Use easyalluvial for visualising model response in up to 4 dimensions.
Mayor Release for easyalluvial with exciting new features. Visualise model response using 4 dimensional partial dependence plots and add marginal histograms to visualise distribution of binned numerical values.
Efficiently explore categorical data in dataframes
We demonstrate how we can use R from within a python jupyter notebook using rpy2 including R html widgets
Here we give a step-by-step tutorial on how to manage R and python packages with conda.
We give an introduction to conda environments and show how they can be used to maintain reproducibility in polyglot data projects using both R and python.
We look at the visualisations options in python with matplotlib and seaborn.
Some reflections on the choice of the python IDE. We end up comparing RStudio to pycharm.
Blogging with jupyter notebooks, hugo_jupyter and some tweaking. Comparison to R and blogdown