deneb VS ggplot2

The network visuals can be a little haphazard.. the other option would be to craft exactly what you want in something like Deneb: https://deneb-viz.github.io/

Voronoi tesselations would potentially be doable in Power BI - there's a well-maintained module for D3 that can do them if you want to try building a Power BI custom visual from the ground up, and Deneb can do Voronoi tesselations if you use Vega rather than Vega-Lite. Deneb can help if you don't want to go through the hassle of bootstrapping a custom visual from scratch and dont necessarily have the web development skills (but you will still need to learn a higher-level language in Vega, which is a visualisation grammar). Deneb is also certified, so your work gets the benefits of running inside a custom visual (as if you build your own visual and need it to be certified, you have to submit it to AppSource first).

Once you "get" the concept of how to think about building visuals, it's very easy to create simple charts, particularly in Vega-Lite. You do need to learn a language but both are very well-documented and have a lot of examples. You can access the doc for either language via Deneb's site: https://deneb-viz.github.io

If your case is straightforward, there are visuals such as Charticulator or Deneb, which allow you to build visuals inside Power BI without having to learn web development and the visuals SDK. As both these visuals are certified, they provide the environment for your designs to run inside, and therefore you can get the benefits of a certified visual without having to publish to AppSource.

Deneb is open source, so if you have ideas or want to contribute, here's the GitHub repo

ggplot2

This might not be at the top of your list, but science fiction often presents advanced data analysis and visualization technologies. Open source data analysis tools such as Python's Pandas and R's ggplot2 have revolutionized the field, making complex data manipulation and visualization accessible to all. In the science fiction novel The Martian, astronaut Mark Watney uses a variety of data analysis and visualization tools to survive on Mars. He uses Python's Pandas to clean and organize data, and he uses R's ggplot2 to create visualizations of his data. These tools allow him to make sense of the vast amounts of data and help him to make critical decisions about his survival.

Python's pandas, NumPy, and SciPy libraries offer powerful functionality for data manipulation, while matplotlib, seaborn, and plotly provide versatile tools for creating visualizations. Similarly, in R, you can use dplyr, tidyverse, and data.table for data manipulation, and ggplot2, lattice, and shiny for visualization. These packages enable you to create insightful visualizations and perform statistical analyses without relying on expensive or proprietary software.

For more information on how to use ggplot2 and create charts consult the ggplot2 official page or the ggplot2 cheat graphic.

ggplot2 (I wish I used more) (https://ggplot2.tidyverse.org/)

R and ggplot2 (and tidyverse in general). Here is the code (towards end of file).

Tools used: various packages in R (tidycensus, dplyr, ggplot2, sf)

Except it’s not a left pad moment. The isoband maintainer has been alerted in advance and the problem is already fixed, ready to be pushed next week.