James Balamuta

@coatless.bsky.social

1184 Followers 16 Following

omnipresent explorer of the unknown, fashionably caffeinated informatics phd, amorous cavalier. design, build, analyze. #rstats on #macos + #webr and #python with #pyodide

Screenshot of the Pyodide REPL, a browser-based Python environment. The left pane shows example code using pandas and matplotlib; the right pane shows an Environment panel of variables and a Plots tab with a line chart titled "n squared."
A split comparison of two ways to share an R project. The top path, 'send the instructions,' runs through install steps to a laptop marked 'runs, if it installs.' The bottom path, 'send a link,' goes straight to a browser already running the plot.
Screenshot showing Apptrope Data Science fixed with a Shiny for Python app open along with 10 other app frameworks.
Screenshot showing part of the ruleset from different R sources regarding CRAN checks that needed a home inside of checktor.
peeky hex logo: cartoon eyes peek over an app whose corner peels back to reveal source code
Screenshot of a Gmail message from a CRAN maintainer, dated Thursday, July 2. The sender's name and address are blacked out. The message reads: "Thanks, on its way to CRAN. Best, [name redacted]. PS: I hope your package will be used a lot!" Below it, the quoted original email shows it was submitted via the CRAN Package Submission Form on 26/06/2026.
Dark terminal screenshot of a checktor() run on a demo package: the Package Doctor diagnosis summary listing five check categories with an overall verdict of "NEEDS ATTENTION (10 issues)."
Screenshot of Charlie's talk posted on Linkedin showing the playground and it's home over at quarto-hub.com
Screenshot of the R Observatory package search page showing results for the query "data.table." The interface lists 133 matching CRAN packages, each shown as a card with the package name, version, download rank, short description, maintainer, and 30-day download count.
The Compare downloads view on R Observatory, showing daily downloads for dplyr, ggplot2, tidyr, and tidyverse, each in its own color. An "Add a package" box with suggestions sits above a "Split Stream" row of source pills (CRAN, r2u, autoCRAN, conda-forge, bioconda, c2d4u) that breaks the comparison out by download source. Two stacked line charts plot the series over the past year, one for CRAN and one for conda-forge, with a hover tooltip listing each package's downloads on a given day and dashed markers for releases and R versions.
An author page on R Observatory for Kurt Hornik. A header notes 85 packages, time on CRAN and Bioconductor, and an ORCID link. Cards show downloads (4M in 30 days, 13.4M in 90 days, 53.9M in a year), a Roles breakdown (maintains, co-authors, contributes), and an Impact panel: 4.9K packages depend on their work, a dependency h-index of 27, and the most-depended-on packages. Further panels list top packages by downloads, an aggregate Portfolio Check Status across maintained packages (error, note, and OK counts, plus packages needing attention), frequent collaborators with a link to the collaborator network, and recent package activity.
A version comparison on R Observatory for ggplot2, set to 3.0.0 versus 4.0.0. From and To version selectors sit at the top. A Metadata panel summarizes what changed between the releases: the dates (flagged a major bump), a license change (from GPL-2 to MIT), a higher minimum R version, a larger download size, and contributors added and removed (four new, one removed, with roles shown). A Structure table then compares counts side by side with a change column, for exported and internal functions, total and R lines, compiled lines, and test lines (for example, exported functions 451 to 643, test lines 5,221 to 17,565). A Documentation table does the same for doc lines, roxygen coverage, and references coverage.
The Dependency licenses page for ggplot2 on R Observatory. A green verdict reads that all 16 installed dependencies are permissively licensed, with no copyleft obligations in the install tree. A make-up bar shows 15 MIT and 1 Apache, plus 6 base or recommended packages that ship with R. Below, an "Explore the dependency graph" panel walks the tree with toggles for view (by dependency or by license family), which kinds to include (Depends, Imports, LinkingTo, Suggests), and depth (one level through all levels), followed by an indented, license-tagged listing of the imports and their own dependencies.
