R vs Python Last Updated : 09 Sep, 2024 Comments Improve Suggest changes Like Article Like Report R Programming Language and Python are both used extensively for Data Science. Both are very useful and open-source languages as well. For data analysis, statistical computing, and machine learning Both languages are strong tools with sizable communities and huge libraries for data science jobs. A theoretical comparison between R and Python is provided below:R vs PythonIn this article, we will cover the following topics:R Programming LanguagePython Programming LanguageDifference between R Programming and Python ProgrammingEcosystem in R Programming and Python ProgrammingAdvantages and disadvantages in R Programming and Python ProgrammingR and Python usages in Data ScienceExample in R and PythonR Programming LanguageR Programming Language is used for machine learning algorithms, linear regression, time series, statistical inference, etc. It was designed by Ross Ihaka and Robert Gentleman in 1993. R is an open-source programming language that is widely used as a statistical software and data analysis tool. R generally comes with the Command-line interface. R is available across widely used platforms like Windows, Linux, and macOS. Also, the R programming language is the latest cutting-edge tool.Python Programming LanguagePython is a widely-used general-purpose, high-level programming language. It was created by Guido van Rossum in 1991 and further developed by the Python Software Foundation. It was designed with an emphasis on code readability, and its syntax allows programmers to express their concepts in fewer lines of code.Difference between R Programming and Python ProgrammingBelow are some major differences between R and Python:FeatureRPythonIntroductionR is a language and environment for statistical programming which includes statistical computing and graphics.Python is a general-purpose programming language for data analysis and scientific computingObjectiveIt has many features which are useful for statistical analysis and representation.It can be used to develop GUI applications and web applications as well as with embedded systemsWorkabilityIt has many easy-to-use packages for performing tasksIt can easily perform matrix computation as well as optimizationIntegrated development environmentVarious popular R IDEs are Rstudio, RKward, R commander, etc.Various popular Python IDEs are Spyder, Eclipse+Pydev, Atom, etc.Libraries and packagesThere are many packages and libraries like ggplot2, caret, etc.Some essential packages and libraries are Pandas, Numpy, Scipy, etc.ScopeIt is mainly used for complex data analysis in data science.It takes a more streamlined approach for data science projects.Ecosystem in R Programming and Python ProgrammingPython supports a very large community of general-purpose data science. One of the most basic uses for data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas and NumPy are one of those packages that make importing and analyzing, and visualization of data much easier.R Programming has a rich ecosystem to use in standard machine learning and data mining techniques. It works in statistical analysis of large datasets, and it offers a number of different options for exploring data and It makes it easier to use probability distributions, apply different statistical tests. R vs PythonFeaturesR PythonData collectionIt is used for data analysts to import data from Excel, CSV, and text files.It is used in all kinds of data formats including SQL tablesData explorationIt optimized for the statistical analysis of large datasetsYou can explore data with PandasData modelingIt supports Tidyverse, making it easy to import, manipulate, visualize, and report on data.You can use NumPy, SciPy, scikit-learn, TansorFlowData visualizationYou can use ggplot2 and ggplot tools to plots complex scatter plots with regression lines.You can use Matplotlib, Pandas, SeabornStatistical Analysis and Machine Learning In R and PythonStatistical analysis and machine learning are critical components of data science, involving the application of statistical methods, models, and techniques to extract insights, identify patterns, and draw meaningful conclusions from data. Both R and Python have widely used programming languages for statistical analysis, each offering a variety of libraries and packages to perform diverse statistical and machine learning tasks. Some comparison of statistical analysis and modeling capabilities in R and Python.CapabilityRPythonBasic StatisticsBuilt-in functions (mean, median, etc.)NumPy (mean, median, etc.)Linear Regressionlm() function and FormulasStatsmodels (OLS)Ordinary Least Squares (OLS) MethodGeneralized Linear Models (GLM)glm() functionStatsmodels (GLM)Time Series AnalysisTime Series packages (forecast)Statsmodels (Time Series)ANOVA and t-testsBuilt-in functions (aov, t.test)SciPy (ANOVA, t-tests)Hypothesis TestsBuilt-in functions (wilcox.test, etc.)SciPy (Mann-Whitney, Kruskal-Wallis)Principal Component Analysis (PCA)princomp() functionscikit-learn (PCA)Clustering (K-Means, Hierarchical)kmeans(), hclust()scikit-learn (KMeans, AgglomerativeClustering)Decision Treesrpart() functionscikit-learn (DecisionTreeClassifier)Random ForestrandomForest() functionscikit-learn (RandomForestClassifier)Advantages in R Programming and Python ProgrammingR ProgrammingPython ProgrammingIt supports a large dataset for statistical analysisGeneral-purpose programming to use data analyzePrimary users are Scholar and R&DPrimary users are Programmers and developersSupport packages like tidyverse, ggplot2, caret, zooSupport packages like pandas, scipy, scikit-learn, TensorFlow, caretSupport RStudio and It has a wide range of statistics and general data analysis and visualization capabilities.Support Conda environment with Spyder, Ipython NotebookDisadvantages in R Programming and Python ProgrammingR ProgrammingPython ProgrammingR is much more difficult as compared to Python because it mainly uses for statistics purposes.Python does not have too many libraries for data science as compared to R.R might not be as fast as languages like Python, especially for computationally intensive tasks and large-scale data processing.Python might not be as specialized for statistics and data analysis as R. Some statistical functions and visualization capabilities might be more streamlined in R.Memory management in R might not be as efficient as in some other languages, which can lead to performance issues and memory-related errorsPython visualization capabilities might not be as polished and streamlined as those offered by R's ggplot2.R and Python usages in Data SciencePython and R programming language is most useful in data science and it deals with identifying, representing, and extracting meaningful information from data sources to be used to perform some business logic with these languages. It has a popular package for Data collection, Data exploration, Data modeling, Data visualization, and statical analysis.Example in R and PythonProgram for the addition of two numbers Python # Python program to add two numbers numb1 = 8 numb2 = 4 # Adding two numbers sum = numb1 + numb2 # Printing the result print("The sum is", sum) R # R program to add two numbers numb1 <- 8 numb2 <- 4 # Adding two numbers sum <- numb1 + numb2 print(paste("The sum is", sum)) OutputThe sum is 12 Comment More infoAdvertise with us Next Article Environments in R Programming A achintkaur18 Follow Improve Article Tags : Python R Language Practice Tags : python Similar Reads R Tutorial | Learn R Programming Language R is an interpreted programming language widely used for statistical computing, data analysis and visualization. R language is open-source with large community support. R provides structured approach to data manipulation, along with decent libraries and packages like Dplyr, Ggplot2, shiny, Janitor a 4 min read IntroductionR Programming Language - IntroductionR is a programming language and software environment that has become the first choice for statistical computing and data analysis. Developed in the early 1990s by Ross Ihaka and Robert Gentleman, R was built to simplify complex data manipulation and create clear, customizable visualizations. Over ti 4 min read Interesting Facts about R Programming LanguageR is an open-source programming language that is widely used as a statistical software and data analysis tool. R generally comes with the Command-line interface. R is available across widely used platforms like Windows, Linux, and macOS. Also, the R programming language is the latest cutting-edge to 4 min read R vs PythonR Programming Language and Python are both used extensively for Data Science. Both are very useful and open-source languages as well. For data analysis, statistical computing, and machine learning Both languages are strong tools with sizable communities and huge libraries for data science jobs. A th 5 min read Environments in R ProgrammingThe environment is a virtual space that is triggered when an interpreter of a programming language is launched. Simply, the environment is a collection of all the objects, variables, and functions. Or, Environment can be assumed as a top-level object that contains the set of names/variables associat 3 min read Introduction to R StudioR Studio is an integrated development environment(IDE) for R. IDE is a GUI, where you can write your quotes, see the results and also see the variables that are generated during the course of programming. R Studio is available as both Open source and Commercial software.R Studio is also available a 4 min read How to Install R and R Studio?Navigating the R language installation process and setting up R Studio is crucial for anyone looking to delve into data analysis, statistical computing, and graphical representation with R. In this article, we provide a step-by-step guide on how to install R and configure R Studio on your system. Wh 6 min read Creation and Execution of R File in R StudioR Studio is an integrated development environment(IDE) for R. IDE is a GUI, where you can write your quotes, see the results and also see the variables that are generated during the course of programming. R is available as an Open Source software for Client as well as Server Versions. Creating an R 5 min read Clear the Console and the Environment in R StudioR Studio is an integrated development environment(IDE) for R. IDE is a GUI, where you can write your quotes, see the results and also see the variables that are generated during the course of programming. Clearing the Console We Clear console in R and RStudio, In some cases when you run the codes us 2 min read Hello World in R ProgrammingWhen we start to learn any programming languages we do follow a tradition to begin HelloWorld as our first basic program. Here we are going to learn that tradition. An interesting thing about R programming is that we can get our things done with very little code. Before we start to learn to code, le 2 min read Fundamentals of RBasic Syntax in R ProgrammingR is the most popular language used for Statistical Computing and Data Analysis with the support of over 10, 000+ free packages in CRAN repository. Like any other programming language, R has a specific syntax which is important to understand if you want to make use of its features. This article assu 3 min read Comments in RIn R Programming Language, Comments are general English statements that are typically written in a program to describe what it does or what a piece of code is designed to perform. More precisely, information that should interest the coder and has nothing to do with the logic of the code. They are co 3 min read R-OperatorsOperators are the symbols directing the compiler to perform various kinds of operations between the operands. Operators simulate the various mathematical, logical, and decision operations performed on a set of Complex Numbers, Integers, and Numericals as input operands. R supports majorly four kinds 5 min read R-KeywordsR keywords are reserved words that have special meaning in the language. They help control program flow, define functions, and represent special values. We can check for which words are keywords by using the help(reserved) or ?reserved function.Rhelp(reserved) # or "?reserved"Output:Reserved Key Wor 2 min read R-Data TypesData types in R define the kind of values that variables can hold. Choosing the right data type helps optimize memory usage and computation. Unlike some languages, R does not require explicit data type declarations while variables can change their type dynamically during execution.R Programming lang 5 min read VariablesR Variables - Creating, Naming and Using Variables in RA variable is a memory location reserved for storing data, and the name assigned to it is used to access and manipulate the stored data. The variable name is an identifier for the allocated memory block, which can hold values of various data types during the program’s execution.In R, variables are d 5 min read Scope of Variable in RIn R, variables are the containers for storing data values. They are reference, or pointers, to an object in memory which means that whenever a variable is assigned to an instance, it gets mapped to that instance. A variable in R can store a vector, a group of vectors or a combination of many R obje 5 min read Dynamic Scoping in R ProgrammingR is an open-source programming language that is widely used as a statistical software and data analysis tool. R generally comes with the Command-line interface. R is available across widely used platforms like Windows, Linux, and macOS. Also, the R programming language is the latest cutting-edge to 5 min read Lexical Scoping in R ProgrammingLexical scoping means R decides where to look for a variable based on where the function was written (defined), not where it is called.When a function runs and it sees a variable, R checks:Inside the function, is the variable there?If not, it looks in the environment where the function was created.T 4 min read Input/OutputTaking Input from User in R ProgrammingDevelopers often have a need to interact with users, either to get data or to provide some sort of result. Most programs today use a dialog box as a way of asking the user to provide some type of input. Like other programming languages in R it's also possible to take input from the user. For doing s 7 min read Printing Output of an R ProgramIn R there are various methods to print the output. Most common method to print output in R program, there is a function called print() is used. Also if the program of R is written over the console line by line then the output is printed normally, no need to use any function for print that output. T 4 min read Print the Argument to the Screen in R Programming - print() Functionprint() function in R Language is used to print out the argument to the screen. Syntax: print(x, digits, na.print) Parameters: x: specified argument to be displayed digits: defines minimal number of significant digits na.print: indicates NA values output format Example 1: Python3 # R program to illu 2 min read Control FlowControl Statements in R ProgrammingControl statements are expressions used to control the execution and flow of the program based on the conditions provided in the statements. These structures are used to make a decision after assessing the variable. In this article, we'll discuss all the control statements with the examples. In R pr 4 min read Decision Making in R Programming - if, if-else, if-else-if ladder, nested if-else, and switchDecision making in programming allows us to control the flow of execution based on specific conditions. In R, various decision-making structures help us execute statements conditionally. These include:if statementif-else statementif-else-if laddernested if-else statementswitch statement1. if Stateme 3 min read Switch case in RSwitch case statements are a substitute for long if statements that