R and Python are among the most-used programming languages in the world, particularly in the era of data analysis and artificial intelligence. This article provides an analysis and evaluation of the programming languages R and Python. It discusses the advantages and disadvantages of each language. You will also learn the 12 key differences between R and Python and their five similarities.
R is an open-source programming language with an environment that is useful for performing statistical computing and data visualization. This environment is built around a basic command-line interface that programmers utilize to read data, specify commands, and receive results. The environment also allows programmers to combine individual operations into a single function that can be reused and perform looping functions.
R runs and compiles on various operating systems such as UNIX platforms, macOS, and Windows. It is a popular language in academic settings due to its robust features. R has various statistical and graphical methods, such as time series, machine learning algorithms, and linear regression.
Ross Ihaka and Robert Gentleman officially released the first version of R in 1995. Over the years, several versions have been released, with each introducing new or improved features. On 22nd April 2022, the latest R version was released: 4.2.0.
Companies such as Meta, Google, and Uber use the R programming language.
Pros and cons of R
The R programming language has the following advantages:
- Through ggplot2 R, users can visualize their data with attractive graphs with notations and formulas.
- R users convert unstructured code into structured ones through packages such as readr and dplyr.
- R is an open-source language that allows several people to optimize and improve its source code and features.
However, it does have a few drawbacks:
- R takes more time to give an output when compared to other languages, such as MATLAB, as it is a slowing processing language.
- R consumes more memory as objects are stored in the random access memory (RAM); the process slows down as more data is added.
- R is not ideal for use with big data. It also requires that all the data be in one place, thus making the process of data handling tedious. Although, users can use integration to make this process easier.
Python is a high-level, general-purpose programming language. It is an open-source, flexible, and object-oriented programming language that emphasizes code readability with a decluttered visual structure and simple syntax.
Python runs in operating systems such as macOS, UNIX- based systems, MS-DOS, and various versions of Windows. Python is a popular language applied in data science, data analysis, web application development, machine learning, and system scripting. It is preferred by programmers for its versatility, debugging capabilities, embeddable code, code efficiency, and other complex functionalities.
On 20th February 1991, Guido Van Rossum released the first version of Python language. Since then, several versions of Python have been released, as others have been discontinued due to security concerns. For example, on 7th September 2022, four new releases of Python were made due to a possible denial of service attack: 3.10.7, 3.9.14, 3.8.14, and 3.7.14. Python 2.0 was released on 16th October 2000, while Python 3.0 was released on 3rd December 2008.
Python programming language is used by companies such as Netflix, Spotify, and NASA.
Pros and cons of Python
By using Python, you can gain the following advantages:
- Python is open source, allowing several people to contribute and improve its libraries and features.
- Python has many essential libraries to perform data science-related functions.
- It enhances productivity with its integration and control capabilities.
- Users can embed Python codes with other programming languages, such as C++ when needed.
However, do keep in mind the following shortcomings:
- It is relatively slower than other programming languages as it is an interpreted language.
- It consumes a large amount of memory which may cause it to respond slowly when more objects need to be accessed.
- Python’s database access layers are underdeveloped compared to other databases, such as Java Database Connectivity (JDBC) and Open Database Connectivity (ODBC).
The following are the differences between R and Python programming languages.
1. Differences in introduction
Python is a general-purpose language for scientific computing and data analysis. It is primarily used to analyze data or code into machines for machine learning (ML). For instance, programmers can use python to develop ML or mobile applications.
On the other hand, R is a programming language and an environment for statistical programming, which includes statistical computing and graphics. Statisticians created R; thus, its functions lean heavily into statistical models and specialized analytics. Data scientists mostly use it to perform statistical analysis. Its output consists of beautiful data visualizations. For instance, it can be used by bioinformaticians to conduct genomics research.
2. Overall objectives
Python primarily creates graphical user interface (GUI) applications, web applications, machine learning, and data analysis. For instance, users can use Python’s Tkinter GUI framework library to develop GUI applications. They can use Tkinter to create widgets to display text and images. Python frameworks such as Django, CherryPy, and Grok are used in web development. The Python SciPy package is used in machine learning in Python.
In contrast, R has several features that enable it to be primarily used in statistical analysis and representation. It has arrays, lists, vectors, and matrices that allow calculations. It also has packages such as ggplot2, lattice, highcharter, and plotly, enabling users to create data visualization. Additionally, R consists of tools such as bar graphs, histograms, scatter plots, and heat maps that are also useful in data representation.
3. The degree of ease of use
Python is a standard programming language that beginners can quickly learn and understand due to its simple syntax. It requires programmers to write fewer lines of code and is easy to read. Python uses a more streamlined approach for its data science projects. It has a robust array of libraries that enable users to input the library’s action into the code, allowing it to perform matrix computations and optimization easily.
R is less popular than Python but is still widely recognized. It is not beginner friendly and has a steep learning curve as its syntax is difficult to read and requires programmers to write more lines of code even for simple operations. R is mainly used for complex data analysis in data science. Its command-line scripting enables users to store complex analytical methods to be recalled later when needed.
4. The degree of ease of learning
The ease with which an individual can learn programming hugely depends on background and programming mastery. However, there is a consensus that Python has a smoother learning curve, making learning more accessible. It is a time-efficient language that demands less coding time as its syntax is similar to the English Language, which allows programmers to finish coding tasks quickly and, in turn, get more time to explore Python.
On the other hand, beginners report finding it more challenging to learn and master the R programming language due to its non-standardized code. The non-standardized code makes R look clunky and awkward to these new programmers and thus may require an extended learning period. However, R is easier to learn for people with a background in statistics.
5. Popularity of the language
Python is more popular according to The Importance Of Being Earnest (TOIBE) index. It has a rating of 17.08% in the October 2022 report, representing an increase in demand of 5% in the past year. Its versatility, ease of use, and huge community have contributed to this immense growth. Python has a more extensive user base that is diverse; they include developers and programmers.
Python is the go-to language in the production sector because its simple syntax allows programmers to perform complex operations using fewer lines of code. It is also ranked as one of the most in-demand tech skills by employers, making it an in-demand skill.
R is less popular than Python. According to the October 2022 report by TIOBE, it ranks as the 12th most popular language with a rating of 1.27%, representing a 0.03% change in the past year. These percentage increases show the increased demand for Python language by programmers and data scientists. R’s user base is mainly in the academic sector, consisting of data scientists and research and development (R&D) who perform data analytics.
6. Use with integrated development environments (IDEs)
An integrated development environment is a software tool that equips users with an interface for coding, testing, and debugging features. An IDE comprises an editor for the source code, build automation tools, and a debugger. The source code editor is a text editor that assists programmers in writing code.
It has features that allow for checking bugs when writing code and an auto-completion feature. The build automation tools allow for the automation of recurring tasks, such as compiling source code into the eventual binary code and packaging the code. The debugger displays the location of the bug in the original code.
The IDE features enable programmers to organize their workflow and solve problems. Python uses IDEs such as Spyder, Eclipse+Pydev, and Atom, while R uses IDEs such as Rstudio, RKward, and R commander.
7. Libraries and packages
Libraries and packages comprise a collection of precompiled codes that programmers can use to perform specific and defined operations. Library also includes documentation, message templates, classes, and configuration data. Python libraries and packages comprise a collection of related modules of code that are used repeatedly in different programs to perform specified tasks.
The Python Matplotib library is responsible for plotting numerical data, thus valuable for data analysis. The Pandas library provides flexible high-level data structures and tools that are useful in data analysis, cleaning, and manipulation. The NumPy library is a machine-learning library that supports multi-dimensional data and large matrices. It has in-built mathematical functions for computations.
In contrast, R has libraries such as the ggplot2 library that are useful in data visualization, the Shiny package that is used to create interactive web applications, and the Rcrawler package that is used for domain-based web crawling and web scraping. R stores its packages in repositories such as the comprehensive R archive network (CRAN), the official repository, Github, and Bioconductor for topic-specific repositories.
