Professional & Continuing Studies

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Learn to utilize the R programming language

  • Button: Learn more about virtual information sessionsPerform data analysis in a work context
  • No prior programming experience required
  • Eight-week live-online course — September 27-November 15 – REGISTER NOW
  • Discounts, scholarships available – First 10 people to register with COUPON CODE R500 receive a $500 discount
  • LEARN MORE Free Virtual Info Session, July 21, 7 p.m.

With the rise of data science and data analytics over the past decade, the R programming language has become increasingly valuable for organizations. R is an open-source, statistical programming language that allows for users to easily extract, clean, visualize and model data. Whether your goal is to become a data scientist or to move beyond Excel, R is a valuable tool to acquire.

In this course, participants will learn to utilize the R programming language in order to perform data analysis in a work context. They will understand how to perform an exploratory data analysis with a focus on the tidyverse. In addition, participants will gain an understanding of tools that are necessary to perform these analyses in the workplace, including git and R Markdown.

This foundational level course welcomes anyone to enroll, and no prior programming experience is required. It focuses on the mechanics of programming with R and not on statistical modeling techniques. 

Program details

button: register nowFoundations of R for Data Analysis Certificate – Noncredit Certificate
LOCATION: Live-online
SCHEDULE: September 27-November 15, 2021 — Mondays, 6-9 p.m.
PRICE: $1,495, ($995 for the first 10 people to register with coupon code R500) all materials included. Scholarships and potential discounts available, including: Early registration, Military, UD student or alum, 2 or more (group).
CEUs: 2.4 (24 contact hours)

Who should participate in this program?

  • Excel users looking to switch to more programmatic ways of completing analyses
  • Current college students needing to utilize R for future coursework
  • Anyone considering a career change into data science or data analytics

Consider these applicable use cases:

  • Grow your technical skill set: You are a Microsoft Excel or Google Sheets user and perform complex analyses – from data manipulation to graphing – with those tools. You are reaching the limits of what those tools can do for you and want to extend your skill set. R is a logical next step for you in your journey with data.
  • Enable your statistical work: You are a student who needs to perform statistical analyses for upcoming coursework. You know that you will need R for your coursework but do not feel comfortable with the basics of it. You need a better understanding of how to transform your data so that you can perform statistical tests with it.
  • Automate your analyses: You are responsible for processing the same reports at work each day, week or month. You spend a significant amount of time processing the data in Excel but feel that there is a better way. You want to reduce the amount of time that it takes you to generate the reports while also reducing the likelihood that you’ll make an error while compiling it. Further, you want to be able to automate creating your report instead of manually copying and pasting your graphs into PowerPoint.
  • Produce compelling data visualizations: You are a journalist who is interested in building data-driven stories, similar to what is being produced in outlets like the New York Times. You’re interested in creating beautiful visualizations but need to better understand the tools to make that possible.
  • Break into a growing industry: You are an aspiring data scientist or data analyst and are interested in learning the tools that you need to break into the industry. R is one of the languages that would push your career forward, but you’re finding it difficult to teach it to yourself.
  • Work with larger datasets: You are being asked to work with larger and larger datasets at work. You’re constantly wrestling with your Excel workbooks because they can’t handle the number of calculations or the amount of data that you need to work with. You need a solution that can more seamlessly handle your growing data needs.
  • Create reproducible reports: You are a scientist who wants to make sure that your work can be reproduced by others you work with. The normal tools that you work with make it difficult for others to understand exactly what you did, making reproducibility a challenge. You need a solution that can simplify this process for you and those you want to share your work with.

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Ryan Harrington is the lead data scientist at CompassRed Data Labs, a data science and analytics consulting firm in Wilmington, Delaware. At CompassRed, Harrington’s focus is on developing models using a combination of statistical methods and machine learning techniques to build solutions for client problems. In addition to his work at CompassRed, he co-founded and co-organized Open Data Delaware, an organization dedicated to improving data transparency within government institutions and applying data and technology to solve civic problems.

