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Detect Under-Reporting of Adverse Events in Clinical Trials with simaerep 0.5.0
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Create
plotly.js
Parallel Categories Diagrams Using this Htmlwidget and easyalluvial
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Notes when going through advanced R - Metaprogramming
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Notes when going through advanced R
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Quality Control with R - Notes
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Quality Control with R - Notes
Detect Under-Reporting of Adverse Events in Clinical Trials
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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.
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Create
plotly.js
Parallel Categories Diagrams Using this Htmlwidget and easyalluvial
![](https://www.datisticsblog.com/parcats_logo.png)
Minor Release, maintains compatibility with
tidyr 1.0.0
and a few bug fixes.
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Use easyalluvial for visualising model response in up to 4 dimensions.
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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.
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A preview on the tidymodels meta package
Efficiently explore categorical data in dataframes
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We demonstrate how we can use R from within a python jupyter notebook using rpy2 including R html widgets
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Here we give a step-by-step tutorial on how to manage R and python packages with conda.
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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.
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We look at the plotly API for R and python
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We look at the visualisations options in python with matplotlib and seaborn.
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We look at pandas and compare it to dplyr.
![](https://www.datisticsblog.com/r2py.png)
Some reflections on the choice of the python IDE. We end up comparing RStudio to pycharm.
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Blogging with jupyter notebooks, hugo_jupyter and some tweaking. Comparison to R and blogdown
Short Blogpost describing how to create the logo.
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