# Very Non-Standard Calling in R

Our group has done a lot of work with non-standard calling conventions in `R`.

Our tools work hard to eliminate non-standard calling (as is the purpose of `wrapr::let()`), or at least make it cleaner and more controllable (as is done in the wrapr dot pipe). And even so, we still get surprised by some of the side-effects and mal-consequences of the over-use of non-standard calling conventions in `R`.

Consider the following calls to `stats::lm()`. And notice the third example fails (throws an error).

```# works
lm("y ~ x",
data = data.frame(
x=1:5,
y = c(1, 1, 2, 2, 2)),
weights = numeric(5)+1)
#>
#> Call:
#> lm(formula = "y ~ x", data = data.frame(x = 1:5, y = c(1, 1,
#>     2, 2, 2)), weights = numeric(5) + 1)
#>
#> Coefficients:
#> (Intercept)            x
#>         0.7          0.3

# works
f1 <- function(w = NULL) {
lm(as.formula("y ~ x"),
data = data.frame(
x=1:5,
y = c(1, 1, 2, 2, 2)),
weights = w)
}
f1(numeric(5)+1)
#>
#> Call:
#> lm(formula = as.formula("y ~ x"), data = data.frame(x = 1:5,
#>     y = c(1, 1, 2, 2, 2)), weights = w)
#>
#> Coefficients:
#> (Intercept)            x
#>         0.7          0.3

# fails
f2 <- function(w = NULL) {
lm("y ~ x",
data = data.frame(
x=1:5,
y = c(1, 1, 2, 2, 2)),
weights = w)
}
f2(numeric(5)+1)
```

According the `stats::lm()` documentation (`help(lm)`) the first argument must be:

an object of class “formula” (or one that can be coerced to that class): a symbolic description of the model to be fitted. The details of model specification are given under ‘Details’.

A string appears to be coerce-able into a formula, so all three examples should work. However, typing “`print(lm)`” reveals the issue: `stats::lm()` doesn’t take the “`weights`” argument in a standard way (as the value of a function parameter). It instead grabs it through a sequence of `match.call()` and `eval()` steps. It is a complicated way to get the value, which works until it does not work. Somehow passing in the formula as a string interferes with how the value of `weights` is found. I think we can now see the benefits of isolation and independence of concerns in code.

This over-use of direct environment copying and manipulation is what leads to a great many data-leaks in `stats::lm()` and `stats::glm()`. This is in addition to their weird habit of keeping a copy of all of the training data (which loses quite a few of the merits of these methods). Our group dealt with these issues a long time ago, so we are somewhat familiar with `stats::lm()` and `stats::glm()`.

Of course, one could (as the `stats::lm()` documentation mentions) call `stats::lm.fit()`. However, `stats::lm.fit()` does not seem to accept weights and its own documentation (`help(lm.fit)`) starts ominously:

These are the basic computing engines called by lm used to fit linear models. These should usually not be used directly unless by experienced users.

Having just finished teaching a four day intensive course covering data science in Python, I can’t help but remark that users of `sklearn.linear_model.LinearRegression()` don’t need to worry about issues such as the above. Some of the notational flair of `R` comes at the cost of significant opportunities for user confusion.

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