ggplot2 VS Altair

Pardon me for shooting from the hip here, but IMO if you're using R for something radically different than statistical analysis and data visualization, there might be another tool/language that's more purpose-suited.

> As someone who basically uses R as a nice LISP-y scripting language to orchestrate calling low-level compiled code from other languages

When I read this, I think, would `bash` or something equally portable/universally installed work?

R is a beautiful thing when limited to its core uses (I use it every day ([0]). But in my experience, the more we build away from those core uses, the more brittleness we introduce. I wish the Posit team would focus on the core R experience, resolve some of the hundreds of open issues on its core packages in a timely way, [1,2] and just generally play to R's strengths.

[0] https://github.com/hsflabstanford/vegan-meta

[1] https://github.com/rstudio/rmarkdown/issues

[2] https://github.com/tidyverse/ggplot2/issues

ggplot2

I got the list of five-letter words from the words package in R, created the QWERTY keyboard grid with base R and tibble, and visualized the data with geom_tile in the ggplot2 package.

Thanks, it's an interesting idea! I definitely could implement this with scale_fill_gradientn) in ggplot2.

I used the ggplot2 package in R to create these figures.

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.

It's compatible with a wide range of data libraries, including Pandas, NumPy, and Altair. Streamlit integrates with all the latest tools in generative AI, such as any LLM, vector database, or various AI frameworks like LangChain, LlamaIndex, or Weights & Biases. Streamlit’s chat elements make it especially easy to interact with AI so you can build chatbots that “talk to your data.”

I like Vega-Lite: https://vega.github.io/vega-lite/

It’s built by folks from the same lab as D3, but designed as “a higher-level visual specification language on top of D3” [https://vega.github.io/vega/about/vega-and-d3/]

My favorite way to prototype a dashboard is to use Streamlit to lay things out and serve it and then use Altair [https://altair-viz.github.io/] to generate the Vega-Lite plots in Python. Then if you need to move to something besides Python to productionize, you can produce the same Vega-Lite definitions using the framework of your choice.

Feel free to open an issue to let us know which parts of the documentation you find obscure and if you have suggestions for how to improve them. We did a larger overhaul a few months back and are always open to feedback on how to improve it further! https://altair-viz.github.io/

(disclaimer: I'm a co-maintainer of Altair)

Vega-Altair is pretty great as well. It uses a grammar of graphics that’s slightly different from ggplot, but has most of the same advantages.

https://altair-viz.github.io/

Altair - Declarative statistical visualization library for Python.

Github: Altair