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Working with Data Types in R

R is a widely used programming language for statistical computing. This course will teach you the fundamentals of understanding data types and data structures and how to work with them within R.

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.

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What's inside

Syllabus

Course Overview

Understanding Data Types and Structures

Working with Scalars and Vectors

Working with Factors and Lists

Working with Data Frames, Matrices, and Arrays

Comparing and Contrasting Data Types and Structures

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Helps learners develop a foundational understanding of data types and data structures used in R programming

Taught by Justin Flett, an experienced instructor in statistical computing

Covers a range of data types and structures commonly used in R, including vectors, lists, factors, matrices, arrays, and data frames

Provides a solid foundation for learners looking to work with data in R

Suitable for beginners with little to no prior knowledge of R

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Working with Data Types in R with these activities:

Organize Course Materials

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Organizing course materials will help learners stay organized and improve their ability to find and review important information.

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Create a folder for each module
Download and save lecture slides, notes, and assignments
Create a system for naming and organizing files

RStudio Tutorials

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These tutorials provide a step-by-step guide to using RStudio, the primary development environment for R.

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Complete the RStudio Basics tutorial
Complete the Data Manipulation tutorial

Read Data Manipulation with R

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This book can provide foundational knowledge of R and be used as reference when working on data manipulation tasks throughout the course.

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Read Chapters 1-3
Complete practice exercises
Create a cheat sheet of important functions

Four other activities

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R for Data Science Practice Problems

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These practice problems provide a structured way to review and reinforce the concepts covered in the course.

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Complete 20 practice problems per week
Review solutions to identify areas for improvement

Create a Data Dictionary

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Creating a data dictionary will help learners understand the structure and meaning of the data they are working with.

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Identify the variables in the dataset
Describe the data type, range, and missing values for each variable
Create a table or spreadsheet to document the data dictionary

Data Visualization Project

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This project will allow learners to apply the skills and knowledge learned in the course to a real-world dataset.

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Choose a dataset and research the topic
Clean and prepare the data
Create visualizations to explore the data
Present findings in a report or presentation

Exploratory Data Analysis Project

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This project will allow learners to gain hands-on experience with exploring and understanding a real-world dataset.

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Load and clean the data
Explore the data using summary statistics and visualizations
Identify patterns and trends in the data
Write a report summarizing the findings

Career center

Learners who complete Working with Data Types in R will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists use their expertise in programming and statistics to extract valuable insights from data. They develop and implement machine learning algorithms to analyze data and solve complex problems. This course focuses on understanding data types and structures in R, a programming language widely used in data science. By mastering these concepts, you will gain a solid foundation for building a successful career as a Data Scientist.

See salaries and explore the career path for Data Scientist

Statistician

Statisticians apply mathematical and statistical methods to collect, analyze, and interpret data. They use their findings to inform decisions and solve problems across various industries. This course provides a comprehensive overview of data types and structures in R, a powerful tool for statistical analysis. By gaining proficiency in these concepts, you will enhance your ability to succeed as a Statistician.

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Data Analyst

Data Analysts investigate data patterns and trends using statistical techniques and other tools. They extract valuable insights from data and present their findings to help organizations make better decisions. This course introduces the fundamentals of data types and structures in R, a key tool for data analysis. By gaining proficiency in these concepts, you will develop a solid foundation for working as a Data Analyst.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze and predict financial data. They play a crucial role in the financial industry, helping organizations make informed investment decisions. This course offers a solid foundation in data types and structures in R, a programming language widely used in quantitative finance. By mastering these concepts, you will gain a competitive edge in the field of Quantitative Analysis.

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Machine Learning Engineer

Machine Learning Engineers design, develop, and deploy machine learning models. They use programming languages and statistical techniques to automate complex tasks and optimize outcomes. This course provides a thorough understanding of data types and structures in R, a popular language for machine learning. By mastering these concepts, you will build a solid foundation for success as a Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Data Engineer

Data Engineers design and manage data pipelines and infrastructure. They ensure that data is collected, stored, and processed efficiently to support data-driven decision-making. This course covers the fundamentals of data types and structures in R, a programming language widely used in data engineering. By gaining proficiency in these concepts, you will develop a solid foundation for a successful career as a Data Engineer.

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Business Analyst

Business Analysts bridge the gap between business and technology, translating business requirements into technical specifications. They analyze data and processes to identify inefficiencies and develop solutions. This course introduces the fundamentals of data types and structures in R, a powerful tool for data analysis. By gaining proficiency in these concepts, you will enhance your ability to excel as a Business Analyst.

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Actuary

Actuaries evaluate and manage financial risks using mathematical and statistical techniques. They play a vital role in the insurance industry, advising on risk assessment and pricing. This course provides a solid foundation in data types and structures in R, a programming language used in actuarial science. By mastering these concepts, you will enhance your ability to succeed as an Actuary.

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Software Engineer

Software Engineers design, develop, and maintain software systems. They use a variety of programming languages and tools to build applications that meet specific user needs. This course introduces the fundamentals of data types and structures in R, a programming language used in software development. By gaining proficiency in these concepts, you will build a solid foundation for a successful career as a Software Engineer.

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Market Researcher

Market Researchers gather and analyze data to understand market trends and consumer behavior. They use this information to develop marketing strategies and products that align with customer needs. This course introduces the fundamentals of data types and structures in R, a programming language used in market research. By gaining proficiency in these concepts, you will build a solid foundation for a successful career as a Market Researcher.

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Financial Analyst

Financial Analysts research and evaluate financial data to provide insights and recommendations for investment decisions. They use statistical techniques and financial modeling to assess risk and value. This course provides a solid foundation in data types and structures in R, a programming language used in financial analysis. By mastering these concepts, you will enhance your ability to succeed as a Financial Analyst.

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Data Journalist

Data Journalists use data to tell compelling stories and communicate complex information. They combine journalism skills with data analysis techniques to create data-driven articles, visualizations, and interactives. This course provides a solid foundation in data types and structures in R, a programming language widely used in data journalism. By mastering these concepts, you will enhance your ability to succeed as a Data Journalist.

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Risk Analyst

Risk Analysts identify, assess, and manage risks in various industries. They use data and statistical analysis to evaluate potential threats and develop mitigation strategies. This course provides a solid foundation in data types and structures in R, a programming language used in risk analysis. By mastering these concepts, you will enhance your ability to succeed as a Risk Analyst.

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Economist

Economists study the production, distribution, and consumption of goods and services. They use data and statistical analysis to understand economic trends and develop policies to address economic challenges. This course offers a solid foundation in data types and structures in R, a programming language used in economics. By mastering these concepts, you will gain a competitive edge in the field of Economics.

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Epidemiologist

Epidemiologists investigate the causes and determinants of diseases in populations. They use data and statistical analysis to identify risk factors, track disease outbreaks, and evaluate public health interventions. This course introduces the fundamentals of data types and structures in R, a programming language used in epidemiology. By gaining proficiency in these concepts, you will build a solid foundation for a successful career as an Epidemiologist.

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Reading list

We've selected 12 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Working with Data Types in R.

R for Data Science

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Covers similar topics to the course, including data types, data structures, and working with data in R. Provides more in-depth explanations and examples, making it a valuable reference for those who want to dive deeper into these concepts.

Working with Data Types in R

What's inside

Syllabus

Good to know

Save this course

Activities

Career center

Reading list

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