R’s true power comes from its rich set of built-in data structures. Choosing the right structure for your task not only streamlines code but also maximizes performance. In this section, we’ll dive deep into: Vectors: the simplest one-dimensional object Factors: efficient categorical data handling Lists: heterogeneous collections for complex data Matrices & Arrays: multi-dimensional atomic vectors Data Frames & Tibbles: tabular data ready for analysis 1. Vectors: The Foundation Vectors are R’s basic building blocks. A vector holds elements of a single type—numeric, character, or logical—and supports vectorized operations for speed and clarity. r # Creating vectors numeric_vec <- c(10, 20, 30, 40) char_vec <- c("red", "green", "blue") logical_vec <- c(TRUE, FALSE, TRUE) # Element-wise operations numeric_vec * 2 # [1] 20 40 60 80 # Indexing and subsetting numeric_vec[2] # 20 numeric_vec[numeric_vec > 25] # 30 40 Best pract...
Mastering R’s syntax and core operators sets the foundation for advanced data analysis. In this post, you’ll learn how to assign variables, work with vectors, explore logical operations, and manage your R workspace effectively. By the end, you’ll understand the building blocks that power every script, function, and package you’ll use on your R journey. 1. Basic Arithmetic and Assignment Every calculation in R relies on a handful of arithmetic operators and a clear way to store results. Arithmetic operators + addition - subtraction * multiplication / division ^ exponentiation r # Simple calculations 3 + 5 # 8 10 * 2 # 20 (4 - 1) / 3 # 1 2^3 # 8 Assignment operators <- is the idiomatic arrow for assigning a value to a variable = works similarly in most contexts, though <- is preferred in scripts r # Variable assignment x <- 42 y = x * 2 # Inspect values x # 42 y # 84 Adopting a consistent assignment style makes your code easier to read and...