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Subject ▸ data science
Reading wells from SPE data repository
Okay. There are two ways of downloading the data for all the wells in the SPE repository: the manual way (one file at a time with “Save As”), and the non-interactive automated way. The manual way is the easiest and require that you provide your SPE username and password in your usual login page. Then, you click on the link to the repository https://www.spe.org/datasets/, and start right-clicking on each of the files under the data folders.A Vagrant virtual machine that runs a Shiny server
Introduction This is an example of a straight forward generation of a Vagrant virtual machine. The script necessary to create the VM is written inside the Vagrantfile and has very few lines. The machines was upgraded to Ubuntu xenial64, as well the R Shiny server and the xenial keys to the repository. There are several files that document the changes and problems found during the rebuilt of this machine: README, NEWS, BUILD, and HISTORY, all of them markdown files.A Vagrant virtual machine to run data science on Volve datasets
vagrant-volve-navarro-BI64G20S2JP8201 This is reproducible work of Machine Learning and Data Science applied to data from the Volve field. Features This is a VirtualBox Virtual Machine (VM) that is automatically generated using Vagrant. A few Machine Learning and Deep Learning packages have been installed, such as Scikit-Learn, NLTK, Keras, TensorFlow and Theano. A Vagrant file is used to generate this VM based on Ubuntu 18.04 (bionic64). Additional packages required for this phase of the ML and DS work are welly, pandas, numpy, seaborn, and lasio.A compilation of Machine Learning examples
Introduction This is a compilation of machine learning examples that I found. They are easy to understand, they address a fundamental principle, they explain why they chose a particular algorithm. Some of them you will find very detailed; others are short and straight to the point. Prerequisites I used R-3.6.3 and RStudio Preview 1.4. I also plan to use Anaconda, Miniconda and GNU Python for the parts where I make use of Python code.Docker for R - Minimal book
This is a minimal example of a book based on R Markdown and bookdown (https://github.com/rstudio/bookdown). Please see the page “Get Started” at https://bookdown.org/home/about/ for how to compile this example.