R VS Python
R and Python are both very popular languages among data analysts, data scientists, and programmers. They both deliver excellent performance features, are open source, and thus available to users for free download. This is, in fact, their greatest selling point since others like SAS are available but at a fee which for small businesses and startups could be a deterrent. However, while some people prefer Python for its versatility, others prefer R for its broad statistical analysis and visualization capabilities. Still, greater benefit can be drawn from using R and Python together as they complement each other in certain applications.
While R is explicitly a statistical analysis language, Python is a general-purpose language with a readable syntax and well structured code. It has specialized libraries and frameworks that are suited for a wide variety of development, machine learning, and data science tasks. However, coding knowledge is required before one can use Python. For this reason, professionals should consider undertaking a programming language course or the Python Data Science Course.
Python enjoys immense community support. By 2019, there were about 8.2 million developers using Python to code and the number has since increased. It is no wonder that Python has many modules, libraries, and frameworks for literally all types of development tasks.
R is a popular language among data scientists as it has been designed explicitly for data science applications. At least 43% of data scientists have R in their toolset. This is 3% more than the data scientists using Python. Unlike in Python, coding is not a prerequisite for using R. The core functions of R are statistical analysis and visualization. Thus, it tends to be more oriented to the data science and analysis domain.
Two decades on and the R community has grown immensely. What this means is that many more libraries are being developed and contributed to its open-source repository continuously. Currently, R has over 12000 packages in its repository therefore you are highly likely to find a package for any kind of analysis you would want to perform; special or general. Secondly, R flaunts a wide range of libraries for reporting and visualizing data.
Battle of Programming Languages - R vs Python
Both R and Python are top open-source data science tools. While they do have some similarities in terms of function, several differences exist between them. Let’s now look at the key differences between R and Python.
||Statistical analysis and visualization||General purpose most|
|Main users||Scholars, R&D professionals, and statisticians||Programmers and application developers
|Learning||Steep learning curve||Easy to learn|
|Flexibility||Ease of use of available libraries||Ease of constructing new models
|Integration||Integrated to run locally||Integrates with applications|
|IDE||R Studio||Spyder and Ipython Notebook|
|Packages and Libraries
||Tidyverse, ggplot2, zoo, Caret
||Pandas, NumPy, SciPy, TensorFlow, Caret, Scikit-learn
||Can handle a large database
||Can handle a large database
||Less popular than Python among professionals involved with statistical analysis.
More popular than R among developers and software engineers.
Core uses and users - R vs Python
R is a statistical analysis program. It comes with many built in maths, statistical, and general functions thus it has been designed primarily for data and statistical modeling and graphing tasks. This makes R popular among scholars and researchers who may or may not have a computer science or programming background.
Python, on the other hand, is a general-purpose coding language that is used for a wide range of applications including web application development, data science computing, and machine learning operations among others. Thanks to its board functionality, it is used in application deployment and production environments mainly by programmers and application developers.
Learning curve - R vs Python
Python is easy to learn. It has a simple and easily-readable English-like syntax that makes it easy for users to understand its code. This explains why beginners prefer learning Python first before other languages.
R is an interpreted language with an unconventional syntax that beginners and those without a programming background may find a little hard to grasp.
Flexibility - R vs Python
Python is a flexible language that allows users to develop new models from scratch. This makes it the best option for scripting web and other applications.
R has an array of statistical models and libraries that are readily available for use.
Integrated development environments (IDEs) - R vs Python
While Python is supported by a range of IDEs including Atom, VisualStudio, and Spyder, R only has one IDE, RStudio which is better suited for statistical analysis than for development. However, R supports extensions to IDEs like VisualStudio, Code, and Atom for users who wish to use it for application development.
Data handling - R vs Python
Python has libraries specifically built for data analysis such as NumPy and Pandas. To perform data analysis, users must first install data analysis packages. R comes with inbuilt data analysis packages that a user can use for basic data analysis tasks without having to install packages. However, when handling large datasets, the installation of packages like data.table comes in handy.
Platform dependency - R vs Python
Both R and Python are platform-independent programing languages whose code can run easily on most operating systems like Windows, Linux, and Mac. This eliminates the need to write a separate code for each OS.
Application developers and software engineers tend to prefer Python over R as it is more suited for use in production environments. This is because it has a number of frameworks like Django and Flask that support the development and deployment of models that will be integrated in web applications. R, on the other hands is preferred by scholars and researchers who are more interested in analyzing data than in building models. Still, R and Python can be used together through reticulate; R’s interface to Python or rpy library which embeds R code into Python language.
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