Programming Data Structures in R


Programming Data Structures in R

Authors: Justin Flett, Martin Burger

There are five data structures that are used to store data in an organized fashion. This allows data manipulations and data operations to become more efficient.

What You Will Learn

  • This skill covers the five data structures known as Lists, Martixs, Vectors, Data Frames, and Factors.


  • Fundamentals of computer programming
  • Fundamentals of creating variables
  • Using Terminal/Command Line


Working with basic data types, selecting the appropriate data structures, creating vector variables and storing and accessing data in a factor.

Working with Data Types in R

by Justin Flett

Nov 27, 2019 / 1h 30m

1h 30m

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R is a popular programming language for statistical computing. In this course, Working with Data Types in R, you will gain foundational knowledge of data types and structures used within R. First, you will learn the basics of understanding what exactly data types and structures are. Next, you will discover how to work with commonly used data types and structures such as vectors, lists, factors, matrices, arrays, and data frames. Finally, you will explore how to compare and contrast these different data types and structures within R. When you’re finished with this course, you will have the skills and knowledge of working with some of the most commonly used data types and structures in R.

Table of contents
  1. Course Overview
  2. Understanding Data Types and Structures
  3. Working with Scalars and Vectors
  4. Working with Factors and Lists
  5. Working with Data Frames, Matrices, and Arrays
  6. Comparing and Contrasting Data Types and Structures


Store and access data in a data frame, matrix, list, and arrays.

Managing Data in R Using Data Frames

by Martin Burger

Sep 25, 2019 / 2h 1m

2h 1m

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Learn to work with very popular and versatile tabular data types in R. When using R, there is no way around the data frame and its alternatives. In this course, Managing Data in R Using Data Frames, you will learn that tabular data (data.frame, data.table, tibble) are the standard data type in R and most of the analyses performed in R use this data format. First, you will discover how to manipulate and reshape a raw data frame. Then, you will explore the common tasks such as table import, factor conversion, formatting of a table header, orientation in a table, column splits, column removal and addition, fusion of multiple tables, variable transformations, query, as well as export of a table. Finally, you will produce a clean data frame which can be used for detailed analysis or passed on to team members. When you complete this course, you’ll have the skills and knowledge to perform standard tasks on data frames and can understand the many advantages of a data frame.

Table of contents
  1. Course Overview
  2. Understanding the Concepts Behind a Data Frame
  3. Exploring a Data Frame
  4. Making Changes to a Data Frame
  5. Continuing your Learning Path and Summary
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