ard_compare draft #437

[malanbos]

What changes are proposed in this pull request?

New function to compare two ARDs: ard_compare

Fixes#437 , @malanbos

Comes with top-level ard_compare function, as well as a script with ard_compare_helpers.R, and check_environment.R to modularize it.

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Optional Reverse Dependency Checks:

Install checked with pak::pak("Genentech/checked") or pak::pak("checked")

# Check dev versions of `cardx`, `gtsummary`, and `tfrmt` which are in the `ddsjoberg` R Universe
Rscript -e "options(checked.check_envvars = c(NOT_CRAN = TRUE)); checked::check_rev_deps(path = '.', n = parallel::detectCores() - 2L, repos = c('https://ddsjoberg.r-universe.dev', 'https://cloud.r-project.org'))"

# Check CRAN reverse dependencies but run tests skipped on CRAN
Rscript -e "options(checked.check_envvars = c(NOT_CRAN = TRUE)); checked::check_rev_deps(path = '.', n = parallel::detectCores() - 2, repos = 'https://cloud.r-project.org')"

# Check CRAN reverse dependencies in a CRAN-like environment
Rscript -e "options(checked.check_envvars = c(NOT_CRAN = FALSE), checked.check_build_args = '--as-cran'); checked::check_rev_deps(path = '.', n = parallel::detectCores() - 2, repos = 'https://cloud.r-project.org')"

[ddsjoberg]

Thank you @malanbos for this!

For now, let's skip env handling. I think it's more complex than we need. We can revisit in the future. Let me know if you'd like to discuss

[ddsjoberg]

All of our functions that begin with ard_*() create new ARDs. Let's name the function compare_ard().

[ddsjoberg]

Let's make the default value keys = c(all_ard_groups(), all_ard_variables(), any_of(c("variable", "variable_level", "stat_name"))).

[ddsjoberg]

Let's add an argument of the columns to compare, compare = any_of(c("stat_label", "stat", "stat_fmt")).

(Is there anything else we should compare by default?)

[ddsjoberg]

Let's remove the env checking for now. It's quite complicated.

[ddsjoberg]

Here we can evaluate the keys and compare columns with

keys <- .process_keys_arg(x, y, keys = {{ keys }})
compare <- .process_compare_arg(x, y, compare = {{ compare }})



# outside the function we define these functions

.process_keys_arg <- function(x, y, keys) {
  keys <- intersect(cards_select({{ keys }}, data = x), cards_select({{ keys }}, data = y))
  .check_not_empty(keys)
  cli::cli_inform("The comparison {.arg keys} are {.emph {.val {keys}}}.")
  keys
}

.process_compare_arg <- function(x, y, compare) {
  # add checks and return evaluated compare vector...
}

.check_not_empty <- function(x, arg_name = rlang::caller_arg(x)) {
  if (rlang::is_empty()) {
     cli::cli_abort("The {.arg {arg_name}} argument cannot be empty.")
  }
  invisible(x)
}

[ddsjoberg]

Let's just use the columns provided in the compare argument to assess which comparisons to make. We can compare all columns in the same way.

[ddsjoberg]

Here we can initialize an empty list of results.

results <- rlang::rep_named(c("rows_in_x_not_y", "rows_in_y_not_x"), list(NULL))
results[["compare"]] <- rlang::rep_named(compare, list(NULL))

In this example the "compare" element will also be a named list. The names are the columns that we compare.

We could then follow this up with calls to functions that will populate these parts of the list, e.g.

results[["rows_in_x_not_y"]] <- .compare_rows(x, y) # returns the results of the anti join of x and y on the key columns
results[["rows_in_y_not_x"]] <- .compare_rows(y, x) # same as above, but reversed
results[["compare"]] <- .compare_columns(x, y, compare) # loop through the columns we will compare and return a named list of data frames where each data frame contains the rows that are not equal between x and y. The data frame will have the key columns and the two columns compared (from x and y).

[ddsjoberg]

Lastly, the function will return the results object, and add a class onto this list.

After we get this settled, we will write a print method for class to make it nice.