Vectors in R Programming and types of vectors

What are Vectors?

• Fundamental Data Structure: Vectors in R are the most basic and fundamental data structure. They are one-dimensional arrays that store elements of a single data type.

• Homogeneous: All the elements within a vector must be of the same type (e.g., all numbers, all characters, etc.).

Types of Vectors

R supports six atomic vector types:

1. Numeric: Stores numbers (integers or doubles, i.e., decimals).

• Example: my_numeric <- c(1, 5, 2.7, 10)

2. Integer: Stores whole numbers only.

• Example: my_integer <- c(2L, 6L, 15L) (the 'L' signifies integer)

3. Logical: Stores Boolean values (TRUE or FALSE).

• Example: my_logical <- c(TRUE, FALSE, TRUE)

4. Character: Stores text or strings.

• Example: my_character <- c("hello", "world", "data")

5. Complex: Stores complex numbers (numbers with real and imaginary parts).

• Example: my_complex <- c(2+5i, 3-1i)

6. Raw: Stores raw bytes of data (this type is less commonly used directly).

Creating Vectors

The primary way to create vectors in R is using the c() function (short for "combine"):

Code snippet

# Create a numeric vector
numbers <- c(5, 2, 8.3, 1)

# Create a character vector
greetings <- c("hello", "good morning")

Operations on Vectors

• Accessing Elements: Access specific elements using square brackets [] and their index (position). Indexing starts from 1.
  Code snippet
  numbers[2]  # Accesses the second element
  
  

• Arithmetic Operations: Perform element-wise addition, subtraction, multiplication, division, etc.
  Code snippet
  numbers * 2   # Multiply each element by 2
  
  

• Logical Comparisons: Perform element-wise comparisons (<, >, ==, etc.).
  Code snippet
  numbers > 4   # Check which elements are greater than 4
  
  

• Functions: Apply various built-in functions to vectors.
  Code snippet
  length(numbers)  # Find the length (number of elements)
  sum(numbers)     # Calculate the sum
  sort(numbers)    # Sort in ascending order
  
  

Key Points

• Vectors are an essential building block in R, used extensively in data analysis and manipulation.

• Understanding different vector types ensures you store and work with your data correctly.

• R provides a rich set of functions and operations for working with vectors.

1. Deleting an Entire Vector

To completely remove a vector from your R workspace, use the rm() function:

Code snippet

my_vector <- c(10, 20, 30)  # Create a vector

rm(my_vector)  # Remove the vector

Vectors in R Programming and types of vectors¶

In [1]:

my_numeric <- c(1, 5, 2.7, 10)

In [2]:

my_numeric

1. 1
2. 5
3. 2.7
4. 10

In [3]:

my_numeric1 = c(1, 5, 2.7, 10)

In [4]:

my_numeric1

1. 1
2. 5
3. 2.7
4. 10

In [5]:

my_integer <- c(2L, 6L, 15L)

In [6]:

my_integer

1. 2
2. 6
3. 15

In [7]:

my_integer <- c(2L, 6.5L, 15L)

In [12]:

my_integer

1. 2
2. 6
3. 15

In [9]:

typeof(my_integer)

'double'

In [10]:

my_integer <- c(2L, 6L, 15L)

In [11]:

typeof(my_integer)

'integer'

In [13]:

typeof(my_integer)

'integer'

In [14]:

my_integer <- c(2L, 6.5L, 15L)

In [15]:

typeof(my_integer)

'double'

In [17]:

age <- c(10,20,30,'40')

In [18]:

age

1. '10'
2. '20'
3. '30'
4. '40'

In [19]:

typeof(age)

'character'

In [20]:

age <- c(10,20,30,40)

In [21]:

typeof(age)

'double'

In [22]:

my_logical <- c(TRUE, FALSE, TRUE)

In [23]:

typeof(my_logical)

'logical'

In [24]:

my_logical <- c(TRUE, FALSE, TRUE,20)

In [25]:

typeof(my_logical)

'double'

In [26]:

my_complex <- c(2+5i, 3-1i)

In [27]:

typeof(my_complex)

'complex'

In [28]:

rm(my_complex)

In [30]:

my_complex

Error in eval(expr, envir, enclos): object 'my_complex' not found
Traceback:

In [ ]: