R

R is a programming language and environment designed specifically for statistical computing and data visualization. It's widely used in academia, research, and data science for statistical analysis and graphical representation of data.

Key Characteristics

• Statistical focus: Built-in statistical functions and tests
• Data manipulation: Powerful data frame operations
• Visualization: Publication-quality graphics with ggplot2
• CRAN ecosystem: Comprehensive R Archive Network with 18,000+ packages
• Interactive: REPL and notebook environments (RStudio, R Markdown)

Common Use Cases

• Statistical analysis: Regression, hypothesis testing, ANOVA
• Data visualization: Charts, plots, interactive graphics
• Bioinformatics: Genomic data analysis
• Finance: Quantitative analysis, risk modeling
• Academic research: Reproducible research with R Markdown

Example Code

# Load data and libraries
library(ggplot2)
library(dplyr)

# Data manipulation
summary_stats <- data %>%
  group_by(category) %>%
  summarise(
    mean_value = mean(value),
    sd_value = sd(value),
    n = n()
  )

# Visualization
ggplot(data, aes(x = category, y = value, fill = category)) +
  geom_boxplot() +
  theme_minimal() +
  labs(title = "Value Distribution by Category")

# Statistical test
t.test(value ~ group, data = data)

R vs Python for Data Science

Aspect	R	Python
Statistics	First-class citizen	Via libraries
Visualization	ggplot2 is outstanding	matplotlib/seaborn
Machine learning	caret, tidymodels	scikit-learn, PyTorch
General programming	Limited	Excellent
Production deployment	Challenging	Easier
Learning curve	Steeper for programmers	Gentler

What We Like

• Statistical power: Unmatched for complex statistical analysis
• ggplot2: Grammar of graphics produces beautiful visualizations
• Tidyverse: Modern, consistent data manipulation
• R Markdown: Reproducible reports with code, text, and output
• Academic packages: Cutting-edge statistical methods

What We Don't Like

• Performance: Slower than Python for large datasets
• Non-standard syntax: Different from mainstream languages
• Production challenges: Harder to deploy in production environments
• Memory management: Loads entire datasets into RAM
• General programming: Not ideal outside statistics

When to Use R

• Complex statistical analysis requiring specialised tests
• Publication-quality data visualization
• Academic or research environments
• Exploratory data analysis
• Bioinformatics or specialised scientific domains

For production ML systems or general-purpose programming, Python is often more practical.