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Python vs R: What Language Is Better For Data Science Projects?

Nov 26, 20207 min read

Robert Krajewski

Co-founder and CEO of Ideamotive. Entrepreneur, mentor and startup advisor.

Intro: why this text and what is inside?

Beginners in Data Science often have the question of which programming language to choose as the main one - a specific one created specifically for data processing - R, or the universal Python, which is popular in other areas as well. Tech business owners and startups also have questions like when Python or R is the right choice for Data Science, what specialist will be more useful for the data science team, or which language to choose for machine learning: Python or JavaScript.

 

Both languages (Python and R) are supported by open-source licenses (as opposed to commercial SAS and SPSS tools or proprietary MATLAB) and traditionally are considered the most popular. The rapid development of Data Science leads to a quick change of positions of these two programming languages. In this article, we will analyze the trends in the confrontation Python vs R for data science, the advantages, and disadvantages of these two programming languages and is it possible to combine them in 2020.

Python vs R for data science: what is Python? 

The design of any programming language implies a compromise. Low-level programming languages are difficult to learn, require a programmer to do a lot of manual work, but allow flexible code optimization and performance. High-level languages allow programmers to solve the same tasks more conveniently and simply but have fewer methods and tools for optimization. One of these programming languages is Python.

 

Since its release in 1991, the Python programming language has been extremely popular and widely used in data processing. Here are some reasons for its popularity:

  • An object-oriented language.
  • General-purpose.
  • It has many extensions and incredible community support.
  • Simple and easy to understand and learn.
  • Pandas, NumPy, and scikit-learn make Python a great choice for machine learning.

However, unlike R, Python does not have specialized packages for statistical computations. The main audience of Python is software developers and web developers. Most of the functional modules were created especially for them, which allow Python programmers to download data, perform complex operations with them, model, and analyze. 

Python vs R for data science: what is R? 

R is a programming language for statistical data processing and graphics work, as well as a free open-source computing environment within the GNU Project. The language was created as a similar language to S, developed in Bell Labs, and is its alternative implementation, although there are significant differences between the languages, most of its code in the S language runs in the R environment. 

 

It is widely used as a statistical software for data analysis and has actually become a standard for statistical programs.

 

The language and environment are available under the GNU GPL license. R uses a command-line interface, although several graphical user interfaces are available, such as the R Commander package, RKWard, RStudio, Weka, Rapid Miner, KNIME, as well as tools for integration into office packages.

 

In 2010, R was named one of the winners of Infoworld Magazine's Open Application Development Software category.

 

The first release of R programming for data science took place in 1995, and since then it has become one of the most frequently used tools for data science.

  • It consists of packages that meet the needs of virtually any statistical application.
  • Currently, CRAN contains more than 10 thousand packages.
  • CRAN has excellent visualization libraries such as ggplot2.
  • The possibility of offline analysis.
  • R is also an environment in which there is a set of software packages with which specialists can perform calculations for charting and manipulating data.
  • R is significantly used in statistical research projects.
  • R is very similar to another programming language - S.
  • R compiles and runs on UNIX, Windows, macOS, FreeBSD, and Linux.
  • C can be used to directly update objects in R. Also it provides efficient work with vectors and matrices.
  • The authors of R were inspired by S+, so many R programmers know how to code with S. 

In terms of performance, R programming for data science is not the fastest and can sometimes eat a lot of memory when working with large data sets.

Python vs R for data science: advantages and disadvantages 

Let's consider the advantages and disadvantages of Python and R, noted by the data analysts who use them. Both programming languages have pros and cons, some of them are noticeable, some can be easily ignored. 

Advantages of R for data science

  • The language was created specifically for data analysis: the recording of language constructions is understandable to many specialists. Comfortable and clear language constructions are an undoubted advantage for non-professional programmers.
  • Many functions required for data analysis are built-in language functions. Checking statistical hypotheses often takes only a few lines of code.
  • Installation of IDE (RStudio) and necessary data processing packages is extremely simplified.
  • R has many data structures, operators, and parameters. It includes many things: from arrays to matrices, from loops to recursion, along with integration with other programming languages like C, C++, and Fortran.
  • R is mainly used for statistical computations. It has a set of algorithms that are used by machine learning engineers and consultants. It is used in time series analysis, classification, clustering, linear modeling, etc.
  • A convenient package repository and an abundance of ready-made tests for almost all methods of Data Science and machine learning.
  • Several quality packages for data visualization for different tasks (ggplot2, lattice, ggvis, googleVis, rCharts, etc.). It is possible to build two-dimensional graphics (diagrams, boxplots), as well as three-dimensional models. 
  • Basic statistical methods are implemented as standard functions, which significantly increases the development speed.
  • For R there is a huge number of additional packages for every taste. It can be a package for text analysis (so-called natural language modeling) or a package with data from Twitter. Every day there are more and more packages, and most of them are collected in one place - in a special CRAN repository.

Disadvantages of R for data science

Like any programming language, R has some disadvantages. Each programmer decides on their own what disadvantage can not be ignored and what should not be noticed. 

  • Low performance. However, there are packages in the system which allow programmers to increase the speed (pqR, renjin, FastR, Riposte, etc.). It is recommended to use data.table and dplyr libraries when working with Big Data.
  • Specificity in comparison with standard programming languages, because the language is highly specialized (for example, indexing of vectors starts from number one instead of zero).
  • Since most of the codes in R are written by people who are not familiar with this programming, the readability of some programs leaves much to be desired. Besides, not all users follow the guidelines for the program code design.
  • R is a great tool for statistics and stand-alone applications but works not so well in the areas where are traditionally used general-purpose languages.
  • It is possible to perform the same functionality in different ways. The syntax for some tasks is not quite obvious.
  • Due to a large number of libraries, the documentation of some of the less popular ones cannot be considered complete.

Advantages of Python for data science

Python is a widely-used programming language. A lot of programmers like it for its simpleness. If it is simple, it does not mean it has low functionality. 

  • Universal multipurpose language: you can perform not only data processing but also their search and use the result of processing in a web application.
  • The interactivity of the programming language (calculations without compilation): programmers also appreciate Python for its built-in interpreter, which allows encoding on the go. In Data Science, this is relevant for testing hypotheses interactively.
  • Dynamic language development: this language is rapidly and intensively developing. With each version, the performance of the language increases and the syntax improves. For example, in version 3.8, there is a new walrus operator - := that is a serious enough event for any language. In low-level languages like C++ or Java, the rate of change is noticeably slower - it is approved by a special committee that meets every few years. 
  • Integrated possibilities for source code optimization: the built-in interpreter is useful for developers. Since Python offers implicit and dynamic data typing, it's possible to estimate the degree of optimization only during code execution, for which the interpreter is useful. It translates the source code into machine instructions that can suggest the idea for optimization. For example, by comparing two instructions you can understand why one works faster than the other. This is an important advantage for working with Big Data because in addition to analyzing the data there is a lot of work to improve the algorithms for processing them.
  • In Python, the standardization process is more open to the community, everyone can come up with their own ideas, and their number is growing rapidly.

Disadvantages of Python for data science

Despite Python being an object-oriented programming language, it has some disadvantages for Data Science. Maybe they are not crucial, but each programmer or data science team before they start work on a Data Science project, decide on their own.

  • Visualization. This capability is an important criterion in selecting the software for data analysis. Although Python has pleasant libraries for visualization, such as Seaborn, Bokeh, and Pygal, the choice may be too great. Moreover, compared to R, the visualization in Python is much more complicated, and its results are sometimes not very clear.
  • Lack of a common repository and lack of alternatives for many R libraries.
  • Python is a language with dynamic typing. It significantly speeds up program development but also complicates the search for difficult tracked errors caused by misassigning different data to the same variables.
  • Global Interpriter Lock. At the moment, this is the main performance problem in Python. It is also due to the poor implementation of multithreading. The GIL code has not changed since the first version of the programming language. This clearly indicates that it is out of date.

Python vs R for data science: is it possible to combine the advantages of both languages?

The battle continues Python vs R for Data Analysis. There are people in the Data Science community who use Python and R, but the percentage is small. On the other hand, it is often the case that adherents of only one programming language would like to use some features of the other language.

 

For example, R users sometimes crave object-oriented features built into the Python language. Similarly, some Python users dream about the wide range of statistical distributions available in the R language.

 

Right now, there is a growing number of data scientists who know both languages and use one or the other as needed. The question arises - is it possible to combine the advantages of languages in one application? For example, it would seem logical to be able to call the R library from Python, and for statisticians familiar with Python, to run programs in Python directly from R. Both languages can perform these operations using third-party libraries:

  • rPython, rJython, SnakeCharmR, PythonInR, reticulate - launch the Python code from R;
  • RPy2, pyRserve, PypeR - launch Python code from R.

Such solutions allow not to switch from one system to another and create programs from ready-made solutions within one application, using modern Python modules and previously implemented specific packages from R.

 

Both R and Python are reliable languages, and one of them is actually enough for the task of data analysis. However, they have their pros and cons, and if you use the strengths of each, a data science team can do a much better project. Anyway, knowing both gives more flexibility and increases chances to work in different environments.

Summary

Both languages appeared in the ‘90s, and have already managed to build up powerful ecosystems of users. As an example, both communities have many active members on Stack Overflow (Python, R). In the beginning, R was used only in the academic environment, but as interest in Data Science grew, it came to commercial applications as well. However, in recent years, Python has acquired a lot of tools for data analysis and managed to provide the necessary competition. The ability to use Python to quickly embed data analysis into web applications makes Python even very useful.

 

The programmer you choose depends on the tasks you're going to accomplish and the time you're willing to devote to development. If you plan to create a great project in data science, we recommend a programmer who knows both languages. A person with knowledge of R concepts and libraries will be one step ahead of people working only with Python. A person that is familiar with R will be especially useful if your data science team wants to use not only ready-made algorithms in Python libraries, but also apply all the intellectual power accumulated by statisticians.

 

Still not sure what programmer do you need? Let us know, we will help you decide and provide you with data talents skilled in Python or R, matched with your product and your industry. Our company has the widest list of Python and R specialists who will perfectly fit your company/project/data science team.

Robert Krajewski

Robert is a co-founder and CEO of Ideamotive. Entrepreneur, who with passion spreads digital revolution all around the internet. Mentor and advisor at startup accelerators. Loves to learn and discover new business models.

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