The Rcpp call graph on R Observatory. An interactive network plots the package's 411 functions as nodes tinted by test coverage on a red-to-green scale, with node shape marking language and rings marking exported functions; edges show calls, including dashed edges from R into compiled code. A side panel shows per-function detail on hover, here the exported sourceCpp function with its line count, cyclomatic complexity, 100% line coverage, and call list. Below the graph, summaries break coverage down by type and by file, tally the structure (411 functions, 18 exported, 291 compiled, 497 call edges), and rank functions by lowest coverage.
The Downloads overview on R Observatory, titled "Package download activity across every data source we track." A row of source filters includes CRAN, r2u, Bioconductor, autoCRAN, conda-forge, bioconda, and c2d4u, with Posit P3M and Fedora COPR marked "soon." A headline reads 277.1M downloads in the last 30 days, combined across 7 active sources, broken down by a stacked bar (CRAN 84.7%, conda-forge 10.3%, Bioconductor 2.7%, and smaller shares from the rest). An ecosystem-trend chart runs from 2012 to 2026, and a "Top packages, combined" table lists the most-downloaded packages with per-source columns.
The ggplot2 package page on R Observatory. A header gives the name, version 4.0.3, and tagline, with four headline numbers: 6,665 packages depend on it, 26.4M downloads a year, 81.1% test coverage, and 13 of 13 CRAN checks passing. Sections below cover an Overview (maintainer, first-published date, description), install commands in tabs (CRAN, pak, Ubuntu r2u, Fedora COPR, openSUSE OBS), Health (CRAN check result, code-health facts, and a dated check history), and the start of Downloads. A Vital Signs sidebar summarizes health, coverage, docs, downloads, reverse dependencies (6,665 direct and 1,909 indirect), release, license, and links to reference docs.
Windows Defender SmartScreen dialog for demo-r-app-suite-shinylive-win-x64.exe, "Unknown publisher," showing the "More info" link you click first.
A three-stage pipeline diagram. A Shiny app (app.R or app.py, R or Python) flows through shinyelectron, which converts and packages it with electron-builder via an export() call, into a desktop app shipped as .app, .exe, or AppImage.
The shinyelectron README: hex logo, the tagline "Turn any Shiny app (R or Python) into a standalone desktop application for macOS, Windows, and Linux," a pipeline diagram (Shiny app in R or Python, to shinyelectron which converts and packages with electron-builder, to a desktop app shipped as .app, .exe, or AppImage), and install plus quick-start code using pak::pak and a single export() call.
macOS drag-to-install window for demo-r-app-suite: the app icon with an arrow pointing to the Applications folder.
A GitHub Actions workflow run for the coatless-rpkg/shinyelectron repository, titled "shinyelectron 0.2.0 #22" under the "Build demo installers" workflow. Status reads "In progress," triggered via a release one hour ago when user coatless published v0.2.0. The build-demos.yml workflow shows a completed "setup" job (59 seconds) connected to a "Matrix: build" stage reading "73/76 jobs completed." A left sidebar lists individual jobs for demo-py-app-suite across multiple platforms, including auto-download builds for linux-x64, mac-arm64, mac-x64, and win-x64, plus a bundled linux-x64 build, each marked with a green checkmark.
A Mac screen split between two windows. On the left, the Mac App Store shows an account page with Pyodios (displaying a download button) and webRios (with an Open button), both marked "Designed for iPad. Not verified for macOS." On the right, the webRios app is open to a "Welcome to webRios" onboarding screen reading "Run R on your iOS device," explaining that webRios brings R statistical computing to iPhone and iPad through webR, a WebAssembly distribution of R. A sidebar lists Console, Editor, Plots, Environment, File Browser, Projects, Packages, History, R Help, and Settings.
Terminal session running a curl POST request to https://packagemanager.posit.co/__api__/filter/packages with header Content-Type: application/json and body {"repo":"cran","names":["ggplot2"],"omit_downloads":false}. The server returns a JSON error: {"code":6,"error":"downloads are not available because usage data is disabled; set omit_downloads to true or enable Server.UsageDataEnabled","payload":null}.
