Why R Users Should Tap Python To Up Their Programming Game
Python and R are the two most popular programming languages in data science. Programming language indexes such as TIOBE and IEEE Spectrum have always counted R and Python among the most favoured languages, along with MATLAB and SAS. The popularity of Python and R soared in the 90s with the growing demand for machine learning and effective handling of large datasets. R vs Python Both R and Python are open source programming languages. New libraries and tools are continuously added to their catalogue. R was introduced mainly for statistical analysis in the early 90s. This procedural language breaks a task into smaller steps and subroutines, making it  easier for a user to understand how complex operations are carried out. R is particularly beneficial when dealing with projects that involve data visualization, analysis, and graphical techniques. However, the advantage comes at the expense of performance and code readability. Python, on the other hand, is an object-oriented programming language released in 1989. It groups data and code into objects that interact with each other. This highly versatile language offers stability, modularity, and code readability, with broad applications. However, Python has a lesser number of main packages to accomplish a task compared to R. Why Should R Programmers Consider Learning Python? R is a great language to work with, especially if the project entails statistical analysis, computations, and manipulations. However, organisations are adopting Python at a much greater pace. Advantages include: Aids in working with text data, thanks to suitable text processing modules.Easier to scrape data from webpagesEffortless image processing with libraries such as OpenCV and Python Image Library (PIL) Overall, Python supports the entire machine learning spectrum in a better way “Python is one of the most accessible programming languages available because of its simplified syntax, and even R programmers can use it easily. Due to its ease of use, python codes can be executed much faster than other programming languages. Allowing programmers to analyse and organise data quickly, Python significantly aids in enabling Cloud Computing, Machine Learning, and Big Data, which are some of the hottest trends in the computer science world right now. Making it easier for organisations to transform and improve their processes and workflows,” said Shrey Kapoor, Computer Hacking Forensics Investigator & Certified Cybersecurity Expert. Comparative Analysis Both Python and R have pre-defined data types. Common attributes include: Numbers: Numeric values are stored under four types–integer, long, float, and complex in Python. The corresponding types in R language are integer, bit64, and numeric; however, complex numbers are rarely used in R.Boolean: It stores only two values–True and False. The difference between Boolean values lies in terms of how they are represented (TRUE and FALSE in R; True and False in Python)Strings in Python are similar to character type in R.Lists in both Python and R are similar and used to store multiple variable types such as integer, string, etc. In terms of libraries, R has all the functions readily available to manipulate data and for reading variables; Python derives functions for data analysis, manipulation, and visualization from external libraries. The most important data manipulation and machine learning libraries in Python are: Numpy is used for numerical computations in Python, providing access to mathematical functions such as linear algebra and statistics.Python uses Scipy for scientific computing.Python uses Matplotlib for data visualization, the corresponding library in R is called ggplot2Pandas library is used extensively for data manipulation tasks. It is similar to packages such as dplyr and data.table in RFor machine learning tasks, Scikit Learn in Python is ideal, with all the functions required for model building. A Python practitioner should grasp the concepts of objects, classes, constructors, function calls etc.Other Python concepts include: An array is a collection of same or multiple classes, similar to R’s listsLists in both Python and R are similar. The only difference lies in the way they are created. For example: For Python: one_list = [‘red’,’square’,12] For R: one_list <- list (‘red’,’square’,12) Matrix is a multi-dimensional structure with a combination of arrays, generally containing elements of the same class. It is similar in both Python and R; however, while the indexing in former starts with 0, it starts with 1 in R. In Python, numpy.column_stack function is used for matrix.Data frame is a 2-dimensional structure with several lists. It provides a skeleton to loosely collected data. In Python, Dataframe function from pandas library is used. For the same purpose, R has a built-in function called data.frame. “Use of Python by R Programmer get simpler if they brush up the basics of Data Structure of Python which includes: variables and types, lists, basic operations, conditions, loops, modules and packages, functions, among others. As all the data science packages are available in both Python and R, it is easy to transition. The data scientist would need to learn some basic data structure like creating and manipulating tables (called data frame in Python) and syntax of Python,” said Amish Hansraj Patel, Senior Data Scientist, Ketto.
Python and R are the two most popular programming languages in data science. Programming language indexes such as TIOBE and IEEE Spectrum have always counted R and Python among the most favoured languages, along with MATLAB and SAS. The popularity of Python and R soared in the 90s with the growing demand for machine learning […]
R is a great language to work with, especially if the project entails statistical analysis, computations, and manipulations.
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I am a technology journalist with AIM. I write stories focused on the AI landscape in India and around the world with a special interest in analysing its long term impact on individuals and societies. Reach out to me at shraddha.goled@analyticsindiamag.com.