R vs. Excel: What’s the Difference?

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

Explore each program further and discover R and Excel’s uses, their advantages and disadvantages, and what to consider when choosing between them. 

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 program is free and open-source and 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 Hadoop and Apache. 

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 and Unix-style platforms, and Windows. 

• You can use add-on packages to expand the kinds of statistical techniques you can perform 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 the use of 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 at one time. 

Excel

Excel is spreadsheet software developed by Microsoft and 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 tool allows 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 to use, the full software costs $99.99 per year or $9.99 a month to access it 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 does not have a 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 a better option for quick, point-and-click actions like reporting, budgeting, or cleaning data. However, if you need to perform repetitive scripting, comprehensive statistical analysis, and work with big data sets, R is the better choice. Compare and contrast R versus Excel in the table below:

Learn more about data analysis with Coursera

You can use either R, Excel, or a combination of the two for data analysis. To gain in-demand skills as a data analyst, consider the IBM Data Analytics with Excel and R Professional Certificate on Coursera. Explore how to start with data analytics using Excel for data mining and wrangling and R for statistical analysis and predictive analytics. If you’re looking to build more in-depth data science skills for a data analyst career, you can also opt for the Google Data Analytics Professional Certificate or the IBM Data Analyst Professional Certificate, both on Coursera.