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A data science blog for Petroleum Engineering.

Topics covered

Subject ▸ reproducibility

A Matplotlib book with Rmarkdown

28 December 2020

Introduction The goal of this repo is testing that Python matplotlib works seamlessly from within RStudio. Motivation Making matplotlib run from within RStudio using the R package reticulate and Python Anaconda has improved a lot in the past few months. The package reticulate and RStudio have gone through a thorough transformation and now seems to be an accepted fact that Python and R have to live along. Examples I have included some working examples of Rmarkdown notebooks with Python matplotlib plots.

A Python plotnine book built with Rmarkdown

28 December 2020

reproducibility, python

Motivation Found this interesting article by Jeroen Janssens on applying the Grammar of Graphics introduced to data science by #rstats ggplot2. The amazing thing is that the article has been totally computed with Python as the coding engine, while the text is in Rmarkdown. The plots have been generated by the Python package plotnine, practically duplicating what R ggplot2 does! I liked the article so much that I decided to make it a “minimal book” with R bookdown, maintaining the Python computations.

A book of Python in Rmarkdown

30 October 2020

reproducibility

Motivation I love this book. It’s not only written in Rmarkdown but also explains the fundamentals of Python in a live book in the web; totally reproducible. Kudos to the author Keh-Soon Yong (AP), who published the original code in GitHub here: https://github.com/yongks/python_bookdown What is extraordinary is that this book has been totally computed with Python as the coding engine, while the text is in Rmarkdown. The plots have been generated by the Python package matplotlib, and seaborn.

Building the book dataviz-wilke with Docker

13 July 2020

reproducibility, data science, visualization of data

dataviz-wilke 2020 This book “Fundamentals of Data Visualization` by Claus Wilke has been made fully reproducible using a Docker container. The compiled book can be read online here. The original repository of the book is in GiHub at this link, and can also be read online here. The book is great at learning advanced visualization techniques using R without focusing too much on the code but rather on universal, timeless best practices.

Building a gallery of TikZ graphics with R

1 June 2020

data science, reproducibility, literate programming

Introduction I have been working a lot lately with LaTeX and TikZ graphics. I am preparing a paper and few articles that need some sketches and wasn’t able to find the right tool to do it. I am a markdown guy and almost everything I write is either in Markdown or Rmarkdown files. One of the last documents I wrote using Rmardown was the transcript of the interview to Professor John Hopfield.

Is Windows in the comfort zone?

29 December 2018

machine learning, linux, reproducibility

There is also a very controversial issue in this article such as Windows ecosystem is the comfort zone of developers and people alike. If you are serious about Application Development in Data Science, Machine Learning and Artificial Intelligence -, then you have to be get out of the comfort zone and start doing it in Linux. The terminal means reproducibility. Start with practicing Linux an hour a day. No?

Virtualization brings Reproducibility to your Petroleum Engineering Workflow

3 January 2018

computer science, reproducibility

Motivation By nature, I am curious. I am not only interested in the why-of-things but also in the “how”. Be able to document it and reproducing it later. And that, most of the time, could be a time consuming affair. Pleasurable, rewarding, but time consuming. Add to that data science and deep learning and you get a exponential combination. I own a 8-core, 32GB RAM, 3TB SSD, Quadro K2100M GPU laptop that originally acquired with the intention of running several virtual machines with Windows, Linux and MacOS, as part of my work as an atypical petroleum engineer.

My blogdown adventure

30 April 2017

R, computer science, reproducibility

I have had my notes here and there: Evernote, network drives, LinkedIn, SPE forums. And I could never find the ideal way to put the data and info together until I found Hugo. Blogdown documentation I borrowed the template ideas for my blog from Ron J. Hyndman blog. The source code for the site is now hosted on github. If you find any problem in this site, please feel free to let me know at.

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