r

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:

```r
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

Error	Cause	Solution
Error in ... : could not find function "%>%"	Pipe operator used without loading magrittr or dplyr.	Add library(dplyr) or use native R 4.1+ pipe |>.
cannot allocate vector of size ... (Out of memory)	Dataset exceeds available RAM.	Use data.table or chunk analysis with the arrow package.
object '...' not found	Variable evaluated before declaration or misspelled column name.	Verify spelling and check active environment objects with ls().

References

• R Project
• R for Data Science