Function which runs lm over different variables

Question

I would like to create a function which can run a regression model (e.g. using lm) over different variables in a given dataset. In this function, I would specify as arguments the dataset I'm using, the dependent variable y and the independent variable x. I want this to be a function and not a loop as I would like to call the code in various places of my script. My naive function would look something like this:

lmfun <- function(data, y, x) {
  lm(y ~ x, data = data)
}

This function obviously does not work because the lm function does not recognize y and x as variables of the dataset.

I have done some research and stumbled upon the following helpful vignette: programming with dplyr. The vignette gives the following solution to a similar problem as the one I am facing:

df <- tibble(
  g1 = c(1, 1, 2, 2, 2),
  g2 = c(1, 2, 1, 2, 1),
  a = sample(5),
  b = sample(5)
)

my_sum <- function(df, group_var) {
  group_var <- enquo(group_var)
  df %>%
    group_by(!! group_var) %>%
    summarise(a = mean(a))
}

I am aware that lm is not a function that is part of the dplyr package but would like to come up with a solution similar as this. I've tried the following:

lmfun <- function(data, y, x) {
  y <- enquo(y)
  x <- enquo(x)

  lm(!! y ~ !! x, data = data)
}

lmfun(mtcars, mpg, disp)

Running this code gives the following error message:

Error in is_quosure(e2) : argument "e2" is missing, with no default

Anyone has an idea on how to amend the code to make this work?

Thanks,

Joost.

Answer 1

You can fix this problem by using the quo_name's and formula:

lmfun <- function(data, y, x) {
  y <- enquo(y)
  x <- enquo(x)

  model_formula <- formula(paste0(quo_name(y), "~", quo_name(x)))
  lm(model_formula, data = data)
}

lmfun(mtcars, mpg, disp)

# Call:
#   lm(formula = model_formula, data = data)
# 
# Coefficients:
#   (Intercept)         disp  
#      29.59985     -0.04122