Lecture 7: Working with Lists and Factors in R

Overview

• In this lecture, we’ll cover:
  • Introduction to lists and their flexibility in R.
  • How to create, access, and modify lists.
  • Working with factors, a key data structure for categorical data.
  • Practical examples of using lists and factors in business data analysis.

1. Introduction to Lists

• A list in R is a flexible data structure that can contain elements of different types, including vectors, matrices, data frames, and even other lists.
• Lists are highly versatile, making them useful for managing complex collections of data.

Creating a List

You can create a list using the list() function, which combines various objects into a single list.

Example: Creating a Sales List

# Creating a list with different types of data
sales_list <- list(
  Products = c("A", "B", "C"),
  Sales_Q1 = c(120, 150, 90),
  Sales_Matrix = matrix(c(120, 150, 90, 100, 130, 170), nrow = 3, ncol = 2)
)
sales_list

$Products
[1] "A" "B" "C"

$Sales_Q1
[1] 120 150  90

$Sales_Matrix
     [,1] [,2]
[1,]  120  100
[2,]  150  130
[3,]   90  170

• Explanation: This list contains a vector of product names, a vector of sales for the first quarter, and a matrix of sales data across multiple periods.

Accessing Elements in a List

You can access elements in a list using either: - The $ operator for named elements. - Double square brackets [[ ]] for positional access.

Example: Accessing List Elements by Name

# Accessing the Products vector in the list
sales_list$Products

[1] "A" "B" "C"

Example: Accessing List Elements by Position

# Accessing the Sales_Q1 vector (second element) in the list
sales_list[[2]]

[1] 120 150  90

• Explanation: This code accesses the second element in the list, which is the Sales_Q1 vector.

Modifying Elements in a List

You can modify or add elements to a list by assigning new values to a specific position or name.

Example: Adding a New Element to the List

# Adding a new element for Sales in Q2
sales_list$Sales_Q2 <- c(170, 200, 140)
sales_list

$Products
[1] "A" "B" "C"

$Sales_Q1
[1] 120 150  90

$Sales_Matrix
     [,1] [,2]
[1,]  120  100
[2,]  150  130
[3,]   90  170

$Sales_Q2
[1] 170 200 140

• Explanation: This adds a new vector, Sales_Q2, to the sales_list, which now contains sales data for both Q1 and Q2.

Removing Elements from a List

You can remove an element from a list by setting it to NULL.

Example: Removing an Element from a List

# Removing the Sales_Matrix from the list
sales_list$Sales_Matrix <- NULL
sales_list

$Products
[1] "A" "B" "C"

$Sales_Q1
[1] 120 150  90

$Sales_Q2
[1] 170 200 140

• Explanation: This code removes the Sales_Matrix element from the list.

2. Introduction to Factors

• Factors are used in R to store categorical data, which is data that falls into a limited number of categories (e.g., “High”, “Medium”, “Low”).
• Factors are especially useful in statistical analysis and modeling, ensuring that categorical variables are properly treated.

Creating a Factor

You can create a factor using the factor() function.

Example: Creating a Factor for Customer Satisfaction

# Creating a factor for customer satisfaction levels
satisfaction_levels <- factor(c("High", "Medium", "Low", "Medium", "High"))
satisfaction_levels

[1] High   Medium Low    Medium High  
Levels: High Low Medium

• Explanation: This factor contains customer satisfaction levels (e.g., “High”, “Medium”, “Low”) and treats them as distinct categories.

Working with Levels in a Factor

Factors have levels, which represent the different categories within the data. You can view or modify these levels.

Example: Viewing the Levels of a Factor

# Viewing the levels of the satisfaction factor
levels(satisfaction_levels)

[1] "High"   "Low"    "Medium"

• Explanation: This returns the levels (“High”, “Medium”, “Low”) of the satisfaction_levels factor.

Example: Changing the Order of Levels

# Reordering the levels of the satisfaction factor
satisfaction_levels <- factor(satisfaction_levels, levels = c("Low", "Medium", "High"))
satisfaction_levels

[1] High   Medium Low    Medium High  
Levels: Low Medium High

• Explanation: This reorders the levels of the satisfaction_levels factor to reflect a logical progression from “Low” to “High.”

Working with Ordered Factors

If the categories in a factor have a meaningful order, such as “Poor”, “Fair”, “Good”, “Excellent”, you can create an ordered factor.

Example: Creating an Ordered Factor for Customer Satisfaction

# Creating an ordered factor
ordered_satisfaction <- factor(c("Good", "Excellent", "Fair", "Good", "Poor"),
                               levels = c("Poor", "Fair", "Good", "Excellent"),
                               ordered = TRUE)
ordered_satisfaction

[1] Good      Excellent Fair      Good      Poor     
Levels: Poor < Fair < Good < Excellent

• Explanation: This ordered factor treats the satisfaction levels as having a ranked order, from “Poor” to “Excellent.”

3. Practical Example: Using Lists and Factors in Business Analytics

Example: Combining Product Data in a List

You can use lists to store and organize different types of data related to business operations.

# Creating a list that combines product names, sales, and satisfaction ratings
business_data <- list(
  Products = c("A", "B", "C"),
  Sales = c(150, 200, 130),
  Satisfaction = factor(c("High", "Medium", "Low"))
)
business_data

$Products
[1] "A" "B" "C"

$Sales
[1] 150 200 130

$Satisfaction
[1] High   Medium Low   
Levels: High Low Medium

• Explanation: This list contains product names, sales data, and customer satisfaction levels for each product.

Example: Analyzing Satisfaction Ratings Using Factors

# Summarizing satisfaction levels
summary(business_data$Satisfaction)

  High    Low Medium 
     1      1      1 

• Explanation: This summary provides a count of the different satisfaction levels (“High”, “Medium”, “Low”) in the business_data list.

Key Takeaways

• Lists are flexible data structures that can hold different types of elements, making them ideal for handling complex data.
• Factors are used to store and manage categorical data, ensuring proper handling of non-numeric variables in statistical models.
• You now know how to create, access, modify, and use lists and factors in R for business data analysis.

Looking Forward

• In the next lecture, we’ll explore how to import, clean, and transform business datasets in R, a key skill for data preparation before analysis.