Begun, the Package Manager Wars, Have

There’s a quiet war brewing in the data science community, and no, it’s not about which AI model hallucinates the least.

It’s about package management.

As data science matures, reproducibility and portability are no longer optional. Models, analyses, and pipelines need to run reliably across laptops, clusters, and cloud environments. Tools like VSCode devcontainers, GitHub Codespaces, Google Colab, and Posit Cloud have made development more portable—but they’ve also made dependency management more critical than ever.

At the same time, the field is still figuring out its standards. What used to be a niche concern (package conflicts, environment drift, etc.) is now something even undergraduate students are taught to think about. And with that has come an explosion of tools: pip, virtualenv, renv, conda, mamba, docker, nix, uv, pak, and more. Each brings a different philosophy about how environments should be defined, resolved, and reproduced.

The abundance of tools is powerful, but it’s also overwhelming. It’s increasingly common for data scientists to stitch together multiple tools, often without a clear model of how they interact.

So how should you navigate this landscape?

A good starting point is scope. A small script may only need pip or renv, while a production pipeline might justify Docker or Nix. Personally, I believe the key is to choose something that scales slightly beyond your predicted needs, so you don’t incur technical debt as your work grows.

Next is control. Are you working locally, on a shared HPC cluster, or in the cloud? Do you have root access? Different tools assume different levels of control, and mismatches here are a common source of frustration.

Finally, consider usability and community support. The most powerful tool is not always the most obviously practical, especially when collaborating. I recommend favouring tools that your end-users and collaborators can understand, adopt, and debug collectively without too much headache.

I’m not going to claim there’s a single “best” solution. But I do think there’s a useful way to think about these tools, and it leads naturally to the newest entrant worth paying attention to: rv.

rv is Pretty Fast

Because rv was built in Rust, it is designed to be fast and efficient. If you want to understand why, just watch this video of how memory works in the Rust programming language: https://www.youtube.com/watch?v=5C_HPTJg5ek. While some packages can take up to and even beyond 60 seconds with install.packages(), installing stuff in rv often takes a fraction of that time. This is mainly because, being a Rust-based software, rv is able to manage internal data structures and operations at lightning speed! To demonstrate this, I set up a microbenchmark to compare the time it takes to install a few popular packages using install.packages(), renv, and rv. Even with the necessary init and configure steps, the results were quite impressive.

Thanks ChatGPT for setting up a microbenchmark:

library(microbenchmark)
library(tidyverse)

# microbenchmark_install.R

# -----------------------------
# Configuration
# -----------------------------
pkgs <- c("tidyverse", "tidymodels", "data.table")
repos <- "https://cloud.r-project.org"

# helper to create clean temp dir
make_clean_dir <- function(prefix) {
  path <- file.path(tempdir(), paste0(prefix, "_", as.integer(Sys.time())))
  dir.create(path, recursive = TRUE, showWarnings = FALSE)
  normalizePath(path)
}

# helper to run R in isolated process
run_r_script <- function(code, lib, wd) {
  script <- tempfile(fileext = ".R")
  writeLines(code, script)

  system2(
    command = file.path(R.home("bin"), "Rscript"),
    args = c(script),
    env = c(
      paste0("R_LIBS_USER=", lib),
      paste0("R_LIBS=", lib)
    ),
    stdout = TRUE,
    stderr = TRUE,
    wait = TRUE
  )
}

# -----------------------------
# 1. Bare R
# -----------------------------
bench_bare <- function() {
  wd <- make_clean_dir("bare")
  lib <- file.path(wd, "library")
  dir.create(lib, recursive = TRUE)

  code <- sprintf('
    dir.create("%s", recursive = TRUE, showWarnings = FALSE)
    .libPaths("%s")
    install.packages(c(%s), repos = "%s", Ncpus = parallel::detectCores())
  ',
    lib,
    lib,
    paste(sprintf('"%s"', pkgs), collapse = ", "),
    repos
  )

  message("Running bare R install...")
  t <- system.time(run_r_script(code, lib, wd))
  return(t)
}

