cheatsheets VS nbdev

If you're an R user, stringr + its cheatsheet gets you very close to remembering what to do without needing to look further!

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

I'd suggest you have a look at cheatsheets (or download them from GitHub) if you want to get to know your way around a package or set if functions, it saves you a lot of time.

You could use geom_hline and geom_vline, geom_abline, or geom_segment for this. (The ggplot cheat sheet is very useful for answering these kinds of questions, BTW.)

I can't say for sure because I don't know what your ultimate aim is for your visualization. Check out the cheat sheet for ggplot2 here.

Finally just do your analysis. You should also should give a try and see the cheat sheet for data importing on the tidyverse package.

That said, when I’m trying to come up with an interesting way to visualize data, I find the ggplot cheat sheet very helpful: https://github.com/rstudio/cheatsheets/raw/main/data-visualization-2.1.pdf

Here's a cheat sheet: https://github.com/rstudio/cheatsheets/blob/main/strings.pdf

The cheat sheet of the stringr package should give you good overview of string manipulation/ regex in R.

I would very highly recommend that rather than trying to get started by translating an existing graph, you check out some documentation about ggplot first. If nothing else, the ggplot cheat sheet from RStudio should help explain what the component parts of the code are, and that might help you figure out what you actually want to do.

One example I've seen is ML/DL folks using jupyter notebooks to develop DL libraries in jupyter notebooks, see https://github.com/fastai/nbdev

- https://github.com/fastai/nbdev: Increase developer productivity by 10x with a new exploratory programming workflow.

9. Nbdev: Boost developer productivity with an exploratory programming workflow - https://nbdev.fast.ai/

See if you like nbdev way of working with data through python and jupyter. nbdev is an optional part that will create python packages from jupyter notebooks. Also even the simple tutorials are opinionated and will guide you to unit test your code and write CICD pipelines.

You probably don’t have time for this for the purposes of your task, but I will also throw in the recommendation of nbdev especially if you’re a Python person. I haven’t had a project to use it on yet, but I’ve gone through the docs and the walkthrough and it seems like a great framework for starting potential projects with all the infrastructure needed for if/when they eventually get big and need all the packaging and stuff

nbdev: jupyter notebooks -> python package

Take a look at https://github.com/fastai/nbdev - haven't used it but supposedly the whole if fast.ai library was written that way. It sounds like a natural direction in your scenario - allowing your to keep working in a familiar environment and still producing production ready code (will, at least in paper 😅)