The Lander Lens: Coding Assistants and the R Workflow

In this episode of The Lander Lens, we look at where AI coding assistants actually fit into the R workflow for data analysts and data science teams. AI coding tools can be genuinely useful when you are deep in an analysis and need help with the practical coding work: adjusting a ggplot visualization, formatting axis labels, reordering bars, writing package infrastructure, setting up tests, creating Docker files, building GitHub Actions or scaffolding a Shiny app that follows an existing team pattern. That is where these tools can save time. They help analysts stay in the flow instead of breaking concentration to search documentation, scan old examples or piece together code from multiple sources. But there is also an important line: coding is not the same thing as analysis. An AI assistant can generate valid R code for almost any analysis you describe. That does not mean the analysis is the right one to run. Model specification, transformations, assumptions, confounders and interpretation still require statistical judgment and domain expertise. The code can run, the model can converge and the output can look reasonable while still being wrong in ways that matter. This session covers practical ways to use AI coding assistants more effectively in R workflows, including project-level instructions, team coding conventions, current package documentation and review discipline for generated code. Use these tools freely for coding tasks. Use them carefully for analytical ones. Learn more about Lander Analytics at landeranalytics.com Subscribe for more conversations on AI, R, data science workflows, Shiny applications and production-ready analytics.