JupyterLab (with R Kernel)

To use the R kernel in JupyterLab, you must first install R on your system, and then open an R console to install the necessary package. You simply run the command `install.packages('IRkernel')` followed by `IRkernel::installspec(user = FALSE)` in your R terminal. This registers the kernel with JupyterLab, allowing you to select "R" when creating a new notebook.

JupyterLab is superior for interactive data exploration and sharing executable, step-by-step results in a notebook format (.ipynb), which is heavily favored in the data science and Python communities. However, RStudio remains the better choice for traditional software development in R, such as building Shiny apps, package development, and complex project management. The choice depends on whether you prioritize notebooks (JupyterLab) or a traditional IDE (RStudio).

Yes, you can easily render `ggplot2` visualizations inline within a JupyterLab notebook using the R kernel. As long as the library is installed in your R environment, the plots will display directly beneath the code cell that generated them. You may need to explicitly call `print()` on your plot variable if it is wrapped inside a function or loop.

While JupyterLab is best known for its native `.ipynb` format, it does have extensions that allow you to open and edit `.Rmd` (R Markdown) files. However, the interactive knitting and rendering capabilities of R Markdown are natively integrated into RStudio. If you heavily rely on rendering R Markdown to PDF or HTML, RStudio is still the more robust option.