I'm trying to make a new release of PKNCA, and 8 tests failed on CRAN with this attempt. Do the errors at the bottom make sense to you (I can try to troubleshoot if they're not obvious).
Steps to reproduce the issue are run aNCA with the proposed release version of PKNCA (humanpred/pknca#523).
Package check result: OK
Changes to worse in reverse depends:
Package: aNCA
Check: tests
New result: ERROR
Running ‘testthat.R’ [44s/44s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> # This file is part of the standard setup for testthat.
> # It is recommended that you do not modify it.
> #
> # Where should you do additional test configuration?
> # Learn more about the roles of various files in:
> # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview
> # * https://testthat.r-lib.org/articles/special-files.html
>
> library(testthat)
> library(shinytest2)
> library(aNCA)
>
> test_check("aNCA")
Joining with `by = join_by(ATPTREF)`
* USUBJID -> id
* PARAM -> analyte
* DOSETRT -> drug
* AVAL -> CONC
* AVALU -> CONC_unit
* DOSE -> dose
* ADOSEDUR -> dosedur
* AFRLT -> TIME
* ATPTREF -> DOSNO
Row 1: Converted volume from 1 L to 1000 mL based on concentration unit ug/mL
Row 1: Converted volume from 2 mL to 2 g based on concentration unit mg/g
Row 1: Converted volume from 1 L to 1000 mL based on concentration unit ug/mL
Row 2: Converted volume from 0.5 mL to 0.5 g based on concentration unit mg/g
Loading required namespace: ggh4x
Saving _problems/test-pivot_wider_pknca_results-71.R
Saving _problems/test-pivot_wider_pknca_results-85.R
Saving _problems/test-pivot_wider_pknca_results-92.R
Saving _problems/test-pivot_wider_pknca_results-119.R
Saving _problems/test-pivot_wider_pknca_results-133.R
Saving _problems/test-pivot_wider_pknca_results-147.R
Saving _problems/test-pivot_wider_pknca_results-155.R
Saving _problems/test-pivot_wider_pknca_results-206.R
[ FAIL 8 | WARN 6 | SKIP 19 | PASS 733 ]
══ Skipped tests (19) ══════════════════════════════════════════════════════════
• On CRAN (16): 'test-g_pkcg.R:11:3', 'test-g_pkcg.R:25:3',
'test-g_pkcg.R:40:3', 'test-g_pkcg.R:74:3', 'test-g_pkcg.R:124:3',
'test-g_pkcg.R:132:3', 'test-g_pkcg.R:140:3', 'test-g_pkcg.R:161:3',
'test-g_pkcg.R:180:3', 'test-g_pkcg.R:213:3', 'test-g_pkcg.R:267:3',
'test-g_pkcg.R:279:3', 'test-read_pk.R:23:5', 'test-read_pk.R:36:5',
'test-read_pk.R:49:5', 'test-read_pk.R:62:5'
• Skipping (1): 'test-e2e-runner.R:5:3'
• empty test (2): 'test-PKNCA.R:249:3', 'test-calculate_summary_stats.R:104:3'
══ Failed tests ════════════════════════════════════════════════════════════════
── Failure ('test-pivot_wider_pknca_results.R:71:3'): pivot_wider_pknca_results ──
Expected `pivot_wider_pknca_results(pknca_res)` not to throw any errors.
Actually got a <simpleError> with message:
arguments imply differing number of rows: 1, 2, 7
── Failure ('test-pivot_wider_pknca_results.R:85:5'): pivot_wider_pknca_results / produces a data.frame ──
Expected `pivoted_res` to be an S3 object.
Actual OO type: none.
