Passing the Data Argument in R User Defined Functions - r

Question

For functions like lm() in R, you pass the "data" argument into the function, usually a dataframe, and then R knows all of the columns by name rather than referencing them. So the difference being x=column instead of referencing in the fashion x=df$column. So how can I use that same method in my own user defined functions?
A simple example:
library(tidyverse)
df <- tibble(x=1:100,y=x*(1+rnorm(n=100)))
test_corr <- function(x,y) {
cor(x,y) %>% return()
}
# Right now I would do this
test_corr(df$x,df$y)
# I want to be able to do this
test_corr(data=df, x, y)

Answer 1

Since you are using tidyverse functions, it would make sense to use tidy evaulation for this type of task. For this function you could do
test_corr <- function(data, x, y) {
quo( cor({{x}}, {{y}}) ) %>%
rlang::eval_tidy(data=data)
}
test_corr(df, x, y)
First we make a quosure to build the expression you want to evaluate and we use the {{ }} (embrace) syntax to insert the variable names you pass in to the function into the expression. We then evaluate that quosure in the context of the data.frame you supply with eval_tidy.
You might also be interested in the tidyselect package vignette where more options are discussed.

Answer 2

You could use reformulate
apply_fun <- function(response, terms, data) {
lm(reformulate(terms, response), data)
}
apply_fun("mpg", "cyl", mtcars)
#Call:
#lm(formula = reformulate(terms, response), data = data)
#Coefficients:
#(Intercept) cyl
# 37.885 -2.876
apply_fun("mpg", c("cyl", "am"), mtcars)
#Call:
#lm(formula = reformulate(terms, response), data = data)
#Coefficients:
#(Intercept) cyl am
# 34.522 -2.501 2.567