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Extract values from vector in R, we will delve into extracting specific values from a vector using the nth, first, and last functions from the dplyr package in R programming language.

The article is structured into four examples that demonstrate the extraction of vector elements.

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Extract values from vector in R

To begin, by installing and loading the dplyr package in R.

install.packages("dplyr")        
library("dplyr")

Our example vector, denoted as ‘x’, is a character vector containing NINE letters.

We then proceed to apply the functions to our example vector.

x <- letters[1:9]               # Create example vector
x
# "a" "b" "c" "d" "e" "f" "g" "h" "i"

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Example 1: nth Function

We explore the usage of the nth function, which allows us to extract a vector element from anywhere within the vector.

By applying the nth function and specifying the position of the desired element, we obtain the desired output.

nth(x, 5) # Apply nth function
# "e"     #OUTPUT

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Example 2: nth Function with Negative Value

Example 2 showcases the application of the nth function to extract an element from the end of the vector.

By placing a minus sign before the position, we can retrieve elements from the end of the vector.

nth(x, - 3)   # Apply nth function with negative
# "g"         #output

Example 3: first Function

Example 3 demonstrates the first function, which returns the first element of an input vector.

first(x)  # Apply first function
# "a"     #output

This function works similarly to the nth function but is specifically designed to target the first element.

Example 4: last Function

Lastly, Example 4 illustrates the use of the last function, which returns the last element of a vector.

last(x)  # Last function
# "i"    #return last value

This function complements the first function by focusing on the end of the vector.

Conclusion

This article provides a comprehensive guide on how to use the nth, first, and last functions from the dplyr package in R to extract specific elements from a vector.

By understanding and applying these functions, users can effectively manipulate and analyze their data in the R programming environment.

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