Repeat Rows of DataFrame N Times in R

In this article, we will discuss how to repeat rows of Dataframe for a given number of times using R programming language. 

Method 1 : Using replicate() method

A replication factor is declared to define the number of times the data frame rows are to be repeated. The do.call() method in R is used to perform an R function while taking as input various arguments. The rbind() method is taken as the first argument of this method to combine data frames together. The second argument is the replicate() method which is used to create multiple copies of the rows of the data frames equivalent to the number of times same as replication factor. 

Syntax: replicate(n, expr, simplify) 

Parameter : 

• n - Number of replications to be performed.
• expr - Expression to be performed repeatedly.
• simplify - Type of output the results of expr are saved into.

Example: Repeating rows n times

# creating a data frame
data_frame <- data.frame(col1 = c(6:8),
                        col2 = letters[1:3],
                        col3 = c(1,4,NA))

print ("Original DataFrame")

print (data_frame)

# replication factor
n <- 3

data_frame_mod <- do.call("rbind", replicate(
  n, data_frame, simplify = FALSE))

print ("Modified DataFrame")

print(data_frame_mod)

Output

[1] "Original DataFrame"
col1 col2 col3
1    6    a    1
2    7    b    4
3    8    c   NA
[1] "Modified DataFrame"
col1 col2 col3
1    6    a    1
2    7    b    4
3    8    c   NA
4    6    a    1
5    7    b    4
6    8    c   NA
7    6    a    1
8    7    b    4
9    8    c   NA

Method 2: Using purrr package

The purr package in R is used to simplify the functioning and working with both functions as well as vectors. The package can be installed into the working space using the following command : 

install.packages("purrr")

The seq_len() method in R is used to create a sequence taking as input an integer value and then generating a sequence of numbers beginning from 1 to the specified integer with steps equivalent to 1. 

Syntax:

vec <- seq_len(number)

The map_dfr() function of the purrr in R is used to create a data frame formed by row appending. It is used for row binding. 

Syntax:

map_dfr(vec, ~data-frame)

Example: Repeating rows n times

library("purrr")

# creating a data frame
data_frame <- data.frame(col1 = c(6:8),
                        col2 = letters[1:3],
                        col3 = c(1,4,NA))
 
print ("Original DataFrame")
print (data_frame)

# replication factor
n <- 2
data_frame_mod <- purrr::map_dfr(seq_len(n), ~data_frame)

print ("Modified DataFrame")
print(data_frame_mod)

Output

[1] "Original DataFrame"
col1 col2 col3
1    6    a    1
2    7    b    4
3    8    c   NA
[1] "Modified DataFrame"
col1 col2 col3
1    6    a    1
2    7    b    4
3    8    c   NA
4    6    a    1
5    7    b    4
6    8    c   NA

Method 3: Using rep() method

The nrow() method in R is used to determine the number of rows of the data frame. 

Syntax:

nrow(data-frame)

The vector sequence is then generated using the seq_len() method described in the previous method. This is followed by the application of rep() method which is used to replicate numeric values a specified number of times. The first argument is the vector generated by seq_len() method and n is the replication factor. 

Syntax:

rep(vec, n)

Data frame indexing is used to append the generated rows to the original data frame rows and stored as the modified data frame. 

Example: Repeating rows n times

# creating a data frame
data_frame <- data.frame(col1 = c(6:8),
                        col2 = letters[1:3],
                        col3 = c(1,4,NA))

print ("Original DataFrame")
print (data_frame)

# replication factor
n <- 3
data_frame_mod <- data_frame[rep(seq_len(nrow(data_frame)), n), ]

print ("Modified DataFrame")
print(data_frame_mod)

Output

[1] "Original DataFrame"
col1 col2 col3
1    6    a    1
2    7    b    4
3    8    c   NA
[1] "Modified DataFrame"
col1 col2 col3
1      6    a    1
2      7    b    4
3      8    c   NA
1.1    6    a    1
2.1    7    b    4
3.1    8    c   NA
1.2    6    a    1
2.2    7    b    4
3.2    8    c   NA