Best practice to avoid conflict with user supplied variable names - R - r

Question

I'm trying to create a R function for a package that will take user data and (the right hand side of) a formula, do some processing, and return a model. But, I'm having trouble when the user data or formula contain variables with the same name as I use internally. A reproducible example,
(Note that updating the formula's environment is required to keep R from looking in the user's R_GlobalEnv for my variable y.)
# R Version 3.6.2
my_function <- function(user_data, user_formula){
y <- as.numeric(user_data[,1] > mean(user_data[,1]))
my_formula <- update.formula(user_formula, y ~ .)
environment(my_formula) <- environment()
my_model <- lm(my_formula, data = user_data, model = TRUE)
return(my_model)
}
some_data <- data.frame(x1 = c(1,2,3,3))
some_formula <- response ~ x1
my_function(some_data, some_formula)
The above is what I want to run, and it works as long as there isn't variable in user_formula or user_data with the name "y". But when the user_data contains a variable with the same name, the model will use that variable instead of mine.
some_data <- data.frame(x1 = c(1,2,3,3), y = c(6,7,5,6))
some_formula <- response ~ x1 + y
my_function(some_data, some_formula)$model
# y x1
# 1 6 1
# 2 7 2
# 3 5 3
# 4 6 3
# Warning messages:
# 1: In model.matrix.default(mt, mf, contrasts) :
# the response appeared on the right-hand side and was dropped
# 2: In model.matrix.default(mt, mf, contrasts) :
# problem with term 2 in model.matrix: no columns are assigned
I tried forcing R to search the function's environment for y by using get(),
my_function <- function(user_data, user_formula){
y <- as.numeric(user_data[,1] > mean(user_data[,1]))
e1 <- environment()
my_formula <- update.formula(user_formula, get("y", e1) ~ .)
environment(my_formula) <- environment()
my_model <- lm(my_formula, data = user_data, model = TRUE)
return(my_model)
}
some_data <- data.frame(x1 = c(1,2,3,3), y = c(6,7,5,6))
some_formula <- response ~ x1 + y
my_function(some_data, some_formula)$model
# get("y", e1) x1 y
# 1 0 1 6
# 2 0 2 7
# 3 1 3 5
# 4 1 3 6
But this also fails if the user data has a variable with the same name as my internal environment name,
some_data <- data.frame(x1 = c(1,2,3,3), y = c(6,7,5,6), e1 = c(1,2,3,4))
some_formula <- response ~ x1 + y + e1
my_function(some_data, some_formula)$model
# Error in get("y", e1) : invalid 'envir' argument
What is the proper way to avoid overlapping my internal variables with user-supplied variable names? I'd prefer a method for base R if possible.

Answer 1

Per docs of lm, the data argument handles variables in formula in two ways that are NOT mutually exclusive:
data
an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which lm is called.
Specifically, the calculated y vector in function runs into a name collision with potential y column in data frame. As you noticed and as emphasized in docs above, lm will default to y column before the y vector.
To avoid naming conflict, consider adding a suitable placeholder like response or dependent_variable and raise a tryCatch warning if user supply a data frame with same column name. This approach also allows you to avoid resetting the environment(formula).
my_function <- function(user_data, user_formula){
tryCatch({
user_data$response <- as.numeric(user_data[,1] > mean(user_data[,1]))
my_formula <- update.formula(user_formula, response ~ .)
my_model <- lm(my_formula, data = user_data, model = TRUE)
return(my_model)
}, warning = function(w) message("Please rename column 'response' in your data frame")
, error = function(e) print(e)
)
}
Output
some_data <- data.frame(x1 = c(1,2,3,3))
some_formula <- response ~ x1
my_function(some_data, some_formula)
# Call:
# lm(formula = my_formula, data = user_data, model = TRUE)
# Coefficients:
# (Intercept) x1
# -0.7273 0.5455
some_data <- data.frame(x1 = c(1,2,3,3), y = c(6,7,5,6), e1 = c(1,2,3,4))
some_formula <- response ~ x1 + y + e1
my_function(some_data, some_formula)
# Call:
# lm(formula = my_formula, data = user_data, model = TRUE)
# Coefficients:
# (Intercept) x1 y e1
# 1.667e+00 3.850e-16 -3.333e-01 3.333e-01
some_data <- data.frame(x1 = c(1,2,3,3), y = c(6,7,5,6), response=c(1,1,1,1))
some_formula <- response ~ x1 + y + response
my_function(some_data, some_formula)
# Please rename column 'response' in your data frame