capturing functions using rlang's enexprs - r

Question

I'm writing a function such that callers of this function can write schemas declaratively:
myschema <- Schema(
patientID = character,
temp = numeric,
treated = logical,
reason_treated = factor(levels=c('fever', 'chills', 'nausea'))
)
Later, I'd to be able to assemble dataframes using the types declared in this schema. I think the best candidate for this job is to use the metaprogramming features available in rlang:
Schema = function(...) {
schematypes = rlang::enexprs(...)
}
However, most of the examples pertain to capturing the expression and thereafter using them as arguments to functions, rather than as functions themselves. That is, I'm finding it hard to capture the right side of the following expression:
patientID = character
and then later being able to evaluate it later as character(myvec), whenever I get myvec. The same applies to the following:
reason_treated = factor(levels=c('fever', 'chills', 'nausea'))
which I would later like to evaluate as factor(myvec, levels=c('fever', 'chills', 'nausea'))
Thanks!

Answer 1

If I understand correctly, you are effectively constructing a schema out of functions, and you want to apply those functions to some arguments when those become available. This falls under the umbrella of functional programming rather than rlang metaprogramming.
A large portion of the functionality you want is already captured by purrr::map and its "engine" as_mapper. You can employ it directly to define
Schema <- function(...) { purrr::map( list(...), purrr::as_mapper ) }
You can now employ it to build new schemas like you suggested (with minor modifications to function definitions):
myschema <- Schema(
patientID = as.character, # Note the correct function name
temp = as.numeric, # Ditto
treated = as.logical, # Tritto
reason_treated = ~factor(., levels=c('fever', 'chills', 'nausea'))
)
# $patientID
# function (x, ...)
# as.character(x = x, ...)
# <environment: base>
#
# $temp
# function (x, ...)
# as.double(x = x, ...)
# <environment: base>
#
# $treated
# function (x, ...)
# as.logical(x = x, ...)
# <environment: base>
#
# $reason_treated
# function (..., .x = ..1, .y = ..2, . = ..1)
# factor(., levels = c("fever", "chills", "nausea"))
# <bytecode: 0x00000000027a2d00>
Given your new schema, registering new patients can be done using a sister function of map that lines up arguments from two lists / vectors:
register_patient <- function(myvec) { purrr::map2( myschema, myvec, ~.x(.y) ) }
JohnDoe <- register_patient( c(1234, 100, TRUE, "fever") )
# $patientID
# [1] "1234"
#
# $temp
# [1] 100
#
# $treated
# [1] TRUE
#
# $reason_treated
# [1] fever
# Levels: fever chills nausea
Let's verify the type of each element:
purrr::map( JohnDoe, class )
# $patientID
# [1] "character"
#
# $temp
# [1] "numeric"
#
# $treated
# [1] "logical"
#
# $reason_treated
# [1] "factor"