Before we talk about rep in R, we must know what iteration is. The term iteration means repetition. As in most other programming languages, traditional looping or iteration is a core aspect of R.
While regular loops are an efficient approach to data management, they are costly for the sole reason that iteration is memory and time-consuming. A good alternative is the use of vectorized methods that can achieve the same goals as iteration; the rep() function is a member of one such vectorized looping function.
What is the rep() function?
In simple terms, rep in R, or the rep() function replicates numeric values, or text, or the values of a vector for a specific number of times. The rep() function is a member of the apply() family of functions of R base package. The apply() family contains functions used to manipulate data from arrays, matrices, data frames, and lists repetitively.
The apply() functions dodge the use of loop constructs to act on arrays, matrices, or input lists and apply a named function with optional arguments. The called function could be an aggregating function, transforming function, or vectorized functions such as arrays, vectors, lists, and matrices.
Vectorized calculations versus iterations
Instead of operating on individual elements of a sequence, vectorized methods work on all the vector components simultaneously. Thus, vectorized calculations always fetch faster results.
To illustrate the speed of vectorized calculations, we will use an example that determines the time elapsed of a for() loop for the generation of a large vector. In the example, each element is calculated sequentially as the incremental cumulative sum from 1 to N (where N = 10,000,000). A comparison is drawn between the for() loop iteration and vectorized function through speed tests.
On comparing the results of the speed tests, it is clear that the time elapsed for the vectorized calculation (speed test 2) is significantly faster than the for() loop. In the time taken for one pass of the iterative loop, the vectorized calculation can be repeated 278 times.
Repeat versus Replicate function
The Repeat function or loop in R is used when we want to execute the same block of code repeatedly until a specific condition is met. It is very similar to the for and while loops that repeatedly execute a command block until the break. The basic syntax to create a repeat loop is:
The following example will clarify the use of Repeat function:
In the above example, the repeat loop sums up the value until it reaches 6. Once the loop has reached 6, the loop breaks by printing “repeat loop ends”.
On the other hand, the replicate function or rep in R, is used for replicating values. The basic R syntax for using the rep() function is:
Here are some examples to understand the rep() function:
Example: Using the rep() function to replicate values for a specific number of times
In the above example, the value 2 replicates ten times.
Example: Using the rep() function with a length attribute
In the above example, 1 to 4 gets printed in sequence until the number of elements reaches 20.
Example: Using the rep() function to replicate a list
In the above example, the rating list of 1 to 5 has replicated thrice.
Using rep() function to expand a vector
The rep() function is a flexible way of repeating a vector. Here are some more examples:
In case we need to expand a statistical vector of experimental/observational units into a vector of a data frame with repeated observations of the units, each argument comes in very handy. Example:
Another feature of rep() is that a vector can expand to an unbalanced panel by replacing the length argument with a vector that specifies the number of times each element in the vector will repeat. Example:
Simpler and faster versions of the rep function include rep_len() and rep.int(). These newer versions come without some of the attributes of rep() but prove useful in cases where speed is primal and extra aspects of the repeated vector are undesired.
In this article, we discussed the Repeat and Replicate functions with suitable examples. While traditional iterations are useful for repeated execution of blocks of code, the rep in R is ideal for replicating the values of a vector or list. Efficient and time-saving, the rep() function has simplified vector replication!
If you are curious to learn about R, data science, check out IIIT-B & upGrad’s PG Diploma in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.