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R-Powered Excel for Business Analytics

Augment and automate your spreadsheets for higher quality data

George Mount

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 live, hands-on, online 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 training 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.


  • 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:

Recommended follow-up:

About your instructor

  • George Mount is an independent analyst and data analytics educator helping clients manage their data so they think more creatively. He’s a technical expert and lead curriculum developer for Thinkful’s Data Analytics program and the instructor of the DataCamp course Survey and Measure Development in R. George blogs about data, innovation, and career development at Georgejmount.com. He holds a master’s degree in information systems with a certificate of achievement in quantitative methods from Case Western Reserve University.


The timeframes are only estimates and may vary according to how the class is progressing

Excel and R for data analytics (15 minutes)

  • Lecture: Introduction to R and when to use it; overview of the R Project and the comprehensive R archive network; the relationship between “base R” and additional packages
  • Group discussion: If you’ve done any programming, what languages have you used?
  • Hands-on exercise: Reviewing and installing a project in CRAN task views
  • Q&A

From Excel calculations to R development (35 minutes)

  • Lecture: Introduction to RStudio desktop
  • Hands-on exercises: Use R to perform basic calculations and comparison operations; navigate and customize - RStudio desktop
  • Q&A

Break (5 minutes)

From Excel cells to R vectors (30 minutes)

  • Lecture: Building, inspecting, and manipulating vectors
  • Hands-on exercise: Practice building, inspecting, and manipulating a vector

From Excel tables to R data frames (30 minutes)

  • Lecture: Importing and inspecting a data frame; manipulating data frames and exporting your results
  • Hands-on exercise: Practice reading, inspecting, and manipulating a data frame
  • Q&A

Break (5 minutes)

From Excel lookups and PivotTables to dplyr manipulation (30 minutes)

  • Lecture: Exploring the grammar of dplyr, a tidyverse package for data manipulation
  • Hands-on exercise: Practice building a data cleaning pipeline using dplyr
  • Q&A

From “That’s hard in Excel” to “That’s easy in R” (20 minutes)

  • Lecture: Building a reporting pipeline in R; how to get help in R and further resources

Wrap-up and Q&A (10 minutes)