── Error ('test-pivot_wider_pknca_results.R:92:5'): pivot_wider_pknca_results / produces a data.frame with expected format when only reshaping main intervals ──
<tibble_error_assign_incompatible_size/tibble_error/rlang_error/error/condition>
Error in ``[<-`(`*tmp*`, , mapping_cols, value = structure(list(AUCLST.hr.ng.mL..8 = c(8, 8), AUCLST.hr.ng.mL..5.4425290000238 = c(5.4425290000238, 5.4425290000238), AUCLST.hr.ng.mL..5.5675290000238 = c(5.5675290000238, 5.5675290000238), AUCLST.hr.ng.mL..14.5112775765674 = c(14.5112775765674, 14.5112775765674), AUCLST.hr.ng.mL..14.5112775765674.1 = c(14.5112775765674, 14.5112775765674), AUCLST.hr.ng.mL..9 = c(9, 9), AUCLST.hr.ng.mL..5.07253965992077 = c(5.07253965992077, 5.07253965992077), CMAX.ng.mL..4 = c(4, 4), CMAX.ng.mL..2 = c(2, 2), CMAX.ng.mL..2.1 = c(2, 2), CMAX.ng.mL..5.59016994374947 = c(5.59016994374947, 5.59016994374947), CMAX.ng.mL..5.59016994374947.1 = c(5.59016994374947, 5.59016994374947), CMAX.ng.mL..2.2 = c(2, 2), CMAX.ng.mL..2.5 = c(2.5, 2.5), TMAX.hr..4 = c(4, 4), TMAX.hr..1.5 = c(1.5, 1.5), TMAX.hr..1.5.1 = c(1.5, 1.5), TMAX.hr..0 = c(0, 0), TMAX.hr..0.1 = c(0, 0), TMAX.hr..c.0..0. = c(0, 0), TMAX.hr..1.5.2 = c(1.5, 1.5), TLST.hr..4 = c(4, 4), TLST.hr..4.5 = c(4.5, 4.5), TLST.hr..4.5.1 = c(4.5, 4.5), TLST.hr..4.5.2 = c(4.5, 4.5), TLST.hr..4.5.3 = c(4.5, 4.5), TLST.hr..c.4.5..4.5. = c(4.5, 4.5), TLST.hr..4.5.4 = c(4.5, 4.5), CLST.ng.mL..4 = c(4, 4), CLST.ng.mL..0.5 = c(0.5, 0.5), CLST.ng.mL..0.5.1 = c(0.5, 0.5), CLST.ng.mL..1 = c(1, 1), CLST.ng.mL..1.1 = c(1, 1), CLST.ng.mL..2 = c(2, 2), CLST.ng.mL..0.5.2 = c(0.5, 0.5), LAMZ.1.hr..NA_real_ = c(NA_real_, NA_real_), LAMZ.1.hr..0.549306144334055 = c(0.549306144334055, 0.549306144334055), LAMZ.1.hr..0.549306144334055.1 = c(0.549306144334055, 0.549306144334055), LAMZ.1.hr..0.549306144334055.2 = c(0.549306144334055, 0.549306144334055), LAMZ.1.hr..0.549306144334055.3 = c(0.549306144334055, 0.549306144334055), LAMZ.1.hr..NA_real_.1 = c(NA_real_, NA_real_), LAMZ.1.hr..0.346573590279973 = c(0.346573590279973, 0.346573590279973), R2.NA_real_ = c(NA_real_, NA_real_), R2.0.977653958518262 = c(0.977653958518262, 0.977653958518262), R2.0.977653958518262.1 = c(0.977653958518262, 0.977653958518262), R2.0.977653958518262.2 = c(0.977653958518262, 0.977653958518262 ), R2.0.977653958518262.3 = c(0.977653958518262, 0.977653958518262), R2.NA_real_.1 = c(NA_real_, NA_real_), R2.0.99971691034068 = c(0.99971691034068, 0.99971691034068), R2ADJ.NA_real_ = c(NA_real_, NA_real_), R2ADJ.0.955307917036524 = c(0.955307917036524, 0.955307917036524), R2ADJ.0.955307917036524.1 = c(0.955307917036524, 0.955307917036524), R2ADJ.0.955307917036524.2 = c(0.955307917036524, 0.955307917036524), R2ADJ.0.955307917036524.3 = c(0.955307917036524, 0.955307917036524), R2ADJ.NA_real_.1 = c(NA_real_, NA_real_), R2ADJ.0.999433820681361 = c(0.999433820681361, 0.999433820681361), lambda.z.corrxy.NA_real_ = c(NA_real_, NA_real_), lambda.z.corrxy..0.988763853768058 = c(-0.988763853768058, -0.988763853768058), lambda.z.corrxy..0.988763853768058.1 = c(-0.988763853768058, -0.988763853768058), lambda.z.corrxy..0.988763853768058.2 = c(-0.988763853768058, -0.988763853768058), lambda.z.corrxy..0.988763853768058.3 = c(-0.988763853768058, -0.988763853768058), lambda.z.corrxy.NA_real_.1 = c(NA_real_, NA_real_ ), lambda.z.corrxy..0.999858445151453 = c(-0.999858445151453, -0.999858445151453), LAMZLL.hr..NA_real_ = c(NA_real_, NA_real_), LAMZLL.hr..2.5 = c(2.5, 2.5), LAMZLL.hr..2.5.1 = c(2.5, 2.5), LAMZLL.hr..2.5.2 = c(2.5, 2.5), LAMZLL.hr..2.5.3 = c(2.5, 2.5), LAMZLL.hr..NA_real_.1 = c(NA_real_, NA_real_), LAMZLL.hr..2.5.4 = c(2.5, 2.5), LAMZUL.hr..NA_real_ = c(NA_real_, NA_real_), LAMZUL.hr..4.5 = c(4.5, 4.5), LAMZUL.hr..4.5.1 = c(4.5, 4.5), LAMZUL.hr..4.5.2 = c(4.5, 4.5), LAMZUL.hr..4.5.3 = c(4.5, 4.5 ), LAMZUL.hr..NA_real_.1 = c(NA_real_, NA_real_), LAMZUL.hr..4.5.4 = c(4.5, 4.5), LAMZNPT.count..NA_real_ = c(NA_real_, NA_real_), LAMZNPT.count..3 = c(3, 3), LAMZNPT.count..3.1 = c(3, 3), LAMZNPT.count..3.2 = c(3, 3), LAMZNPT.count..3.3 = c(3, 3), LAMZNPT.count..NA_real_.1 = c(NA_real_, NA_real_), LAMZNPT.count..3.4 = c(3, 3), CLSTP.ng.mL..NA_real_ = c(NA_real_, NA_real_), CLSTP.ng.mL..0.524557531710824 = c(0.524557531710824, 0.524557531710824), CLSTP.ng.mL..0.524557531710824.1 = c(0.524557531710824, 0.524557531710824), CLSTP.ng.mL..1.04911506342165 = c(1.04911506342165, 1.04911506342165), CLSTP.ng.mL..1.04911506342165.1 = c(1.04911506342165, 1.04911506342165), CLSTP.ng.mL..NA_real_.1 = c(NA_real_, NA_real_), CLSTP.ng.mL..0.498319272249044 = c(0.498319272249044, 0.498319272249044), LAMZHL.hr..NA_real_ = c(NA_real_, NA_real_), LAMZHL.hr..1.26185950714292 = c(1.26185950714292, 1.26185950714292), LAMZHL.hr..1.26185950714292.1 = c(1.26185950714292, 1.26185950714292), LAMZHL.hr..1.26185950714292.2 = c(1.26185950714292, 1.26185950714292), LAMZHL.hr..1.26185950714292.3 = c(1.26185950714292, 1.26185950714292), LAMZHL.hr..NA_real_.1 = c(NA_real_, NA_real_), LAMZHL.hr..2 = c(2, 2), LAMZSPN.NA_real_ = c(NA_real_, NA_real_), LAMZSPN.1.58496250072116 = c(1.58496250072116, 1.58496250072116), LAMZSPN.1.58496250072116.1 = c(1.58496250072116, 1.58496250072116), LAMZSPN.1.58496250072116.2 = c(1.58496250072116, 1.58496250072116), LAMZSPN.1.58496250072116.3 = c(1.58496250072116, 1.58496250072116), LAMZSPN.NA_real_.1 = c(NA_real_, NA_real_), LAMZSPN.1 = c(1, 1), AUCIFO.hr.ng.mL..NA_real_ = c(NA_real_, NA_real_), AUCIFO.hr.ng.mL..6.35276822665064 = c(6.35276822665064, 6.35276822665064), AUCIFO.hr.ng.mL..6.47776822665064 = c(6.47776822665064, 6.47776822665064), AUCIFO.hr.ng.mL..16.3317560298211 = c(16.3317560298211, 16.3317560298211), AUCIFO.hr.ng.mL..16.3317560298211.1 = c(16.3317560298211, 16.3317560298211), AUCIFO.hr.ng.mL..NA_real_.1 = c(NA_real_, NA_real_), AUCIFO.hr.ng.mL..6.51523470080973 = c(6.51523470080973, 6.51523470080973 )), class = "data.frame", row.names = c(NA, -2L)))`: Assigned data `as.data.frame(...)` must be compatible with existing data.
