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You’re not a stats genius

Neither am I. I get it.

You just want to up your skills and work on cooler projects. But learning R and data science is a bit overwhelming.

Maybe you’ve tried some tutorials but didn’t really get anywhere.  Or maybe this is your first time coding, so you’re feeling a bit uneasy.  Or maybe you’re not overwhelmed at all, but you know Excel and want to learn R quickly.

Either way, R for Excel Users will get you up and running.

You will finally be able to work with bigger data.  Take on more interesting projects at work and look like a genius to your coworkers.

“I like how you write in an easy-to-understand manner, and your comparisons with how Excel and R work really helped clarify some points in my head.”
– Mark Farrell, Business Intelligence United States Postal Service

How is this different from other R books?

R for Excel Users is unique in a few ways.

  • As much as possible, things are put in context of something you already know: Excel.
  • It’s written with the functional corporate data analyst in mind, not the professional statistician. This means we can ignore large swaths that simply won’t be as important for you (at least not yet), like matrices.
  • It keeps you focused on mastering a really important topic: vectors and data frames. No modeling, no beautiful graphics. Just data. The goal is to build a solid foundation you can easily build on. It’s all about crawling before you run.

Table of Contents

TitleWhat to expect
Part 1 – Introduction & Set Up
1. Getting Set UpDownload and familiarize with the software
2. Programming BasicsCrash course on basic programming concepts
3. Quick Start – Analysis ExamplesJust a few full motivational examples
Part 2 – The Fundamentals: Cells and Formulas
4. Cells are VectorsReview two foundational concepts of R: vectors and functions
5. Formulas are Functions
Part 3 – Data Frames in R are like Data Tables in Excel
6. Import and Create DataGet your data into R, inspect it, process it
7. Inspect Your Data
8. Working with Columns
9. Working with Rows – Filtering and Sorting
10. Manipulating Rows and Columns with dplyr
Part 4 – Shape your Dataset
11. Combine Data TablesMore data frame processing — combining and transposing it.
12. PivotTables – Summarize and Transpose your Data
Part 5 – Advanced Topics
13. Working with listsGet more bang out of your scripts.  Yes, whatever that means!
14. Programming: Loops and Control Flow
15. Writing your own functions
16. Apply Family of Functions
17. Text / String Extraction


Why start with data manipulation in R?

Because the sexy stuff is actually not too hard once you know what you’re doing. Here’s an example. Running a linear regression is a simple command that looks like this:

lm(y ~ x1 + x2 + ... xn, data = dname)

That’s it. And if you understand regression already, then interpreting the output will be easy. But getting your data set in the right format, combining it with other data sets, manipulating columns, filtering rows and working with lists — that is the hurdle I will help you overcome.


I will gladly give you a full refund within 30 days. I am only here to help, and if I can’t help you, I don’t want your money.


Soon there will be add-ons like video courses, source code, etc. Stay tuned.


Yes! You will get a .pdf, .epub and .mobi versions to suit your device of choice. I personally prefer to read technical books on paper though — but that’s just me.


Nope. Where applicable I draw parallels to Excel to aid in the instruction, but in no way are things oversimplified. There is very little on visualization and statistical analysis, and that is intentional.