8. Speed and performance
Python is a high-level language that uses simple syntax. It is the preferred option when building critical and fast applications as it uses less code, which takes less time to execute. In contrast, R is a low-level programming language. It requires longer codes, even for simple processes. A long code takes longer to run. Thus, R can be said to execute code slower than Python.
9. Data collection
On the contrary, R was developed to enable analysts to import data from Excel, text files, and CSV files. Unlike Python, R packages are designed for basic web scraping. Data frames in R can be created by turning files built in SPSS or Minitab format.
10. Data exploration and manipulation
Data exploration involves exploring an extensive data set to discover initial patterns, characteristics, and points of interest. Data manipulation involves organizing the data into structured data so that computer programs can interpret it easily. The Pandas library in Python is used for data manipulation and exploration. It enables users to filter, sort, and display data quickly. Pandas also have capabilities that allow merging and joining datasets and indexing and subsetting data that facilitates data manipulation.
In contrast, R is purpose-built for the statistical analysis of vast datasets and thus offers a wide range of solutions for data exploration and manipulation. The dplyr package in R allows users to select, filter, mutate, group, summarize, and join data. R also enables users to create probability distributions, use different statistical tests, and use data mining techniques.
11. Approach to data modeling
Python comprises standard libraries for data modeling. For example, NumPy is used for numerical modeling analysis, SciPy is used to perform scientific computing and calculations, and scikit-learn is used for machine learning algorithms. The Tidyvserse package makes it easy to import, manipulate, visualize and report data in R. However, sometimes users rely on external packages to perform specific data modeling analyses in R.
12. Data visualization
Python does not enable users to visualize their data as attractively as R. Programmers can use the Matplotlib library to generate basic graphs and charts. At the same time, programmers can also use the Seaborn library to draw more attractive and informative statistical graphics to visualize data.
On the other hand, R was essentially created to visualize statistical analysis results. The base graphics module enables users to create basic charts and plots, while the ggplot2 and ggplot tools allow users to plot complex scatter plots with regression lines to visualize data.
The following are the similarities between R and Python programming languages.
1. They are open-source programming languages
Python is created under an open source license approved by the open source initiative (OSI); this makes it freely distributable, available, and usable even for commercial purposes. Similarly, the R programming language is also known as open-source software under the provisions of the GNU’s Not Unix (GNU) general public license. Open-source languages allow users to contribute to improving and optimizing features, report bugs, or even provide bug fixes to the official sites.
2. They are used for data analysis
Python is used for data analytics due to its simple syntax, flexibility, and scalability. It has an extensive collection of libraries used for computation and data manipulation. Python also offers libraries for graphics and data visualization. Similarly, the R programming language is used for data analytics. It enables users to identify patterns and build practical models. It is used to create and develop software applications that perform statistical analysis. It also supports analytical modeling techniques such as clustering and classical statistical tests.
3. They both have a high demand in the job market
According to a report by TIOBE, Python remains the number one most in-demand programming language. It is also one of the most in-demand tech skills, with several companies using ML and AI to run critical operations. R programming language ranks 12th in that report making it one of the top 20 programming languages. R programmers are also in demand, though at a different rate than those with Python skills. They can apply for positions such as data analysts, data architects, data scientists, or data administrators.
4. They are both suitable for AI and machine learning
Python is often used to support machine learning (ML) and artificial intelligence (AI), as it can be coded to process data in real-time and interact with a wide range of technologies. It also uses simple syntax, has an extensive library, and has a large community of developers. Programmers can also use R to support AI and ML. It is open-sourced and has a growing community. These programming languages facilitate AI tasks such as image and speech recognition.
5. They are both platform-independent
Both Python and R programming languages are platform-independent. They perform similar functions irrespective of the platform that the programmer uses. They are compatible with platforms such as Linux systems, Windows, and macOS versions.
Python and R are preferred languages for developers working with data, ML, and AI, and with good reason. They are both open-source, opening up a massive learning, discussion, and innovation community. However, R has its roots in statistical analysis, making it especially suited for data science applications and visualization. Python is simpler to work with, particularly in a production environment.
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