R: قوائم R: «الحاوية الشاملة» في لغة R

Employee ID:2024001(Numbers)
Name:Alice(Character)
Department:Data Analysis Department(Character)
Salary:15000(Numbers)
Skills:c("R", "Python", "SQL")(Character vector)

# One list Fits all types
employee <- list(
  id = 2024001,
  name = "Alice",
  department = "Data Analysis Department",
  salary = 15000,
  skills = c("R", "Python", "SQL"),
  is_active = TRUE
)

# Access any field
employee$name           # [1] "Alice"
employee$skills[1]      # [1] "R"
employee$salary * 12    # [1] 180000  (Annual Salary)

list(key1 = value1, key2 = value2, ...)

# 1. List of Names(Most Commonly Used)
person <- list(name = "Alice", age = 25, scores = c(85, 90, 92))
person
# $name
# [1] "Alice"
# 
# $age
# [1] 25
# 
# $scores
# [1] 85 90 92

# 2. Anonymous List(Browse by Location)
unnamed <- list("a", 1, TRUE)
# [[1]] "a"
# [[2]] 1
# [[3]] TRUE

# 3. Nested Lists
company <- list(
  hr = list(name = "HR", count = 5),
  it = list(name = "IT", count = 20)
)

# 4. Empty list
empty <- list()
length(empty)  # [1] 0

# 5. Use list() to convert vector (Maintain Heterogeneity)
as.list(c(1, 2, 3))
# [[1]] 1
# [[2]] 2
# [[3]] 3

person <- list(name = "Alice", age = 25, scores = c(85, 90, 92))

# [[ ]] Return the element itself
person[[1]]
# [1] "Alice"  ← Character

# [ ] Return to Sublist
person[1]
# $name
# [1] "Alice"  ← Or a list

# Key Differences: Using [ ] after extraction, attempting to access sub-elements may cause an error
person[1]$name  # ✅ $name Still accessible(Syntax sugar)
person[1][1]    # ❌ This is the 1 element,But there is only one child list $name

# Practical Application:Calculate the average score
mean(person[[3]])      # [1] 89  ← [[3]] Extract the vector
mean(person$scores)    # [1] 89  ← $scores Extract the vector
mean(person[3])        # Error: need numeric data

# $ is syntactic sugar for [[ "name" ]]
person$name           # Equivalent person[["name"]]
person[["name"]]      # Equivalent person$name

# $ Supports partial matches(Not recommended)
person$na             # Automatically matched to "name"
# [1] "Alice"

# [[ ]] Partial matches are not supported(Safer)
person[["na"]]        # Error

company <- list(
  hr = list(name = "HR", count = 5, head = "Alice"),
  it = list(name = "IT", count = 20, head = "Bob")
)

# Accessing Nested Elements
company$hr$head                  # [1] "Alice"
company[["it"]][["head"]]        # [1] "Bob"
company$it$count                 # [1] 20

person <- list(name = "Alice", age = 25)

# Edit ([[ ]] or $ both can be changed)
person$age <- 26
person[["age"]]
# [1] 26

# Batch Edit
person$age <- person$age + 1
person$age  # [1] 27

# Methods 1: Use $ to add
person$email <- "alice@example.com"

# Methods 2: Use [[ ]] to add
person[["phone"]] <- "13800000000"

# Methods 3: Use c() to merge
person <- c(person, address = "Haidian District, Beijing")

# Assign NULL to delete
person$phone <- NULL

# Or
person[["address"]] <- NULL

# View Structure(Important Debugging Functions)
str(person)
# List of 4
#  $ name    : chr "Alice"
#  $ age     : num 27
#  $ email   : chr "alice@example.com"
#  $ address : chr "Haidian District, Beijing"

# View All Names
names(person)
# [1] "name" "age" "email" "address"

mixed_list <- list(
  numbers = 1:5,
  chars = c("a", "b", "c"),
  logical = c(TRUE, FALSE, TRUE)
)

# Calculate the length of each element
lapply(mixed_list, length)
# $numbers
# [1] 5
# 
# $chars
# [1] 3
# 
# $logical
# [1] 3

# CalculateFor each element,"Abstract"
summary_list <- lapply(mixed_list, function(x) {
  if (is.numeric(x)) {
    return(list(mean = mean(x), sum = sum(x)))
  } else {
    return(list(length = length(x), class = class(x)))
  }
})

summary_list$numbers
# $mean
# [1] 3
# 
# $sum
# [1] 15

# lapply Back to List,sapply Simplify to a vector
sapply(mixed_list, length)
# numbers    chars  logical 
#       5        3        3

