How do you code an R function so that it 'knows' to look in 'data' for the variables in other arguments?

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If you
run:

mod <- lm(mpg ~
            factor(cyl),
            data=mtcars)

It runs,
because lm knows to look in mtcars to find both mpg and
cyl.

Yet
mean(mpg) fails as it can't find mpg, so you do
mean(mtcars$mpg).

How
do you code a function so that it knows to look in 'data' for the
variables?

myfun <- function
            (a,b,data){

            return(a+b)
}

This
will work
with:

myfun(mtcars$mpg,
            mtcars$hp)

but will
fail with:

myfun(mpg,hp,
            data=mtcars
            )

Cheers

Answer

Here's how I would code
myfun():

myfun
            <- function(a, b, data) {
 eval(substitute(a + b), envir=data,
            enclos=parent.frame())
}

myfun(mpg, hp,
            mtcars)
# [1] 131.0 131.0 115.8 131.4 193.7 123.1 259.3 86.4 117.8 142.2 140.8
            196.4
# [13] 197.3 195.2 215.4 225.4 244.7 98.4 82.4 98.9 118.5 165.5 165.2
            258.3

# [25] 194.2 93.3 117.0 143.4 279.8 194.7 350.0
            130.4

If you're
familiar with with(), it's interesting to see that it works in
almost exactly the same way:

>
            with.default
# function (data, expr, ...) 
# eval(substitute(expr),
            data, enclos = parent.frame())
# 
#
                        namespace:base>

In
both cases, the key idea is to first create an expression from the symbols passed in as
arguments and then evaluate that expression using data as the
'environment' of the evaluation.

The first part
(e.g. turning a + b into the expression mpg +
hp) is possible thanks to substitute(). The second
part is possible because eval() was beautifully designed, such
that it can take a data.frame as its evaluation
environment.