R
What it is: Statistical computing language. Built by statisticians for statisticians. Best stats packages anywhere.
What It Does Best
Statistical analysis. Every statistical test exists in R. New methods appear in R first.
Tidyverse ecosystem. dplyr, ggplot2, tidyr—modern, consistent data manipulation and visualization.
Academic research. Reproducible research tools. R Markdown for papers. Standard in many fields.
Key Features
ggplot2: Grammar of graphics for publication-quality visualizations
dplyr: Intuitive data manipulation with pipe syntax
R Markdown: Literate programming for reproducible research
CRAN: 20,000+ packages for specialized statistical methods
Shiny: Interactive web applications from R code
Pricing
Free: Open source language and packages
RStudio: Free IDE, or Pro ($995/year) for commercial use
When to Use It
✅ Statistical analysis is your main job
✅ Academic or research environment
✅ Publication-quality visualizations (ggplot2)
✅ Need specific statistical methods
✅ Working with survey data or experimental designs
When NOT to Use It
❌ Building ML production systems (Python better)
❌ General-purpose programming (Python better)
❌ Web development or APIs (wrong tool)
❌ Large-scale data processing (Spark/SQL better)
❌ Team unfamiliar with programming (GUI tools better)
Common Use Cases
Statistical modeling: Regression, ANOVA, mixed models, survival analysis
Data visualization: ggplot2 for publication-ready charts and graphs
Bioinformatics: Bioconductor packages for genomics and proteomics
Survey analysis: Complex sampling designs and weighting
Time series: Forecasting, ARIMA, state space models
R vs Alternatives
vs Python: R better for stats and viz, Python better for ML and production
vs SAS/SPSS: R free and more flexible, SAS/SPSS better enterprise support
vs Stata: R more powerful and free, Stata easier for economists
Unique Strengths
Statistical depth: Every statistical method exists, cutting-edge research first
ggplot2: Best visualization system in any language
Tidyverse: Consistent, readable syntax for data work
R Markdown: Seamlessly mix code, results, and narrative
Bottom line: If statistics is your core work, R is unbeatable. For everything else, Python wins. Many data scientists know both.