R

A language and environment for statistical computing and graphics.

When to Use

• Statistical Analysis
• Data Visualization (ggplot2)
• Bioinformatics
• Academic research

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

Vectorization

R operations are designed to work on entire vectors at once, avoiding explicit loops.

x + 1 # Adds 1 to every element in x

Pipe Operator %>%

Used to clean code by passing output of one function as input to the next (Tidyverse).

data %>%
  filter(users > 100) %>%
  group_by(region) %>%
  summarize(total = sum(users))

Best Practices

Do:

• Use the Tidyverse (dplyr, ggplot2) for modern R
• Document functions with Roxygen2
• Use RStudio IDE

Don’t:

• Use explicit for loops if vectorization is possible (performance)
• Mix naming conventions (snake_case is preferred in tidyverse)

References

• R Project
• R for Data Science