💻Intro to Programming in R Unit 3 – Vectors: Basics and Operations in R

What Are Vectors?

• Vectors are one-dimensional arrays that can hold multiple values of the same data type
• Serve as the most basic data structure in R and are widely used for storing and manipulating data
• Can contain numeric (integer or double), character, logical, or complex values
• Have a length attribute that represents the number of elements in the vector
• Enable efficient storage and processing of data by allowing operations to be performed on multiple values simultaneously
• Provide a foundation for more complex data structures in R, such as matrices and data frames
• Play a crucial role in data analysis and manipulation tasks, such as subsetting, filtering, and transforming data

Creating Vectors in R

• Use the

  c()

  function to create vectors by combining multiple values separated by commas
  • Example:

    my_vector <- c(1, 2, 3, 4, 5)

• Specify the data type of the vector elements using the appropriate function:

  numeric()

  ,

  character()

  ,

  logical()

  , or

  complex()

• Create vectors with repeated values using the

  rep()

  function
  • Example:

    repeated_vector <- rep(1, times = 5)

• Generate sequences of numbers using the

  :

  operator or the

  seq()

  function
  • Example:

    sequence_vector <- 1:10

• Create vectors with specific patterns or sequences using functions like

  seq()

  and

  rep()

• Combine existing vectors using the

  c()

  function to create a new vector
• Create named vectors by assigning names to the elements using the

  names()

  function or during vector creation

Vector Types and Coercion

• R supports four main types of vectors: numeric, character, logical, and complex
• Numeric vectors contain integer or double values and are the most commonly used type
• Character vectors store text or string values enclosed in quotes
• Logical vectors hold TRUE or FALSE values and are often used for conditional operations
• Complex vectors contain complex numbers with real and imaginary parts
• Vector elements must be of the same data type within a single vector
• R performs automatic type coercion when elements of different types are combined in a vector
  • Coercion follows a hierarchy: logical < integer < double < character
• Use explicit coercion functions like

  as.numeric()

  ,

  as.character()

  , or

  as.logical()

  to convert vectors to a specific type
• Be cautious when combining vectors of different types to avoid unintended coercion results

Accessing Vector Elements

• Access individual elements of a vector using square brackets

  []

  and the element's index
  • R uses 1-based indexing, meaning the first element has an index of 1
• Retrieve multiple elements by providing a vector of indices inside the square brackets
  • Example:

    my_vector[c(1, 3, 5)]

• Use negative indices to exclude specific elements from the selection
  • Example:

    my_vector[-c(2, 4)]

• Access elements based on logical conditions using logical vectors or comparison operators
  • Example:

    my_vector[my_vector > 3]

• Extract elements using named indices if the vector has named elements
  • Example:

    my_vector["name"]

• Utilize the

  which()

  function to find the indices of elements satisfying a specific condition
• Subset vectors using the

  subset()

  function and specify the desired condition

Basic Vector Operations

• Perform element-wise arithmetic operations on vectors of the same length
  • Addition:

    vector1 + vector2

  • Subtraction:

    vector1 - vector2

  • Multiplication:

    vector1 * vector2

  • Division:

    vector1 / vector2

• Carry out arithmetic operations between a vector and a scalar value
  • Example:

    my_vector + 10

• Compare vectors element-wise using comparison operators (

  >

  ,

  <

  ,

  >=

  ,

  <=

  ,

  ==

  ,

  !=

  )
  • Returns a logical vector indicating the result of each comparison
• Combine vectors element-wise using logical operators (

  &

  ,

  |

  ,

  !

  )
• Find the length of a vector using the

  length()

  function
• Calculate summary statistics of a vector, such as

  sum()

  ,

  mean()

  ,

  min()

  ,

  max()

  , and

  median()

Vector Functions in R

• length()

  : Returns the number of elements in a vector

• sum()

  : Calculates the sum of all elements in a numeric vector

• mean()

  : Computes the arithmetic mean of a numeric vector

• min()

  and

  max()

  : Find the minimum and maximum values in a vector, respectively

• sort()

  : Sorts the elements of a vector in ascending or descending order

• unique()

  : Returns a vector with duplicated elements removed

• rev()

  : Reverses the order of elements in a vector

• paste()

  : Concatenates elements of a character vector into a single string

• grep()

  and

  grepl()

  : Search for patterns within a character vector

• is.na()

  : Checks for missing values (NA) in a vector

• any()

  and

  all()

  : Test for the presence of any or all TRUE values in a logical vector

Applying Functions to Vectors

• Apply functions to each element of a vector using the

  sapply()

  or

  lapply()

  functions

  • sapply()

    simplifies the output to a vector, matrix, or list

  • lapply()

    always returns a list
• Use anonymous functions or pre-defined functions as the first argument to

  sapply()

  or

  lapply()

  • Example:

    sapply(my_vector, function(x) x^2)

• Perform conditional operations on vector elements using the

  ifelse()

  function
  • Applies a different function or value based on a logical condition
• Utilize the

  apply()

  function to apply a function over the margins of an array or matrix
• Employ vectorized functions that automatically operate on each element of a vector without the need for explicit looping
  • Examples:

    sqrt()

    ,

    log()

    ,

    exp()

    ,

    round()

    ,

    floor()

    ,

    ceiling()

• Leverage the

  Vectorize()

  function to create vectorized versions of non-vectorized functions

Common Vector Mistakes and How to Avoid Them

• Forgetting to use the

  c()

  function when creating vectors, leading to unexpected results
  • Make sure to combine elements using

    c()

    to create a proper vector
• Mixing data types within a vector, causing automatic coercion and potential loss of information
  • Ensure that all elements in a vector have the same data type
• Accessing vector elements using incorrect indices or forgetting that R uses 1-based indexing
  • Double-check the indices and remember that the first element has an index of 1
• Attempting to perform operations on vectors of different lengths without proper recycling
  • Ensure that vectors have compatible lengths or use appropriate recycling mechanisms
• Ignoring warning messages about coercion or length mismatches, leading to unexpected behavior
  • Pay attention to warning messages and investigate the cause of any issues
• Forgetting to handle missing values (NA) appropriately in vector operations and functions
  • Use functions like

    is.na()

    and

    na.omit()

    to detect and handle missing values
• Incorrectly assuming that operations on vectors are performed in a element-wise manner
  • Be aware of the behavior of different operators and functions when applied to vectors