compare a variable to several integral values. Switch case in R is a multiway branch statement. It allows a variable to be tested for equality against a list of values. Switch statement follows the approach of mapping and searching 2 min read For loop in RFor loop in R Programming Language is useful to iterate over the elements of a list, data frame, vector, matrix, or any other object. It means the for loop can be used to execute a group of statements repeatedly depending upon the number of elements in the object. It is an entry-controlled loop, in 5 min read R - while loopWhile loop in R programming language is used when the exact number of iterations of a loop is not known beforehand. It executes the same code again and again until a stop condition is met. While loop checks for the condition to be true or false n+1 times rather than n times. This is because the whil 5 min read R - Repeat loopRepeat loop in R is used to iterate over a block of code multiple number of times. And also it executes the same code again and again until a break statement is found. Repeat loop, unlike other loops, doesn't use a condition to exit the loop instead it looks for a break statement that executes if a 2 min read goto statement in R ProgrammingGoto statement in a general programming sense is a command that takes the code to the specified line or block of code provided to it. This is helpful when the need is to jump from one programming section to the other without the use of functions and without creating an abnormal shift. Unfortunately, 2 min read Break and Next statements in RIn R Programming Language, we require a control structure to run a block of code multiple times. Loops come in the class of the most fundamental and strong programming concepts. A loop is a control statement that allows multiple executions of a statement or a set of statements. The word ‘looping’ me 3 min read FunctionsFunctions in R ProgrammingA function accepts input arguments and produces the output by executing valid R commands that are inside the function. Functions are useful when we want to perform a certain task multiple times.In R Programming Language when we are creating a function the function name and the file in which we are c 5 min read Function Arguments in R ProgrammingArguments are the parameters provided to a function to perform operations in a programming language. In R programming, we can use as many arguments as we want and are separated by a comma. There is no limit on the number of arguments in a function in R. In this article, we'll discuss different ways 4 min read Types of Functions in R ProgrammingA function is a set of statements orchestrated together to perform a specific operation. A function is an object so the interpreter is able to pass control to the function, along with arguments that may be necessary for the function to accomplish the actions. The function in turn performs the task a 6 min read Recursive Functions in R ProgrammingRecursion, in the simplest terms, is a type of looping technique. It exploits the basic working of functions in R. Recursive Function in R: Recursion is when the function calls itself. This forms a loop, where every time the function is called, it calls itself again and again and this technique is 4 min read Conversion Functions in R ProgrammingSometimes to analyze data using R, we need to convert data into another data type. As we know R has the following data types Numeric, Integer, Logical, Character, etc. similarly R has various conversion functions that are used to convert the data type. In R, Conversion Function are of two types: Con 4 min read Data StructuresData Structures in R ProgrammingA data structure is a particular way of organizing data in a computer so that it can be used effectively. The idea is to reduce the space and time complexities of different tasks. Data structures in R programming are tools for holding multiple values. R’s base data structures are often organized by 4 min read R StringsStrings are a bunch of character variables. It is a one-dimensional array of characters. One or more characters enclosed in a pair of matching single or double quotes can be considered a string in R. It represents textual content and can contain numbers, spaces, and special characters. An empty stri 6 min read R-VectorsR Vectors are the same as the arrays in R language which are used to hold multiple data values of the same type. One major key point is that in R Programming Language the indexing of the vector will start from '1' and not from '0'. We can create numeric vectors and character vectors as well. R - Vec 4 min read R-ListsA list in R programming is a generic object consisting of an ordered collection of objects. Lists are one-dimensional, heterogeneous data structures. The list can be a list of vectors, a list of matrices, a list of characters, a list of functions, and so on. A list in R is created with the use of th 6 min read R - ArrayArrays are important data storage structures defined by a fixed number of dimensions. Arrays are used for the allocation of space at contiguous memory locations.In R Programming Language Uni-dimensional arrays are called vectors with the length being their only dimension. Two-dimensional arrays are 7 min read R-MatricesR-matrix is a two-dimensional arrangement of data in rows and