Tidyplots empowers life scientists with easy code-based data visualization

Background:

Data visualization is essential for understanding and sharing experimental findings. Unfortunately, existing open-source tools often have complex syntax, posing a barrier for life scientists especially those not used to work with code. Traditional data analysis workflows can’t really cope with the increasing data volume and current reproducibility standards. Additionally, many scientists are not able to afford commercial licenses for more mainstream programs that take care of data analysis and visualization, which can sometimes lead to inadequate data assessing and analysis.

As described in the preprint highlighted here, ‘tidyplots’ addresses this by providing a user-friendly, code-based interface for creating plots. It aims to empower researchers by allowing them to use automated data visualization pipelines.

Key features of tidyplots:

Easy crafting of graphs

Tidyplots introduces a novel interface based on consistent and intuitive grammar that minimizes the need for extensive programming experience. The human-like syntax facilitates the creation of plots through a series of function calls connected in a pipeline, using verbs like “add,” “remove,” and “adjust”. (Fig 1.)

Fig.1 from the preprint. Illustrates the basic syntaxis and arguments for generating intuitive graphs and visualizations with tidyplots. Image is made available under a a CC-BY-ND 4.0 International license.

Code economy and visualization flexibility

The tidyplots R package requires less code compared to ggplot2 to achieve equivalent plots and thereby reduces code complexity. It offers over 50 add functions for plotting raw data, statistics, distributions, and annotations.

Tidyplots also provides demo datasets, color schemes for color vision deficiencies, and thorough documentation. Jan Broder Engler, the creator of tidyplots and author of this preprint, provides constant examples on its X account (https://x.com/JanBroderEngler) and one can find more use cases on the webpage of the project (https://tidyplots.org/). To hear directly from Jan, please check out the ‘spotLights’ episode above.

Why we (see spotLight above!) highlight this work:

Tidyplots focuses on human code readability, making the source code easier to read and write. We like that Tidyplots prioritizes specialized functions, which—combined with the consistent naming scheme and code editor auto-completion— eliminates the need to consult function documentation. This is important because it lowers the barrier to entry for scientists who may not have extensive programming experience, enabling them to create effective visualizations and improve reproducibility.

Future Directions and Questions:

Will tidyplots integrate with other data analysis tools and workflows commonly used in specific life science domains?

Could tidyplots be expanded to include interactive plotting capabilities for enhanced data exploration?

 

doi: https://doi.org/10.1242/prelights.39673

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