updating docs, making it CRAN ready

Author: edwindj

.github/workflows/R-CMD-check.yaml

@@ -4,9 +4,10 @@ on:
   push:
     branches: [main, master]
   pull_request:
-    branches: [main, master]
 
-name: R-CMD-check
+name: R-CMD-check.yaml
+
+permissions: read-all
 
 jobs:
   R-CMD-check:
@@ -29,7 +30,7 @@ jobs:
       R_KEEP_PKG_SOURCE: yes
 
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
 
       - uses: r-lib/actions/setup-pandoc@v2
 
@@ -47,3 +48,4 @@ jobs:
       - uses: r-lib/actions/check-r-package@v2
         with:
           upload-snapshots: true
+          build_args: 'c("--no-manual","--compact-vignettes=gs+qpdf")'

.github/workflows/test-coverage.yaml

@@ -4,9 +4,10 @@ on:
   push:
     branches: [main, master]
   pull_request:
-    branches: [main, master]
 
-name: test-coverage
+name: test-coverage.yaml
+
+permissions: read-all
 
 jobs:
   test-coverage:
@@ -15,36 +16,47 @@ jobs:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
 
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
 
       - uses: r-lib/actions/setup-r@v2
         with:
           use-public-rspm: true
 
       - uses: r-lib/actions/setup-r-dependencies@v2
         with:
-          extra-packages: any::covr
+          extra-packages: any::covr, any::xml2
           needs: coverage
 
       - name: Test coverage
         run: |
-          covr::codecov(
+          cov <- covr::package_coverage(
             quiet = FALSE,
             clean = FALSE,
-            install_path = file.path(Sys.getenv("RUNNER_TEMP"), "package")
+            install_path = file.path(normalizePath(Sys.getenv("RUNNER_TEMP"), winslash = "/"), "package")
           )
+          print(cov)
+          covr::to_cobertura(cov)
         shell: Rscript {0}
 
+      - uses: codecov/codecov-action@v5
+        with:
+          # Fail if error if not on PR, or if on PR and token is given
+          fail_ci_if_error: ${{ github.event_name != 'pull_request' || secrets.CODECOV_TOKEN }}
+          files: ./cobertura.xml
+          plugins: noop
+          disable_search: true
+          token: ${{ secrets.CODECOV_TOKEN }}
+
       - name: Show testthat output
         if: always()
         run: |
           ## --------------------------------------------------------------------
-          find ${{ runner.temp }}/package -name 'testthat.Rout*' -exec cat '{}' \; || true
+          find '${{ runner.temp }}/package' -name 'testthat.Rout*' -exec cat '{}' \; || true
         shell: bash
 
       - name: Upload test results
         if: failure()
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
           name: coverage-test-failures
           path: ${{ runner.temp }}/package

DESCRIPTION

@@ -9,7 +9,7 @@ Authors@R: c( person("Edwin", "de Jonge", email = "[email protected]", role
 Description: Rule sets with validation rules may contain redundancies or contradictions. 
   Functions for finding redundancies and problematic rules are provided, 
   given a set a rules formulated with 'validate'.
-Depends: validate
+Depends: validate, R (>= 4.1.0)
 License: MIT + file LICENSE
 Encoding: UTF-8
 URL: https://github.com/data-cleaning/validatetools
@@ -18,3 +18,4 @@ Imports: methods, stats, utils, lpSolveAPI
 Suggests: testthat,
     covr
 RoxygenNote: 7.3.2
+Roxygen: list(markdown = TRUE)

NEWS.md

@@ -1,6 +1,6 @@
 # validatetools 0.6.0
 
-* added a fix when categorical variables where used, without a rule that specified the domain. Thanks to
+* added a fix when categorical variables where used, without a rule that specified the domain. (issue #15) Thanks to
 Romina Filippini and Simona Toti for reporting. 
 
