cleaning BiocCheck(); linting revision; GHA bump to Bioc devel

.github/workflows/check-bioc.yml

@@ -52,9 +52,9 @@ jobs:
       fail-fast: false
       matrix:
         config:
-          - { os: ubuntu-latest, r: '4.3', bioc: '3.17', cont: "bioconductor/bioconductor_docker:RELEASE_3_17", rspm: "https://packagemanager.rstudio.com/cran/__linux__/jammy/latest" }
-          - { os: macOS-latest, r: '4.3', bioc: '3.17'}
-          - { os: windows-latest, r: '4.3', bioc: '3.17'}
+          - { os: ubuntu-latest, r: '4.3', bioc: '3.18', cont: "bioconductor/bioconductor_docker:devel", rspm: "https://packagemanager.rstudio.com/cran/__linux__/jammy/latest" }
+          - { os: macOS-latest, r: '4.3', bioc: '3.18'}
+          - { os: windows-latest, r: '4.3', bioc: '3.18'}
           ## Check https://github.com/r-lib/actions/tree/master/examples
           ## for examples using the http-user-agent
     env:
@@ -105,16 +105,16 @@ jobs:
         uses: actions/cache@v3
         with:
           path: ${{ env.R_LIBS_USER }}
-          key: ${{ env.cache-version }}-${{ runner.os }}-biocversion-RELEASE_3_17-r-4.3-${{ hashFiles('.github/depends.Rds') }}
-          restore-keys: ${{ env.cache-version }}-${{ runner.os }}-biocversion-RELEASE_3_17-r-4.3-
+          key: ${{ env.cache-version }}-${{ runner.os }}-biocversion-devel-r-4.3-${{ hashFiles('.github/depends.Rds') }}
+          restore-keys: ${{ env.cache-version }}-${{ runner.os }}-biocversion-devel-r-4.3-
 
       - name: Cache R packages on Linux
         if: "!contains(github.event.head_commit.message, '/nocache') && runner.os == 'Linux' "
         uses: actions/cache@v3
         with:
           path: /home/runner/work/_temp/Library
-          key: ${{ env.cache-version }}-${{ runner.os }}-biocversion-RELEASE_3_17-r-4.3-${{ hashFiles('.github/depends.Rds') }}
-          restore-keys: ${{ env.cache-version }}-${{ runner.os }}-biocversion-RELEASE_3_17-r-4.3-
+          key: ${{ env.cache-version }}-${{ runner.os }}-biocversion-devel-r-4.3-${{ hashFiles('.github/depends.Rds') }}
+          restore-keys: ${{ env.cache-version }}-${{ runner.os }}-biocversion-devel-r-4.3-
 
       # - name: Install Linux system dependencies
       #   if: runner.os == 'Linux'
@@ -202,7 +202,7 @@ jobs:
       - name: Install pkgdown
         if: github.ref == 'refs/heads/devel' && env.run_pkgdown == 'true' && runner.os == 'Linux'
         run: |
-          remotes::install_cran("pkgdown")
+          remotes::install_github("r-lib/pkgdown")
         shell: Rscript {0}
 
       - name: Session info
@@ -285,7 +285,7 @@ jobs:
         if: failure()
         uses: actions/upload-artifact@master
         with:
-          name: ${{ runner.os }}-biocversion-RELEASE_3_17-r-4.3-results
+          name: ${{ runner.os }}-biocversion-devel-r-4.3-results
           path: check
 
 

DESCRIPTION

@@ -3,8 +3,9 @@ Package: tidySingleCellExperiment
 Title: Brings SingleCellExperiment to the Tidyverse 
 Version: 1.11.4
 Authors@R: c(person("Stefano", "Mangiola", 
+    comment=c(ORCID="0000-0001-7474-836X"),
     email="[email protected]",
-    role=c("aut", "cre")))
+    role=c("aut", "cre"))) 
 Description: 'tidySingleCellExperiment' is an adapter that abstracts
     the 'SingleCellExperiment' container in the form of a 'tibble'.
     This allows *tidy* data manipulation, nesting, and plotting.
@@ -61,10 +62,10 @@ VignetteBuilder:
 RdMacros:
     lifecycle
 Biarch: true
-biocViews: AssayDomain, Infrastructure, RNASeq, DifferentialExpression, 
+biocViews: 
+    AssayDomain, Infrastructure, RNASeq, DifferentialExpression, SingleCell,
     GeneExpression, Normalization, Clustering, QualityControl, Sequencing
 Encoding: UTF-8
-LazyData: true
 RoxygenNote: 7.2.3
 URL: https://github.com/stemangiola/tidySingleCellExperiment
 BugReports: https://github.com/stemangiola/tidySingleCellExperiment/issues

