What’s the difference between Python vs R?
Python and R are two of the most popular programming languages used for data analysis and Machine Learning. Both languages have their strengths and weaknesses, and the choice between them often comes down to personal preference and use case. In this post, we will examine the key differences between Python and R and guide our users on which language may be more suitable for their project.
Head-to-Head Comparison Between Python and R
Let’s find the comparison table between Python and R.
|Easy to learn and versatile
|Specialized syntax for statistics and data
|Large and active community
|Active community, particularly in statistics
|Extremely popular in various domains
|Widely used in academia and statistics
|Extensive libraries for various purposes
|Comprehensive packages for statistics
|Pandas’ library provides powerful tools
|Tidyverse packages for data manipulation
|Matplotlib, Seaborn, Plotly, etc.
|ggplot2, base R graphics, and more
|Extensive libraries like scikit-learn
|A wide range of statistical models in-built
|Strong support with libraries like TensorFlow, PyTorch
|Limited but growing support
|Leading libraries like TensorFlow, PyTorch
|Emerging libraries like Keras and TensorFlow
|Django, Flask, and others
|Limited but growing support for web apps
|Natural Language Processing
|SpaCy, NLTK, Gensim
|NLP libraries like tm, tidytext, and others
|Big Data Processing
|PySpark, Dask, and Hadoop integration
|Limited support, but tools like SparkR exist
|Easily integrates with other languages
|Less seamless integration with other tools
|Generally, faster for numerical computations
|Slightly slower for some tasks, but efficient
Detailed Comparison between Python vs R
Popularity and Adoption: Python vs R
Python and R have both seen immense growth in popularity and usage over the past decade, especially in the fields of data science, machine learning, and artificial intelligence.
- Python has become one of the most widely used multi-purpose programming languages. According to the TIOBE Index, Python is currently the 3rd most popular programming language globally as of September 2023. Python adoption has grown rapidly in recent years due to its easy syntax and versatility for scripting, web development, data analysis, and more.
- R retains its dominance in statistical computing and data visualization. It is the lingua franca of statisticians and data analysts. In the Kaggle ML & DS Survey 2021, R was the 2nd most popular language for data science and machine learning practitioners after Python.
Ease of Use: R vs Python
- Python has a relatively gentle learning curve. It is designed for readability and easy-to-master syntax. Python’s syntax allows developers to write programs with fewer lines of code compared to other languages. Python also provides high-level data structures like lists, dictionaries, and sets that make data analysis easy.
- R has a steeper learning curve than Python. The syntax can be difficult to read as it was initially designed for statisticians. Furthermore, R’s flexibility comes with a less structured code. R relies heavily on specialized packages, which need to be mastered for productive data analysis.
In surveys, Python generally scores higher than R on ease of use and learning. However, both languages have extensive learning resources and community support available.
Data Analysis and Visualization: Python vs. R
Python and R offer excellent libraries for data analysis and visualization.
- For data analysis, Python has Pandas for data manipulation and modeling. R has a variety of packages like dplyr, data.table, and more for working with data frames. Both languages provide statistical modeling capabilities via dedicated packages.
- For data visualization, Python has Matplotlib, Seaborn, Plotly, and Bokeh. R has ggplot2, Lattice, and ggvis among other packages. The grammar of graphics implemented in ggplot2 is extremely popular for publication-quality visualizations.
In data analytics and visualization functionality, Python and R are quite close competitors. Each language has its strengths – Python for general-purpose programming productivity and scalability, R for statistical depth, and specialized packages.
Libraries and Packages: R and Python
- Python has a vast collection of open-source libraries for data science, ML, AI, etc. hosted on repositories like PyPI. Popular ones include Numpy, Pandas, Matplotlib, Scikit-Learn, Keras, PyTorch, TensorFlow, and more. Python packages are installed using pip or conda.
- R has over 16000 user-contributed packages for data analysis hosted on CRAN. Some popular packages include dplyr, data.table, ggplot2, caret, tidyverse, shiny, and more. R packages are installed using the install.packages() function.
