R vs. Python for data science
R and Python are open-source programming languages programmers use for various purposes — but which language is more effective to use for data science?
Learn more about what R and Python are and explore their main differences so you can better understand these common programming languages.
Why choose R?
R is a programming language originally developed by two professors who wanted a better statistical platform for their students. Today, R is used in statistical computing, machine learning, visualization, and data analysis.
Industries such as data science, healthcare, and academic research often use R. Because it is open-source, it is free for users without requiring licenses or fees.
Why choose Python?
Python is a general-purpose, high-level programming language used for a range of tasks, such as scientific computing, artificial intelligence (AI), data analysis, task automation, website building, and software creation.
Like R, Python is open-source, meaning that it is free to use and distribute without licenses or fees. Its other advantages are that it is easy to learn, versatile, flexible, and fast.
What are the main differences between R and Python?
Here are some of the main differences between these two programming languages:
Purpose
R was created explicitly for statistical analysis and data visualization. It allows specialization for the analysis of big data. By contrast, Python is a general-purpose language that applies to many tasks, including web development, machine learning, and data analysis.
Types of users
Statisticians, researchers, academics, and data analysts utilize R in complex data visualization and statistical analysis. Python is typically used by new programmers, general programmers, and data scientists seeking a more flexible language that applies to a broader range of uses, such as machine learning, web development, and data manipulation.
The degree of ease
Because of its similarity to human language, Python is straightforward for most to learn and use. New programmers who have no coding experience commonly appreciate Python's ease of use.
R is a more specialized language that is considered more complicated to learn, with its unique syntax, steeper learning curve, and potentially confusing commands. Those with a background in statistics may find that R is easier to understand.
Popularity
Python is more commonly used in a wide range of applications, is easier to learn, and operates faster than R. It is more common than R among general users and new programmers.
R is a more common language among data scientists who use statistical modeling and data visualization.
Common libraries
There are over 300,000 available packages in the Python Package Index (PyPI). Python's popular libraries include:
- NumPy, used for numerical analysis
- Keras, used in deep learning and artificial intelligence
- Pandas, used in data analysis
- PyCharm, an integrated development environment (IDE) for Python
- SciKit-learn, used in predictive analysis
- Matplotlib, used with object-oriented application programming interface (API) for embedding plots
- SciPy, used in scientific computing
- Folium, used for geospatial data visualization
- Seaborn, used for statistical data visualization
Approximately 19,000 packages are available in the Comprehensive R Archive Network (CRAN). R's popular libraries include:
- Tidyverse, a popular collection of R packages
- dplyr, a set of functions that enable data frame manipulation
- Ggplot2, an open-source data visualization package
- RStudio, an R-based IDE
- R packages, reproducible R codes, and functions
- caret, used with machine learning workflow
Get started with online programs on edX
edX offers online courses in R Shiny and Python to help you learn these programming languages more easily. Alternatively, you can choose to pursue a bachelor's or master's in data science degree on edX instead.