llr VS rmarkdown

This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see .

The polished package now supports Rmarkdown documents that use the shiny runtime. This includes flexdashboard!

The package ggplot2 in R platform is a good choice. You can make any type of figure, here are some examples, https://www.r-graph-gallery.com/heatmap.html . Combined with rmarkdown (https://rmarkdown.rstudio.com/), you can combine together all of the steps together -- data input, data cleaning, data analysis, the final report of various formats, PDF, HTML, Word, PowerPoint, or journal articles. See here https://github.com/rstudio/rticles

One option for Markdown-esque input and Latex output is RMarkdown. RStudio does a nice job of allowing you to write markdown, embed references, code cells, and visualizations. I used it in grad school and only rarely had to drop down to Latex to do something more customized.

https://rmarkdown.rstudio.com/

If you are not familiar with Shiny, it is an R package that makes it easy to build interactive web apps straight from R. You can host standalone apps on a webpage or embed them in R Markdown documents or build dashboards.

What are you writting the research paper in? One way is to use Rmarkdown so that you can easily integrate the results. Rmarkdown is generally a really good tool, so it pays of to learn it anyway.

Meanwhile, other languages took markdown and ran with it, using (and here lies the tragedy) pandoc to embed it in their rapid development processes. They've gone on and built empires off of it, such as https://jupyter.org/ and https://rmarkdown.rstudio.com/. We can't even begin to compete. We have to re-route everything through ihaskell to even get to the starting line of the rapid, general data analytics race.

Have you heard of knitr (https://yihui.org/knitr/)? It's the gold standard as far as I'm concerned for dynamic report generation that needs to run code. Since it supports running arbitrary shell commands, it can already be used to query remote databases as long as you have a CLI to query them with. Combined with RMarkdown (https://rmarkdown.rstudio.com/), which augments Markdown with support for LaTeX typesetting, it's the ultimate toolset for doing this kind of thing. You can read a blog post here on how to use knitr within RMarkdown: https://kbroman.org/knitr_knutshell/pages/Rmarkdown.html

I'm not trying to be a downer, but it seems like your product is just duplicating the functionality of these existing products but does less since it only supports SQL and Markdown.

I guess you autogenerate charts, but it says you're targeting a technical audience that is presumably comfortable calling functions in Python and R for graphical data visualization.

This is nitpicky, and I'm sure you have some command line option to choose another port (though your "get started" doesn't show how), but mdbook also uses 3000. I'm sure they probably weren't the first to default to that, either.

I hope this doesn't come across as downplaying your product. It looks nice. I just don't see what you offer here that can't already be done with existing data ecosystem tools. I was using RMarkdown with knitr to generate all of my papers when I was an ML grad student years ago. It felt back then like I was the only person at Georgia Tech who realized these tools existed, and now it still feels that way.