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The table below shows a comparison of some essential libraries/packages for data science in Python and R:
While Python offers more general-purpose libraries, R provides domain-specific packages tailored for statistics and data analysis. Both languages allow loading packages to extend functionality as required.
Performance: Python and R
Python and R have different performance characteristics:
- Python code typically runs slower than R. This is due to Python being dynamically typed and interpreted at runtime. But Python performance has improved with just-in-time compilers like PyPy.
- R code is compiled to byte code before execution, giving it significant performance benefits over Python. However, looping operations in R can be slow compared to Python.
- For big data and high-performance statistical computing, Python’s NumPy library with vectorization, Numba, and Dask provide optimizations. R has packages like bigmemory, ff, and more for large data.
- For parallel computing, Python supports multiprocessing and Joblib. In R, parallel computing is possible via foreach, doParallel, and other packages.
So while R enjoys some performance advantages currently, Python is fast catching up via continued optimization of its numerical and scientific libraries.
Applications and Use Cases: R and Python
Python and R are both highly capable and productive languages for data analysis. Some key differences in their application include:
- Python is preferred for general-purpose programming in addition to data analysis, AI, and web development. R is more domain-specific for statistical analysis and visualization.
- Python has traditionally been strong for production machine learning systems deployment with its libraries like Scikit-Learn and Community. R is used more for developing ML models than deployment in production systems.
- For big data and production-scale data processing, Python has several optimized libraries like NumPy, Dask, and Modin. While R can work with Big Data, Python seems better suited for large-scale processing.
- Python’s data visualization libraries like matplotlib produce static visualizations suitable for publication and reporting. R’s ggplot2 is considered industry-leading for interactive and exploratory data visualization.
- R’s domain-specific packages are extensively used in academic research and statistics. But Python is gaining traction in academia as well.
Developer Productivity: Python Vs R
Python and R also differ in the development experience they provide:
- Python has a large developer community, extensive documentation, and an abundance of libraries that enhance productivity. Code reuse is simplified via Python’s packages.
- R provides great support for interactive data analysis with RStudio and Jupyter notebooks. It has domain-specific packages tailored for statisticians’ workflows.
- Python’s interpreted nature allows for rapid prototyping during development. R compiles code that demands some structure before executing.
- Python code tends to have higher readability due to its content-descriptive style. R’s functional programming style needs some ramp-up for developers to understand.
Both languages have their strengths when it comes to developer experience. Python offers great general-purpose programming facilities while R provides specialized support for statistical computing.
Salary and Job Trends: Python or R
In terms of salary and job trends, Python and R developers are in high demand with lucrative salaries:
- For data scientists, average salaries are over $120,000 in the US according to Payscale. Python is the most in-demand skill, but R competency is also valued.
- The average base salary for Python developers in the US is $120k, as per Dice. For R developers it is $106k as per ZipRecruiter.
- IT infrastructure firm HashiCorp’s 2021 State of Infrastructure report found Python to be the most in-demand tech skill. LinkedIn’s 2020 Emerging Jobs Report found Data Science roles expanding the fastest with Python and R key skills.
- Burning Glass data indicates over 90,000 job openings for Python developers and over 40,000 for R developers in the US currently.
Hence, there is high demand and salaries for both Python and R skills which is expected to continue growing. While Python edges past R in job openings, having competency in both languages is valued.
Final Thoughts on Python vs R
Python is known for its simplicity, readability, and large community support. It has many libraries for numerical computing and data analysis like NumPy, Pandas, SciPy, and scikit-learn. Python can also be used for web development and has frameworks like Django and Flask. However, Python can be slower for complex statistical modeling and machine learning tasks compared to R.
R is a programming language specifically designed for statistical computing and graphics. It has a wide variety of statistical and graphical techniques available through packages like ggplot2, dplyr, and caret. R also has strong capabilities for developing statistical models like linear and nonlinear models, time series, classification, and clustering. The R community is also quite robust. However, R has a steeper learning curve than Python and code can be more difficult to read at times.
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Amelie Lamb is an experienced technical content writer at SoftwareStack.co who specializes in distilling complex software topics into clear, concise explanations. She has a talent for taking dense technical jargon and making it engaging and understandable for readers through her informative, lively writing style.