 # validatetools 0.5.2

R/contradicted.R

@@ -4,10 +4,10 @@
 #' of the rules should stay and which should go? 
 #' @export
 #' @example ./examples/feasible.R
-#' @param x \code{\link{validator}} object with rules.
-#' @param rule_name \code{character} with the names of the rules that are causing infeasibility.
+#' @param x [validate::validator()] object with rules.
+#' @param rule_name `character` with the names of the rules that are causing infeasibility.
 #' @family feasibility
-#' @return \code{character} with conflicting rules.
+#' @return `character` with conflicting rules.
 is_contradicted_by <- function(x, rule_name){
   rn <- rule_name %in% names(x)
 

R/detect_boundary.R

@@ -1,20 +1,20 @@
 #' Detect the range for numerical variables
 #' 
 #' Detect for each numerical variable in a validation rule set, what its maximum and minimum values are.
-#' This allows for manual rule set checking: does rule set \code{x} overly constrain numerical values?
+#' This allows for manual rule set checking: does rule set `x` overly constrain numerical values?
 #' 
 #' This procedure only finds minimum and maximum values, but misses gaps.
 #' 
-#' @seealso \code{\link{detect_fixed_variables}}
+#' @seealso [detect_fixed_variables()]
 #' @references Statistical Data Cleaning with R (2017), Chapter 8, M. van der Loo, E. de Jonge
 #' @references Simplifying constraints in data editing (2015). Technical Report 2015|18, Statistics Netherlands, J. Daalmans
 #' @example ./examples/detect_boundary.R
 #' @export
-#' @param x \code{\link{validator}} object, rule set to be checked
+#' @param x [validate::validator()] object, rule set to be checked
 #' @param eps detected fixed values will have this precision.
 #' @param ... currently not used
 #' @family feasibility
-#' @return \code{\link{data.frame}} with columns "variable", "lowerbound", "upperbound".
+#' @return [data.frame()] with columns "variable", "lowerbound", "upperbound".
 detect_boundary_num <- function(x, eps = 1e-8, ...){
   x <- check_validator(x)
   prec <- -log(eps, 10)
@@ -62,11 +62,11 @@ detect_boundary_num <- function(x, eps = 1e-8, ...){
 #' 
 #' Detect viable domains for categorical variables
 #' @example ./examples/detect_boundary.R
-#' @param x \code{\link{validator}} object with rules
+#' @param x [validate::validator()] object with rules
 #' @param as_df return result as data.frame (before 0.4.5)
 #' @param ... not used
 #' @family feasibility
-#' @return \code{data.frame} with columns \code{$variable}, \code{$value}, \code{$min}, \code{$max}. Each row is a 
+#' @return `data.frame` with columns `$variable`, `$value`, `$min`, `$max`. Each row is a 
 #' category/value of a categorical variable.
 #' @export
 detect_boundary_cat <- function(x, ..., as_df = FALSE){

R/detect_fixed_variables.R

@@ -3,8 +3,8 @@
 #' Detects variables that have a fixed value in the rule set. 
 #' To simplify a rule set, these variables can be substituted with their value.
 #' @example ./examples/detect_fixed_variables.R
-#' @seealso \code{\link{simplify_fixed_variables}}
-#' @param x \code{\link{validator}} object with the validation rules.
+#' @seealso [simplify_fixed_variables()]
+#' @param x [validate::validator()] object with the validation rules.
 #' @param eps detected fixed values will have this precission.
 #' @param ... not used.
 #' @family redundancy
@@ -38,11 +38,11 @@ detect_fixed_variables <- function(x, eps = x$options("lin.eq.eps"), ...){
 #'   
 #' @export
 #' @example ./examples/detect_fixed_variables.R
-#' @param x \code{\link{validator}} object with validation rules
+#' @param x [validate::validator()] object with validation rules
 #' @param eps detected fixed values will have this precission.
-#' @param ... passed to \code{\link{substitute_values}}.
+#' @param ... passed to [substitute_values()].
 #' @family redundancy
-#' @return \code{\link{validator}} object in which 
+#' @return [validate::validator()] object in which 
 simplify_fixed_variables <- function(x, eps = 1e-8, ...){
   x <- check_validator(x)
   fv <- detect_fixed_variables(x, eps = eps, ...)

