Weightlifter WeightLifter: Visual Weight Space Exploration for Multi-Criteria Decision Making.

    S. Pajer, M. Streit, T. Torsney-Weir, F. Spechtenhauser, T. Möller, and H. Piringer, “WeightLifter: Visual Weight Space Exploration for Multi-Criteria Decision Making,” IEEE Transactions on Visualization and Computer Graphics, vol. 23, no. 1, pp. 611–620, Jan. 2017, doi: 10.1109/TVCG.2016.2598589.

  • Visualization through aggregation techniques for tabular data.

    K. Furmanova et al., “Taggle: Combining overview and details in tabular data visualizations,” Information Visualization, vol. 19, no. 2, pp. 114–136, Apr. 2020, doi: 10.1177/1473871619878085.

  • Controlling level of detail information when creating visualizations using drag and drop methods for regions of tabular visualizations.

    A. Whilden, D. Karis, V. Setlur, R. Degtyar, J. Que, and F. Lymperopoulos, Blocks: Creating Rich Tables with Drag-and-Drop Interaction. The Eurographics Association, 2022. doi: 10.2312/evs.20221094.

  • Integrated visualization techniques for hierarchical tables.

    G. Li, R. Li, Z. Wang, C. H. Liu, M. Lu, and G. Wang, “HiTailor: Interactive Transformation and Visualization for Hierarchical Tabular Data,” IEEE Transactions on Visualization and Computer Graphics, vol. 29, no. 1, pp. 139–148, Jan. 2023, doi: 10.1109/TVCG.2022.3209354.

Understanding of Tabular Visualization

  • Gives some indication of how tables are perceived but not experimentally based. Also gives indications of how tables are used and should be designed but not experimentally based.

    J. Bertin, “Semiologie Graphique”, 1967

  • Perception of bar charts, pie charts, and tables for the task of comparing proportions of components and combinations of components. They found poor performance for tables compared with the two summary visualizations for such tasks. They did not find ordering to have a significant effect on the task performance.

    I. Spence and S. Lewandowsky, “Displaying proportions and percentages,” Applied Cognitive Psychology, vol. 5, no. 1, pp. 61–77, 1991, doi: 10.1002/acp.2350050106.

  • Survey paper of perception studies for visualizations including tabular visualizations.

    G. J. Quadri and P. Rosen, “A Survey of Perception-Based Visualization Studies by Task,” IEEE Transactions on Visualization and Computer Graphics, vol. 28, no. 12, pp. 5026–5048, Dec. 2022, doi: 10.1109/TVCG.2021.3098240.

  • Comparison of task performance on value retrieval, range, correlation, and a decision task comparing parallel coordinates, scatterplot matrix and tabular visualizations found that tables perform well for in terms of speed and accuracy for decision based tasks.

    E. Dimara, A. Bezerianos, and P. Dragicevic, “Conceptual and Methodological Issues in Evaluating Multidimensional Visualizations for Decision Support,” IEEE Transactions on Visualization and Computer Graphics, vol. 24, no. 1, pp. 749–759, Jan. 2018, doi: 10.1109/TVCG.2017.2745138.

  • Comparison of Amar-Eagan-Stasko low level task performances for tables, bar charts, pie charts, scatterplots and line charts reporting user accuracy, speed and preferences found that tables perform well for retrieval and computation tasks compared to other basic charts.

    B. Saket, A. Endert, and Ç. Demiralp, “Task-Based Effectiveness of Basic Visualizations,” IEEE Transactions on Visualization and Computer Graphics, vol. 25, no. 7, pp. 2505–2512, Jul. 2019, doi: 10.1109/TVCG.2018.2829750.

  • Includes experiments comparing tables, bar tables, and bar charts for comparing ratio tasks. The findings show that tables promote unbiased reasoning and avoid problems of confirmation bias in comparison with bar charts by promoting the use of the ratio strategy.

