perf: optimized core tabulation and summary functions; added benchmark suite

[kpagacz]

• optimized core tabulation and summary functions, added a benchmark suite (related to Performance improvement of gtsummary nestdevs-tasks#118)

The below 5 functions from the cards package contributed the most to runtime of the below gtsummary benchmark:

    suppressPackageStartupMessages({
      library(profvis)
      library(dplyr)
      library(gtsummary)
    })

    # Replicate the trial dataset 10x to simulate a moderately sized dataset
    data_big <- trial[rep(seq_len(nrow(trial)), 10), ]

    # Run profvis to generate the interactive flame graph
    profvis({
      for(i in 1:2) {
        tbl_strata(
          data_big,
          strata = grade,
          .tbl_fun = ~ .x |> tbl_summary(by = trt, include = c(age, marker))
        )
      }
    })

A more detailed list:

• ard_summary.data.frame : Replaced deeply nested purrr::map() , purrr::map2() , and dplyr::bind_rows() iterations within .calculate_stats_as_ard and .lst_results_as_df with lapply() and data.frame() assignments. (~36% speedup)

• ard_tabulate.data.frame : refactored .calculate_tabulation_statistics to remove tidyr::pivot_longer and dplyr::mutate(across()) (~36% speedup)

• tidy_ard_column_order & tidy_ard_row_order : removed tidyselect operations and dplyr::arrange , substituting them base R string searches ( grepl ) and order() vector-subsetting. (~28x speedup)

• ard_tabulate_value.data.frame : replaced pmap with a vectorized O(N) which() / %in% base R scan. (~16x speedup)

• ard_total_n.data.frame : Completely avoided a full-dataframe dplyr::mutate scan by creating a direct 1-row tibble object for the total N ARD object. (~45x speedup)

  CI Additions:

• Added .github/workflows/benchmark.yaml to trigger automated profiling workflows on PRs prefixed with perf: .

• Unified benchmark code into .github/scripts/benchmark.R , which runs bench::mark() on core functions and automatically posts a markdown table reporting itr/sec and memory allocation directly to the PR comment thread.

Related to insightsengineering/nestdevs-tasks#118

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Pre-review Checklist (if item does not apply, mark is as complete)

• All GitHub Action workflows pass with a ✅
• PR branch has pulled the most recent updates from master branch: usethis::pr_merge_main()
• If a bug was fixed, a unit test was added.
• Code coverage is suitable for any new functions/features (generally, 100% coverage for new code): devtools::test_coverage()
• Request a reviewer

Reviewer Checklist (if item does not apply, mark is as complete)

• If a bug was fixed, a unit test was added.
• Run pkgdown::build_site(). Check the R console for errors, and review the rendered website.
• Code coverage is suitable for any new functions/features: devtools::test_coverage()

When the branch is ready to be merged:

• Update NEWS.md with the changes from this pull request under the heading "# cards (development version)". If there is an issue associated with the pull request, reference it in parentheses at the end update (see NEWS.md for examples).
• All GitHub Action workflows pass with a ✅
• Approve Pull Request
• Merge the PR. Please use "Squash and merge" or "Rebase and merge".

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')"

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[kpagacz]

I have read the CLA Document and I hereby sign the CLA