A laptop on a wooden desk displaying a Microsoft Excel spreadsheet, densely filled with rows of numerical data and a green toolbar across the top. A person's hand rests on the trackpad, with a tablet, a small potted plant, and a calculator on the desk nearby. The BBC caption beneath reads that the R programming language can be used to analyse data easily, even though the screen is clearly showing Excel rather than R.
Screenshot of GitHub pull request #284 in rstudio/r-builds, titled "feat: portable, relocatable macOS and Windows R builds," merged by jmwoliver with 19 commits from the branch portable-r-mac-windows into main. A "Files changed" table lists paths and descriptions, including macos/build.sh (main macOS build orchestrator), macos/patch-mach-o.sh (Mach-O load command rewriting and codesigning), macos/make-relocatable.sh, macos/install-rprofile-hook.sh, macos/notarize.sh, macos/entitlements.plist, macos/test.sh, windows/build.ps1, windows/test.ps1, several .github/workflows YAML files, a Makefile, README.md, and a CLAUDE.md labeled "Developer notes." Below the table, an "Existing art for reference" section links to github.com/portable-r/portable-r-macos and github.com/portable-r/portable-r-windows.
Screenshot of ipywidgets inside of a Pyodios beta notebook not generating the interactive toggle but instead showing code.
Pyodios website homepage. A yellow banner at the top reads "Pyodios is in App Store review. Coming soon to your iPhone and iPad." Below: the blue Python app icon on a white tile, the title "pyodios" with tagline "Python on iOS", a v1.0.0 badge, and a description about running Python via Pyodide WebAssembly fully on-device. A "Coming soon to the App Store" pill replaces the usual download button. Two iPhone mockups show a Jupyter-style notebook running numpy and matplotlib with an inline sine wave plot, and a plot gallery with six charts including a damped wave, heatmap, distribution histogram, language bar chart, trig functions, and scatter plot.
Screenshot of a macOS Electron window titled "Python Shiny Demo Suite" showing a launcher screen with three app cards: Dashboard (analytics overview with live-updating stats and charts), Data Explorer (browse and analyze built-in datasets), and About (system information and runtime details), each labeled PY-SHINY. A terminal window behind it shows npm dependencies being installed and the Electron app starting in development mode.
Screenshot of a Windows 11 desktop tiled with eight windows, each running the same Shiny dashboard inside an Electron app under a different runtime configuration. Every window shows four colored summary cards at the top (Backend or Runtime, R or Python version, Platform, and Packages or Arch) above an Interactive Plot with a slider and scatter chart, plus a Runtime Details panel. The eight variants pair R Shiny and Python Shiny with four packaging strategies, shinylive, system install, auto-download, and bundled runtime, running on Windows across Emscripten/wasm32, x86-64, and ARM64 architectures, demonstrating that the same Shiny app can be shipped as a Windows desktop application through any of these modes. A PowerShell terminal is visible at the bottom of the screen showing the commands used to launch each Electron app.
Screenshot of the announcement by Simon Urbanek regarding Tomáš Kalibera’s untimely passing. It is with great sadness that we announce the passing of Tomáš Kalibera on 1 April, a valuable member of the R core team for close to 10 years and a good friend, after a short but aggressive illness. Tomáš brought in fresh perspective and knowledge, enabling him to improve many aspects of R, including performance and reliability. He created many tools aimed at aiding package authors to make their packages more reliable, and was instrumental in modernizing the Windows build of R. He was an active member of CRAN and the R community, providing help to package authors, and he was the most prolific writer on the R core blog. He will be remembered for his profound contributions to R by millions of users. He is survived by his wife and 1 year old son. A full obituary will be posted in due time. Respectfully, Simon Urbanek
Screenshot of the webR assembly playground for the litedown package showing an Rmd document being 'fused' in browser to show a rendered HTML page mirroring what would be present if rmarkdown or quarto were used locally to render a page (albeit with different styles).