    C. Xiong, E. Lee-Robbins, I. Zhang, A. Gaba, and S. Franconeri, “Reasoning Affordances with Tables and Bar Charts,” IEEE Transactions on Visualization and Computer Graphics, pp. 1–13, 2022, doi: 10.1109/TVCG.2022.3232959.

Design and Uses of Tabular Visualization

  • Gives some indication of how tables are perceived but not experimental results. Also gives indications of how tables are used and should be designed but again not experimental results.

    J. Bertin, “Semiologie Graphique”, 1967

  • Includes notions of both good and bad table design practices but is asserted without experimental results. Asserts that ordering can be important in improving tabular visualization.

    A. S. C. Ehrenberg, “Rudiments of Numeracy,” Journal of the Royal Statistical Society. Series A (General), vol. 140, no. 3, pp. 277–297, 1977, doi: 10.2307/2344922.

  • Tufte includes advice on the design and uses of tabular visualization although he makes a lot of assertions without experimental evidence.

    E. Tufte “The visual display of quantitative information”, Graphics Press, 1983.

  • The Derivation Problem for Summary Data.

    F. M. Malvestuto, “The derivation problem of summary data,” in Proceedings of the 1988 ACM SIGMOD international conference on Management of data, in SIGMOD ’88. New York, NY, USA: Association for Computing Machinery, Jun. 1988, pp. 82–89. doi: 10.1145/50202.50211.

  • Includes notions of good and bad table design practices, and includes some theory about what you can and can’t do with tabular visualizations.

    H. Wainer, “Understanding Graphs and Tables,” ETS Research Report Series, vol. 1992, no. 1, pp. 4–20, 1992, doi: 10.1002/j.2333-8504.1992.tb01443.x.

  • Includes a discussion of designing tables based on goals.

    H. Wainer, “Improving Tabular Displays, With NAEP Tables as Examples and Inspirations,” Journal of Educational and Behavioral Statistics, vol. 22, no. 1, pp. 1–30, Mar. 1997, doi: 10.3102/10769986022001001.

  • Ubiquity and uses of tables as spreadsheets and online visualizations.

    L. M. Koesten, E. Kacprzak, J. F. A. Tennison, and E. Simperl, “The Trials and Tribulations of Working with Structured Data: -a Study on Information Seeking Behaviour,” in Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, in CHI ’17. New York, NY, USA: Association for Computing Machinery, May 2017, pp. 1277–1289. doi: 10.1145/3025453.3025838.

  • How interactive tables and spreadsheets are used by data workers and how effective they are: Untidy Data.

    L. Bartram, M. Correll, and M. Tory, “Untidy Data: The Unreasonable Effectiveness of Tables,” IEEE Transactions on Visualization and Computer Graphics, vol. 28, no. 1, pp. 686–696, Jan. 2022, doi: 10.1109/TVCG.2021.3114830.

  • An analysis with design and experiments of the accessibility of web tables for blind and low vision users.

    Y. Wang, R. Wang, C. Jung, and Y.-S. Kim, “What makes web data tables accessible? Insights and a tool for rendering accessible tables for people with visual impairments,” in Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, in CHI ’22. New York, NY, USA: Association for Computing Machinery, Apr. 2022, pp. 1–20. doi: 10.1145/3491102.3517469.

History of Tabular Visualization

  • The history of tables are explored through the examples of four early tables from 1900 BCE to 1300 CE.

    F. T. Marchese, “Exploring the Origins of Tables for Information Visualization,” in 2011 15th International Conference on Information Visualisation, Jul. 2011, pp. 395–402. doi: 10.1109/IV.2011.36.

Other

  • Synopses for Massive Data: Samples, Histograms, Wavelets, Sketches
  • E^3: Towards the Metrication of Graphical Presentation Techniques for Large Data Sets
  • How Numbers Are Shown: A Review of Research on the Presentation of Quantitative Data