RStudio Server VS NumPy

This particular issue should be resolved in the latest daily builds of RStudio. The underlying issue here was a conda patch included in the conda-provided builds of R, which interfered with the way RStudio attempted to load R. Please see https://github.com/rstudio/rstudio/issues/13184#issuecomment... for more details.

It is a bug that was introduced in RStudio 2023.06.0 Build 421. See Error with 'cacheKey' in .rs.WorkingDataEnv and .rs.CachedDataEnv. The current advice is to ignore or add options(rstudio.help.showDataPreview = FALSE) to your ~/.Rprofile ... so RStudio can ignore it for you.

A quick google for details from other app: https://github.com/rstudio/rstudio/wiki/Writing-Good-Feature-Requests :)

solution found here: https://github.com/rstudio/rstudio/issues/9367

u/acemachine123, if you’re using Rmarkdown or Rprojects over a network drive, connected by VPN, it’s going to be slow like u/ThatDeadDude said. Best thing you can do is thumbs up the issue here https://github.com/rstudio/rstudio/issues/10417 and use an Rscript file instead.

Hey! I can not select the Noto Sans Mono font in RStudio's Editor Font options, despite it being installed in my system, as you can see in the picture. RStudio only offers me "Noto Color Emoji" instead. When looking for this online, I only found this: https://github.com/rstudio/rstudio/issues/9512, but I am not sure if this really a related issue. Or does RStudio not support TrueType fonts? I can't find anything about this after a quick search

Next (2023.03) changelog - https://github.com/rstudio/rstudio/blob/main/version/news/NEWS-2023.03.0-cherry-blossom.md .

for example in this post they have dark menus aswell

In NumPy with @, dot() or matmul():

In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication.

Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:

numpy: A library for scientific computing in Python

Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy.

I was looking at all the big projects like numpy, pytorch, flask, etc.

I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]