Named Collections (Keys and Sets)

Why Learn Named Collections in Data Analytics?

Imagine you are configuring settings for a data analysis database pipeline. You need to store:

  • Host address: "db.company.com"
  • Port number: 5432
  • Connection status: TRUE

If you use a simple list or vector, you must access them by their positions (e.g., config[[1]] for host, config[[2]] for port). This makes your code hard to read and prone to bugs if the order of elements changes.

Instead, you want to retrieve these values using descriptive names—like config$host or config["port"].

In R, this key-value association is achieved using Named Vectors and Named Lists. Let's learn how to create and query named collections, and how to perform set operations like finding common values between groups.

1. Named Vectors

A named vector allows you to assign a label (key) to each item in a vector. All items must still be of the same data type.

# Creating a named character vector
country_codes <- c(US = "United States", UK = "United Kingdom", CA = "Canada")

# Retrieving values using keys
print(country_codes["US"])  # "United States"

# Modifying or adding key-value pairs
country_codes["MX"] <- "Mexico"
print(country_codes)

2. Named Lists & The $ Operator

For heterogeneous data, Named Lists are highly popular. You can name the items inside a list and access them using R's signature dollar sign $ operator:

# Creating a named list representing a customer profile
customer <- list(
  name = "Alice Smith",
  age = 34,
  purchases = c(12.50, 45.00, 110.20)
)

# Accessing values using the $ operator
print(customer$name)       # "Alice Smith"
print(customer$age)        # 34
print(mean(customer$purchases)) # 55.9

# Adding a new key-value pair
customer$is_premium <- TRUE
print(customer)

3. Set Operations on Vectors

In data analytics, you often need to compare categories across datasets (e.g., finding customers who purchased in both January and February). R provides native set operations on vectors:

  • unique(x): Removes duplicate elements from vector x.
  • intersect(x, y): Returns elements common to both x and y.
  • union(x, y): Returns all unique elements from both x and y.
  • setdiff(x, y): Returns elements in x that are not in y.
  • setequal(x, y): Checks if the sets contain the exact same elements.
jan_customers <- c("Alice", "Bob", "Charlie", "Alice")
feb_customers <- c("Charlie", "David", "Eve")

# Remove duplicates
print(unique(jan_customers)) # "Alice" "Bob" "Charlie"

# Shared customers (Intersection)
print(intersect(jan_customers, feb_customers)) # "Charlie"

# All unique customers across both months (Union)
print(union(jan_customers, feb_customers)) # "Alice" "Bob" "Charlie" "David" "Eve"

# Customers who bought in Jan but NOT Feb (Difference)
print(setdiff(jan_customers, feb_customers)) # "Alice" "Bob"

Hands-on Exercises

Exercise 1: Querying a Database Config

Create a named list representing a server configuration with the keys: host (value "localhost"), port (value 5432), and ssl (value TRUE). Write R code to:

  1. Initialize the named list.
  2. Retrieve and print the port value using the $ operator.
  3. Update the host value to "127.0.0.1".
  4. Print the updated config list.
# Write your code below and click Run Code
Click to view Answer
config <- list(
  host = "localhost",
  port = 5432,
  ssl = TRUE
)

print(config$port) # 5432

config$host <- "127.0.0.1"
print(config)

Exercise 2: Customer Cohort Comparison

You have two customer vectors representing product views:

  • Product A buyers: c("ID_101", "ID_102", "ID_103", "ID_104")
  • Product B buyers: c("ID_103", "ID_104", "ID_105", "ID_106")

Write R code to:

  1. Find customers who bought Product A but did not buy Product B (Difference).
  2. Find customers who bought both products (Intersection).
  3. Find the total number of unique customers across both groups.
# Write your code below and click Run Code
Click to view Answer
prod_a <- c("ID_101", "ID_102", "ID_103", "ID_104")
prod_b <- c("ID_103", "ID_104", "ID_105", "ID_106")

only_a <- setdiff(prod_a, prod_b)
print(only_a) # "ID_101" "ID_102"

both <- intersect(prod_a, prod_b)
print(both) # "ID_103" "ID_104"

total_unique <- length(union(prod_a, prod_b))
print(total_unique) # 6