Screenshot of a Mac desktop tiled with eight windows, each running the same Shiny dashboard inside an Electron app under a different runtime configuration. Every window shows four colored summary cards at the top (Backend/Runtime, R or Python Version, Platform, and Packages) above an Interactive Plot with a slider and scatter chart, plus a Runtime Details panel underneath. The eight variants pair R Shiny and Python Shiny with different packaging strategies, shinylive, system install, bundled runtime, auto-download, and container, running across Darwin/arm64, Linux, and Emscripten/wasm32 architectures, demonstrating that the same Shiny app can be shipped as a desktop application through any of these modes.
Windows 11 desktop showing three windows: a PowerShell terminal compiling the Rcpp package from source using g++ from the bundled Rtools45 toolchain, a File Explorer window showing the portable-r-4.5.3-win-x64-full directory containing R files alongside an rtools45 folder, and a second Explorer window showing the library folder with Rcpp and other installed packages.
Screenshot of the CRAN package page for shinylive on cran.rstudio.com. The page reads: "Package 'shinylive' was removed from the CRAN repository. Formerly available versions can be obtained from the archive. Archived on 2026-04-06 for policy violation."
A macOS desktop showing two windows side by side. On the left, a Finder window displays the contents of a "portable-r-4.5.3-macos-arm64" folder, with a directory tree expanded under "library" revealing subfolders like base, boot, class, cluster, codetools, compiler, datasets, foreign, graphics, grDevices, grid, and jsonlite (which is expanded to show CITATION, DESCRIPTION, doc, help, and html items). On the right, a Terminal window (zsh shell) shows commands for downloading and extracting Portable R 4.5.3 for macOS ARM64 from GitHub using curl, verifying the R home directory with Rscript, and installing the jsonlite package. The terminal output confirms jsonlite was successfully downloaded (1.1 MB) and installed, with the final line printing {"portable":true} to verify the portable configuration is working.
Screenshot of a Windows 11 desktop showing portable R in action. A PowerShell terminal displays the full workflow: curl downloading the portable-r-4.5.3-win-x64.zip from GitHub, tar extracting it, R.home() returning C:/Users/ronin/Desktop/portable-r-4.5.3-win-x64, and install.packages successfully installing jsonlite with the output {"works":[true]}. Behind the terminal, two File Explorer windows show the portable-r-4.5.3-win-x64 folder on the Desktop alongside the zip file, and a second Explorer window browsing the library subfolder where the jsonlite package folder is highlighted among 31 installed packages.
Screenshot of a Parallels Windows VM showing shinyelectron output for a Native Shiny for Python App on Windows 11.
Screenshot from the R contributors slack with the GitHub repo being discussed.
Contributor profile page for Brian Ripley on the R Mailing List Archives, showing 17,941 messages from June 1998 to March 2026. A stacked bar chart displays activity over time peaking around 2003 to 2006, with a breakdown table showing 11,715 messages on R-help (65.3%) and 5,495 on R-devel (30.6%) across 10 lists.
The GitHub data repository README showing R and Python code snippets for reading Parquet files, followed by a data overview table listing all 31 mailing lists with message counts, author counts, and date ranges. R-help leads with 398,517 messages dating back to April 1997.
A thread view on the R Mailing List Archives showing a message titled "macos 26 considerations" from March 2026 on the R-SIG-Mac list, with replies from Simon Urbanek, Brian Ripley, Gabor Csardi, and Peter Dalgaard visible in the thread panel below.
Homepage of the R Mailing List Archives showing a search bar, three popular lists (R-help with 398,500 messages, R-devel with 63,425, and R-package-devel with 12,125), and a full directory of core lists and special interest groups.
Screenshot showing a new webRoid version (v1.0.1) containing a fix for the startup issue in Google Play Store queue.