Harrington holds a B.S in environmental and business economics from Rutgers University and an M.S. in business analytics from Drexel University. Prior to his work as a data scientist, he was a high school math teacher through Teach for America and spent time building Delaware’s entrepreneurial ecosystem at 1313 Innovation.

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Course outline

Week 1: Understanding the R Programming Environment
We discuss the basics of the R programming language and the most common integrated development environment (IDE) used with R – RStudio. Topics include:

  • Understanding differences between R and RStudio
  • Understanding the RStudio environment
  • Differences between R scripts and R Markdown
  • Typical analytics workflows
  • Basic operations in R
  • Data types and classes

Week 2: Introduction to Exploratory Data Analysis
We use the Exploratory Data Analysis (EDA) framework as a way to begin working with R. A key component of EDA is visualizing data. Topics include

  • Understanding packages and how to download, manage and maintain them
  • Understanding the Exploratory Data Analysis lifecycle
  • Importing data into R from Excel files and .csvs
  • Visualizing data using ggplot2
  • Sharing code using git and Github

Week 3: Introduction to the tidyverse
The tidyverse is an opinionated collection of R packages that share a similar design philosophy. These tools allow for complex data transformations that aid in the EDA process. Topics include:

  • Data transformation using the dplyr package
  • Common functions for data transformation (select, filter, arrange, mutate, summarize)
  • Piping functions using magrittr
  • Creating reproducible examples for troubleshooting (reprex)

Week 4: Data Cleaning Tools
No two datasets require the same data cleaning to be prepared for analysis. A variety of tools are needed to perform data transformations. We explore a variety of functions that aid in this process. Topics include:

  • Utilizing conditional statements using if_else and case_when
  • Manipulating data across columns
  • Addressing missing data using replace and replace_na
  • Addressing missing data using fill

Week 5: Joining and Reshaping Datasets
Oftentimes, the data we work with is not contained in a single table, but multiple. In cases like this, we must “join” data together. Further, data is frequently in the wrong “shape” for analysis; sometimes it must be “wide” and sometimes it must be “long.” The tidyverse provides tools to more easily shape data to support analysis. Topics include:

  • Understanding types of joins – right, left, inner, etc.
  • Identifying “tidy” data
  • Reshaping data using pivot_wider and pivot_longer
  • Using tidy data to create more complex data visualizations with ggplot2

Week 6: Creating Functions
While packages provide a wide variety of functions, R becomes more powerful when you begin to build your own functions. Topics include:

  • Understanding what functions are
  • Best practices for creating functions
  • Understanding available tools for debugging functions
  • Building functions using non-standard evaluation using the embrace paradigm {{

Week 7: Manipulation of Strings, Factors and Datetimes
Numeric values are not the only type of data we must learn to manipulate. Data is often presented as strings, factors or datetimes. Each of these data types can be manipulated in different ways. Topics include:

  • Understanding differences between strings and factors
  • String manipulation utilizing the stringr package and string matching concepts utilizing regex
  • Using forcats for factor manipulation
  • Using lubridate to manipulate dates

Week 8: Functional Programming
As our analyses advance, we will frequently find situations where we need to write the same code multiple times. This can cause our code to be error-prone. Functional programming allows us to create more concise code through iteration. Topics include:

  • The relationship between loops and functional programming
  • Vectorizing code
  • The map paradigm

Individual Project

As a final requirement of the course, students will conduct their own analysis on a dataset of their choosing. Students will select a dataset and propose an analysis to be completed utilizing the tools taught throughout the course. Students will submit their analysis as a knitted R markdown HTML document through GitHub pages.

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Learner outcomes

Students will learn to analyze data using the R programming language. Upon completion of this course, students will be able to:

  • Navigate the R programming environment through RStudio
  • Utilize the exploratory data analysis framework
  • Transform data utilizing the tidyverse
  • Join and reshape datasets
  • Create and debug functions
  • Manipulate a variety of data types
  • Utilize functional programming

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Technology and platform requirements

  • Computer access: Windows or macOS only (no tablets or mobile devices)
  • R
  • RStudio
  • git
  • git bash (if using a Windows machine)
  • Slack

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