R programming is one of the most popular languages when it comes to data science, statistical computations or scientific research. R programming is widely used in machine learning and it is very efficient and user-friendly. It provides flexibility in doing big statistical operations with a few lines of code.

Vectors in R are the same as the arrays in C language which are used to hold multiple data values of the same type. One major key point is that in R the indexing of the vector will start from ‘1’ and not from ‘0’. We can create numeric vectors and character vectors as well.

#### Types of vectors

Vectors are of different types which are used in R. Following are some of the types of vectors:

**Numeric vectors**

Numeric vectors are those which contain numeric values such as integer, float, etc.

`# R program to create numeric Vectors`

`# creation of vectors using c() function.`

`v1 <`

`-`

`c(`

`4`

`,`

`5`

`,`

`6`

`,`

`7`

`)`

`# display type of vector`

`typeof(v1)`

`# by using 'L' we can specify that we want integer values.`

`v2 <`

`-`

`c(`

`1L`

`,`

`4L`

`,`

`2L`

`,`

`5L`

`)`

`# display type of vector`

`typeof(v2)`

*chevron_right**filter_none***Output:**[1] "double" [1] "integer"

**Character vectors**

Character vectors contain alphanumeric values and special characters.`# R program to create Character Vectors`

`# by default numeric values`

`# are converted into characters`

`v1 <`

`-`

`c(`

`'geeks'`

`,`

`'2'`

`,`

`'hello'`

`,`

`57`

`)`

`# Displaying type of vector`

`typeof(v1)`

*chevron_right**filter_none***Output:**[1] "character"

**Logical vectors**

Logical vectors contain boolean values such as TRUE, FALSE and NA for Null values.`# R program to create Logical Vectors`

`# Creating logical vector`

`# using c() function`

`v1 <`

`-`

`c(TRUE, FALSE, TRUE, NA)`

`# Displaying type of vector`

`typeof(v1)`

*chevron_right**filter_none***Output:**[1] "logical"

#### Creating a vector

There are different ways of creating vectors. Generally, we use ‘c’ to combine different elements together.

`# R program to create Vectors ` ` ` `# we can use the c function ` `# to combine the values as a vector. ` `# By default the type will be double ` `X <` `-` `c(` `61` `, ` `4` `, ` `21` `, ` `67` `, ` `89` `, ` `2` `) ` `cat(` `'using c function'` `, X, ` `'\n'` `) ` ` ` `# seq() function for creating ` `# a sequence of continuous values. ` `# length.out defines the length of vector. ` `Y <` `-` `seq(` `1` `, ` `10` `, length.out ` `=` `5` `) ` `cat(` `'using seq() function'` `, Y, ` `'\n'` `) ` ` ` `# use':' to create a vector ` `# of continuous values. ` `Z <` `-` `2` `:` `7` `cat(` `'using colon'` `, Z) ` |

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**Output:**

using c function 61 4 21 67 89 2 using seq() function 1 3.25 5.5 7.75 10 using colon 2 3 4 5 6 7

#### Accessing vector elements

Accessing elements in a vector is the process of performing operation on an individual element of a vector. There are many ways through which we can access the elements of the vector. The most common is using the ‘[]’, symbol.

Note:Vectors in R are 1 based indexing unlike the normal C, python, etc format.

`# R program to access elements of a Vector ` ` ` `# accessing elements with an index number. ` `X <` `-` `c(` `2` `, ` `5` `, ` `18` `, ` `1` `, ` `12` `) ` `cat(` `'Using Subscript operator'` `, X[` `2` `], ` `'\n'` `) ` ` ` `# by passing a range of values ` `# inside the vector index. ` `Y <` `-` `c(` `4` `, ` `8` `, ` `2` `, ` `1` `, ` `17` `) ` `cat(` `'Using combine() function'` `, Y[c(` `4` `, ` `1` `)], ` `'\n'` `) ` ` ` `# using logical expressions ` `Z <` `-` `c(` `5` `, ` `2` `, ` `1` `, ` `4` `, ` `4` `, ` `3` `) ` `cat(` `'Using Logical indexing'` `, Z[Z>` `4` `]) ` |

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** Output**

Using Subscript operator 5 Using combine() function 1 4 Using Logical indexing 5

#### Modifying a vector

Modification of a Vector is the process of applying some operation on an individual element of a vector to change its value in the vector. There are different ways through which we can modify a vector:

`# R program to modify elements of a Vector ` ` ` `# Creating a vector ` `X <` `-` `c(` `2` `, ` `7` `, ` `9` `, ` `7` `, ` `8` `, ` `2` `) ` ` ` `# modify a specific element ` `X[` `3` `] <` `-` `1` `X[` `2` `] <` `-` `9` `cat(` `'subscript operator'` `, X, ` `'\n'` `) ` ` ` `# Modify using different logics. ` `X[X>` `5` `] <` `-` `0` `cat(` `'Logical indexing'` `, X, ` `'\n'` `) ` ` ` `# Modify by specifying ` `# the position or elements. ` `X <` `-` `X[c(` `3` `, ` `2` `, ` `1` `)] ` `cat(` `'combine() function'` `, X) ` |

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** Output**

subscript operator 2 9 1 7 8 2 Logical indexing 2 0 1 0 0 2 combine() function 1 0 2

#### Deleting a vector

Deletion of a Vector is the process of deleting all of the elements of the vector. This can be done by assigning it to a NULL value.

`# R program to delete a Vector ` ` ` `# Creating a Vector ` `M <` `-` `c(` `8` `, ` `10` `, ` `2` `, ` `5` `) ` ` ` `# set NULL to the vector ` `M <` `-` `NULL ` `cat(` `'Output vector'` `, M) ` |

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**Output:**

Output vector NULL

#### Sorting elements of a Vector

** sort()** function is used with the help of which we can sort the values in ascending or descending order.

`# R program to sort elements of a Vector ` ` ` `# Creation of Vector ` `X <` `-` `c(` `8` `, ` `2` `, ` `7` `, ` `1` `, ` `11` `, ` `2` `) ` ` ` `# Sort in ascending order ` `A <` `-` `sort(X) ` `cat(` `'ascending order'` `, A, ` `'\n'` `) ` ` ` `# sort in descending order ` `# by setting decreasing as TRUE ` `B <` `-` `sort(X, decreasing ` `=` `TRUE) ` `cat(` `'descending order'` `, B) ` |

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**Output:**

ascending order 1 2 2 7 8 11 descending order 11 8 7 2 2 1

## Recommended Posts:

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- Check if Elements of a Vector are non-empty Strings in R Programming - nzchar() Function
- Make Elements of a Vector Unique in R Programming - make.unique() Function
- Compute the gamma value of a Non-negative Numeric Vector in R Programming - gamma() Function
- Replicate elements of vector in R programming - rep() Method
- Adding elements in a vector in R programming - append() method

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