Promotional card for webRoid v1.0.0 with the tagline "R on Android" on a dark navy background with green circuit-trace accents. Three phone screenshots show the R console after running a plot command (with a notification badge on the Plots tab), the plots gallery displaying the resulting scatter plot, and the code editor with syntax-highlighted R script and output panel.
Screenshot of Posit's blog post showing the Posit AI pricing breakdown. The service costs $20 per month and includes unlimited Next Edit Suggestions, a credit allowance for Posit Assistant described as covering 'a full month of typical use' for many users, and an automatic recharge option that charges additional money when the usage cap is reached. The Posit Assistant and Automatic Recharge sections are highlighted.
Positron documentation page for Positron Assistant showing the section 'Positron AI agents' with highlighted text reading: 'The Positron AI tools are BYO-key, meaning that the tools can interact with a number of different model providers (e.g., Anthropic, AWS Bedrock, GitHub Copilot, or OpenAI).' This contrasts with Posit's RStudio announcement which does not support BYOK.
Screenshot of Posit’s blog post on posit.co highlighting the text: ‘Posit Assistant and NES in RStudio Desktop and RStudio Server require the managed Posit AI service and do not support custom model providers via Bring Your Own Key (BYOK).’
Screen recording of the pyodios iOS app demonstrating kernel switching. The app toggles between a local Pyodide-based Python kernel, which runs self-contained on the device, and a remote Jupyter kernel connected over the network. Code is executed under both modes, showing the app seamlessly handling the switch between local and remote computation backends.
Screenshot of the webRios documentation website at webrios.caffeinatedmath.com. The page shows the webRios app icon, the tagline 'R on Your Device,' version 1.1.1, and a description explaining it runs R code locally on iPhone and iPad via WebAssembly with no server required. Below are two iPhone mockups: the left showing an R console with code examples including print statements, warnings, errors, and ggplot2 commands; the right showing a plot gallery view with generated scatter plots and line charts.
Screenshot of webR-based Android application demonstrating inline graphics within the console window instead of having graphics being outsourced to the "Plots" tab.
Android Studio IDE showing an AndroidManifest.xml file for an app called webrios, alongside a Pixel 8 Pro emulator running the app. The emulator displays an R console with several executed commands: basic arithmetic (1 + 1 returning 2), a message function printing 'Hello Mike!', a warning call, a stop call producing an error, variable assignment, and a print statement. Each produced the expected R output. An input field at the bottom reads 'Enter R code...' with a send button.
More -> Settings -> Send Feedback
View of available platforms filtered to iOS 17 that has simulator images available to install within Xcode.
iPad Pro with iOS 26 running the code within webRios’ editor: r <- matrix(0, nrow=100, ncol=2) for(i in 1:100){ x <- rnorm(100) y <- rpois(100, 3) m <- glm(y ~ x, family=poisson) r[i,] <- coef(m) print(r[i,]) } plot(r) summary(r)
Screenshot of webRios running on an iPad, showing a four-pane layout. The top-left Editor pane has an R script with code completion suggestions visible for the plot function. The bottom-left Console pane shows executed R output. The top-right Environment pane displays a variable x with a numeric value of 1. The bottom-right area shows Plots, Files, Packages, and Help tabs. A custom R keyboard row with operators like the assignment arrow, pipe, brackets, and common R symbols is visible at the bottom.
App Store Connect screenshot showing the webRios app with version 1.1.0 in "Waiting for Review" status.
Screenshot of a Pyodide enabled Python Notebook mode on an iPhone App. A code cell with 'import this' sits above a Markdown cell currently being edited with the text 'Hello!' typed in. The Markdown cell is selected with a blue border and shows reorder, add, and delete controls. Below the cells is an 'Add Cell' button. he toolbar at top shows Run All, Code, Markdown, and Clear Outputs buttons. A bottom tab bar displays Console, Editor, Plots, and More options.
Screenshot of webRios showing autocomplete for the data.frame() functionc all in console mode.