R Community Rejoice: Coding Assistants for R Users

What Claude Code gets right (and wrong) as an R coding assistant

By Jared Lander and Joe Marlo

You're three hours into an analysis. You've found something interesting in the data and need to adjust a visualization. The legend needs to move, the axis labels need formatting and you can't remember the exact ggplot2 theme arguments. This is where AI coding tools are genuinely, uncomplicatedly useful for R users.

But five minutes later you're deciding whether to log-transform a variable or how to handle missingness in a join, and the calculus changes. The tool will happily generate code for either choice. It won't tell you which one is appropriate for your data.

That tension between coding assistance and analytical judgment is the central thing R teams need to understand about tools like Claude Code. We've been using it across projects at Lander Analytics, and what follows is our assessment of where it helps, where it falls short and what we've learned about fitting it into R workflows.

The Syntax Assistant

The most immediate payoff is also the least dramatic: syntax lookup during analysis. Every R user has lost momentum to minor formatting questions. How do I reorder factor levels for this chart? How do I customize the color scale? What's the tidymodels recipe syntax for tuning hyperparameters? You describe what you need in plain English and get back working code

# "Reorder the bars by value and format the axis as percentages"
ggplot(survey_data, aes(x = reorder(response, pct), y = pct)) +
  geom_col(fill = "#2c5f8a") +
  scale_y_continuous(labels = scales::percent) +
  coord_flip()

# "Set up a random forest with cross-validation, tuning mtry and min_n"
rand_forest(mtry = tune(), min_n = tune()) |>
  set_engine("ranger") |>
  set_mode("classification")

These aren't impressive demonstrations of AI capability. They're just useful. The old workflow – open a browser, search Stack Overflow, scan three answers, copy-paste, adapt – has quietly become a relic. Stack Overflow served the R community extraordinarily well for over a decade, but the friction of leaving your editor to search for syntax is exactly the kind of interruption that compounds. Now analysts stay in their analytical train of thought. For teams doing heavy visualization or modeling work, that continuity adds up across a day.

The Engineering Side of R

Where Claude Code genuinely excels is the software engineering work that R teams increasingly need to do: package development, unit testing with testthat, CI/CD pipelines, containerization and Shiny application development. These are tasks with clear specifications where "correct" is objectively verifiable. The code works or it doesn't.

Shiny is the standout use case. Shiny apps are notoriously varied in architecture. Every team develops its own conventions for module structure, reactive data flow and database connections. There is no single right way to organize one. We've had excellent results pointing Claude Code at an existing production app and asking it to map the architecture: how modules communicate, where reactive values flow, how database connections are managed, how variables are named. From that understanding, it scaffolds a new app following the same patterns. This matters because consistency across Shiny apps is hard to maintain as teams grow, and "read this codebase and build something that matches" is a task Claude Code handles remarkably well.

The same applies to package infrastructure (DESCRIPTION files, test scaffolding, vignette templates), Dockerfiles for Shiny deployment and GitHub Actions for R CMD check. Claude Code also gives R users a lower-friction way to explore unfamiliar engineering territory (containerization, deployment pipelines, CI/CD) without drowning in documentation.

Most R teams work in RStudio, VS Code with the R extension, or Positron (the newer IDE from Posit). All now support LLM integration in some form. Positron's Assistant feature supports Anthropic Claude for chat and GitHub Copilot for inline completions. We typically run Claude Code in a terminal pane alongside our IDE rather than replacing anything in the existing workflow.

The Analysis Gap

Here's where it gets complicated. Everything described above is fundamentally a coding task. But the core of what R teams do is analysis, and analysis is a different kind of problem.

When an analyst explores a new dataset, chooses between model specifications, decides which transformations make sense or interprets coefficients, they're making judgment calls grounded in domain knowledge and statistical training. Claude Code can generate syntactically valid code for any analysis you describe. It cannot (yet) tell you whether that analysis is the right one to run.

In software engineering, there's a productive mode of working where you move fast, fix bugs as they surface and iterate aggressively. Steve Yegge illustrates this in his Gas Town framework: chaotic, high-throughput, agent-driven development where some bugs get fixed twice and it doesn't matter because the code either works or it doesn't. Analysis doesn't operate that way. An analysis can "work" perfectly (the code runs, the model converges, the output looks plausible) while being wrong in ways that matter: missing confounders, violated assumptions, inappropriate transformations. These are silent failures that no test suite catches.

We're actively developing workflows for the analytical side of this: how to use LLMs during exploratory analysis, when to trust modeling suggestions, how to verify that generated code does what the analysis actually requires. That deserves its own treatment and we plan to write about it in the future. For now, the guidance is: use Claude Code freely for coding tasks, and cautiously for analytical ones. Your analysts' judgment is the thing that makes analysis trustworthy, and no tool changes that.

Making It Work

A few practices make a measurable difference in the quality of LLM-generated R code.

Project-level instructions: A CLAUDE.md file at your project root defines your team's conventions and loads automatically every session. The file stores instructions like "Use tidyverse with the native pipe |>," "Use testthat 3rd edition" and "No unnecessary code comments." Simon P. Couch, an engineer at Posit, maintains CLAUDE.md files with instructions like "read every file in R/" and references to preferred patterns. This prevents the slow accumulation of inconsistencies that happens when different team members get different suggestions from the model.

Current documentation: R packages evolve faster than training data. Ask for API code and you'll get httr instead of httr2. Request data manipulation and you might get plyr or reshape2 instead of dplyr and tidyr. Context7 and the R environment aware btw package's MCP server pull current, version-specific documentation into prompts so the model works with how packages behave today, not two years ago.

Review discipline: LLM-generated R code needs the same scrutiny as human-written code, with extra attention to package versions (httr vs httr2, plyr vs dplyr), mixed pipe syntax (%>% vs |>) and data assumptions. The model doesn't see your actual data – unless you direct it to – and will reference columns that don't exist or apply transformations that don't fit your types. Posit's experimental gander package begins to address this by giving the LLM access to your R environment, but it's early days.

Where This Lands

Hadley Wickham's keynote at useR! 2025 framed LLMs as tools that "augment rather than replace" R workflows. We'd sharpen that: they augment the coding parts of R work reliably, and the analytical parts much less so. The teams that benefit most from these tools will be the ones that understand where that line falls in their own work and manage accordingly.

Jared P. Lander

Founder and Chief Data Scientist

Lander Analytics

Joe Marlo

Director of Data Science

Lander Analytics

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About the author: Jared P. Lander is Chief Data Scientist and founder of Lander Analytics, where he helps organizations build practical, measurable AI workflows grounded in strong data foundations.

About the author: Joe Marlo is Director of Data Science at Lander Analytics, where he designs agentic workflows, statistical models, and interactive frontends that put rigorous analysis into production.