ecent developments in data-driven science have led researchers to integrate data from several sources, over diverse experimental procedures, or databases. This alone poses a major challenge in truthfully visualizing data, especially when the number of data points varies between classes. To aid the representation of datasets with differing sample size, we have developed a new type of plot overcoming limitations of current standard visualization charts. SinaPlot is inspired by the strip chart and the violin plot and operates by letting the normalized density of points restrict the jitter along the x-axis. The plot displays the same contour as a violin plot but resembles a simple strip chart for a small number of data points. By normalizing jitter over all classes, the plot provides a fair representation for comparison between classes with a varying number of samples. In this way, the plot conveys information of both the number of data points, the density distribution, outliers and data spread in a very simple, comprehensible, and condensed format. The package for producing the plots is available for R through the CRAN network using base graphics package and as geom for ggplot through ggforce. We also provide access to a web-server accepting excel sheets to produce the plots (http://servers.binf.ku.dk:8890/sinaplot/).
Sidiropoulos, N., Sohi, S. H., Pedersen, T. L., Porse, B. T., Winther, O., Rapin, N., & Bagger, F. O. (2018). SinaPlot: an enhanced chart for simple and truthful representation of single observations over multiple classes. Journal of Computational and Graphical Statistics, 27(3), 673-676.https://doi.org/10.1080/10618600.2017.1366914