R Objects

Overview

In R, everything is an object. Data, functions, models, and even results of calculations are all stored as objects. Understanding R objects is fundamental to working with data, writing reproducible code, and performing statistical analyses.

This article introduces what R objects are, common object types, how to create them, and how to inspect them.

What Is an R Object?

An R object is anything that R can store in memory and work with. When you assign a value to a name, you are creating an object.

# store 10 in x

x <- 10

• 10 is the value

• x is the object name

• <- is the assignment operator
  

​

x + 5

Object names in R:

• Can contain letters, numbers, . and _

• Must start with a letter or .

• Are case-sensitive (Data ≠ data)

​

​

student_scores

mean_value

​

Common Types of R Objects

1. Vectors

A vector is a one-dimensional collection of values of the same type.

scores <- c(85, 90, 88, 92)

Common vector types:

• Numeric: c(1, 2.5, 3)

• Character: c("A", "B", "C")

• Logical: c(TRUE, FALSE, TRUE)

2. Data Frames

A data frame is a table-like object where:

• Columns can be different types

• Each column is a vector

• Each row represents an observation

students <- data.frame(

  name = c("Alex", "Jordan", "Taylor"),

  score = c(85, 90, 88),

  passed = c(TRUE, TRUE, TRUE)

)

3. Matrices

A matrix is a two-dimensional object where all values must be the same type.

M <- matrix(c(1, 2, 3, 4), nrow = 2)

Matrices are often used in linear algebra and statistical modeling.

4. Lists

A list can store different types of objects together.

my_list <- list(

  numbers = c(1, 2, 3),

  text = "hello",

  flag = TRUE

)

Lists are flexible and commonly used to store model outputs.

5. Functions

Functions are also objects in R.

square <- function(x) {

  x^2

}

You can store, pass, and modify functions like any other object.

Inspecting R Objects

Viewing an Object

Students

Checking the Object Type

class(students)

Viewing Structure

str(students)

Getting a Summary

summary(students)

These commands help you understand what kind of data an object contains and how it is organized.

Modifying Objects

Objects can be overwritten or updated:

x <- 5

x <- x + 1

You can also modify parts of objects:

students$score <- students$score + 5

Removing Objects

To remove an object from memory:

rm(x)

To see all objects currently in your environment:

ls()

Why R Objects Matter in Data Science

Understanding R objects allows you to:

• Store and manipulate data efficiently

• Understand outputs from statistical models

• Debug errors by checking object structure

• Write clear and reproducible code

Most errors in beginner R programming occur because an object is not the expected type or structure.

Key Takeaways

• Everything in R is an object

• Objects store data, functions, and results

• Common objects include vectors, data frames, matrices, lists, and functions

• Use class(), str(), and summary() to inspect objects

• Mastery of R objects is essential for statistics and data science workflows