Believe it or not, Excel is still my go-to analysis tool a lot of times, because it’s great at what it does. I’m a shortcut fiend, so I can do things pretty quickly. So when do I opt for R? People have asked me this many times. Here is my unofficial checklist I loop through in my head to decide whether to Excel or not to Excel:

- Is the data
**not well structured**or PivotTable-ready? Does it have a**lot of stuff within cells**that needs to get broken out?

If yes,**R**, unless I can work my Excel magic to clean it up. - Is this a
**quick and dirty one-time analysis**? Including quick visuals.

If Yes, then**Excel**, as long as the data is not gigantic. - Do I need anything
**beyond basic statistical analysis**? Regression, clustering, text mining, time series analysis, etc

If Yes, then**R**. No contest. - Do I have to
**crunch a few disparate datasets**to do my work?

Depends on complexity. If data sets are small and a simple vlookup can handle it, then**Excel**. If more than three tables, most likely**R**. If more than 1-2 columns vlookup’ing from each table, also**R**. - Something I will want to
**share in a web-based, interactive format**that is nice to look at?

**R**with the Shiny framework **Unique and beautiful visuals**the world has rarely seen?

**R**.

While I was learning R, I used a hybrid approach … doing the heavy-lifting data prep work in R, then using the write.csv() function to send my data frames back to Excel for visuals and basic analysis. Over time, I have learned to do more complete analysis in R, from beginning to end.

I hope this helps! What scenarios did I miss?

## Leave a Reply

2 Comments on "Excel vs R: When to use what"

Nice post, John.

I use a lot of Excel when I have to present scenarios and change a “final” table in front of clients. Looking at the data is easier in Excel than in R, but of course I’m referring to a “final, condensed, aggregated” dataset (< ~30000 I guess?).

Knowing how to conduct a PCA analysis in R does not mean you understand the dataset and can play with human-generated scenarios on it in a spreadsheet.

For that sense, R is overrated. There's a reason the industry still relies heavily on spreadsheets. They are easy. They support the creation of scenarios and conversation about the data.

R programming will be gibbreish in front of a meeting room with the CEO. Unless you spent days/weeks working on a Shiny dashboard. And still you can be surprised with a simple question on "change a parameter x, please".

What you have said is true about scenario analysis, but then R is meant for statisticians and written by statisticians. Using it for a simple think like that is like using a thermonuclear bomb to kill a fly.

R shines truly when one actually have to use statistics to infer information, say creation of neural networks or forecasting or the likes.

Also when working in a file with more then 1000 line items when it is necessary to have a birds eye view then R works well for me (this is my personal thought and given that I am proficient in R this is biased).