add docker explanation and compare to renv

_drafts/docker-for-R.md

@@ -11,4 +11,26 @@ machines (VMs) and Docker might have seemed like the domain of software
 engineers, far removed from the world of statistical models, tidy data, and
 ggplots. But as the complexity of data science workflows has grown, the ability
 to manage environments, ensure reproducibility, and collaborate effectively has
-become critical. That’s where Docker comes in.
+become critical. That’s where Docker comes in.
+
+At its core, Docker is a platform that helps you package and run applications in
+isolated environments called containers. A container is like a virtual box that
+holds everything your R project needs to run—R itself, libraries, system
+dependencies, and even a specific operating system. Unlike traditional virtual
+machines, Docker containers are lightweight, efficient, and portable.
+
+For R users, Docker offers a way to ensure that your code runs exactly the same
+anywhere, from your laptop to a cloud server. No more "it works on my machine"
+excuses!
+
+You may be thinkging, "But I alredy have R installed on my computer and I use
+renv. That's enough, right?" Well, not quite. While renv is excellent for
+managing R package dependencies and ensuring reproducibility at the package
+level, it falls short in addressing system-level dependencies, R versioning, and
+cross-platform compatibility. Docker, by contrast, packages the entire
+environment—including the operating system, R version, and required system
+libraries—ensuring your project runs identically anywhere. Additionally, Docker
+offers seamless integration with other tools and workflows, such as deploying
+Shiny apps or APIs, and avoids dependency conflicts by isolating projects
+completely. While renv is easier for quick package management, Docker provides a
+comprehensive solution for full-stack reproducibility and deployment.