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I recently encountered a situation where I wanted to run several linear models, but where the response variables would depend on previous steps in the data analysis pipeline. Let me illustrate using the `mtcars` dataset:

```data(mtcars)
#>                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
#> Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
#> Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
#> Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
#> Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
#> Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
#> Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1
```

Let’s say I wanted to fit a linear model of `mpg` vs. `hp` and get the coefficients. This is easy:

```lm(mpg ~ hp, data = mtcars)\$coefficients
#> (Intercept)          hp
#> 30.09886054 -0.06822828
```

But what if I wanted to fit a linear model of `y` vs. `hp`, where `y` is a response variable that I won’t know until runtime? Or what if I want to fit 3 linear models: each of `mpg`, `disp`, `drat` vs. `hp`? Or what if I want to fit 300 such models? There has to be a way to do this programmatically.

It turns out that there are at least 4 different ways to achieve this in R. For all these methods, let’s assume that the responses we want to fit models for are in a character vector:

```response_list <- c("mpg", "disp", "drat")
```

Here are the 4 ways I know (in decreasing order of preference):

1. as.formula()

`as.formula()` converts a string to a formula object. Hence, we can programmatically create the formula we want as a string, then pass that string to `as.formula()`:

```for (y in response_list) {
lmfit <- lm(as.formula(paste(y, "~ hp")), data = mtcars)
print(lmfit\$coefficients)
}
#> (Intercept)          hp
#> 30.09886054 -0.06822828
#> (Intercept)          hp
#>    20.99248     1.42977
#> (Intercept)          hp
#>  4.10990867 -0.00349959
```

2. Don’t specify the `data` option

Passing the `data = mtcars` option to `lm()` gives us more succinct and readable code. However, `lm()` also accepts the response vector and data matrix themselves:

```for (y in response_list) {
lmfit <- lm(mtcars[[y]] ~ mtcars\$hp)
print(lmfit\$coefficients)
}
#> (Intercept)          hp
#> 30.09886054 -0.06822828
#> (Intercept)          hp
#>    20.99248     1.42977
#> (Intercept)          hp
#>  4.10990867 -0.00349959
```

3. get()

`get()` searches for an R object by name and returns that object if it exists.

```for (y in response_list) {
lmfit <- lm(get(y) ~ hp, data = mtcars)
print(lmfit\$coefficients)
}
#> (Intercept)          hp
#> 30.09886054 -0.06822828
#> (Intercept)          hp
#>    20.99248     1.42977
#> (Intercept)          hp
#>  4.10990867 -0.00349959
```

4. eval(parse())

This one is a little complicated. `parse()` returns the parsed but unevaluated expressions, while `eval()` evaluates those expressions (in a specified environment).

```for (y in response_list) {
lmfit <- lm(eval(parse(text = y)) ~ hp, data = mtcars)
print(lmfit\$coefficients)
}
#> (Intercept)          hp
#> 30.09886054 -0.06822828
#> (Intercept)          hp
#>    20.99248     1.42977
#> (Intercept)          hp
#>  4.10990867 -0.00349959
```

Of course, for any of these methods, we could replace the outer loop with `apply()` or `purrr::map()`.

References: