Finally, an R book that’s not overwhelming
You want to learn R and do more with your data. But it’s all so 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.
– 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.
Print and Kindle Format
Available on Amazon
Questions?
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.
Is it available in hard copy?
Yes! On Amazon.
Is this a watered-down book on R?
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.
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