![]() The choice: do I put the code in the Rmd, or do I just report intermediate results? More often than not, the latter makes the most sense. (It is one I have to make a lot.) For the sake of example, let’s pretend I did a large machine learning project on some RNA-Seq data, and analyzed the data in a JupyterLab notebook on an EC2 instance with a lot of memory and many CPUs (more than a laptop or desktop). Load up our Diamond Age-branded RMarkdown ( Rmd) template.Things look great – like ROC AUC = 0.999 great – and now I need to write up the report of how all of this magic happened and why it matters to our clients. So, let’s set the stage: Here I am, happily developing code in JupyterLab (sometimes in %%R cells, but mostly not), and I finish my analysis. More info on this type of setup can be found here. Functionality also exists to use a native R kernel in JupyterLab (i.e., no Python interface), but that is outside the scope of this post. (I have some cred here - the first application I wrote professionally was in Visual Basic, over 20 years ago.)įor those of you how may have not seen this R-from-Python scenario in action before, here’s a taste using IPython %%R magic in a JupyterLab notebook backed by an IPython kernel.īriefly, you load the extension from rpy2 into the notebook, annotate a code cell with %%R, and use R as you normally would. ![]() R dropped 0.52% between 20 and was overtaken by (gasp) Visual Basic. Recent TIOBE metrics may tell part of the story. Pair the narrative with the analysis possibilities of the R ecosystem, and one may wonder why more complete Python-to-R switches are not made by professionals doing this kind of work. However, it has always been a pain point to create the (admittedly) beautiful reports that come almost stock with RMarkdown. I used it in both my PhD and postdoc work, as well as professionally, and it’s great. R and Pythonįor many years now, the Python community has had really nice integration with R through rpy2 and the cell magic of %%R in Jupyter/IPython. This is a lot, though, and hopefully those without the full suite of knowledge above can still gain some appreciation of the system I’m going to describe. To proceed through this document with ease, you should be familiar with: I am aware of the many improvements that have been made to the Python tooling in RStudio, but to me it still feels unnatural - and, though it’s much improved, it still has a way to go (in my opinion) before it is appropriate for a full-time, Python-centric, computational biology workload. Am I motivated to do so? Not at the moment. I work in Python 99.9% of the time, I’ve been programming in Python for a long time now, and I spend a lot of development time working interactively with JupyterLab. They also enable our customers to customize their views of our work (e.g., hiding or showing all code along with a narrative) and choose how they interact with the data (e.g., downloading tables directly to Excel, viewing dynamic plots).īut what about Python-oriented developers and data scientists who don’t want to use R? There’s a way they can also get the benefits of the RMarkdown reporting system - but it requires a bit of trickery, which I’ll show you in this tutorial. These tools let us merge our analyses and reporting into a single framework and give our customers a way to inspect our methodology and reproduce our analyses on their own (if they wish). At Diamond Age Data Science, we make extensive use of RMarkdown and RStudio.
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