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# cbind / rbind
left  <- data.frame(id = 1:3, x = c(10, 20, 30))
right <- data.frame(id = 1:3, y = c("a", "b", "c"))
cbind(left, right[ , -1])            # add column y

extra_row <- data.frame(id = 4, x = 40)
rbind(left, extra_row)               # append a row

# merge() — SQL-style joins
orders <- data.frame(
    cust_id = c(1, 2, 2, 3),
    amount  = c(100, 200, 150, 300)
)
customers <- data.frame(
    cust_id = c(1, 2, 4),
    name    = c("Alice", "Bob", "Dave")
)
merge(orders, customers, by = "cust_id")             # inner join
merge(orders, customers, by = "cust_id", all.x = TRUE) # left join
merge(orders, customers, by = "cust_id", all = TRUE)   # full outer

# apply family
m <- matrix(1:12, nrow = 3)
apply(m, 1, sum)   # row sums    -> length 3
apply(m, 2, sum)   # column sums -> length 4

df <- data.frame(a = c(1,2,3), b = c(4,5,6), c = c(7,8,9))
sapply(df, mean)   # mean of each column -> named numeric vector
lapply(df, range)  # min/max of each column -> named list

# tapply — grouped summary
scores <- c(85, 90, 78, 92, 88, 76)
groups <- c("A", "B", "A", "B", "A", "B")
tapply(scores, groups, mean)   # mean score per group

# do.call — assemble a data frame from a list of rows
rows <- list(
    data.frame(x = 1, y = "one"),
    data.frame(x = 2, y = "two")
)
do.call(rbind, rows)