The R Vectors are a very basic data structure in R Programming and are used extensively in data manipulation, analysis, and visualization. It is a one-dimensional array and is an essential building block for many other data structures and operations in R. For example the Data Frame and Matrix are created from the Vector.

R is a free programming language mainly used for statistical computing and graphics. R is an interpreter similar to Python where you don’t have to compile first in order to run your program. Once you create your program you can just run it on a wide variety of UNIX platforms, Windows, and MacOS.

R was initially started by statisticians to make statistical processing easier but later other programmers were involved and evolved it to a wide variety of non-statistical tasks, including data processing, graphic visualization, and analytical processing.

## 1. What is R Vectors?

The Vectors in R is the simplest basic type of object that is used to store a sequence of data elements of the same type. A Vector is similar to a sequence object in Python Pandas. Members of a Vector are called Components.

Below are some key points to note and remember about Vectors.

• Vector is a basic data structure in R.
• It is a one-dimensional data structure.
• It holds elements of the same type.
• Members in the vector are called components.
• It is not recursive.
• We can easily convert vectors into data frames or matrices.
• In DataFrame, each column is considered a vector.

In the next sections, I will explain the below points in detail with working examples.

• Homogeneous Data Type: All elements within a vector must be of the same data type. This means that you cannot have a mix of integers, characters, and other data types in the same vector.
• Atomic Data Types: Vectors can store elements of atomic data types, such as numeric (e.g., 1.23), integer (e.g., 42), character (e.g., “hello”), logical (e.g., TRUE), and complex (e.g., 2 + 3i).
• Creation of Vectors: Vectors can be created in various ways, including using functions like c(), seq(), and rep(). For example, c(1, 2, 3) creates a numeric vector with elements 1, 2, and 3.
• Accessing Elements: You can access individual elements of a vector using indexing. R uses 1-based indexing, so the first element is accessed with [1], the second with [2], and so on.
• Vector Operations: You can perform various operations on vectors, such as arithmetic operations, comparison operations, and more. These operations are applied element-wise.

## 2. Creating Vector

Let’s create a simple integer vector using c(). Note that the c() function stands for concatenate. but it is one of the most used ways to create a vector in R.

# Creating Integer vector
v_int = c(1,3,6,7)
print(v_int)

# Output:
# [1] 1 3 6 7

And, you can also use this to create a character vector. Alternatively, you can also use the character() function.

# Creating Character vector
v_char = c('One','Two','Three')
print(v_char)

# Output:
# [1] "One"   "Two"   "Three"

Use the length() function to get the length of the vector. Pass the vector as the argument to this function to get the length.

# Length of the vector
length(v_char)

# Output
#[1] 3

You can get the type of the vector using typeof() function. This takes the object as an argument and returns its type.

# Type of the vector
typeof(v_char)

# Output:
[1] "character"

If you have a character vector, you can easily convert the character vector to double by using as.vector() function with a mode value "numeric".

# Syntax of as.vector()
as.vector(x, mode = "any")

Here is an example.

# Convert character vector to double
x <- c("1","2")
print(typeof(x))
x  <- as.vector(x,"numeric")
print(typeof(x))

# Output:
# [1] "character"
# [1] "double"

Use is.vector() to check the type of the vector a specific type.

# Syntax of is.vector()
is.vector(x, mode = "any")

Example

# Check type of vector
x <- c("1","2")
is.vector(x,'character')
is.vector(x,'numeric')

# Output:
# [1] TRUE
# [1] FALSE

## 3. Empty Vector

If the length of the vector is zero then it is considered an empty vector in R. It can be created in several ways in R programming. One way to create an empty vector is by using c() function without any arguments.

# Create empty vector
v_empty = c()
print(v_empty)

# Output:
# NULL

Now let’s get the length of the vector and check if it is empty.

# Length of the vector
length(v_empty)

# Output:
# [1] 0

## 4. Convert Vectors into DataFrame

By using data.frame() function we can create a DataFrame from Vectors. A data frame is a list of variables of the same number of rows with unique row names. so all vectors you used to convert to DataFrame should have the same length, not having the same length will result in an error.

# Create Vectors
id <- c(10,11,12,13)
name <- c('sai','ram','deepika','sahithi')
dob <- as.Date(c('1990-10-02','1981-3-24','1987-6-14','1985-8-16'))

# Create DataFrame
df <- data.frame(id,name,dob)

Yields below output. In the above example, I have used the vectors id, name and dob as arguments to the data.frame() function, separated by commas. The above example yields the below output. R will create a data frame with the column names/variables with the same names we used for Vector. You can use df or print(df) to print the DataFrame to the console.

# Output:
id    name        dob
1 10     sai 1990-10-02
2 11     ram 1981-03-24
3 12 deepika 1987-06-14
4 13 sahithi 1985-08-16

## 6. Repeating Vectors

By using rep() function we can repeat the members in a vector with the specified number of times, and create a new vector with the result.

# Repeat vectors
v_rep = rep(c(1, 5), times = 3)
print(v_rep)

# Output:
#[1] 1 5 1 5 1 5

Let’s pass a vector to times and see what happens. This is used to specify how often the values are to be repeated. You can also use each argument to specify how many times to repeat each value.

# Repeat vectors
v_rep = rep(c(1, 5), times = c(2,3))
print(v_rep)

# Output:
# [1] 1 1 5 5 5

## 7. Types of Vector in R

You can create a vector with all basic types.

• Integer Vectors – Stores only integer values.
• Numeric Vectors – For all numeric values.
• Character Vectors – Stores alphanumeric characters.
• Logical Vectors – Stores TRUE/FALSE values.
• Datetime Vectors – These are used to store date-time values
• Factor Vectors – To work with categories.

