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Dear reader,
As a fellow data science practitioner, you likely grapple with the million dollar question: R or Python? These two programming languages dominate in data analytics and statistical modeling. Both boast large communities pumping out new libraries and tools daily.
So how do you decide when to use R versus Python?
In this comprehensive guide, I‘ll compare R and Python across several aspects – from background and design to strengths and new trends. My goal is to provide insights that help you utilize both languages based on the specific needs of each data task.
Let‘s dive in!
History and Origins
First, a quick historical overview to understand where R and Python came from:
R: By Statisticians, For Statisticians
- Created in 1990s at University of Auckland for statistical computing
- Designed by and for academic statisticians
- Goal was to make statistical analysis and graphics easier
- Packages hosted on Comprehensive R Archive Network (CRAN)
- Now 16,000+ packages available, still overseen by R Foundation
Python: Generalist With a Data Science Evolution
- Conceived in late 1980s as a general programming language
- Designed by developer Guido Van Rossum for wider applications
- Key goal was code readability and simplicity
- Third-party libraries like NumPy (2006) sparked data science use
- Pandas (2008) and SciKit-Learn (2010) cemented Python for analytics
So in a nutshell, R was built specifically for statistics while Python took a more general path that organically grew to envelop data science.
Now let‘s examine how each language has been embraced.
Popularity and Growth Trends
In terms of raw popularity, Python has raced ahead of R in recent years.
Python boasts a 21.69% surge in popularity score from 2016 to 2017 per IEEE Spectrum‘s ranking. This rocketed Python to 1st place as most popular overall language in 2017, compared to R in 6th place.
Python also edges out R in data science jobs. An analysis by code submission site Kaggle shows over 60% more data job listings asking for Python skills than R skills over the past 5 years.
However, for strictly statistics-oriented data science roles, R still holds more positions. Over 65% of data scientist job ads on statistics job board StatisticsJobs specifically ask for R skills.
So while Python may have broader usage, R remains entrenched where hardcore statistics understanding is required. Some surveys actually suggest more established data scientists gravitate to R while younger coders pick up Python first.
As data veteran Domino Data Lab described, "Python is winning the war for new users while R remains the weapon of choice for most academic data scientists".
Indeed, R and Python continue to thrive in tandem within the overall data landscape. The chart below shows the growth trend for both:
With innovation in data science accelerating, R and Python packages equip us with more and more capabilities year after year.
Now let‘s analyze the core strengths and weaknesses behind each language.
Strengths and Weaknesses
In this section I‘ll break down the key advantages and limitations of both R and Python. Understanding these tradeoffs helps us pick one or the other by data task.
R: Specialist Statistical Beast
Key Strengths: statistical depth, custom analysis, beautiful visualization, reproducible reporting
R‘s greatest feature is its rigorous statistical functionality. With over 16,000 packages spanning advanced analytical techniques, R offers unparalleled depth. Packages like zoo, maxLik, and forecast equip time series capabilities. ggplot2 makes flexible, publication-ready graphics seamless. R Notebooks generated via RMarkdown integrate analysis with reporting.
For a statistical specialist, R has all the tools to conduct bespoke analysis while communicating results with striking visuals and documents. R‘s consistency and strictness lend well to the scientific method.
However, R‘s focused specialization comes at a cost:
Weaknesses: performance penalties, production deployment limitations, idiosyncratic syntax
Heavier computations slow down in R, creating performance drags. Production deployment of full R applications can suffer too. While RShiny packages help create interactive web apps, translating analysis into production still lags Python‘s pipelines.
Additionally, those versed in Python or other languages often find R‘s syntax frustratingly opaque. The formulaic language common to statisticians feels foreign to most programmers. This alienation limits collaboration with non-R users.
So in summary, R provides unrivaled statistical functionality at the cost of computational speed and production readiness.
Python: A Flexible Data Science Pipeline
Key Strengths: scalability, versatility, programmer-friendly syntax, wide application
As a general-purpose language, Python offers extreme flexibility: from early experimentation to scaled deployment, Python can do it all.
It comes baked in with programmer-friendly syntax, inviting collaboration. Python‘s established credibility across fields like web development give it strong community support.
The Python data science stack brings powerful libraries for tasks ranging from data cleaning (Pandas) to visualization (Matplotlib, Seaborn) to robust modeling (SciKit-Learn, StatsModels, Tensorflow). Notebooks like Jupyter integrate analysis with documentation.
Once models are built, Python seamlessly transports them into production-ready applications. The cross-capability makes Python a flexible choice.
Of course, there are gaps in Python‘s scope:
Weaknesses: fragmented packsge ecosystem, statistic gaps, visualization learning curve
Behind that flexibility lies fragmentation. Important statistics or techniques may lack support across scattered Python packages. Cohesion lags behind R‘s CRAN unified ecosystem.
Statistical depth trails dedicated tools like R too; Python generalizes across many domains. Hardcore statistical customization therefore proves difficult.
Finally, Python‘s diverse visualization options bog users down in choice paralysis. The multitude of syntaxes create a frustrating learning curve. R‘s visualization stands apart in simplicity.
So in summary, Python provides extreme flexibility and scalability while sacrificing statistical specialization and cohesion.
Learning Curve and Skill Building
Conventional wisdom says Python is easier to initially pick up than R.
With standard programming syntax shared across languages, Python greets newcomers with familiar variable assignments, loops, and functions. Code reads like mathematical pseudo-code.
R relies on unique formula syntax less intuitive to novice coders. The formulaic style directly conveys statistical concepts – helpful later on but obstructing as an entry point.
However, both languages present learning curves steeper than advertized.
