Heike Hofmann

Mosaic plots in the 'ggplot2' framework. Mosaic plot functionality is provided in a single 'ggplot2' layer by calling the geom 'mosaic'. This extends the now-defunct 'ggmosaic' package by allowing fitted loglinear models which can be used to show patterns of association among variables in frequency tables.

Provides dual-stroke and contrast-aware extensions to 'ggplot2', designed for improved visibility and accessibility in complex visualizations. Includes geoms for dual-stroke segments, regression lines, curved annotations, function plots, paths, and adaptive text. Also includes utility functions for computing contrast-aware color pairs and perceptually distinct highlight palettes using Web Content Accessibility Guidelines (WCAG)-based contrast logic.

As cognitive decline progresses, older adults may self-regulate their driving. Avoidance of left turns across traffic is observable in naturalistic driving data but rarely self-reported. We studied 106 older adults using baseline and one-year follow-up neuropsychological assessments. In-vehicle sensors passively recorded driving behavior over 12 weeks. We identified 295,112 turns from vehicle heading changes. We used mixed-effects logistic regression to model the odds of turning left, with cognitive status category change from baseline to one-year follow-up as the predictor. Greater cognitive impairment, represented by movement to a more severe cognitive status category at one-year follow-up, was associated with reduced odds of turning left (odds ratio = 0.984, 95% confidence interval = 0.969–0.999; P value = .037). Left-turn avoidance may be a behavioral marker of early cognitive decline. Passive driving data could help detect functional changes, enabling intervention to preserve mobility and independence. Further research is needed to establish a clinical threshold of concern for decreasing trends in left turn frequency in older drivers.

In this paper, we discuss considerations and methods for experimentally testing visualizations. We discuss levels of user engagement with graphics, common issues when developing a sampling or data generation model, the importance of pilot testing, and data analysis methods. Along the way, we also provide recommendations of how to avoid some of the unique pitfalls of human testing in statistical and visualization research.This article is categorized under: Statistical and Graphical Methods of Data Analysis > Statistical Graphics and Visualization Statistical and Graphical Methods of Data Analysis > Modeling Methods and Algorithms

Presenting data visually is a cornerstone of effective science communication. While prior studies have investigated humans' ability to effectively perceive values in charts, fewer have focused on the translation of perceived values to real-world conclusions. Those that do focus on real-world understanding often utilize convenience samples or focus on very simple graphic formats, resulting in an incomplete understanding of how viewers translate data graphics into meaningful conclusions. We utilize a probability-based sample of over 3,000 participants in the U.S. to test user understanding of three chart types and find that both educational attainment and age play a role in ability to interpret data graphics. Our work demonstrates a need for further study on how chart comprehension and comfort with drawing real-world conclusions differs across demographic groups and commonly-used chart types. Additionally, this work highlights that complex charts can be inaccessible to viewers who lack confidence in reading a chart.

We propose a reproducible pipeline for extracting representative signals from 2D topographic scans of the tips of cut wires. The process fully addresses many potential problems in the quality of wire cuts, including edge effects, extreme values, trends, missing values, angles, and warping. The resulting signals can be further used in source determination, which plays an important role in forensic examinations. With commonly used measurements such as the cross-correlation function, the procedure controls the false positive rate and false negative rate to the desirable values as the manual extraction pipeline but outperforms it with robustness and objectiveness.

The use of visuals is a key component in scientific communication. Decisions about the design of a data visualization should be informed by what design elements best support the audience’s ability to perceive and understand the components of the data visualization. We build on the foundations of Cleveland and McGill’s work in graphical perception, employing a large, nationally-representative, probability-based panel of survey respondents to test perception in stacked bar charts. Our findings provide actionable guidance for data visualization practitioners to employ in their work.

Parallel coordinate plots (PCP) are a valuable tool for exploratory data analysis of high-dimensional numerical data. The use of PCPs is limited when working with categorical variables or a mix of categorical and continuous variables. In this paper, we propose generalized parallel coordinate plots (GPCP) to extend the ability of PCPs from just numeric variables to dealing seamlessly with a mix of categorical and numeric variables in a single plot. In this process we find that existing solutions for categorical values only, such as hammock plots or parsets become edge cases in the new framework. By focusing on individual observations rather than a marginal frequency we gain additional flexibility. The resulting approach is implemented in the R package ggpcp.