NAMESPACE

@@ -50,10 +50,15 @@ importFrom(S4Vectors,"metadata<-")
 importFrom(S4Vectors,DataFrame)
 importFrom(S4Vectors,metadata)
 importFrom(SingleCellExperiment,cbind)
+importFrom(SingleCellExperiment,reducedDims)
+importFrom(SummarizedExperiment,"assays<-")
 importFrom(SummarizedExperiment,"colData<-")
+importFrom(SummarizedExperiment,assay)
+importFrom(SummarizedExperiment,assayNames)
 importFrom(SummarizedExperiment,assays)
 importFrom(SummarizedExperiment,colData)
 importFrom(dplyr,add_count)
+importFrom(dplyr,any_of)
 importFrom(dplyr,arrange)
 importFrom(dplyr,contains)
 importFrom(dplyr,count)
@@ -74,12 +79,10 @@ importFrom(dplyr,rowwise)
 importFrom(dplyr,sample_frac)
 importFrom(dplyr,sample_n)
 importFrom(dplyr,select)
-importFrom(dplyr,select_if)
 importFrom(dplyr,slice)
 importFrom(dplyr,summarise)
 importFrom(dplyr,summarize)
 importFrom(dplyr,vars)
-importFrom(ellipsis,check_dots_unnamed)
 importFrom(ellipsis,check_dots_used)
 importFrom(fansi,strwrap_ctl)
 importFrom(ggplot2,aes)
@@ -90,6 +93,7 @@ importFrom(magrittr,"%>%")
 importFrom(magrittr,equals)
 importFrom(magrittr,set_rownames)
 importFrom(methods,getMethod)
+importFrom(methods,is)
 importFrom(pillar,align)
 importFrom(pillar,get_extent)
 importFrom(pillar,style_subtle)
@@ -127,6 +131,7 @@ importFrom(tidyr,extract)
 importFrom(tidyr,nest)
 importFrom(tidyr,pivot_longer)
 importFrom(tidyr,separate)
+importFrom(tidyr,spread)
 importFrom(tidyr,unite)
 importFrom(tidyr,unnest)
 importFrom(tidyselect,eval_select)

R/attach.R

@@ -1,22 +1,23 @@
 core <- c("dplyr", "tidyr", "ttservice", "ggplot2")
 
 core_unloaded <- function() {
-  search <- paste0("package:", core)
-  core[!search %in% search()]
+    search <- paste0("package:", core)
+    core[!search %in% search()]
 }
 
 # Attach the package from the same library it was loaded from before.
 # [source: https://github.com/tidy-biology/tidyverse/issues/171]
 same_library <- function(pkg) {
-  loc <- if (pkg %in% loadedNamespaces()) dirname(getNamespaceInfo(pkg, "path"))
-  library(pkg, lib.loc=loc, character.only=TRUE, warn.conflicts=FALSE)
+    loc <- if (pkg %in% loadedNamespaces()) 
+        dirname(getNamespaceInfo(pkg, "path"))
+    library(pkg, lib.loc=loc, character.only=TRUE, warn.conflicts=FALSE)
 }
 
 tidyverse_attach <- function() {
-  to_load <- core_unloaded()
-
-  suppressPackageStartupMessages(
-    lapply(to_load, same_library))
-
-  invisible(to_load)
+    to_load <- core_unloaded()
+    
+    suppressPackageStartupMessages(
+        lapply(to_load, same_library))
+    
+    invisible(to_load)
 }

R/data.R

@@ -1,35 +1,41 @@
 #' pbmc_small
 #'
-#' PBMC single cell RNA-seq data in SingleCellExperiment format
+#' PBMC single cell RNA-seq data in `SingleCellExperiment` format.
 #'
-#' @format  A SingleCellExperiment object containing 80 Peripheral Blood 
-#'          Mononuclear Cells (PBMC) from 10x Genomics. Generated by subsampling the PBMC dataset of 2,700 single cells.
+#' @format A `SingleCellExperiment` object containing 80 Peripheral Blood 
+#'   Mononuclear Cells (PBMC) from 10x Genomics. Generated by subsampling
+#'   the PBMC dataset of 2,700 single cells.
 #' @source \url{https://satijalab.org/seurat/v3.1/pbmc3k_tutorial.html}
 #' @usage data(pbmc_small)
+#' @return `tidySingleCellExperiment`
 "pbmc_small"
 
 #' Cell types of 80 PBMC single cells
 #' 
 #' A dataset containing the barcodes and cell types of 80 PBMC single cells.
 #'
-#' @format  A tibble containing 80 rows and 2 columns. Cells are a subsample of 
-#'          the Peripheral Blood Mononuclear Cells (PBMC) dataset of 2,700 single 
-#'          cell. Cell types were identified with SingleR.
+#' @format A tibble containing 80 rows and 2 columns.
+#'   Cells are a subsample of the Peripheral Blood Mononuclear Cells (PBMC) 
+#'   dataset of 2,700 single cell. Cell types were identified with SingleR.
 #' \describe{
 #'   \item{cell}{cell identifier, barcode}
 #'   \item{first.labels}{cell type}
 #' }
 #' @source \url{https://satijalab.org/seurat/v3.1/pbmc3k_tutorial.html}
 #' @usage data(cell_type_df)
+#' @return `tibble`
 "cell_type_df"
 
-#' Intercellular ligand-receptor interactions for 38 ligands from a single cell RNA-seq cluster.
+#' Intercellular ligand-receptor interactions for 
+#' 38 ligands from a single cell RNA-seq cluster.
 #'
-#' A dataset containing ligand-receptor interactions withibn a sample. There are 38 ligands from a single cell cluster versus 
-#' 35 receptors in 6 other clusters.
+#' A dataset containing ligand-receptor interactions within a sample.
+#' There are 38 ligands from a single cell cluster versus 35 receptors 
+#' in 6 other clusters.
 #'
-#' @format  A tibble containing 100 rows and 9 columns. Cells are a subsample of 
-#'         the PBMC dataset of 2,700 single cells. Cell interactions were identified with SingleCellSignalR.
+#' @format A `tibble` containing 100 rows and 9 columns.
+#'   Cells are a subsample of the PBMC dataset of 2,700 single cells. 
+#'   Cell interactions were identified with `SingleCellSignalR`.
 #' \describe{
 #'   \item{sample}{sample identifier}
 #'   \item{ligand}{cluster and ligand identifier}
@@ -43,4 +49,5 @@
 #' }
 #' @source \url{https://satijalab.org/seurat/v3.1/pbmc3k_tutorial.html}
 #' @usage data(pbmc_small_nested_interactions)
+#' @return `tibble`
 "pbmc_small_nested_interactions"