# -----------------------------
# 2. renv
# -----------------------------
bench_renv <- function() {
  wd <- make_clean_dir("renv")

  code <- sprintf('
    cd %s
    install.packages("renv", repos = "%s")
    renv::init(bare = TRUE)
    install.packages(c(%s), repos = "%s", Ncpus = parallel::detectCores())
  ',
    wd,
    repos,
    paste(sprintf('"%s"', pkgs), collapse = ", "),
    repos
  )

  message("Running renv install...")
  t <- system.time({
    system2(
      file.path(R.home("bin"), "Rscript"),
      args = c("-e", shQuote(code)),
      stdout = TRUE,
      stderr = TRUE
    )
  })
  # delete the renv folder to clean up
  unlink(file.path(wd, "renv"), recursive = TRUE, force = TRUE)

  return(t)
}

# -----------------------------
# 3. rv (CLI)
# -----------------------------
bench_rv <- function() {
  wd <- make_clean_dir("rv")

  message("Running rv install...")

  t <- system.time({
    old <- setwd(wd)
    on.exit(setwd(old), add = TRUE)

    system("rv init", intern = TRUE)

    # explicitly configure a repository
    system(
      paste(
        "rv configure repository add --url https://packagemanager.posit.co/cran/latest cran-latest"
      ),
      intern = TRUE
    )

    system(
      sprintf("rv add %s", paste(pkgs, collapse = " ")),
      intern = TRUE
    )

    system("rv sync", intern = TRUE)

    # delete the rv folder to clean up
    unlink(file.path(wd, "rv"), recursive = TRUE, force = TRUE)
  })

  t
}
# -----------------------------
# Run benchmarks
# -----------------------------
results <- microbenchmark(
    bare = bench_bare(),
    renv = bench_renv(),
    rv = bench_rv(),
    times = 50
)

autoplot(results)

rv is Encapsulated

Something that I would not have argued in my early R days is that it’s a good thing that rv (and to its credit, renv as well) is encapsulated and technically separate from the interface you use to interact with R. For a noob, this can seem daunting — why would you want to EXIT R to do something related to your R project? But the more comfortable I get with the terminal, the more I appreciate the separation of concerns. This framework allows you to manage your packages and environments as a separate task, allowing you to focus and narrow down on specific problems. You don’t have to worry if conda, your conda r-base, or some other factor is interfering with your R session. Instead, you install rv once, and use it only when you begin a project and install a package. When you advance through your analysis, you simply leave R, use rv to add or remove packages, and then return to R to continue your work. This forces you to evaluate your package management decisions deliberately, and should reveal any borked environments before you get into the weeds of your analysis. Better still, using tools like Quarto is more likely to work on the first try since the rv binary is just sitting in your regular path and the library itself is in your project folder.

rv is Cross-Platform

Again, due to its Rust-based architecture, rv is designed to be cross-platform and work seamlessly on different operating systems, including Windows, macOS, and Linux. This means that you can use rv to manage your packages and environments regardless of the platform you’re working on, which can be especially useful if you’re collaborating with others who may be using different operating systems. With rv, you can ensure that your project is reproducible and consistent across different environments, making it easier to share your work and collaborate with others in the data science community.

rv is Declarative

What does it mean for a program to be declarative? Why does it even matter? Consider your typical workflow with base R and no additional tools. First, you start R, then you load your data. You find that you want to plot something, so you install ggplot2 with install.packages(). Then you load ggplot2 with library(), and make your plot. You save your script, but you haven’t recorded what packages are used or how they are installed. No worry, you may think. That’s what renv is for! You can just run renv::snapshot() and it will record the state of your packages in the project, and then you can share that with your collaborators. Problem solved?

Not so fast.

The issue with building up your package library step by step is that by the time you have installed package number 14, written 200 lines of code, fit 4 models, and made 6 plots, you have iteratively changed the state of your software environment in a way that is not guaranteed to be reproducible. Sure, you recorded what packages and versions you have at the end, but what if installing package number 4 actually caused a problem with package number 2 by updating an upstream dependency?

This is why declarative package management is so powerful. With rv, you are forced to declare the final build state of your project, and anytime rv is called, it attempts to rebuild that state from scratch, with exact package versions and dependencies respected.

Conclusion

I’m loving the idea of a declarative, Rust-based package manager for R, and I think rv has a lot of potential to be a game-changer in the data science community. Rust in general offers significant performance improvements, and when applied to package management, it may provide a more efficient and reliable solution. I’m going to keep an eye out for any obvious pitfalls, of course, but for now, I’m excited to see how rv evolves and how it can help data scientists manage their packages and environments more effectively.

Tip

Bonus: If you are developing packages, the rv team has recommended that you use rv like this to install the package in your local library.