If you’re using Excel to collect, analyze, and interpret business data, you may be running into limitations that stop you from performing advanced, reproducible analysis. Speed up, automate, and validate your reporting and analytics with R, the popular open source programming language for data science.

Join expert George Mount to learn how to utilize R for data manipulation by focusing on the most common data structures in business analytics: vectors and data frames. You’ll gain hands-on experience in the RStudio Desktop and learn how to use R’s popular tidyverse collection of packages for data analytics. Along the way, you’ll walk through the various windows in RStudio, discover how to navigate an R development environment, and understand how R can augment and automate common data preparation and manipulation tasks often done in Excel.

What you’ll learn and how you can apply it

By the end of this course, you’ll understand:

• R’s powerful open source tools and packages
• The R equivalent of common Excel tasks such as PivotTables, sorting, and filtering
• The role of vectors, objects, and functions in R

And you’ll be able to:

• Load, view, and write Excel files from R
• Perform common data wrangling tasks including sorting, filtering, and aggregation using the dplyr library
• Navigate the RStudio integrated development environment
• Identify, install, and implement packages

This course is for you because…

• You're an analyst responsible for collecting, analyzing, and interpreting business data.
• You want to learn how to use R with RStudio and its most common packages.

Prerequisites

• A machine with R, RStudio Desktop, and the core tidyverse packages installed (instructions)
• Familiarity with basic tasks and functions in Excel, including sorting and filtering, and the use of IF statements
• Experience with conditional aggregates like SUMIF() and COUNTIF(), as well as PivotTables and VLOOKUP() (useful but not required)
• No prior programming knowledge needed

Recommended preparation:

• Read “A Scientific Calculator” and “Inspecting Variables and Your Workspace” (chapters 2 and 3 in Learning R)
• Watch “Excel Tables” and “Pivot Tables” in Big Data Analytics with Excel (video)

Recommended follow-up:

• Read R for Data Science (book)
• Read R Cookbook, second edition (book)