The Best IDE for Data Science Still Needs to be Built

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Digital representation of the ideal IDE for data science, titled 'The best IDE for data science still needs to be built'. The image illustrates a high-tech interface with screens displaying integrated code environments for SAS, R, and Python. Each screen shows unique code snippets and data visualizations, emphasizing a seamless multilingual workflow. A digital world map in the background symbolizes global collaboration in data science.


Data science involves many programming languages and each comes with its preferred integrated development environment (IDE). I know this because I’ve been doing it for over 20 years and I’ve used a lot of them. I started with SPSS and Stata in grad school before taking my first real job as a SAS programmer. I settled on R as my primary programming tool via Rstudio, but I’ve had jobs that required Python with Jupyter notebooks, VS Code, and others. I’m a multilingual data scientist because that is the reality of the career.

Today there are three pillars in data science: SAS, R, and Python. Many of us specialize in one or another, either due to preference or industry standard, but many of us work across all three. Unfortunately, there is no single IDE to serve us all. This makes any multilingual workflow cumbersome and inefficient because we must navigate data types, programming environments, and operating systems. Oftentimes we choose the one with which we are most comfortable, and just argue about which is better. But in reality, we simply don’t have a platform that will let us always use the best tool for the job.

I believe that the best IDE for data science will soon be available. This IDE will integrate these three dominant data science tools into a seamless workflow. We can take advantage of the best that each of these tools has to offer to more efficiently explore and understand our data.

The Three Pillars of Data Science

SAS, R, and Python are very different but each is best suited for different tasks. In most cases, each can technically do the job of the rest, but they are not equally efficient.


SAS is the best tool for the management and cleaning of tabular data. The SAS data step and PROC SQL are powerful tools for manipulating data and deriving new variables. SAS only includes two data types: numeric and character so the programmer rarely has to worry about type errors. SAS doesn’t have complicated rules for coding missing values. The IF – THEN – ELSE syntax is easy to understand and easy to read. SAS arrays and SAS macros provide a mechanism to automate repetitive tasks across similar datasets and variables. The SAS data step is memory efficient. Simply put, the SAS data step is easier to use and provides fewer opportunities for coding errors than any other data science tool.

That is where the advantages of SAS end. The SAS PROC system of statistical models is based on programming syntax from the 1970s. SAS is slow to incorporate the latest and greatest models. SAS PROC reliance on a tabular datasets makes advanced algorithms impossible. The ODS output system is difficult to navigate. And SAS graphics are difficult to program and generally look terrible.

R Programming

I fell in love with R because I could make beautiful plots. When ggplot2 and plotly came out, these beautiful graphics were even easier. As a statistical analyst, the basic modeling formula, y ~ x1 + x2 + x3 matches how I was taught to understand my first linear model. The reporting tools in R are powerful and flexible: Quarto makes it easy to create professional reports and presentations. The creativity and variety of the 20,000+ packages on CRAN constantly amazes and delights me. And I could argue that Shiny is the coolest thing ever invented.


These days, Python is the most popular programming language for data science. It is the lingua franca in computer science which allows data scientists to communicate among a multidisciplinary group of engineers and IT professionals. Python includes libraries like Pytorch and scikit-learn for high performance computing and machine learning. The bleeding edge of data science is developed in Python. Sure, there are packages in R that can do these things too, but these tasks are usually accomplished in Python.

The IDE's (Integrated Development Environments)

Given these tools, data scientists have three primary IDEs to choose from. The SAS language runs in the SAS proprietary system. Posit provides R and Python support in Workbench and is by and large the perennial favorite in the R community. VS Code developed by Microsoft, is the the most popular IDE among all code developers and data scientists.

Yet, only one of these will execute code from all three pillars of data science, and that is SAS through its SAS Viya product. SAS Viya is a cloud-based system that runs the SAS language, R, and Python. SAS understood that open source analytics was a threat to their business model for the reasons outlined above. After initial efforts to squash the upstarts SAS decided to embrace multilingual data science and incorporate R and Python into their ecosystem.

But it is still SAS. It is still running on a closed system. It is still very expensive. Using it does not provide me the same efficient experience I’ve come to expect from Posit and VS Code. It was built by a company that has been around for 50 years and has a reputation for being slow to innovate. It is not the best IDE for data science, but it is the only tool that can do all three.

The Future of Multilingual Data Science

In a world where we seamlessly drift between programming languages, incorporating generative AI will become a vital tool. I’ve discussed using AI tools as a code migrator: GitHub Copilot is fully integrated into VS Code and Posit Workbench, and other LLMs are available as a Rstudio plugin through the chattr package. These tools are already helpful in generating a good first-pass code migration, but incorporating a back end testing and evaluation service is a logical next step. It would require an IDE that could run all three languages in parallel and a back-end algorithm for testing and evaluating results. Statisticians trained in SAS could request their GLM procedures in SAS return an equivalent glm() call in R, and rest comfortably knowing their models were tested and approved prior to deployment. Our team at ProCogia is working on just such a tool. Many of us are more comfortable programming in our preferred language; AI will provide us with just the little bit of help we need transition to a more effective and efficient workflow. Early results are very promising and we are refining a series of general-purpose prompts for finer instructions to the LLM.


It is time to build the best IDE for data science. The tools are there and we can do it with an open-source ethos. The Altair SLC compiler is the key to unlocking this opportunity and I am excited to start developing this integration with our community.

I will be at the R Stats conference representing the R Consortium in New York City, May 2024. I would love a chance to talk with you and your team about this exciting opportunity to integrate all our tools into a single master application. I am looking forward to hearing from you soon.

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