ndexr from the bottom up
This is the index of everything. R knowledge, infrastructure patterns, the story of ndexr — assembled from recordings made over years of building. Start anywhere. The chapters have an order but the story has no ending.
Setup
Every production system starts the same way — someone opens a terminal and types. These are those moments. R and RStudio, git, Docker, WSL. Nothing clever here, but nothing skippable either. The difference between a data scientist and an engineer is usually just this: one of them has done this part enough times that it's boring.
Installing WSL2
Installing R on Ubuntu
Installing R and RStudio
Install R and RStudio
Installing GIT on Ubuntu
Installing Git on Windows
Install Docker on Ubuntu
Installing Docker on Windows
Introduction to the RStudio Debugger
R programming by voice with mouse eye tracking
VoiceCode R
R in Production
The R in Production series was an attempt to write down the gap between R as a data science tool and R as something you'd trust with real work. Packages, version control, PostgreSQL, Python interop — six episodes that treat R the way it deserves to be treated. The discipline here is what makes everything else in ndexr possible.
R in Production Intro
Intro - R in Production
R in Production Introduction
R in Production - Episode 1 - Gentle Introduction to R Packages
R in Production - Episode 2 - Setting up Git and Git Workflows
R in Production - Episode 3 - Setting up PostgreSQL with Docker Compose
R in Production - Episode 4 - Connecting to PostgreSQL from R
R in Production - Episode 4.1 - A Brief Detour into Git and Github
R in Production - Episode 5 - Adding connection_postgres
R in Production - Episode 6 - Installing Reticulate and Python
A Better RShiny Template
Shiny App with raw bootstrap 5
Infrastructure
Infrastructure is where most R projects go to die. Not because it's hard, but because no one writes it down. These recordings cover the actual work: spinning up EC2, configuring HTTPS, running NGINX in front of Shiny, Docker Compose on production iron, RStudio on AWS. The parts that are boring until they break — and then they're everything.
Retrieving AWS Access Credentials
Load balanced R plumber API with NGINX
Running Caret on Multiple Servers
Installing Shiny Server and RStudio Server on AWS
Installing RStudio on AWS EMR / Running ml_als
RBOX AMI
Scaling R with NGINX and Docker Compose on EC2
EC2 Launch and HTTPS setup
Breaking in EC2 and Cost Explorer
ndexr AWS Setup
Building ndexr
These are the live sessions. Unedited, unglamorous, watching the platform actually take shape. A better Shiny template. R infrastructure managed from a phone. An HPC spun up in under ten minutes. This is what building looks like — not the polished version, the real one. The decisions, the restarts, the parts that worked on the first try and the parts that didn't.
How fast from nothing to R super computer?
Build an HPC in less than 10 minutes with ndexr
ndexr app quick start
Build R infra with ndexr (or something)
Managing R Infrastructure from my phone
Scaling up and down a supercomputer workstation at the park