If you're an Excel user (or any other spreadsheet, really), adapting to learn R can be hard. As this blog post by Gordon Shotwell explains, one of the reasons is that simple things can be harder to do in R than Excel. But it's worth perservering, because complex things can be easier.

While Excel (ahem) excels at things like arithmetic and tabulations (and some complex things too, like presentation), R's programmatic focus introduces concepts like data structures, iteration, and functions. Once you've made the investment in learning R, these abstractions make reducing complex tasks into discrete steps possible, and automating repeated similar tasks much easier. For the full, compelling argument, follow the link to the Yhat blog, below.

Yhat blog: R for Excel Users

It may be easy to "do things" in Excel, but it is even easier to foul things up without even knowing you did. Most people who think they're doing good things in Excel are sadly mistaken. Excel "code", i.e. relationships between cells via formula, are next to impossible to trace or debug. And that's just for starters.

Further, there are exactly **zero** charts that are easier to produce or better laid out in Excel than R. The number of times I've had to explain to people that a line chart in Excel is NOT a scatterplot, and that their x-axis is not representing their data....

Posted by: Carl WItthoft | February 24, 2017 at 04:32

R and Excel are both great tools for Data Science and Data Analysis. Thanks for sharing this great article on comparing these two tools it is quite helpful.

Posted by: Proquotient | February 27, 2017 at 22:11

I think a combination of Excel and R is very useful. With Excel, the data can be processed very nicely, in order to "analyze" it with R. It is time that Microsoft R is integrated into Excel!:-)

Posted by: Günter Faes | March 10, 2017 at 23:33