What is R
R is a language and environment for statistical computing, reporting, and graphical representations. It is an open-source scripting language.
It was created in 1993 by Ross Ihaka and Robert Gentleman at the University of Auckland. The R Development Core Team currently develops it. R is freely available under the GNU General Public License. Pre-compiled, binary versions are provided for various operating systems - Linux, Windows, and Mac.
R has a comprehensive catalog of statistical and graphical methods, including machine learning algorithms, linear regression, time series, and statistical inference.
What is Python
Following the definition from the official Python website:
Python is an interpreted, object-oriented, high-level programming language. Its high-level built-in data structure, combined with dynamic typing (it is also a strongly typed language) and dynamic bidding, make it very attractive for Rapid Application Development and for use as a scripting or glue language to connect existing components.
You will find more information about Python on our blog. We recommend the articles:
- All you need to know about Python - part 1 and rest
- Is Python FAST? - introduction to the comparison of Python with other languages
Difference between R and Python
In general, we’ll focus on the differences between these two languages but let’s briefly look at the similarities.
Python and R are both high-level, open-source programming languages. Two are preferred in Data Science, Data Analysis, Machine Learning, and Statistics.
Although they are generally used for similar purposes, there are some significant differences.
The main difference between these two languages is that Python is a general purpose, and R comes from statistical analysis.
Another considerable distinction is its approach to data science. Both have supportive communities and are constantly working on making their libraries and tools more valuable. But: R is used for statistical analysis while Python is reaching more towards general data wrangling.
Python is a multi-purpose language whose syntax is readable and easy to learn and understand. Programmers worldwide love to use Python in data analysis or machine learning in scalable production environments.
R bends towards statistical models and specialized analytics. Data scientists use R for deep analysis, which is supported by a few lines of code and beautiful data visualizations.
Packages and libraries
Due to many packages, the advantage of using formulas, and easily usable tests, R can be used for basic data analysis even without installation packages.
R has an extensive library repository that is constantly updated in CRAN (Comprehensive R Archive Network). It includes dplyr, mice, and ggplot2, just to name a few. R has a very active user base that is working on updating its colossal database (over 10,000 packages).
The Pip package index has all the libraries for Python. You’ll find there matplotlib, seaborn, and many more. R has many users making sure that its repository is up to date. With Python’s list, this issue is a bit more complicated.
R’s visualization packages include ggplot2, ggvis, googleVIS, and rCharts. Using R for your visualization will help you make the most complex raw data set look informative and, well, just pretty.
Python has a lot of interactive options, e.g., Bokeh but picking the best for your project and most relevant for your current needs is sometimes exhausting and very complex.
Honestly, data visualization is more straightforward and more pleasing to the eye with R.
Speed and performance
R requires more extended codes for simple procedures because it is a low-level programming language. A longer code takes more time when it comes to execution; reduced speed is a reason for that.
Python is a high-level programming language, and when it comes to building fast apps, it is a chosen language. Python code is simpler and shorter, so it takes less time to run.
Which one is the right choice for you?
R is a programming language made by statisticians for statistics. Business analysts, data analysts, and scientists use it. Primarily for working with all sorts of data - starting with statistical computing and finishing with data visualization.
Academic teachers, analysts all over the world but also researchers, and top brands like Uber, Meta, Google, Accenture, and Airbnb, to name a few, are using the R Programming Language. It can be used in banking, e-commerce, finance, healthcare, and even manufacturing (companies like Ford or Modelez use R to analyze customer “sentiment”).
Python was built for those who are looking for simplicity and quick development. The biggest world-known organizations - NASA, Spotify, and Google - use Python daily. According to the TIOBE index, since 2001, Python has had one of the highest positions.
Python is used in all fields - data analytics and visualization, AI, language development, and even design and web development.
So, as you can see, both languages are great and perfect for data analysis. But, there is always a but…
If you are interested in statistical calculations and data visualization - R should be your choice. If you’re considering becoming a data scientist and want to work with big data, AI, and deep learning algorithms, go with Python.
Remember that choosing the right language depends not only on your skills but also on your project. Things worth considering are: your experience - Python is very easy to learn while working out the complexity of advanced functionalities in R, which makes it a bit more challenging to learn fast.
As mentioned, R is used by academics, engineers, and scientists who often do not have a programming background because R is characterized as a statistical tool. Python is a production-ready language that has an influential position in the programming world and is used in many applications.
Instead of wondering which one is better, we perhaps should wonder how to make the best use of both for each individual case. Most companies and developers use both to solve their issues and work on their projects. The ideal way is to learn both.
At the end of our comparison, let’s think: Is R worth learning?
R focuses on the statistical side of the project, while Python finds itself useful in data analysis tasks. But these are two similar languages, and both are definitely worth learning.
R is often called the “golden child” of data science, and (probably) there is no better programming language for statisticians to learn. Data scientists skilled in R can easily find projects in top trier companies like New York Times or Google.
Python is highly recommended to enter the big data world or data science. The learning curve for Python is way easier than for R, yet this high-level programming language has proven its worth over the years, and it is a preferred language amongst web and game developers.
To sum up, if you are starting or want to give your career in the field of big data and data science a boost - go and learn two of the most popular (and, at the same time, the most important) programming languages dedicated to these fields - Python and R.
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