columns. In a matrix, rows are the ones that run horizontally and columns are the ones that run vertically. In R programming, matrices are two-dimensional, homogeneous data structures. These are some examples of matrices:R - MatricesCreat 10 min read R-FactorsFactors in R Programming Language are used to represent categorical data, such as "male" or "female" for gender. While they might seem similar to character vectors, factors are actually stored as integers with corresponding labels. Factors are useful when dealing with data that has a fixed set of po 4 min read R-Data FramesR Programming Language is an open-source programming language that is widely used as a statistical software and data analysis tool. Data Frames in R Language are generic data objects of R that are used to store tabular data. Data frames can also be interpreted as matrices where each column of a matr 6 min read Object Oriented ProgrammingR-Object Oriented ProgrammingIn R, Object-Oriented Programming (OOP) uses classes and objects to manage program complexity. R is a functional language that applies OOP concepts. Class is like a car's blueprint, detailing its model, engine and other features. Based on this blueprint, we select a car, which is the object. Each ca 7 min read Classes in R ProgrammingClasses and Objects are core concepts in Object-Oriented Programming (OOP), modeled after real-world entities. In R, everything is treated as an object. An object is a data structure with defined attributes and methods. A class is a blueprint that defines a set of properties and methods shared by al 3 min read R-ObjectsIn R programming, objects are the fundamental data structures used to store and manipulate data. Objects in R can hold different types of data, such as numbers, characters, lists, or even more complex structures like data frames and matrices.An object in R is important an instance of a class and can 3 min read Encapsulation in R ProgrammingEncapsulation is the practice of bundling data (attributes) and the methods that manipulate the data into a single unit (class). It also hides the internal state of an object from external interference and unauthorized access. Only specific methods are allowed to interact with the object's state, en 3 min read Polymorphism in R ProgrammingR language implements parametric polymorphism, which means that methods in R refer to functions, not classes. Parametric polymorphism primarily lets us define a generic method or function for types of objects we haven’t yet defined and may never do. This means that one can use the same name for seve 6 min read R - InheritanceInheritance is one of the concept in object oriented programming by which new classes can derived from existing or base classes helping in re-usability of code. Derived classes can be the same as a base class or can have extended features which creates a hierarchical structure of classes in the prog 7 min read Abstraction in R ProgrammingAbstraction refers to the process of simplifying complex systems by concealing their internal workings and only exposing the relevant details to the user. It helps in reducing complexity and allows the programmer to work with high-level concepts without worrying about the implementation.In R, abstra 3 min read Looping over Objects in R ProgrammingOne of the biggest issues with the “for†loop is its memory consumption and its slowness in executing a repetitive task. When it comes to dealing with a large data set and iterating over it, a for loop is not advised. In this article we will discuss How to loop over a list in R Programming Language 5 min read S3 class in R ProgrammingAll things in the R language are considered objects. Objects have attributes and the most common attribute related to an object is class. The command class is used to define a class of an object or learn about the classes of an object. Class is a vector and this property allows two things:  Objects 8 min read Explicit Coercion in R ProgrammingCoercing of an object from one type of class to another is known as explicit coercion. It is achieved through some functions which are similar to the base functions. But they differ from base functions as they are not generic and hence do not call S3 class methods for conversion. Difference between 3 min read Error HandlingHandling Errors in R ProgrammingError Handling is a process in which we deal with unwanted or anomalous errors which may cause abnormal termination of the program during its execution. In R Programming, there are basically two ways in which we can implement an error handling mechanism. Either we can directly call the functions lik 3 min read Condition Handling in R ProgrammingDecision handling or Condition handling is an important point in any programming language. Most of the use cases result in either positive or negative results. Sometimes there is the possibility of condition checking of more than one possibility and it lies with n number of possibilities. In this ar 5 min read Debugging in R ProgrammingDebugging is a process of cleaning a program code from bugs to run it successfully. While writing codes, some mistakes or problems automatically appears after the compilation of code and are harder to diagnose. So, fixing it takes a lot of time and after multiple levels of calls. Debugging in R is t 3 min read Like