# Factorial Implementations

fac <- function(n) {
  ans <- 1
  for(i in seq(n)) ans <- ans * i
  ans
}

fac <- function(n)
if (n <= 0) 1 else n * fac(n - 1)

fac <- function(n) prod(seq(n))

fac <- function(n) gamma(n+1)


#  These are slightly modified versions of Peter Dalgaard's
#  subset and transform functions for data frames.

subset <-
function (x, subset, select, ...) 
{
    if (missing(subset)) 
        r <- TRUE
    else {
        e <- substitute(subset)
        r <- eval(e, x, sys.frame(sys.parent()))
        r <- r & !is.na(r)
    }
    if (missing(select)) 
        vars <- TRUE
    else {
        nl <- as.list(1:ncol(x))
        names(nl) <- names(x)
        vars <- eval(substitute(select), nl, sys.frame(sys.parent()))
    }
    x[r, vars, drop = FALSE]
}


transform <-
function (x, ...) 
{
    e <- eval(substitute(list(...)), x, sys.frame(sys.parent()))
    tags <- names(e)
    inx <- match(tags, names(x))
    matched <- !is.na(inx)
    if (any(matched)) {
        x[inx[matched]] <- e[matched]
        x <- data.frame(x)
    }
    if (!all(matched)) 
        data.frame(x, e[!matched])
    else x
}


# Examples, using the air data frame.

# 1) Select all cases where ozone > 3
high1 <- subset(air, ozone > 3)


# 2) Select all cases where ozone > 3
# Variables from ozone to temperature only
high2 <- subset(air, ozone > 3, select = ozone:temperature))

# 3) Transform, creating one new variable and replace one old one
names(air)
new.air <- transform(air, new =  -ozone,
                 temperature = (temperature-32)/1.8)
names(new.air)