R

A language and environment for statistical computing and graphics.

When to Use

Statistical Analysis & Econometrics: Hypothesis testing, regression modeling, survival analysis, and statistical research.
Publication-Ready Data Visualization (ggplot2): Creating high-fidelity, publication-quality statistical charts and graphics.
Data Wrangling with the Tidyverse: Transforming and aggregating complex datasets using dplyr, tidyr, and pipes (|>).
Interactive Dashboards & Reports (Shiny / Quarto): Publishing dynamic web dashboards and reproducible scientific documents.

Quick Start

print("Hello, World!")

# Vector
x <- c(1, 2, 3, 4, 5)

# Mean
mean(x)

# Data Frame
df <- data.frame(
  Name = c("Alice", "Bob"),
  Age = c(25, 30)
)

Core Concepts

The Grammar of Graphics (ggplot2)

Composes statistical visualizations by layering data, aesthetic mappings, geometries, and facets:

library(ggplot2)

# Compose visual layers declaratively
ggplot(mpg, aes(x = displ, y = hwy, color = class)) +
  geom_point(size = 3, alpha = 0.8) +
  geom_smooth(method = "lm", se = FALSE) +
  labs(
    title = "Engine Displacement vs Highway Fuel Economy",
    x = "Displacement (Liters)",
    y = "Highway MPG"
  ) +
  theme_minimal()

Tidy Data Transformations with `dplyr` and Native Pipe (`|>`)

Transforms tables using standardized verbs and R 4.1+ native forward pipes:

library(dplyr)

summary_stats <- starwars |>
  filter(!is.na(height), !is.na(mass)) |>
  group_by(species) |>
  summarise(
    count = n(),
    avg_height = mean(height),
    median_mass = median(mass)
  ) |>
  filter(count >= 2) |>
  arrange(desc(avg_height))

Fast Columnar In-Memory Processing with `data.table`

Blazing fast processing of multi-gigabyte datasets:

library(data.table)
dt <- data.table(iris)
dt[Species == "setosa", .(Avg_Petal_Length = mean(Petal.Length)), by = Species]

Common Patterns

Tidyverse Data Transformation Pipeline

Problem: Multiple intermediate dataframe copies cluttering memory during analysis.

Solution: Use dplyr pipe operator (%>% or |>) for stream processing:

library(dplyr)

summary_stats <- mtcars %>%
  filter(mpg > 15) %>%
  group_by(cyl) %>%
  summarise(
    mean_mpg = mean(mpg),
    mean_hp = mean(hp),
    count = n()
  ) %>%
  arrange(desc(mean_mpg))

print(summary_stats)

Best Practices

Do:

Use the Native Pipe (|>): Standardize on R 4.1+ native pipe (|>) over legacy magrittr (%>%).
Use renv for Reproducible Environments: Lock exact package versions in renv.lock to ensure reproducible research.
Adopt data.table or arrow for Big Data: Avoid out-of-memory crashes on multi-million row datasets.
Publish with Quarto: Author reproducible reports, slides, and websites using modern Quarto (.qmd).

Don't:

Use attach(): attach() pollutes search namespaces and causes silent variable masking bugs.
Grow arrays inside iterative loops: Pre-allocate vector sizes or use vectorized vectorized functions (lapply, purrr::map).
Use 1:length(x): If x is empty, 1:0 creates an invalid 2-element vector; use seq_along(x) instead.

Troubleshooting