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.

A preview on the tidymodels meta package

Efficiently explore categorical data in dataframes

There has been a lot of fuzz about jupyter notebooks lately, so lets revisit some of its features and use-cases.

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 into some techniques for scikitlearn that allow us to write more generalizable code that executes faster and helps us to avoid numpy arrays.

We take scikitlearn for a spin, and try out the whole modelling workflow.

We look at the plotly API for R and python

We look at the visualisations options in python with matplotlib and seaborn.

We look at pandas and compare it to dplyr.

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

Short Blogpost describing how to create the logo.

In this tutorial I want to show how you can use alluvial plots to
visualise model response in up to 4 dimensions. `easyalluvial`

generates
artificial data space using fixed values for unplotted variables or uses
the partial dependence plotting method. It is model agnostic but offers
some convenient wrappers for `caret`

models.