Looping over Objects in R Programming Last Updated : 28 Apr, 2025 Comments Improve Suggest changes Like Article Like Report One 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 provides many alternatives to be applied to vectors for looping operations that are pretty useful when working interactively on a command line. function and its variants: apply()lapply()sapply()tapply()mapply() Let us see what each of these functions does. apply(): This function applies a given function over the margins of a given array. Looping FunctionOperationapply()Applies a function over the margins of an array or matrixlapply()Apply a function over a list or a vectorsapply()Same as lapply() but with simplified resultstapply()Apply a function over a ragged arraymapply()Multivariate version of lapply()apply(): This function applies a given function over the margins of a given array. apply(array, margins, function, ...) array = list of elements margins = dimension of the array along which the function needs to be applied function = the operation which you want to perform R # R program to illustrate # apply() function # Creating a matrix A = matrix(1:9, 3, 3) print(A) # Applying apply() over row of matrix # Here margin 1 is for row r = apply(A, 1, sum) print(r) # Applying apply() over column of matrix # Here margin 2 is for column c = apply(A, 2, sum) print(c) Output: [, 1] [, 2] [, 3][1, ] 1 4 7[2, ] 2 5 8[3, ] 3 6 9[1] 12 15 18[1] 6 15 24lapply(): This function is used to apply a function over a list. It always returns a list of the same length as the input list. lapply(list, function, ...)list = Created list function = the operation which you want to perform R # R program to illustrate # lapply() function # Creating a matrix A = matrix(1:9, 3, 3) # Creating another matrix B = matrix(10:18, 3, 3) # Creating a list myList = list(A, B) # applying lapply() determinant = lapply(myList, det) print(determinant) Output: [[1]][1] 0[[2]][1] 5.329071e-15sapply(): This function is used to simplify the result of lapply(), if possible. Unlike lapply(), the result is not always a list. The output varies in the following ways:- sapply(list, function, ...) list = Created list function = the operation which you want to perform R # R program to illustrate # sapply() function # Creating a list A = list(a = 1:5, b = 6:10) # applying sapply() means = sapply(A, mean) print(means) Output: a b3 8 A vector is returned since the output had a list with elements of length 1. If output is a list containing elements having length 1, then a vector is returned.If output is a list where all the elements are vectors of same length(>1), then a matrix is returned.If output contains elements which cannot be simplified or elements of different types, a list is returned.tapply(): This function is used to apply a function over subset of vectors given by a combination of factors. tapply(vector, factor, function, ...) vector = Created vector factor = Created factor function = the operation which you want to perform R # R program to illustrate # tapply() function # Creating a factor Id = c(1, 1, 1, 1, 2, 2, 2, 3, 3) # Creating a vector val = c(1, 2, 3, 4, 5, 6, 7, 8, 9) # applying tapply() result = tapply(val, Id, sum) print(result) Output: 1 2 3 10 18 17 How does the above code work? mapply(): It's a multivariate version of lapply(). This function can be applied over several list simultaneously. mapply(function, list1, list2, ...) function = the operation which you want to performlist1, list2= Created lists. R # R program to illustrate # mapply() function # Creating a list A = list(c(1, 2, 3, 4)) # Creating another list B = list(c(2, 5, 1, 6)) # Applying mapply() result = mapply(sum, A, B) print(result) Output: [1] 24Loop Through List & Display All Sub-Elements on Same Line R my_list <- c(1, 2, 3, 4, 5) for (element in my_list) { cat(element, " ") } Output: 1 2 3 4 5 Loop Through List & Display All Sub-Elements on Different Lines R my_list <- c(1, 2, 3, 4, 5) for (element in my_list) { cat(element, "\n") } Output: 1 2 3 4 5Loop Through List and Only Display Specific Values R my_list <- c(1, 2, 3, 4, 5) for (element in my_list) { if (element %% 2 == 0) { # Display only even values cat(element, "\n") } } Output: 2 4 First we creates a list my_list with values from 1 to 5. It then iterates through each element using a for loop, and if the element is even (determined by element %% 2 == 0), it is printed on a new line using cat. The output displays only the even values (2 and 4) from the list. Comment More infoAdvertise with us Next Article S3 class in R Programming A AmiyaRanjanRout Follow Improve Article Tags : Programming Language R Language 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. 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We can think of class like a sketch of a car. It contains all the details about the model_name, model_no, engine, etc. Based 4 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 havenât 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 3 min read Abstraction in R ProgrammingPeople who've been using the R language for any period of time have likely grown to be conversant in passing features as arguments to other functions. However, people are a whole lot much less probably to go back functions from their personal custom code. This is simply too horrific because doing so 5 min read Looping over Objects in R ProgrammingOne of the biggest issues with the âforâ 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