The ggplot2 vs plotly decision shapes your R data viz workflow in 2026. Compare static publication graphics with interactive strengths of both libraries.
Articles
Articles
News and deep dives on the R ecosystem.
- ggplot2 vs plotly: When to Use Each in R
- Quarto vs R Markdown in 2026: Which Should You Use?
Quarto vs R Markdown: the official successor is here. This 2026 comparison helps you choose the right tool for your reproducible reporting workflow.
- R for Machine Learning in 2026: A Complete Guide
R machine learning has matured into a serious production platform in 2026. From tidymodels to torch, here is a complete guide to the R ML ecosystem.
- Data Visualization Best Practices in R
Create effective, informative data visualizations in R. This guide covers ggplot2, color theory, chart selection, and common pitfalls to avoid.
- data.table vs dplyr: Performance Showdown
data.table vs dplyr performance showdown: benchmarks on 10M-row datasets — filtering, grouping, joins, memory. Pick the right R data manipulation tool.
- Polars R: High-Performance DataFrames with Lazy Evaluation
Polars R brings Rust-powered DataFrame performance to R: handle large datasets, use lazy evaluation, and replace dplyr for memory-intensive data tasks.
- The Best R Packages in 2026: A Curated List
The best R packages in 2026, from data manipulation to machine learning and web APIs. A curated guide to the essential R tools every developer needs.
- State Quarto 2026: Where the Platform Stands Today
The state Quarto occupies in 2026 as the dominant publishing system for R, Python, and Julia. Here is where the state Quarto stands and why it is the go-to.
- R DevOps: Reproducible Pipelines in 2026
R DevOps practices for building reproducible data science pipelines with Docker, GitHub Actions, CI/CD, and containerized deployment.
- Using R and Python Together with reticulate
R and Python work together via reticulate: call Python from R, share data frames across languages, and run Python code in Quarto and R Markdown documents.
- R for Quantitative Finance in 2026: A Complete Guide
R quantitative finance in 2026: quantmod, tidyquant, PortfolioAnalytics, and rugarch for data retrieval, technical analysis, backtesting, and risk modeling.
- Running R in Production: Best Practices for 2026
Running R in production takes scheduling, containers, and monitoring. Deploy R scripts reliably with cron, Docker, plumber, and renv.
- R Machine Learning Packages in 2026: The Complete Landscape
Explore the R machine learning ecosystem in 2026: tidymodels, xgboost, torch, h2o, and more. Covers classification, regression, deep learning, and AutoML.
- R vs Julia for Data Science 2026: How to Choose
The R vs Julia data science debate has settled into a clear pattern by 2026. Here is how the R vs Julia choice depends on your goals, team, and use case.
- R vs Python for Data Science in 2026
R vs Python in 2026: compare strengths in statistics, machine learning, and visualization. Decide which language fits your data science career and projects.
- Shiny vs Streamlit for Data Apps in 2026: Which Should You Use?
Shiny vs Streamlit in 2026: compare architecture, state management, and deployment to choose the right data app framework for your R or Python project.
- Tidyverse vs Base R: When to Use Each
The tidyverse vs base R debate shapes how R programmers write code. Learn when each approach excels and how to combine both for cleaner, faster R workflows.
- What's New in R 4.4 — R 4.4.0 (Puppy Cup) brings experimental
What's new in R 4.4.0 (Puppy Cup): experimental tail-call optimization with Tailcall(), improved NULL and complex value handling, and stricter package checks.
- What's New in R 4.5 — R 4.5.0 adds the penguins dataset, a new
What's new in R 4.5.0: the penguins dataset, use() for selective package loading, parallel downloads, grepv(), and performance improvements.
- Why R docs should show the shape of data early
R docs improve when examples reveal data structure before transformations. Good R docs show data shape early, cutting confusion and helping examples land.