Why R is a Natural Fit for Excel Users Entering Data Science

Over the last 2 years, I’ve trained many professionals transitioning from Excel to programming for data analysis. One common pattern I’ve noticed? Excel users often struggle with Object-Oriented Programming (OOP) concepts, making Python feel overwhelming at first.

This is where R shines as an entry point. Unlike Python, R allows users to dive deep into data analysis without needing to load external packages or understand OOP. A typical workflow like reading a CSV, manipulating data, running a regression, and visualizing results, can all be done using R’s base functions. This makes the transition from Excel smoother and less intimidating. If you find my perspective too simplified, consider this from a business standpoint: The Time-to-Productivity for Python is significantly higher than for R.

Here are a few reasons why R is easier for Excel users compared to Python:

? No need for complex data structures: R works naturally with vectors and data frames, much like Excel’s columns and tables. In Python, users must first learn about lists, dictionaries, and NumPy arrays before working with data effectively.

? Simple, spreadsheet-like operations: R’s indexing (1-based) is more intuitive for non-programmer Excel users compared to Python’s 0-based indexing. Also, operations on entire columns or rows are straightforward in R, whereas in Python, users often need to loop or use NumPy/Pandas.

? Built-in statistical and data visualization functions: R provides functions like lm() for linear regression and plot() for visualization right out of the box. In Python, users must install libraries like NumPy, Pandas, Matplotlib, and Scikit-learn before they can even begin.

? Functional Programming is more intuitive for Excel users: R follows a functional programming approach, where operations are performed using functions that take inputs and return outputs. Excel formulas like =SUM(A1:A10) have an equivalent sum(vector) in R. In contrast, Python leans heavily on dot-chained methods df['col'].sum(), which can feel less intuitive for Excel users who are accustomed to standalone function calls rather than object-oriented method chaining. This makes it easier for Excel users to transition into coding with R without needing to grasp complex object structures early on.

? R's powerful packages are shaping Python's ecosystem: Many advanced statistical and data analysis packages originated in R and are now being ported to Python due to their effectiveness. Libraries like tidymodels, ggplot2 (via plotnine), and data.table (via datatable) have inspired similar implementations in Python. Additionally, tools like pyjanitor (inspired by R's janitor package for data cleaning) and Shiny for Python (adapting R's powerful Shiny framework for interactive web apps) show how R's ecosystem has influenced Python. This highlights how R has been a pioneer in statistical computing, making it an excellent choice for beginners looking to leverage well-established, battle-tested tools before transitioning to Python.

I recognize that Python is the go-to language for large-scale data science, but not every Excel user aspires to be a Data Scientist. My priority isn’t pushing everyone toward that path. Instead, I focus on building a solid foundation in data handling and best practices first. Once learners have a firm grasp of these fundamentals, transitioning to Python becomes much smoother and more meaningful, as they can apply their knowledge effectively.

For Excel users stepping into the world of programming, R isn’t just an alternative; It is the bridge to a deeper data-driven mindset.

What’s your take? Have you found R or Python easier to learn when transitioning from Excel? ??

I am a full-time Business Data Analyst and part-time Online IT Trainer specializing in Excel, PowerPoint, SQL, PowerBI, R and Python for Data Analysis and Reporting. Let's connect if you would like to know more about how to make the jump.

I started my career as a Business Analyst back in 2010 and eventually changed my career track over to the Data track. You can read more about it in my other LinkedIn article: https://www.dhirubhai.net/pulse/how-i-switched-my-career-track-from-business-analysis-vishal-katti-ratkf/