Choosing and Installing a Programming Language

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This guide introduces the primary languages used in statistical research (R and Python) along with a few others you may encounter in the department. Every programming language has its relative strengths and weaknesses, so it may not be wise (or possible) to do all of your statistical research in one language alone. Beyond the information provided here, you can find additional explanations and alternate programming languages by consulting your favorite search engine or AI.

Important: IDEs Matter
Many installation instructions will ask you to install an integrated development environment (IDE) along with the programming language itself (e.g. RStudio for R). The IDE is the workspace in which you will create, edit, and execute most of your code, so choosing the right IDE is just as important as choosing the right programming language. See Installing and Configuring an Integrated Development Environment for more information.

R

R is an open-source language built by statisticians, for statisticians. It is widely used in academia in both theoretical and applied research.

Strengths	Weaknesses
Unmatched Statistical Ecosystem: The Comprehensive R Archive Network (CRAN) hosts thousands of packages for almost every statistical method imaginable, from basic linear regression to advanced modeling of Gaussian processes and spatial statistics. Data Visualization: Packages like ggplot2 offer incredibly powerful and customizable plotting capabilities (though getting your aesthetics and dimensions perfectly mapped can sometimes take a little troubleshooting!). Community: Because it is so dominant in statistical academia, you will find extensive tutorials, forums, and peers who can help you debug. Many faculty in the CMU Statistics department have extensive experience with R.	Memory Constraints: Base R loads data entirely into memory (RAM). If you are working with massive datasets, you can easily crash your session unless you use specialized packages. Inconsistent Syntax: Because R has grown organically over decades, you will encounter drastically different coding paradigms (like Base R versus the tidyverse), which can be confusing to navigate at first. Speed: R is notoriously slow at running iterative loops. To write fast code, you have to learn to "vectorize" your operations.
🔗 Tutorial: Getting started with R and RStudio

Python

Python is an open-source, general-purpose programming language that has become the dominant force in machine learning, deep learning, and data science.

Strengths	Weaknesses
Machine Learning & Deep Learning: If your research involves extensive machine learning, deep learning, or GPU-heavy workloads, Python is the absolute standard. Libraries like PyTorch and TensorFlow have massive ecosystems and community support. Versatility: Python excels at data wrangling, web scraping, and integrating seamlessly with high-performance computing environments (like submitting script jobs via Slurm). Readability: Its clean syntax makes it a great foundational language to learn general computer science principles and write easily maintainable scripts.	Environment Management: Managing packages, dependencies, and environments (via conda or pip) can frequently lead to version conflicts and frustrating debugging sessions. Statistical Ergonomics: While it dominates machine learning, everyday statistical tasks (like running an ANOVA or extracting p-values from a regression) often require more boilerplate code and lack the intuitive, out-of-the-box summaries R provides. Clunky Visualization Libraries: The standard Python tools for creating visualizations can be hard to navigate for beginners, especially for plots with any nonstandard additions.
🔗 Getting Started Guide | Official Installation Instructions

MATLAB

MATLAB (Matrix Laboratory) is a proprietary programming language heavily used in applied mathematics, engineering, and certain areas of computational statistics.

Strengths	Weaknesses
Linear Algebra Out-of-the-Box: Matrix operations are incredibly fast and syntactically intuitive, making mathematical translations from paper to code very straightforward. Reliability: As a paid, commercially developed product, its official toolboxes are highly optimized, rigorously tested, and thoroughly documented. Interactive Environment: The graphical interface makes it easy to inspect variables, matrices, and plots in real-time.	Cost and Accessibility: It is proprietary. While the department provides licenses, relying entirely on MATLAB can be restrictive if you collaborate with researchers who don't have a license or move to an industry role that won't pay for it. General Software Engineering: It is designed primarily as a mathematical scratchpad. Building large, modular software projects or using object-oriented programming concepts can feel clunky. Community Ecosystem: It lacks the massive, community-driven open-source package repositories of R and Python, meaning you may have to hand-code newer statistical methods yourself.
🔗 CMU MATLAB License and Installation Page

🛠️ Other Notable Languages

• Julia: A newer, open-source language designed to combine the raw speed of C/C++ with the usability of Python and R. It is rapidly gaining traction in statistics for computationally heavy tasks like complex Markov Chain Monte Carlo (MCMC) simulations.
• C++: While rarely used for everyday data analysis due to its steep learning curve, C++ is the engine running under the hood of many computationally intensive R and Python packages.

How to Choose Your First Language

If you are starting with little to no programming experience, the best choice usually depends on your immediate goals:

Start with R if: You are focusing primarily on statistical modeling, data visualization, or traditional coursework. It will get you up and running with data analysis faster than almost any other tool.

Start with Python if: You are leaning toward machine learning, plan to work with deep learning frameworks, or want a versatile language that handles large datasets and cluster computing exceptionally well.

Start with MATLAB if: You are collaborating closely with engineering departments, working on highly matrix-driven applied math, or if a specific course or lab strictly requires it.

Note: You don't have to choose just one forever! Many researchers use R for their data exploration and plotting, while relying on Python for heavy model training. Start by mastering the language most commonly used by your advisor or your current classes, and expand your toolkit from there.