Data analytics are an increasingly important component of decision making in any business. Whether you’re a part of a marketing team that needs to generate visuals to highlight industry trends, or you’re looking to generate financial statements, you will need an analytic program to simplify report development.
Both R and Excel are great tools to handle data analytics. Excel is already well known and used by many professionals, whereas R is an open-source programming language that’s frequently used in big data analysis with more advanced functions. When choosing between R and Excel, it’s important to understand the functions of each, and how either software can get you the results you need.
1. Ease of Use
Most people have likely already learned at least a few basic tips in Microsoft Excel. That’s what’s great about Excel – the initial learning curve is quite easy, and most can be done via point-and-click on the top panel. Once you bring your data in, it’s not too hard to make a few basic graphs or charts.
R is a programming language, meaning the initial learning curve is a bit difficult. It will take a few weeks to get used to the interface and to remember the different functions, but it becomes second-nature once you’re accustomed to it.
As Excel’s user interface is point-and-click, you’re going to have to remember where everything is. You can’t import codes and scripts as you would with R, so you’ll have to ‘reinvent the wheel’ frequently. It’s not bad if you’re doing basic stats, but it may become time-consuming with more complicated tasks.
R on the other hand, while still less user-friendly with a more intimidating user interface, is reproducible. It may take a while to write your scripts and to figure those mechanics out, but once you do, you can save the process to be used for future data sets. It’s incredibly helpful for large projects with multiple data sets, as you’ll keep everything consistent and clean, without having to re-write the script each time.
How detailed do you need to go? In Excel, you can quickly highlight a group of cells and make a simple chart for PowerPoint, but R can make some incredibly attractive, detailed visuals that will blow someone away.
It comes down to what you need your graphics to do. If you’re just looking to throw a presentation together to visualize data for your coworkers, then making simple charts in Excel will be fine. For those planning to publish your visuals to the public to really highlight your big data, then spending a bit more time in R would be worth it.
3. Price, Community, and Customization
Excel is going to run you at least $70 a year as part of Office 365, which isn’t too bad. You can get Word, PowerPoint, and other software with that package so you’re not just buying Excel. You can download free add-ins for Excel also, including options to improve visualization, among others. Excel is part of Microsoft, giving you access to their community of forums and technical help professionals.
R is free to download. As R is open-source and open to the public, it has gathered a vibrant community, with many forums, websites, and Reddit boards devoted to sharing resources and tips. If there’s something you want to do in R, then someone is out there to help.
R’s open source nature, engaged community, and free pricetag are welcome, but Excel’s price is affordable and the community is strong.
4. Statistical Analysis
How detailed do you want to be? What information are you looking for? If you just want to run some quick statistics and arithmetic, then Excel might be better for you since it’s an easy point-and-click away to run numbers. Though, it’s not hard to get that same info from R, either.
Pivot tables in Excel have been a buzzword the past few years, and it’s one of the program’s more useful features for stats. You can easily compute large amounts of data, define variables, and easily choose what rows/columns you want to compare and gather reports from. It’s fairly easy to make a pivot table, and it has awesome rewards.
R programming can do a lot of analysis and is great for identifying trends that you might not have thought to look for, and even deciding how reliable said statistics are. R allows you ways to clean and organize data, gives more visualization options, and if there’s a topic you want to explore, then there’s likely a way to do it in R.
Both are incredibly valuable skills that are in-demand across a variety of industries. Countless jobs are looking for applicants with at least some Excel experience (note: pivot tables look really good on a resumé), but R has a higher earning potential, and is more in-demand than Excel.
R is now the most popular programming language, making it an industry standard for data analytics and data science. If you want to enter either field, there’s a good chance you’ll need to know R. Entry-level jobs for those focusing on R also tend to make a high salary, frequently starting off with $75,000+.
When you think about it, how many job listings have you seen that require Excel? Administrative assistants, marketers, academics, and more, everyone is expected to use Excel to some degree. It’s universal and is now an expected skillset, whereas 10-15 years ago it was optional. Having a good background in Excel is still attractive on a resume and will help to land a career with a high earning potential, but there are not many jobs looking for Excel skills alone.
Summarizing R and Excel
R and Excel are beneficial in different ways. Excel starts off easier to learn and is frequently cited as the go-to program for reporting, thanks to its speed and efficiency. R is designed to handle larger data sets, to be reproducible, and to create more detailed visualizations. It’s not a question of choosing between R and Excel, but deciding which program to use for different needs.
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