Data analytics are increasingly important components 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 analytics program to help you develop your reports and effectively communicate your findings.
Both R and Excel are excellent data analytics tools, but they each have distinct functionality.
Excel is a well-known software program included in the Microsoft Office Suite. Used to create spreadsheets, execute calculations, produce charts, and perform statistical analysis, Excel is used by many professionals across a variety of industries.
R is a free, open-source programming language and software environment that’s frequently used in big data analysis and statistical computing. R has many advanced functions and capabilities.
Read on to learn more about their unique features and which is the better tool to help you solve your current data analysis problems.
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Differences Between R and Excel
When choosing between R and Excel, it’s important to understand how either software can get you the results you need. Here are some key differences between R and Excel to help you decide which makes the most sense to use.
1. Ease of Use
Learning the Software
Most people have likely already learned at least a few basic tips in Microsoft Excel. That’s one substantial benefit of using Excel—the initial learning curve is quite minimal, and most analysis can be done via point-and-click on the top panel. Once a user imports their data into the program, it’s not exceedingly hard to make basic graphs and charts.
R is a programming language, however, meaning the initial learning curve is steeper. It will take most at least a few weeks to familiarize themselves with the interface and master the various functions. Luckily, using R can quickly become second-nature with practice.
Replicating Analysis
R, while less user-friendly with a more intimidating user interface, has the capability to reproduce analyses repeatedly and with very different datasets. This can be incredibly helpful for large projects with multiple data sets, as you’ll keep everything consistent and clean, without having to rewrite the script each time.
Since Excel’s user interface is point-and-click, you’ll need to rely on memory and repetition frequently. You cannot import codes and scripts as you would with R, so you’ll have to “reinvent the wheel” to perform the same analysis across different data sets. This is not detrimental if you are doing basic statistics, but it may become time-consuming with more complicated analyses.
2. Visualization
When deciding between R and Excel, ask yourself, “How detailed do my visualizations need to be in order to achieve my goal(s)?” In Excel, for example, you can quickly highlight a group of cells and make a simple chart for PowerPoint. If you need a more comprehensive graph, however, R may be your best bet. R can produce incredibly attractive, detailed visuals that can help stakeholders understand your findings.
It all comes down to what you need your graphics to do. If you’re just looking to cobble together a quick-and-dirty presentation to visualize data for your coworkers, then making simple straightforward charts in Excel will suffice. For those planning to publish large amounts of complicated data to various stakeholders, spending the time in R to create impressive interactive visual representations will likely be worth your while.
3. Price, Community, and Customization
Excel costs at least $70 a year as part of Office 365, which also includes Outlook, Word, PowerPoint, and other software. You can download free add-ins for Excel also, including options to improve visualization, among others. Since Excel is part of Microsoft, you also gain access to their community of forums and technical help professionals.
R, however, 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, someone is out there to help.
R’s open-source nature, engaged community, and free price tag are welcoming, but Excel’s price is affordable and the community is also strong.
4. Statistical Analysis
When comparing R and Excel, it’s important to define the level of information you are looking for. If you simply want to run statistics and arithmetic quickly, Excel might be the better choice, since it’s an easy point-and-click way to run numbers.
Pivot tables in Excel have been gaining traction 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 and columns you want to compare and gather reports from. It’s fairly easy to make a pivot table, and they provide powerful benefits. Excel’s spreadsheets have a finite number of rows and columns, however, so you’ll be unable to analyze massive datasets that can be handled with R.
R programming can do a tremendous amount of analysis, is great for identifying trends you might not have thought to look for, and it can even decide how reliable said statistics are. R allows you 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. If you’re looking to do anything beyond basic statistical analysis, such as regression, clustering, text mining, or time series analysis, R may be the better bet.
5. Careers
Aptitude with Excel and R are incredibly valuable competencies that are in-demand across a variety of industries. Countless jobs are looking for applicants with at least some Excel experience (pivot tables look really good on a resumé), but R has a higher earning potential and is more in-demand than Excel.
R is one of the most popular programming languages and is an industry-standard for data analytics and data science. If you want to enter either field, there’s a good chance you’ll have a competitive advantage by knowing R. Entry-level jobs for those focusing on R also tend to make a high salary, frequently starting off earning more than $75,000.
Countless job listings also require Excel competency. From administrative assistants, marketers, academics, and more, everyone is expected to use Excel to some degree, whereas 10 to 15 years ago it was optional. Having a good background in Excel is still attractive on a resumé and will help to land a career with a high earning potential, but there are not many jobs looking for Excel skills alone.
Using 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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