R/dnf.R

@@ -262,8 +262,15 @@ fix_cat_domain <- function(rules){
     lapply(function(x){
       a <- sapply(x, \(x) 1)
       type <- sapply(x, \(x) "binary")
-      mip_rule(a = a, op = "<=", b = 1, rule = "domain", type = type)
+      mip_rule(a = a, op = "<=", b = 1, rule = ".domain.", type = type)
     })
+  cv <- 
+    lapply(names(cv), function(n){
+      mr <- cv[[n]]
+      mr$rule <- paste0(".domain.", n)
+      mr
+    })
+  
   c(rules, unname(cv))
 }
 

R/feasible.R

@@ -5,7 +5,7 @@
 #' categorical and conditional rules in a rule set are consistent.
 #'  
 #' @example ./examples/feasible.R
-#' @param x \code{validator} object with validation rules.
+#' @param x `validator` object with validation rules.
 #' @param ... not used
 #' @family feasibility
 #' @return TRUE or FALSE
@@ -28,13 +28,13 @@ is_feasible <- function(x, ...){
 #' 
 #' Make an infeasible system feasible, by removing the minimum (weighted) number of rules, such that the remaining
 #' rules are not conflicting.
-#' This function uses \code{\link{detect_infeasible_rules}} for determining the rules to be removed.
+#' This function uses [detect_infeasible_rules()] for determining the rules to be removed.
 #' @export
-#' @param x \code{\link{validator}} object with the validation rules.
-#' @param ... passed to \code{\link{detect_infeasible_rules}}
+#' @param x [validate::validator()] object with the validation rules.
+#' @param ... passed to [detect_infeasible_rules()]
 #' @family feasibility
 #' @example ./examples/feasible.R
-#' @return \code{\link{validator}} object with feasible rules.
+#' @return [validate::validator()] object with feasible rules.
 make_feasible <- function(x, ...){
   dropping <- detect_infeasible_rules(x, ...) 
 
@@ -54,12 +54,12 @@ make_feasible <- function(x, ...){
 #' to you than others. This can be mitigated by supplying weights for the rules. Default weight is 1.
 #' @export
 #' @example ./examples/feasible.R
-#' @param x \code{\link{validator}} object with rules
-#' @param weight optional named \code{\link{numeric}} with weights. Unnamed variables in the weight are given the default
-#' weight \code{1}.
+#' @param x [validate::validator()] object with rules
+#' @param weight optional named [numeric()] with weights. Unnamed variables in the weight are given the default
+#' weight `1`.
 #' @family feasibility
 #' @param ... not used
-#' @return \code{character} with the names of the rules that are causing infeasibility.
+#' @return `character` with the names of the rules that are causing infeasibility.
 detect_infeasible_rules <- function(x, weight = numeric(), ...){
   if (!is_infeasible(x)){
     return(character())

R/implied_by.R

@@ -3,11 +3,11 @@
 #' Find out which rules are causing rule_name(s) to be redundant.
 #' @example ./examples/redundancy.R
 #' @export
-#' @param x \code{\link{validator}} object with rule
-#' @param rule_name \code{character} with the names of the rules to be checked
+#' @param x [validate::validator()] object with rule
+#' @param rule_name `character` with the names of the rules to be checked
 #' @param ... not used
 #' @family redundancy
-#' @return \code{character} with the names of the rule that cause the implication.
+#' @return `character` with the names of the rule that cause the implication.
 is_implied_by <- function(x, rule_name, ...){
   check_validator(x)
   idx <- match(rule_name, names(x), 0)