Data Structure

-3:3

## Output
[1] -3 -2 -1 0 1 2 3

# Basic list creation
my_list <- list("apple", 42, TRUE, 15.7)
print(my_list)

# List with named elements
person <- list(
  name = "Imdad",
  age = 45,
  married = TRUE,
  scores = c(85, 92, 78)
)
print(person)

# Generate list using lapply
my_list <- lapply(1:5, function(x) x)
print(my_list)

squared_list <- lapply(1:5, function(x) x^2)
print(square_list)

my_list <- list(a = 1:5, b = "hello", c = TRUE)

length(my_list)        # Number of elements
names(my_list)         # Element names
str(my_list)           # Structure
is.list(my_list)       # Check if object is a list

# List to vector
num_list <- list(1, 2, 3, 4)
num_vector <- unlist(num_list)

# Vector to list
char_vector <- c("a", "b", "c")
char_list <- as.list(char_vector)

# Multiple models
models <- list(
  linear = lm(mpg ~ wt, data = mtcars),
  quadratic = lm(mpg ~ wt + I(wt^2), data = mtcars)
)

# Extract coefficients
coefs <- lapply(models, coef)
r_squared <- sapply(models, function(x) summary(x)$r.squared)

# Predictions for new data
new_data <- data.frame(wt = c(2.5, 3.0, 3.5))
predictions <- lapply(models, predict, newdata = new_data)

my_list <- list("apple", 42, TRUE, c(1, 2, 3))

# Update second element
my_list[[2]] <- 100
print(my_list)

# Update using single bracket (returns list)
my_list[2] <- list(999)  # Note: must wrap in list()

person <- list(name = "Imdad", age = 40, city = "Multan")

# Update using double bracket
person[["age"]] <- 31

# Update using dollar notation
person$city <- "Dera Ghazi Khan"

# Update using single bracket
person["name"] <- list("Ullah")  # Must wrap in list()

nested_list <- list(
  personal = list(name = "rfaqs.com", age = 25),
  professional = list(job = "Engineer", salary = 50000)
)

# Update nested elements
nested_list$personal$age <- 26
nested_list[["professional"]][["salary"]] <- 55000
nested_list[[1]][[2]] <- 27  # Update age by position

student <- list(
  name = "Ali",
  scores = c(85, 92, 78, 88)
)

# Update specific element in vector
student$scores[3] <- 95  # Change third score from 78 to 95
student$scores <- c(90, 94, 96, 89)  # Replace entire vector

my_list <- list(a = 1, b = 2, c = 3, d = 4)

# Delete element 'b'
my_list$b <- NULL

# Delete using double bracket notation
my_list[["c"]] <- NULL

print(my_list)  # Only a and d remain

my_list <- list("first", "second", "third", "fourth")

# Remove second element
my_list <- my_list[-2]

# Remove multiple elements
my_list <- my_list[-c(1, 3)]  # Remove first and third elements

mixed_list <- list(
  num1 = 10,
  text = "hello",
  num2 = 20,
  flag = TRUE
)

# Delete elements that are not numeric
for(name in names(mixed_list)) {
  if(!is.numeric(mixed_list[[name]])) {
    mixed_list[[name]] <- NULL
  }
}

# Alternative using lapply and filtering
numeric_elements <- mixed_list[sapply(mixed_list, is.numeric)]

my_list <- list(
  temp_data1 = c(1, 2, 3),
  perm_data = c(4, 5, 6),
  temp_data2 = c(7, 8, 9),
  important = "keep this"
)

# Delete elements with names starting with "temp"
temp_indices <- grep("^temp", names(my_list))
my_list <- my_list[-temp_indices]

# Or using logical indexing
my_list <- my_list[!grepl("^temp", names(my_list))]

my_list <- list(a = 1, b = 2)

# These are different!
my_list["a"] <- list(10)  # Correct for single bracket
my_list[["a"]] <- 10      # Correct for double bracket

my_list <- list(a = 1, b = 2, c = 3)

my_list$b <- NULL    # Completely removes b
my_list$b <- NA      # Keeps b but sets value to NA

# Sometimes it is easier to rebuild than delete multiple elements
my_list <- list(a = 1, b = 2, c = 3, d = 4, e = 5)

# Keep only a, c, e
my_list <- my_list[c("a", "c", "e")]