Programmatically generate formulas for lmer

Machine Learning and Modeling
1

Is there a robust way to programmatically create non-standard formulas? I know about reformulate but that doesn't handle random effects specifications, etc. I want to do something like the following:

library(lme4)

outcome <- "Sepal.Width"
fixed_effects <- c("Species", "Petal.Width")
random_intercepts <- c("Sepal.Length", "Petal.Length")

randomify <- function(feats) paste0("(1|", feats, ")", collapse = " + ")
fixed <- paste0(fixed_effects, collapse = " + ")
random <- randomify(random_intercepts)

formula <- as.formula(paste(outcome, "~", fixed, "+", random))
lmer(formula, iris)
#> Linear mixed model fit by REML ['lmerMod']
#> Formula: Sepal.Width ~ Species + Petal.Width + (1 | Sepal.Length) + (1 |  
#>     Petal.Length)
#>    Data: iris
#> REML criterion at convergence: 76.0282
#> Random effects:
#>  Groups       Name        Std.Dev.
#>  Petal.Length (Intercept) 0.04213 
#>  Sepal.Length (Intercept) 0.08105 
#>  Residual                 0.28806 
#> Number of obs: 150, groups:  Petal.Length, 43; Sepal.Length, 35
#> Fixed Effects:
#>       (Intercept)  Speciesversicolor   Speciesvirginica  
#>            3.2558            -1.5048            -1.8273  
#>       Petal.Width  
#>            0.7613
2

Since this will all live in a dataframe anyway, maybe I just hit it with a pass of janitor::clean_names first?

3

Your code above is basically what I end up doing. I don't know of anything else.