## 8. Combine Two or Multiple Vectors

As explained above c() is actually used to combine members hence, let’s use this to combine two or more vectors into a single vector. By using c() or append() you can also add element to vector in R.

# Create character vectors
v_char = c('One','Two','Three')
v_char2 = c('Four','Five','Six')

# Combine vectors
v_combine = c(v_char,v_char2)
print(v_combine)

# Output
#[1] "One"   "Two"   "Three" "Four"  "Five"  "Six"

## 9. Access Vector

By using R bracket notation you can access the Vector members by index. Note that, unlike other programming languages, the R index starts from 1. To get the range of values use startindex:endindex, this returns all values between the start and end index. Let’s access it by giving an index inside a single square bracket [].

# Access vector elements
v_char[1]   # Returns [1] "One"
v_char[1:3] # Returns [1] "One"   "Two"   "Three"

When you use a negative index, it just removes the specified index vector and returns the result. For example, the following creates a vector slice with the second member removed.

# Access vector elements
v_char2 = c('Four','Five','Six')
v_char2[-2]

# Output:
#[1] "Four" "Six"

If an index is used that is not present, NA is returned as a result.

# Access vector elements
v_char2[5]

# Output:
#[1] NA

## 10. Change Values in Vector

By using an index let’s see how to modify or change the value of an index. The following example changes the value of the first member of the vector to the character string Oneeeeeeee.

# Change vector elements
v_char[1]  <- "Oneeeeeeee"
print(v_char)

# Output
[1] "Oneeeeeeee" "Two"        "Three"

## 11. R Vectors vs list

Below are some differences between vector vs list.

• Vector can hold only elements of the same type. whereas the List can hold elements of different types like numeric, character, logical e.t.c
• Vector is a single dimension, whereas the List is a multi-dimensional object.
• Vector is not recursive, whereas the List is recursive.

# Create list
list_obj = list('One',2,TRUE)
print(list_obj)

# Output[[1]]
#[[1]]
#[1] "One"

#[[2]]
#[1] 2

#[[3]]
#[1] TRUE

## 12. Handling NA Values in R Vector

NA is considered a ‘Missing Values’ or ‘Not Available’ in R and to remove NA values from the vector use na.rm() or is.na() functions. Additionally, R also supports several other methods to delete NA from the vector.

When you run an arithmetic operation on the R vector that has NA values, you will get results as NA. In general, any operation on an NA becomes a NA. hence, It’s best practice to clean up the vector by removing all NA values before performing an arithmetic operation.

# NA Examples

# Create Vector
ids <- c(10,5,NA,15,10)

# Using is.na()
x <- ids[!is.na(ids)]

# Using na.rm=TRUE
x <- max(ids, na.rm=TRUE)

# Using na.omit()
x <- na.omit(ids)

# Remove NA from string Vector.
states = c('NY','DC',NA,'CA','NV',NA)
x <- na.omit(states)

# Using exclude
x <- na.exclude(states)
print(x)

## 13. Sorting Vectors

To sort a vector alphabetically using the sort() function that takes a vector as an argument and returns an alphabetically ordered value for the character vector and ascending order for numeric.

# Sort Character Vector
v <- c('D','R','G','A','X')
sort(v)
sort(v, decreasing=TRUE)

# Output
#> sort(v)
#[1] "A" "D" "G" "R" "X"
#>sort(v, decreasing=TRUE)
#[1] "X" "R" "G" "D" "A"

## 14. Subsetting Vectors

To subset elements from the vector use bracket notation [], by using this notation we can subset a single element from a vector, multiple elements, and subset by range, select elements from a list e.t.c.

# Create a vector
v <- c('A','B','C','D','E','F')

# Subset by Index Position
v[1]

# Output
#> v[1]
#[1] "A"

# Subset elements from list
v[c(1,3)]
v[c(2.3,4.5)]

# Output
#> v[c(1,3)]
#[1] "A" "C"
#> v[c(2.3,4.5)]
#[1] "B" "D"

## 15. Removing Duplicates

Handling duplicate values is one of the challenging tasks when analyzing data. Removing duplicates comes under data cleaning which is a challenging task in data analytics. Data cleaning needs to be done before performing any operations on data as having duplicate values results in inconsistent results. In the R vector, By using duplicated() function we can remove duplicates from the vector

Remove duplicates
v <- c('A','B','D','C','A','F','G','C','d','E','E')
v[!duplicated(v)]

# Output
#[1] "A" "B" "D" "C" "F" "G" "d" "E"

## 16. Remove Elements

To remove multiple values from a vector, define a vector or list with the values you wanted to remove and use it with bracket notation [] and %in% operator. The %in% operator is used to check what values to remove.

# Remove specific value from vector
x <- c('A','B','C','D','E','F','B')
x[ !x == 'A']

# Output
#[1] "B" "C" "D" "E" "F" "B"

# Remove Multiple Values
x <- c('A','B','C','D','E','F','B')
x[! x %in% c('A', 'D', 'E')]

# Output
#[1] "B" "C" "F" "B"

## 17. Conclusion

In this article, you have learned what is the vector in R? The vector is a basic one-dimensional data structure, It holds elements of the same type. Members in the vector are called components. It is not recursive and we can easily convert vectors into data frames or matrices.

Also, learned the differences between lists. Vector can hold only elements of the same type. whereas the List can hold elements of different types like numeric, character, logical e.t.c Vector is a single dimension, whereas the List is a multi-dimensional object. Vector is not recursive, whereas the List is recursive.

You can find several examples of R vectors at Github R Programming Examples Project.