x Existing data has 7 rows.
x Element 1 of assigned data has 2 rows.
i Only vectors of size 1 are recycled.
Caused by error in `vectbl_recycle_rhs_rows()`:
! Can't recycle input of size 2 to size 7.
── Failure ('test-pivot_wider_pknca_results.R:119:5'): pivot_wider_pknca_results / reshapes PKNCA results correctly when also considering AUC intervals ──
Expected `pivoted_res` to be an S3 object.
Actual OO type: none.
── Error ('test-pivot_wider_pknca_results.R:133:5'): pivot_wider_pknca_results / reshapes PKNCA results correctly when also considering AUC intervals ──
Error in `expect_setequal(colnames(pivoted_res), expected_colnames)`: `object` must be a vector, not `NULL`.
Backtrace:
▆
1. └─testthat::expect_setequal(object = colnames(pivoted_res)) at test-pivot_wider_pknca_results.R:133:5
2. └─testthat:::check_vector(act$val, error_arg = "object")
3. └─testthat:::stop_input_type(x, "a vector", arg = error_arg, call = error_call)
4. └─rlang::abort(message, ..., call = call, arg = arg)
── Error ('test-pivot_wider_pknca_results.R:147:5'): pivot_wider_pknca_results / rounds numeric values to three decimals ──
Error in `round(actual_values, 3)`: non-numeric argument to mathematical function
── Error ('test-pivot_wider_pknca_results.R:155:5'): pivot_wider_pknca_results / adds appropriate labels to columns (CDISC PPTEST) ──
Error in `formatters::var_labels(pivoted_res)`: is.data.frame(x) is not TRUE
Backtrace:
▆
1. └─formatters::var_labels(pivoted_res) at test-pivot_wider_pknca_results.R:155:5
2. └─base::stopifnot(is.data.frame(x))
── Error ('test-pivot_wider_pknca_results.R:206:5'): pivot_wider_pknca_results / excludes the presence of the column PPANMETH ──
Error in `(function (..., row.names = NULL, check.rows = FALSE, check.names = TRUE, fix.empty.names = TRUE, stringsAsFactors = FALSE) { data.row.names <- if (check.rows && is.null(row.names)) function(current, new, i) { if (is.character(current)) new <- as.character(new) if (is.character(new)) current <- as.character(current) if (anyDuplicated(new)) return(current) if (is.null(current)) return(new) if (all(current == new) || all(current == "")) return(new) stop(gettextf("mismatch of row names in arguments of 'data.frame', item %d", i), domain = NA) } else function(current, new, i) { current %||% if (anyDuplicated(new)) { warning(gettextf("some row.names duplicated: %s --> row.names NOT used", paste(which(duplicated(new)), collapse = ",")), domain = NA) current } else new } object <- as.list(substitute(list(...)))[-1L] mirn <- missing(row.names) mrn <- is.null(row.names) x <- list(...) n <- length(x) if (n < 1L) { if (!mrn) { if (is.object(row.names) || !is.integer(row.names)) row.names <- as.character(row.names) if (anyNA(row.names)) stop("row names contain missing values") if (anyDuplicated(row.names)) stop(gettextf("duplicate row.names: %s", paste(unique(row.names[duplicated(row.names)]), collapse = ", ")), domain = NA) } else row.names <- integer() return(structure(list(), names = character(), row.names = row.names, class = "data.frame")) } vnames <- names(x) if (length(vnames) != n) vnames <- character(n) no.vn <- !nzchar(vnames) vlist <- vnames <- as.list(vnames) nrows <- ncols <- integer(n) for (i in seq_len(n)) { xi <- if (is.character(x[[i]]) || is.list(x[[i]])) as.data.frame(x[[i]], optional = TRUE, stringsAsFactors = stringsAsFactors) else as.data.frame(x[[i]], optional = TRUE) nrows[i] <- .row_names_info(xi) ncols[i] <- length(xi) namesi <- names(xi) if (ncols[i] > 1L) { if (length(namesi) == 0L) namesi <- seq_len(ncols[i]) vnames[[i]] <- if (no.vn[i]) namesi else paste(vnames[[i]], namesi, sep = ".") } else if (length(namesi)) { vnames[[i]] <- namesi } else if (fix.empty.names && no.vn[[i]]) { tmpname <- deparse(object[[i]], nlines = 1L)[1L] if (startsWith(tmpname, "I(") && endsWith(tmpname, ")")) { ntmpn <- nchar(tmpname, "c") tmpname <- substr(tmpname, 3L, ntmpn - 1L) } vnames[[i]] <- tmpname } if (mirn && nrows[i] > 0L) { rowsi <- attr(xi, "row.names") if (any(nzchar(rowsi))) row.names <- data.row.names(row.names, rowsi, i) } nrows[i] <- abs(nrows[i]) vlist[[i]] <- xi } nr <- max(nrows) for (i in seq_len(n)[nrows < nr]) { xi <- vlist[[i]] if (nrows[i] > 0L && (nr%%nrows[i] == 0L)) { xi <- unclass(xi) fixed <- TRUE for (j in seq_along(xi)) { xi1 <- xi[[j]] if (is.vector(xi1) || is.factor(xi1)) xi[[j]] <- rep(xi1, length.out = nr) else if (is.character(xi1) && inherits(xi1, "AsIs")) xi[[j]] <- structure(rep(xi1, length.out = nr), class = class(xi1)) else if (inherits(xi1, "Date") || inherits(xi1, "POSIXct")) xi[[j]] <- rep(xi1, length.out = nr) else { fixed <- FALSE break } } if (fixed) { vlist[[i]] <- xi next } } stop(gettextf("arguments imply differing number of rows: %s", paste(unique(nrows), collapse = ", ")), domain = NA) } value <- unlist(vlist, recursive = FALSE, use.names = FALSE) vnames <- as.character(unlist(vnames[ncols > 0L])) if (fix.empty.names && any(noname <- !nzchar(vnames))) vnames[noname] <- paste0("Var.", seq_along(vnames))[noname] if (check.names) { if (fix.empty.names) vnames <- make.names(vnames, unique = TRUE) else { nz <- nzchar(vnames) vnames[nz] <- make.names(vnames[nz], unique = TRUE) } } names(value) <- vnames if (!mrn) { if (length(row.names) == 1L && nr != 1L) { if (is.character(row.names)) row.names <- match(row.names, vnames, 0L) if (length(row.names) != 1L || row.names < 1L || row.names > length(vnames)) stop("'row.names' should specify one of the variables") i <- row.names row.names <- value[[i]] value <- value[-i] } else if (!is.null(row.names) && length(row.names) != nr) stop("row names supplied are of the wrong length") } else if (!is.null(row.names) && length(row.names) != nr) { warning("row names were found from a short variable and have been discarded") row.names <- NULL } class(value) <- "data.frame" if (is.null(row.names)) attr(value, "row.names") <- .set_row_names(nr) else { if (is.object(row.names) || !is.integer(row.names)) row.names <- as.character(row.names) if (anyNA(row.names)) stop("row names contain missing values") if (anyDuplicated(row.names)) stop(gettextf("duplicate row.names: %s", paste(unique(row.names[duplicated(row.names)]), collapse = ", ")), domain = NA) row.names(value) <- row.names } value })(`AUCLST[hr*ng/mL]` = structure(list(8, 5.4425290000238, 5.5675290000238, 14.5112775765674, 14.5112775765674, 9, 5.07253965992077), label = "AUC to Last Nonzero Conc"), `CMAX[ng/mL]` = structure(list(4, 2, 2, 5.59016994374947, 5.59016994374947, 