Abstract
AbstractIn research involving data-rich assays, exploratory data analysis is a crucial step. Typically, this involves jumping back and forth between visualizations that provide overview of the whole data and others that dive into details. For example, it might be helpful to have one chart showing a summary statistic for all samples, while a second chart provides details for points selected in the first chart. We present R/LinkedCharts, a framework that renders this task radically simple, requiring very few lines of code to obtain complex and general visualization, which later can be polished to provide interactive data access of publication quality.
Funder
Deutsche Forschungsgemeinschaft
Klaus Tschira Stiftung
Ruprecht-Karls-Universität Heidelberg
Publisher
Springer Science and Business Media LLC
Reference42 articles.
1. Hegarty M. The cognitive science of visual-spatial displays: implications for design. Top Cogn Sci. 2011;3(3):446–74.
2. Newman WM, Sproull RF. Principles of interactive computer graphics. New York: McGraw-Hill; 1979.
3. Becker RA, Cleveland WS. Brushing scatterplots. Technometrics. 1987;29(2):127–42.
4. Caldarola EG, Rinaldi AM. Big Data Visualization Tools: A Survey. In: Proceedings of the 6th International Conference on Data Science, Technology and Applications. Setubal, Portugal: SCITEPRESS - Science and Technology Pulications; 2017. p. 296–305.
5. Noronha A, Daníelsdóttir AD, Gawron P, Jóhannsson F, Jónsdóttir S, Jarlsson S, et al. ReconMap: an interactive visualization of human metabolism. Bioinformatics. 2017;33(4):605–7.