Docker driven Data Science with R

Cascading Docker images adapted from Matrix DS to create a reproducible, standard, consistent environment to run datascience projects and cater to development and production modes. The images enable deploying dashboard frameworks like shiny with rapidity & ease.

Federal R&D Spending on climate change

An EDA using `R` of federal government data of the R&D budget towards Climate Change.

Using ESS for Data Science

RStudio is a formidable IDE to work with and offers an environment to seamlessly work with multiple languages beyond R. It is especially convenient for tasks involving frequent visualisation of data frames and plots, and for use with Shiny app development. However, the text (i.e code) editing capabalities are still significantly lacking compared to the likes of Emacs and Vim. Besides this, it does not offer a seamless interface integrating task, time management and multi-language programming environments to the extent available within Org-mode via Emacs.

Notes - What they forgot to teach you about R

The book, ‘What they forgot to teach you about R’ being co-authored by <https://twitter.com/JennyBryan @JennyBryan> is not yet completed, however I was still compelled to go through the existing material as it was an engaging read. These are some notes captured from the book. Verbatim quotes from the book are encapsulated. My notes and observations are added in plain text. I recommend you cultivate a workflow in which you treat R processes (a.

A graphic overview of the 'binary' with respect to R packages

Recently there was a question as to what a Binary is, building off a question posted on the Rstudio community forum. I’ve always found these aspects interesting, and a little hard to keep track of the connections and flow - So I’ve made a flowchart that will help me remember and hopefully explain what is happening to a noob. In this process, I was able to remember One of the first documents I really enjoyed reading when I started learning how to use Linux.

Some notes on research-compendium

These are my notes while studying the research-compendium concept, which is essentially a bunch of guidelines to produce research that is ‘easily’ reproducible. The notes are mostly based on marwick-2018-packag-r , which is one canonical reading on the concept. Other references are mentioned throughout the text, and also collected separately. These notes were prepared a few weeks ago during a foray into Docker. They are neither complete not comprehensive - but will serve as a good refresher of the principle concepts.

R notes and snippets

Lubridate - introductory technical paper This paper (Grolemund and Wickham) offers a good introduction and comparison between using lubridate and not using it, as well as several examples of using the library. It also offers some case studies which can serve as useful drill exercises. Importing multiple excel sheets from multiple excel files This is one approach to importing multiple sheets from multiple excel files into a list of tibbles. The goal is that each sheet is imported as a separate tibble.