R vs. Excel: What’s the Difference?

Key takeaways

When distinguishing between R versus Excel, it’s important to consider the needs of your data analysis tasks.

• While R is ideal when working with big data, Excel is advantageous for smaller tasks thanks to its simplicity.

• R is open source and free to use, whereas you must pay for the full version of Excel.

• You can use both R and Excel to extract insights from your data, with R requiring programming skills to do so.

Explore each program further and discover R and Excel’s uses, advantages, and disadvantages, and what to consider when choosing between them. Then, if you’re ready to start developing your data analysis skills, earn an IBM Data Analytics with Excel and R Professional Certificate, where you will have the opportunity to grow your skills in data manipulation, analysis, visualization, and more, to help you prepare for a career in data analytics.

What is R vs. Excel?

R and Excel are two different software tools used in data analytics. While both can perform data analysis tasks, each has distinct uses. R is an entire programming language for statistical computing, while you likely know Excel primarily as a spreadsheet program in the Microsoft Office Suite. However, you can use either program to perform statistical analyses, data visualizations, and calculations. 

What is R?

Developed as a language for statistical computing, R is an open-source alternative to the older S language developed by John Chambers and colleagues at Bell Laboratories. R offers many different kinds of statistical analysis and plotting techniques. The free and open-source program has become popular for data analysis and machine learning applications.

What is R used for?

R has many different uses, although its popular uses include statistical analysis and data visualization. Some other uses of R include the following:

• Data analysis

• Machine learning and predictive analytics

• Market research

• Biostatistics

• Big data analytics and exploratory data analytics

R integrates well with data science, offering practitioners compatibility with other analytics software. It also works with distributed computing big data analytics platforms like Apache Hadoop.

Advantages of R

R is a mature, open-source programming language that offers many advantages when it comes to statistics and data science, some of which include:

• Since R is open-source and free to use, it’s accessible to anyone who needs to run R code.

• R works on all operating systems, including macOS, Linux, Unix-style platforms, and Windows.

• You can use add-on packages to expand your statistical techniques with R.

• R has extensive documentation to help you learn the language.

• R has its own integrated development environment (IDE) to help you write R code.

Disadvantages of R

Many of R’s advantages come from its design as a fully functional programming language; however, this can also lead to some limitations in using R, such as the need to know coding. Some other disadvantages of R include the following:

• R is sometimes slow at compiling large data sets, as its basic version can only use one central processing unit (CPU) at a time. 

• Since R is a programming language based on a command-line interface (CLI), learning before a beginner starts doing statistics will take some time. 

• R has a problem with poor memory management since it stores all the data in the random access memory (RAM) during each session, limiting the amount of data you can analyze simultaneously. 

R vs. Python

Python is another popular programming language for data work, a general-purpose language capable of performing a wide range of tasks, including data analysis and visualization. However, while Python serves a variety of programming purposes, R has a more direct focus, specializing in statistical analysis and data-related tasks, such as visualization, making it a popular option for data scientists.

What is Excel?

Excel is a spreadsheet software developed by Microsoft. It was first released in 1985 for Apple Macintosh computers and, in 1987, for Windows. Excel’s main purpose is to organize data into an order of rows and columns that contain boxes called cells. This makes data easy to view, manipulate, and calculate. 

What is Excel used for?

Excel is a popular spreadsheet program used by organizations and individuals for things like business analytics, reporting, human resource management, and operations analysis. Some of its specific uses include the following:

• Excel makes it easy to collect, enter, and store data for later analysis. 

• Excel can be used for data cleaning since its tools allow you to scrub data, remove duplicates, and automate data cleaning processes. 

• Excel has popular statistical analysis tools to perform functions like regression and a suite of data analysis tools in the Analysis ToolPak. 

• Excel is popular for budgeting, financial reporting, and accounting.

Advantages of Excel

Excel’s ease of use is what makes it such a popular program for basic data storage and statistics; however, it has other advantages as well:

• Excel has a lower learning curve than full-blown programming software, and its design is familiar, including a full user interface. 

• You can produce quick graphs and plots by highlighting groups of cells.

• Excel has free downloads and add-ons for more complicated features.

• Since Excel is a part of the Microsoft 365 suite, you gain access to community forums and even technical support. 

Disadvantages of Excel

Despite Excel’s advantages, it was never designed as a comprehensive statistics software, which gives it some disadvantages: 

• While the online browser version of Excel is free, the full software costs $99.99 per year or $9.99 a month to access as part of Microsoft 365 [1]. 

• Excel spreadsheets have a limited number of rows and columns, making the program unable to handle extensive data sets for big data. 

• Excel produces basic everyday charts, but cannot produce publication-ready plots and graphs. 

• Excel has no scripting feature, so you must manually perform all analysis tasks in every spreadsheet. 

R vs. Excel: Other things to consider

When it comes to choosing between R and Excel, you need to consider your use cases, needs, and the kinds of data analysis you need to do. Excel is better for quick, point-and-click actions like reporting, budgeting, or cleaning data. However, R is a better choice if you need to perform repetitive scripting, comprehensive statistical analysis, and work with big data sets. Compare and contrast R versus Excel in the table below:

Explore our free data analysis resources

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• Watch on YouTube: Business Intelligence vs. Data Analytics vs. Business Analytics

• Take the quiz: Which Excel Course Should You Take? Find Out in 1 Minute

• Gain expert insights: 6 Questions with a Microsoft Data Analytics Leader

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