# Merge Multiple Lists
list1 <- list(a = 1, b = 2)
list2 <- list(c = 3, d = 4)

merged <- c(list1, list2)
merged
# $a 1
# $b 2
# $c 3
# $d 4

# Flatten the list into a vector(Only for"Atom"Element Valid)
numbers_list <- list(a = 1, b = 2, c = 3)
unlist(numbers_list)
# a b c
# 1 2 3

# Note:The type will be coerced after flattening.
mixed <- list(1, "a", TRUE)
unlist(mixed)
# [1] "1"    "a"    "TRUE"  ← Convert All to Strings

config <- list(
  host = "localhost",
  port = 3306,
  user = "root",
  password = "secret"
)

# Convert list elements to environment variables
list2env(config, envir = .GlobalEnv)

# You can use it right away
host    # [1] "localhost"
port    # [1] 3306

# A list of vectors of equal length can be converted data.frame
df <- as.data.frame(list(
  name = c("A", "B", "C"),
  age = c(20, 25, 30),
  score = c(85, 90, 78)
))
print(df)
#   name age score
# 1    A  20    85
# 2    B  25    90
# 3    C  30    78

# ============================================
# Employee File Management
# Features:Manage with Lists 3 Complete information about each employee
# ============================================

# 1. Create 3 List of employees
employees <- list(
  alice = list(
    id = 2024001,
    name = "Alice",
    department = "Data Analysis Department",
    salary = 15000,
    skills = c("R", "Python", "SQL"),
    projects = c("User Profiles", "Sales Forecast")
  ),
  bob = list(
    id = 2024002,
    name = "Bob",
    department = "Engineering Department",
    salary = 18000,
    skills = c("Java", "Go", "Docker"),
    projects = c("API Refactoring", "Microservice Migration")
  ),
  charlie = list(
    id = 2024003,
    name = "Charlie",
    department = "Product Department",
    salary = 20000,
    skills = c("Figma", "Axure"),
    projects = c("V2.0 Design", "User Research")
  )
)

# 2. View a specific employee's full profile
cat("=== Alice Full Information ===\n")
str(employees$alice)
# List of 6
#  $ id         : num 2024001
#  $ name       : chr "Alice"
#  $ department : chr "Data Analysis Department"
#  $ salary     : num 15000
#  $ skills     : chr [1:3] "R" "Python" "SQL"
#  $ projects   : chr [1:2] "User Profiles" "Sales Forecast"

# 3. Use sapply for batch processing
cat("\n=== Annual Salaries of All Employees ===\n")
annual_salaries <- sapply(employees, function(emp) emp$salary * 12)
print(annual_salaries)
#    alice      bob  charlie 
#   180000   216000   240000

# 4. Use sapply to count skills
cat("\n=== Number of skills per employee ===\n")
skill_counts <- sapply(employees, function(emp) length(emp$skills))
print(skill_counts)
#    alice      bob  charlie 
#        3         3         2

# 5. Use split for grouping by department
cat("\n=== Grouped by Department ===\n")
dept_groups <- split(
  sapply(employees, function(emp) emp$name),
  sapply(employees, function(emp) emp$department)
)
print(dept_groups)
# $Data Analysis Department
# [1] "Alice"
# 
# $Engineering Department
# [1] "Bob"
# 
# $Product Department
# [1] "Charlie"

# 6. Add a New Employee
employees$diana <- list(
  id = 2024004,
  name = "Diana",
  department = "Data Analysis Department",
  salary = 16000,
  skills = c("R", "Tableau"),
  projects = c("Data Visualization")
)

cat("\n=== Add Diana after ===\n")
cat("Total Number of Employees:", length(employees), "\n")
cat("Diana Department:", employees$diana$department, "\n")

# 7. Identify the employee with the highest salary
top_salary <- which.max(sapply(employees, function(emp) emp$salary))
cat("\nHighest Salary:", names(employees)[top_salary], "(", max(sapply(employees, function(emp) emp$salary)), "USD)\n")

# 8. Retrieve the skills of all employees(Merge all skill vectors)
all_skills <- unlist(lapply(employees, function(emp) emp$skills))
cat("\n=== Company-wide Skills Summary ===\n")
print(table(all_skills))
# all_skills
#   Axure  Docker    Figma      Go    Java   Python       R     SQL Tableau 
#       1       1       1       1       1       1       2       1       1

=== Alice Full Information ===
List of 6
 $ id         : num 2024001
 $ name       : chr "Alice"
 ...

=== Annual Salaries of All Employees ===
   alice      bob  charlie 
  180000   216000   240000 

=== Company-wide Skills Summary ===
all_skills
  Axure  Docker  Figma      Go   Java  Python     R   SQL Tableau 
      1       1       1       1       1       1       2       1       1