As an Excel user for many years, I’m periodically trying new things to work with data and one of them was R. If you are interested in R, then here are a few tips and inspirational words that worked for me and may work for you.
Be aware that R comes with a command-line based user interface, but Excel is a spreadsheet program that is menu-driven where you can achieve many things by clicking. If you are familiar with a programming language (for example VBA), then it is easier to adapt. Excel is alive and by default doing all necessary recalculations automatically while in R you have to do recalculations manually.
Besides, there are R features that are tricky, and here are a few of them:
1. R language is case-sensitive.
2. Backslashes don’t behave like in Excel when addressing files. If you need to specify the path to a text file then instead of this
in R it will work like this.
3. Assignment operator usage is different and the most common is <-.
Here are nine tips on how to begin your journey from Excel to R
1. Watch online courses or YouTube videos for basic understanding. I can recommend the course Learning R from Lynda.com.
You have to understand the basics and good habits. Many ways how R stores the data. For example, one of the most commonly used is a data frame that is similar to a table. What are packages and how to use them? It’s important to understand those things and other basic concepts before digging deep.
2. Don’t try to shift from Excel to R for no reason or to look smarter. Figure out when R is the better tool. For example, complicated data transformation automatization, specific visualizations, forecasting, and sophisticated data analysis algorithms in general.
4. You can begin work with R independently by focusing on the same functions you use in Excel. It is a little bit boring, but it’s great for comparing results and understanding that you are doing it correctly. Excel is an excellent tool for learning analysis methods, basic descriptive statistics, or data transformations because you can comfortably build them from scratch. For example, in a simple way you can do regression analysis in Excel, but when you decided to use some sophisticated forecasting algorithms, then it is time to practice R. It’s a lot easier when you already gained experience with Excel.
5. Learn how to use Excel with familiar data sources in R. For example Excel and CSV files or copy data from R to Excel.
6. It’s important to fall in love with R. Try to use it in your favorite areas.
7. Figure out solutions that may be simpler to do with R ( sometimes nearly impossible with Excel). For example, you can do some data transformations and then automate them by running R scripts with the Windows command line. Try to implement R scripts in Power BI.
9. Try to practice a lot with R. Hard work pays off.