ISSN: 1797-4453

This paper draws attention to an underused display type for corpus data visualization: the line plot. While this graph type is commonly associated with time series data, its true potential arguably unfolds in the application to multifactorial data sets involving discrete (categorical) variables. Data layouts of this kind are typical of corpus-based work and the preferred vehicle for their visualization is currently the bar chart. It is sometimes argued that line plots should only be used when the horizontal axis represents a continuous trait. However, once we allow for the levels of binary and categorical variables to be connected by lines, we recognize that this form offers several distinct advantages over bar charts. This is especially true for visualization tasks involving multiple predictor variables. The paper starts out by providing some theoretical background for the comparative evaluation of graph types, with a focus on quantitative comparisons and perceptual processing. Drawing on empirical insights into visual perception, evidence-based recommendations for the design of line plots are given. These include the choice of line types and plotting symbols, the use of direct labeling, and the arrangement of variables in the display. Following this, key advantages of line plots are illustrated. These include pictorial minimalism, the availability of extended encoding strategies and the scaffolding provided by perceptual grouping laws. The paper closes by emphasizing limitations of this display type. These concern the depiction of non-continuous x-variables and the asymmetric perception of interactions among predictor variables. While ample attention should be paid to these issues, we argue for a (more) routine use of line plots in corpus data visualization.