Detect Under- and Over- Reporting of Clinical Events in Clinical Trials with simaerep 0.6.0
Detect Under-Reporting of Adverse Events in Clinical Trials with simaerep 0.5.0
Create
plotly.js Parallel Categories Diagrams Using this Htmlwidget and easyalluvial
Notes when going through advanced R - Metaprogramming
Notes when going through advanced R
Quality Control with R - Notes
Quality Control with R - Notes
Detect Under-Reporting of Adverse Events in Clinical Trials
Minor Release, maintains compatibility with
dplyr 1.0.0 and now has a slick pkgdown documentation website and makes better use of Travis CI using multiple builds to ensure compatibilty with package dependencies.
Create
plotly.js Parallel Categories Diagrams Using this Htmlwidget and easyalluvial
Minor Release, maintains compatibility with
tidyr 1.0.0 and a few bug fixes.
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
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 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.