For R users, absorbing plethoric analysis packages proves an endless learning climb. New pre-built statistical tools constantly enter the ecosystem via CRAN. Keeping R skills current and leverage the full language requires perpetual vigilance.
For Python users, traversing the library landscape introduces fresh challenges. Just piecing together coherent pipelines from various packages occupies initial years. Each new advance means tool reconfiguration. Only later does this glue-work pay dividends.
Ultimately adept statistical coders develop literacy in both languages. R provides precise statistical understanding while Python promotes general computational thinking – two symbiotic skill sets.
Specializing in one language provides short-term job market advantages. But expansive analytics careers capitalize on both R and Python fluency applied at different data life cycle stages.
Now let‘s overview some key packages enabling R and Python analytics.
Packages and Libraries
Both languages owe their analytics capacities to curated sets of third party packages adding functionality:
R Packages
R libraries all stream through CRAN, the standardized public repository of 16,000+ packages. Some most popular analysis packages include:
- tidyverse – RStudio‘s R package collection for wrangling, plotting, modeling and more
- ggplot2 / lattice / plotly – elegant and customizable graphics
- caret – unified interface for machine learning including preprocessing and models
- zoo / xts – tools for financial time series analysis
- randomForest / e1071 – implementations of machine learning algorithms
- knitr / RMarkdown – literate programming formats for reproducible analysis reporting
Python Libraries
Python analytics packages have emerged more independently, requiring savvier stitching together. Among the most essential libraries:
- NumPy – foundational math/stats functionality and multi-dimensional arrays
- Pandas – data structures and analysis routines
- Matplotlib / Seaborn – visualization and graphics
- StatsModels – statistical modeling
- SciKit-Learn – vast machine learning algorithms
- Tensorflow – leading neural network/deep learning library
- Gensim – robust text mining and natural language processing
- Pipeline – meta-package for smooth dataframe analysis syntax
This snapshot highlights the comprehensive analytics toolsets both languages now provide.
Next let‘s move towards practical application – when should you use one language or the other?
When Should I Use R vs Python?
Based on their design tradeoffs, certain data tasks naturally lend themselves more to R or Python.
Here are some guidelines on when to use each:
R Tends to Excel At:
- Inferential statistics and significance testing
- Cutting edge statistical research
- Statistical learning methods and experimental analysis
- Custom graphics and visualizations
- Dashboards expressing complex statistical results
- Notebooks, presentations, and reports explaining statistical findings
Python Tends to Excel At:
- Building end-to-end analytic pipelines
- Production-grade machine learning application development
- Deploying predictive services at scale
- Cloud-based analytics and computing
- Gathering, cleaning, and munging large datasets
- Interactive data visualization dashboards
As you can see, R dominates statistically-focused analysis while Python leads operationally-focused engineering.
Of course, these guidelines describe the "centers of gravity" where each language orbits. In practice, usage depends case-by-case.
Often a hybrid approach makes sense: conduct research and iteration in R, then port successful models to Python for production. Such a tandem process maximizes strengths of both languages.
Now let‘s examine job prospects.
Job Market Outlook
In general data science job listings, Python holds the advantage.
Surveys like 2019 Kaggle‘s State of Data Science report show 63% of data jobs asking for Python compared to 41% seeking R skills.
However, for specifically statistics-oriented roles, R still holds sway. Over 65% of statistics job board listings explicitly request R.
Ultimately, average salaries for data scientists end up comparable between R and Python coders – right around $100,000.
But in terms of finding that first job, Python provides a wider funnel of opportunities today. Seasoned professionals can always switch into R-centric statistics from a Python springboard.
So for those newer to data science, starting with Python offers a smoother on-ramp – with the planning to layer-in R later on. Such a strategic foundation in both languages helps sustain adaptable careers.
Current Innovations and Emerging Trends
Both R and Python ecosystems continue advancing analytics frontiers across areas like:
Big Data Integration
- R packages like sparklyr and DistributedR facilitate big data analysis by integrating R with Spark
- Python also links up with Spark and other platforms like Dask for large scale computation
Cloud Computing
- RStudio‘s RCloud allows running R code in the cloud while using local tools
- Python links natively to cloud through services like AWS SageMaker
Machine Learning Deployment
- Realtime deployment of R models made possible through Docker containers
- Python dominates productionized ML through frameworks like Django and Flask
Data Visualization
- R visuals continue excelling through htmlwidgets likeplotly integrating D3 visualizations
- Python explores more advanced graphics via JavaScript libraries like Altair
Notebooks
- R Notebooks provide literate programming merging analysis with narrative
- Jupyter nbconvert exports Python notebooks into presentation formats
Natural Language Processing
- R packages like quanteda and tidytext ease text mining
- Python‘s Gensim, NLTK and spaCy equip robust NLP capabilities
As you can see, R and Python drive innovations across nearly all aspects of applied statistics and machine learning. Both show no sign of slowing momentum.
Key Takeaways
Alright – we‘ve covered a lot of ground comparing R and Python!
Let‘s recap the key takeaways:
- R leads advanced statistical analysis while Python enables scalable engineering
- R‘s cohesive CRAN centralizes packages while Python‘s libraries run independently
- R‘s visualization outshines but Python smoothes deployment
- Python offers more job opportunities but R provides specialization upside
- Both continue fast innovation across analytics frontiers
- Ideally, leverage both languages based on the task needs
The versatile 21st century data scientist flits between both languages fluidly – designing in R, building in Python. As analytics challenges evolve, so do the strengths of each language. There‘s no need forcing a single winner.
I hope mapping the R vs Python choice equips you to best utilize both languages‘ superpowers. Whether just beginning your journey or sharpening skills, integrate these tools as a flexible framework. Master this blueprint, and no analytics challenge stands a chance!
Now go show those data sets who‘s boss!