Introduction to R for Data Science

Overview

R is rapidly becoming the leading language in data science and statistics. Today, R is the tool of choice for data science professionals in every industry and field. Whether you are full-time number cruncher, or just the occasional data analyst, R will suit your needs. This introduction to R programming course will help you master the basics of R. In seven sections, you will cover its basic syntax, making you ready to undertake your own first data analysis using R. Starting from variables and basic operations, you will eventually learn how to handle data structures such as vectors, matrices, data frames and lists. In the final section, you will dive deeper into the graphical capabilities of R, and create your own stunning data visualizations.

Prerequisites

No prior knowledge in programming or data science is required.

Course duration

2 Days

Course outline

Section 1: Introduction to Basics

Take your first steps with R. Discover the basic data types in R and assign your first variable.

Section 2: Vectors

Analyze gambling behaviour using vectors. Create, name and select elements from vectors.

Section 3: Matrices

Learn how to work with matrices in R. Do basic computations with them and demonstrate your knowledge by analyzing the Star Wars box office figures.

Section 4: Factors

R stores categorical data in factors. Learn how to create, subset and compare categorical data.

Section 5: Data Frames

When working R, you’ll probably deal with Data Frames all the time. Therefore, you need to know how to create one, select the most interesting parts of it, and order them.

Section 6: Lists

Lists allow you to store components of different types. Section 6 will show you how to deal with lists.

Section 7: Basic Graphics

Discover R’s packages to do graphics and create your own data visualizations.


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