عوامل الإسناد والعوامل الخاصة

# Both assign a value, <- is preferred
name <- 'Alice'
age = 30
cat('Name:', name, '
')
cat('Age:', age, '
')

# <- creates a variable outside function calls
x <- 42

# = inside function call sets argument, does NOT create x
mean(x = c(1, 2, 3))  # x is an arg here, not a variable

# But <- inside a function call DOES create a variable
mean(y <- c(1, 2, 3))  # y is now also in workspace
cat('y was created:', y, '
')

# Right assignment operator
100 -> max_score
'R Programming' -> course_name
cat('Max score:', max_score, '
')
cat('Course:', course_name, '
')

# Same as:
# max_score <- 100
# course_name <- 'R Programming'

counter <- 0

increment <- function() {
  counter <<- counter + 1  # modifies the global counter
}

increment()
increment()
increment()
cat('Counter after 3 increments:', counter, '
')

fruits <- c('apple', 'banana', 'cherry', 'date', 'elderberry')

# Check membership
cat('Is mango in fruits?', 'mango' %in% fruits, '
')
cat('Is cherry in fruits?', 'cherry' %in% fruits, '
')

# Check multiple values at once
search <- c('banana', 'mango', 'date')
cat('Which are in fruits:', search %in% fruits, '
')

countries <- c('USA', 'UK', 'Canada', 'Germany', 'France', 'Japan')
populations <- c(331, 67, 38, 83, 67, 126)  # millions

# Keep only G7 European members
g7_europe <- c('UK', 'Germany', 'France', 'Italy')
mask <- countries %in% g7_europe
cat('G7 Europe countries:', countries[mask], '
')
cat('Populations:', populations[mask], '
')

A <- matrix(c(1, 2, 3, 4), nrow = 2, ncol = 2)
B <- matrix(c(5, 6, 7, 8), nrow = 2, ncol = 2)

cat('Matrix A:
')
print(A)
cat('Matrix B:
')
print(B)
cat('A %*% B (matrix mult):
')
print(A %*% B)
cat('A * B (element-wise):
')
print(A * B)

# Without pipe
result1 <- round(sqrt(abs(-16)), 2)
cat('Without pipe:', result1, '
')

# With native pipe |>
result2 <- -16 |> abs() |> sqrt() |> round(2)
cat('With pipe:', result2, '
')

# Reading left to right: take -16, get abs, sqrt, then round

scores <- c(88, 45, 92, 61, 73, 55, 84, 79)

# Chain: keep passing scores, then compute mean
passing_mean <- scores |> \(x) x[x >= 60] |> mean()
cat('Mean of passing scores:', round(passing_mean, 2), '
')

# Another chain: sort, reverse, take top 3
top3 <- scores |> sort(decreasing = TRUE) |> head(3)
cat('Top 3 scores:', top3, '
')

# Define a custom infix operator for 'not in'
'%notin%' <- function(x, table) !(x %in% table)

cities <- c('Paris', 'London', 'Berlin', 'Tokyo', 'Sydney')
exclude <- c('Tokyo', 'Sydney')

cat('Cities not in exclude list:', cities[cities %notin% exclude], '
')

# Demonstrate %in% as a filter
all_nums <- 1:20
special <- c(3, 6, 9, 12, 15, 18)
cat('Multiples of 3 up to 20:', all_nums[all_nums %in% special], '
')

# Demonstrate pipe
1:10 |> mean() |> cat('
')

# Pipe + %in% combined
data <- c(5, 12, 3, 8, 15, 7, 20, 1)
valid_range <- 5:15

result <- data |> \(x) x[x %in% valid_range] |> sort()
cat('Values in range 5-15:', result, '
')