Untidy Data: The Unreasonable Effectiveness of Tables

Bartram, Lyn; Correll, Michael; Tory, Melanie

doi:10.1109/tvcg.2021.3114830

Cited by 27 publications

(11 citation statements)

References 74 publications

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“…Consequently, we join previous works [59,70,71] in calling for rethinking how we design and develop visualization interfaces, especially when the goal is real-world adoption. Instead of developing yet-another-tool, we argue for meeting analysts where they are at in their analysis workflows.…”

Section: Design Opportunitiesmentioning

confidence: 88%

Here’s what you need to know about my data: Exploring Expert Knowledge’s Role in Data Analysis.

Lin¹,

Lisnic²,

Akbaba³

et al. 2023

Preprint

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Data driven decision making has become the gold standard in science, industry, and public policy. Yet data alone, as an imperfect and partial representation of reality, is often insufficient to make good analysis decisions. Knowledge about the context of a dataset, its strengths and weaknesses, and its applicability for certain tasks is essential. In this work, we present an interview study with analysts from a wide range of domains and with varied expertise and experience inquiring about the role of contextual knowledge. We provide insights into how data is insufficient in analysts workflows and how they incorporate other sources of knowledge into their analysis. We also suggest design opportunities to better and more robustly consider both, knowledge and data in analysis processes.

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Section: Design Opportunitiesmentioning

confidence: 88%

Here’s what you need to know about my data: Exploring Expert Knowledge’s Role in Data Analysis.

Lin¹,

Lisnic²,

Akbaba³

et al. 2023

Preprint

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show abstract

“…The process of moving between and among different visual representations (and visual metaphors [ZK08]) of the same data is an important but understudied assumption undergirding the use of raincloud plots. Similarly, while there is some work in how people “sanity check” [CLKS19] and “eyeball” [BCT22] raw data, and further work in how people build up ensemble statistical pictures from raw data [SHGF16] and use that data to make inferences [KKH21], it unclear how the empirical lessons for these disparate lines of work intersect or interact. It is possible, even, that these tasks are diametrically opposed: it could be the case that a viewer looking at visualizations in order to make inferential statistical judgments does not need or want more information about the raw values in the distribution (and in fact this additional information could introduce biases if such judgments are based on inappropriate or inaccurate perceptual proxies).…”

Section: Discussionmentioning

confidence: 99%

Teru Teru Bōzu: Defensive Raincloud Plots

Correll

2023

Computer Graphics Forum

Self Cite

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Univariate visualizations like histograms, rug plots, or box plots provide concise visual summaries of distributions. However, each individual visualization may fail to robustly distinguish important features of a distribution, or provide sufficient information for all of the relevant tasks involved in summarizing univariate data. One solution is to juxtapose or superimpose multiple univariate visualizations in the same chart, as in Allen et al.'s [APW*19] “raincloud plots.” In this paper I examine the design space of raincloud plots, and, through a series of simulation studies, explore designs where the component visualizations mutually “defend” against situations where important distribution features are missed or trivial features are given undue prominence. I suggest a class of “defensive” raincloud plot designs that provide good mutual coverage for surfacing distributional features of interest.

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“…Systems focusing on these activities usually need to visualize the runtime behavior of the program including object states, function calls, etc. Another example is that, data workers [4], [52] and education practitioners expect an effective method for comprehending or learning the semantics of a program. We note that the concerns of different roles are not strictly differentiated.…”

Section: Program Visualizationmentioning

confidence: 99%

Visualizing the Scripts of Data Wrangling with SOMNUS

Xiong,

Fu,

Ding

et al. 2022

Preprint

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Data workers use various scripting languages for data transformation, such as SAS, R, and Python. However, understanding intricate code pieces requires advanced programming skills, which hinders data workers from grasping the idea of data transformation at ease. Program visualization is beneficial for debugging and education and has the potential to illustrate transformations intuitively and interactively. In this paper, we explore visualization design for demonstrating the semantics of code pieces in the context of data transformation. First, to depict individual data transformations, we structure a design space by two primary dimensions, i.e., key parameters to encode and possible visual channels to be mapped. Then, we derive a collection of 23 glyphs that visualize the semantics of transformations. Next, we design a pipeline, named SOMNUS, that provides an overview of the creation and evolution of data tables using a provenance graph. At the same time, it allows detailed investigation of individual transformations. User feedback on SOMNUS is positive. Our study participants achieved better accuracy with less time using SOMNUS, and preferred it over carefully-crafted textual description. Further, we provide two example applications to demonstrate the utility and versatility of SOMNUS.

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Untidy Data: The Unreasonable Effectiveness of Tables

Cited by 27 publications

References 74 publications

Here’s what you need to know about my data: Exploring Expert Knowledge’s Role in Data Analysis.

Here’s what you need to know about my data: Exploring Expert Knowledge’s Role in Data Analysis.

Teru Teru Bōzu: Defensive Raincloud Plots

Visualizing the Scripts of Data Wrangling with SOMNUS

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