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Merge pull request #30 from tdhock/alevels
add alevels, tests, examples, version++
2 parents d135f09 + 93bb9f4 commit c6cd10e

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‎.github/workflows/test-coverage.yaml‎

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GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
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steps:
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- uses: actions/checkout@v3
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- uses: actions/checkout@v4
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- uses: r-lib/actions/setup-r@v2
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with:
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- name: Upload test results
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if: failure()
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uses: actions/upload-artifact@v3
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uses: actions/upload-artifact@v4
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with:
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name: coverage-test-failures
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path: ${{ runner.temp }}/package

‎DESCRIPTION‎

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person("Toby", "Hocking",
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email="toby.hocking@r-project.org",
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role=c("aut", "cre")))
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Version: 2025.1.21
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Version: 2025.3.24
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License: GPL-3
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Title: Named Capture to Data Tables
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Description: User-friendly functions for extracting a data

‎NAMESPACE‎

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export(alevels)
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export(altlist)
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export(alternatives_with_shared_groups)
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export(measure)

‎NEWS‎

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Changes in version 2025.3.24
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- alevels() helper creates a pattern string that matches literal alternatives, and a conversion function that outputs a factor.
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Changes in version 2025.1.21
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- capture_first_vec, capture_all_str, capture_first_df, capture_first_glob, measure, capture_melt_single, capture_melt_multiple now support type.convert argument. TRUE means to use utils::type.convert(x,as.is=TRUE) as default conversion function (as.is=TRUE means to return character instead of factor), FALSE means identity, and otherwise can be any function to use as default conversion.

‎R/alevels.R‎

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alevels <- structure(function # Alternative levels
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### Create a pattern and a conversion function based on alternative
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### string literals.
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(...
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### Optional names are string literals to match; values are
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### corresponding factor levels.
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){
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old2new <- c(...)
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if(is.null(names(old2new))){
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names(old2new) <- old2new
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}
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to.rep <- names(old2new)==""
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names(old2new)[to.rep] <- old2new[to.rep]
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list(
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paste(names(old2new), collapse="|"),
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function(x)factor(old2new[x], old2new))
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### List of pattern and conversion function that returns factor.
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}, ex=function(){
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## Example 0: melt iris data with literal alternatives -> chr columns.
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ichr <- nc::capture_melt_single(
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iris[1,],
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part="Sepal|Petal",
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"[.]",
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dim="Length|Width")
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factor(ichr$part)#default factor levels are alphabetical.
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## Example 1: melt iris data with alevels() -> factor columns.
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(ifac <- nc::capture_melt_single(
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iris[1,],
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part=nc::alevels("Sepal","Petal"),
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"[.]",
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dim=nc::alevels("Length","Width")))
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ifac$part #factor with levels in same order as given in alevels().
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## Example 2: alevels(literals_to_match="levels_to_use_in_output").
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tv_wide <- data.frame(
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id=0,
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train.classif.logloss = 1, train.classif.ce = 2,
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valid.classif.logloss = 3, valid.classif.ce = 4)
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nc::capture_melt_single(
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tv_wide,
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set=nc::alevels(valid="validation", train="subtrain"),
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"[.]classif[.]",
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measure=nc::alevels(ce="error_prop", auc="AUC", "logloss"))
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## Example 3: additional groups which output character columns.
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nc::capture_melt_single(
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tv_wide,
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set_chr=list(set_fac=nc::alevels(valid="validation", train="subtrain")),
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"[.]classif[.]",
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measure_chr=list(measure_fac=nc::alevels(ce="error_prop", auc="AUC", "logloss")))
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})
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‎man/alevels.Rd‎

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\name{alevels}
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\alias{alevels}
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\title{Alternative levels}
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\description{Create a pattern and a conversion function based on alternative
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string literals.}
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\usage{alevels(...)}
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\arguments{
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\item{\dots}{Optional names are string literals to match; values are
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corresponding factor levels.}
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}
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\value{List of pattern and conversion function that returns factor.}
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\author{Toby Hocking <toby.hocking@r-project.org> [aut, cre]}
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\examples{
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## Example 0: melt iris data with literal alternatives -> chr columns.
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ichr <- nc::capture_melt_single(
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iris[1,],
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part="Sepal|Petal",
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"[.]",
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dim="Length|Width")
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factor(ichr$part)#default factor levels are alphabetical.
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## Example 1: melt iris data with alevels() -> factor columns.
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(ifac <- nc::capture_melt_single(
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iris[1,],
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part=nc::alevels("Sepal","Petal"),
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"[.]",
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dim=nc::alevels("Length","Width")))
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ifac$part #factor with levels in same order as given in alevels().
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## Example 2: alevels(literals_to_match="levels_to_use_in_output").
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tv_wide <- data.frame(
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id=0,
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train.classif.logloss = 1, train.classif.ce = 2,
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valid.classif.logloss = 3, valid.classif.ce = 4)
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nc::capture_melt_single(
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tv_wide,
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set=nc::alevels(valid="validation", train="subtrain"),
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"[.]classif[.]",
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measure=nc::alevels(ce="error_prop", auc="AUC", "logloss"))
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## Example 3: additional groups which output character columns.
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nc::capture_melt_single(
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tv_wide,
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set_chr=list(set_fac=nc::alevels(valid="validation", train="subtrain")),
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"[.]classif[.]",
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measure_chr=list(measure_fac=nc::alevels(ce="error_prop", auc="AUC", "logloss")))
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}

‎tests/testthat/test-CRAN-alternatives.R‎

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expect_identical(match.dt[["day"]], "17")
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expect_identical(match.dt[["year"]], "1983")
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})
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test_that("alevels ok with no names", {
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ifac <- nc::capture_melt_single(
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iris[1,],
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part=nc::alevels("Sepal","Petal"),
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"[.]",
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dim=nc::alevels("Length","Width"))
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expect_identical(levels(ifac$part), c("Sepal","Petal"))
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expect_identical(levels(ifac$dim), c("Length","Width"))
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})
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test_that("alevels() ok with all and some names", {
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tv_wide <- data.frame(
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id=0,
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train.classif.logloss = 1, train.classif.ce = 2,
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valid.classif.logloss = 3, valid.classif.ce = 4)
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tv_long <- nc::capture_melt_single(
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tv_wide,
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set_chr=list(set_fac=nc::alevels(valid="validation", train="subtrain")),
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"[.]classif[.]",
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measure_chr=list(measure_fac=nc::alevels(ce="error_prop", auc="AUC", "logloss")))
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expect_is(tv_long$set_chr, "character")
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expect_is(tv_long$measure_chr, "character")
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expect_identical(levels(tv_long$set_fac), c("validation","subtrain"))
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expect_identical(levels(tv_long$measure_fac), c("error_prop","AUC","logloss"))
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})

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