Programming R for Data Analysts


Programming R for Data Analysts

Authors: Charlotte Wickham, Mihaela Danci, Christopher Venturini, Emilee McWilliams, Chase DeHan

The R language is widely used among statisticians and data miners for developing statistical software and data analysis.

What You Will Learn

  • You will learn analytical skills with one of the most widely used languages in data


  • Fundamentals of computer programming
  • Fundamentals of creating variables


Learn the structure of an R solution, and the core of the R programming language.

Building Your First R Analytics Solution

by Charlotte Wickham

Mar 23, 2020 / 2h 6m

2h 6m

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You’ve been given the task of building your next analytics report in R. Your colleague has even shared some R code they think will help. But where do you start? You’re not even sure what to do with the .R file they shared. In this course, Building Your First R Analytics Solution, you’ll see the entire workflow that data professionals use to produce their R analytics solutions. First, you’ll start right at the beginning — setting up your own computer to run R code. Next, you’ll learn how to run, read, and write R code in RStudio. Finally, you'll discover how to organize your work with RStudio projects and how to communicate your work with R Markdown. By the end of the course, you’ll have not only produced a shareable report with R Markdown, you’ll have built your project so that anyone else can recreate the report too. You’ll be using the same tools and workflows as R professionals, and along the way, you’ll start building habits that ensure your analysis is organized, documented, and reproducible.

Table of contents
  1. Course Overview
  2. Setting up Your Computer to Use R
  3. Working with R Code in RStudio
  4. Organizing Your Work with RStudio Projects
  5. Communicating and Documenting Your Analysis with R Markdown

Programming with R

by Mihaela Danci

Feb 28, 2020 / 2h 2m

2h 2m

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At the core of data analysis is a thorough knowledge of R programming. In this course, Programming with R, you will learn how to manipulate different objects.

First, you will learn the basic syntax.
Next, you will explore data types and data structures available in R.
Finally, you will discover how to write your own functions by implementing control flow statements.

When you are finished with this course, you will have a foundational knowledge of R programming that will help you as you move forward to data science.

Table of contents
  1. Course Overview
  2. Getting Started with R
  3. Exploring Vectors and Factors
  4. Using Matrices, Arrays, and Lists
  5. Working with Data Frames
  6. Managing Control Statements
  7. Building Your First Function


Debug your R programs, and customize their running environment.

Creating and Debugging R Programs

by Christopher Venturini

Aug 21, 2020 / 2h 1m

2h 1m

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Every good developer knows that being able to troubleshoot their code effectively is crucial to being productive. In this course, Creating and Debugging R Programs, you'll gain an understanding of how independently running scripts works within the R platform and how to quickly debug them in the case the need arises. First, you’ll explore the various data types that R provides and learn how to leverage them when processing data. Once you have those understood, you’ll learn how variables are leveraged within functions and how they’re encapsulated within a function’s environment. Finally, you'll discover how to write a non-interactive R script and schedule it to run on multiple platforms. When you’re finished with this course, you’ll have the skills and knowledge to successfully write and run your own independent scripts and troubleshoot running them in a production environment.

Table of contents
  1. Course Overview
  2. Understanding the R Platform
  3. Data Types
  4. Processing Data with R
  5. Outputting Data with R
  6. Debugging R Interactively
  7. R Environments
  8. Running R Non-interactively
  9. Debugging R Non-interactively
  10. Troubleshooting and Avoiding Common Debugging Issues

Customizing R Environments

by Emilee McWilliams

May 29, 2020 / 26m


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Understanding the differences in data analysis tools can be confusing. In this course, Customizing R Environments, you will gain the ability to understand the R framework, learn how to customize an environment, and finally discover the best data tool to suit your goals. First, you will learn how to use a function to identify environments. Next, you will discover how library packages are accessible to R. Finally, you will explore a powerful package within R. When you are finished with this course, you will have the skills and knowledge to see how R is different from other data analysis tools.

Table of contents
  1. Course Overview
  2. Presenting Environments and Binding Values in R
  3. Identifying Types of Environments in R
  4. Listing Environments and Presenting Libraries from a CRAN in R


Build tables and capture reusable logic with R functions.

Building Tables with R

by Emilee McWilliams

Sep 25, 2020 / 32m


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Quickly understanding variables in a dataset can be time-consuming. In this course, Building Tables in R, you’ll learn to develop tables, proportions, and marginal frequencies in R. First, you’ll explore the table function with two- and three-way tables. Next, you’ll discover proportions for these tables. Finally, you’ll learn how to build a marginal frequency table. When you’re finished with this course, you’ll have the skills and knowledge of developing tables for data in R needed to understand customer data and prepare for advanced analysis.

Table of contents
  1. Course Overview
  2. Creating a Two-way and a Three-way Frequency Table in R
  3. Creating Proportions for a Table in R
  4. Developing Marginal Frequency Table in R

Applying Functions in R

by Chase DeHan

Jan 16, 2020 / 1h 11m

1h 11m

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The functional approach to programming is something that makes R unique and powerful. In this course, Applying Functions in R, you will learn foundational knowledge of functional programming in R. First, you will understand how the apply functions work. Next, you will discover why the functional approach is far superior to for loops in R. Finally, you will explore a multitude of functions that apply in different use cases. When you are finished with this course, you will have the skills and knowledge of using the apply family of functions needed to write clear and concise code without a for loop.

Table of contents
  1. Course Overview
  2. Understanding Functional Programming and Apply
  3. Adding Anonymous Functions to Apply
  4. Using Common Apply Functions
  5. Using and Understanding Which Function to Use
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