shibu99 1. Introduction Sampling and Sampling Distribution form the backbone of modern statistical analysis. In real-world situations, collecting information from every individual in a population is rarely possible due to limitations such as time, cost, accessibility, and feasibility. Instead, analysts, researchers, and data scientists rely on samples. These samples provide information that helps us understand how … Read more
Understanding how Inf, NaN, and Lists work in R is essential for data cleaning, debugging, and structuring complex information. These concepts often appear when handling real‑world datasets where missing values, undefined results, and mixed‑type data structures are common. The following notes provide clear, point‑wise explanations along with practical R examples. 1. Understanding Inf in R … Read more
1. Introduction Missing observations are common in empirical datasets originating from surveys, experiments, sensor records, and transactional systems. In R, missing values are represented using the symbol NA, indicating that the information for that particular position is unavailable or undefined. When reading external files such as CSVs or spreadsheets, R automatically converts empty fields or … Read more
Introduction Vectors in R are ordered data structures in which each element occupies a fixed position. The process of extracting or altering values relies on the language’s 1‑based indexing system. In this system, the first element of a vector is referenced by index 1, the second by index 2, and so forth. This indexing approach … Read more
R offers several helpful built‑in functions that allow you to examine vector objects. These functions tell you how many elements a vector has, what type of data it stores, and how R handles that data internally. Understanding these functions is important when working with datasets, debugging code, or verifying the structure of your objects. Understanding … Read more
R provides several built-in data structures designed to store, organise, and analyse data effectively. These structures use basic data types such as numeric, integer, character, and logical. For anyone learning R for analytics, statistics, or data science, a strong understanding of these structures is essential. The five primary data structures in R are: Among these, … Read more
Goal of This Chapter Before doing real data science, you must understand: This chapter explains EVERYTHING clearly. How R Executes Code R is an interpreted language, meaning: Try this: Output: Comments in R Comments are notes for humans, ignored by R. Used for: Assignment Operators. Assignment means: store a value inside a variable. R has … Read more
Understanding data starts with understanding types. When you enter a value in R, the language needs to know what kind of data it is. This classification determines how R stores it, how much memory it uses, and what operations can be performed on it. In this article, we explore the five fundamental data types in … Read more
When starting with R, one concept shapes everything you will learn: R treats every entity—numbers, data frames, models, functions—as an object. This principle forms the backbone of R’s programming structure. Understanding it early makes your learning smoother, helps you debug confidently, and allows you to work with complex data operations easily. Think of R as … Read more
R Data Types and Data Structures Understanding how data is stored, organized, and manipulated is the foundation of every programming language—especially when working in data science. R, being a powerful statistical computing language, provides a rich system of data types and data structures that allow you to store anything from simple values to complex datasets. … Read more