2, 2.5), label = "Max Conc"), `TMAX[hr]` = structure(list(4, 1.5, 1.5, 0, 0, c(0, 0), 1.5), label = "Time of CMAX"), `TLST[hr]` = structure(list(4, 4.5, 4.5, 4.5, 4.5, c(4.5, 4.5), 4.5), label = "Time of Last Nonzero Conc"), `CLST[ng/mL]` = structure(list( 4, 0.5, 0.5, 1, 1, 2, 0.5), label = "Last Nonzero Conc"), `LAMZ[1/hr]` = structure(list(NA_real_, 0.549306144334055, 0.549306144334055, 0.549306144334055, 0.549306144334055, NA_real_, 0.346573590279973), label = "Lambda z"), R2 = structure(list(NA_real_, 0.977653958518262, 0.977653958518262, 0.977653958518262, 0.977653958518262, NA_real_, 0.99971691034068), label = "R Squared"), R2ADJ = structure(list(NA_real_, 0.955307917036524, 0.955307917036524, 0.955307917036524, 0.955307917036524, NA_real_, 0.999433820681361), label = "R Squared Adjusted"), lambda.z.corrxy = structure(list(NA_real_, -0.988763853768058, -0.988763853768058, -0.988763853768058, -0.988763853768058, NA_real_, -0.999858445151453), label = "lambda.z.corrxy"), `LAMZLL[hr]` = structure(list(NA_real_, 2.5, 2.5, 2.5, 2.5, NA_real_, 2.5), label = "Lambda z Lower Limit"), `LAMZUL[hr]` = structure(list(NA_real_, 4.5, 4.5, 4.5, 4.5, NA_real_, 4.5), label = "Lambda z Upper Limit"), `LAMZNPT[count]` = structure(list(NA_real_, 3, 3, 3, 3, NA_real_, 3), label = "Number of Points for Lambda z"), `CLSTP[ng/mL]` = structure(list(NA_real_, 0.524557531710824, 0.524557531710824, 1.04911506342165, 1.04911506342165, NA_real_, 0.498319272249044), label = "Clast pred"), `LAMZHL[hr]` = structure(list(NA_real_, 1.26185950714292, 1.26185950714292, 1.26185950714292, 1.26185950714292, NA_real_, 2), label = "Half-Life Lambda z"), LAMZSPN = structure(list(NA_real_, 1.58496250072116, 1.58496250072116, 1.58496250072116, 1.58496250072116, NA_real_, 1), label = "Lambda z Span"), `AUCIFO[hr*ng/mL]` = structure(list(NA_real_, 6.35276822665064, 6.47776822665064, 16.3317560298211, 16.3317560298211, NA_real_, 6.51523470080973), label = "AUC Infinity Obs"), `AUCINT_0-2[hr*ng/mL]` = structure(c(2, 2.681, 2.869, 7.594, 8.344, 3.5, 3.003), label = "AUC from T1 to T2"), `AUCINT_2-4[hr*ng/mL]` = structure(c(6, 2.462, 2.462, 4.925, 4.925, 4, 1.797), label = "AUC from T1 to T2"), check.names = TRUE, fix.empty.names = TRUE, stringsAsFactors = FALSE)`: arguments imply differing number of rows: 1, 2, 7
Backtrace:
▆
1. └─aNCA::pivot_wider_pknca_results(res_with_ppanmeth) at test-pivot_wider_pknca_results.R:206:5
2. └─aNCA:::add_label_attribute(pivoted_res, myres)
3. ├─base::as.data.frame(...)
4. └─base::as.data.frame.list(...)
5. ├─base::do.call(data.frame, c(x, alis))
6. └─base (local) `<fn>`(...)
[ FAIL 8 | WARN 6 | SKIP 19 | PASS 733 ]
Error:
! Test failures.
Execution halted
Description
I'm trying to make a new release of PKNCA, and 8 tests failed on CRAN with this attempt. Do the errors at the bottom make sense to you (I can try to troubleshoot if they're not obvious).
To reproduce
Steps to reproduce the issue are run aNCA with the proposed release version of PKNCA (humanpred/pknca#523).
Expected behaviour
All tests pass
Errors
Originally from https://win-builder.r-project.org/incoming_pretest/PKNCA_0.12.2_20260323_150409/reverseDependencies/summary.txt (but this link will disappear in about a week)