Fix sparse matrix cast and ggplot aes_string deprecations

.github/workflows/check-bioc.yml

@@ -24,7 +24,6 @@ on:
   push:
     branches: [ master, RELEASE_* ]
   pull_request:
-    branches: [ master, RELEASE_* ]
 
 name: R-CMD-check-bioc
 
@@ -49,16 +48,21 @@ jobs:
     name: ${{ matrix.config.os }} (${{ matrix.config.r }})
     container: ${{ matrix.config.cont }}
     ## Environment variables unique to this job.
+
+    concurrency:
+      group: ${{ github.workflow }} ${{ matrix.config.os }} ${{ github.event.pull_request.head.label || github.head_ref || github.ref }}
+      cancel-in-progress: true
 
     strategy:
       fail-fast: false
       matrix:
         config:
-          - { os: ubuntu-latest,  r: '4.2', bioc: '3.15', bioc_release: RELEASE_3_15, cont: "bioconductor/bioconductor_docker:RELEASE_3_15", rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest" }
-          - { os: macOS-latest,   r: '4.2', bioc: '3.15', bioc_release: RELEASE_3_15 }
-          - { os: windows-latest, r: '4.2', bioc: '3.15', bioc_release: RELEASE_3_15 }
+          - { os: ubuntu-latest, r: 'release', bioc: '3.18', cont: "bioconductor/bioconductor_docker:RELEASE_3_18", rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest" }
+          - { os: macOS-latest, r: 'release', bioc: '3.18' }
+          - { os: windows-latest, r: 'release', bioc: '3.18' }
           ## Check https://github.com/r-lib/actions/tree/master/examples
           ## for examples using the http-user-agent
+
     env:
       R_REMOTES_NO_ERRORS_FROM_WARNINGS: true
       RSPM: ${{ matrix.config.rspm }}

DESCRIPTION

@@ -41,6 +41,7 @@ Imports:
     SingleCellExperiment,
     ggplot2,
     ggplot.multistats,
+    rlang,
     tidyr,
     tidyselect,
     ggthemes,
@@ -120,4 +121,4 @@ Collate:
     'projection-dist.r'
     'rankcor.r'
     'sigmas-plotting.r'
-RoxygenNote: 7.1.2
+RoxygenNote: 7.2.3

NAMESPACE

@@ -140,7 +140,7 @@ importFrom(SummarizedExperiment,colData)
 importFrom(VIM,hotdeck)
 importFrom(ggplot.multistats,stat_summaries_hex)
 importFrom(ggplot2,aes)
-importFrom(ggplot2,aes_string)
+importFrom(ggplot2,after_stat)
 importFrom(ggplot2,element_blank)
 importFrom(ggplot2,element_line)
 importFrom(ggplot2,element_rect)
@@ -163,7 +163,6 @@ importFrom(ggplot2,scale_fill_identity)
 importFrom(ggplot2,scale_fill_manual)
 importFrom(ggplot2,scale_x_continuous)
 importFrom(ggplot2,scale_y_continuous)
-importFrom(ggplot2,stat)
 importFrom(ggplot2,theme)
 importFrom(ggplot2,theme_minimal)
 importFrom(ggthemes,extended_range_breaks)
@@ -207,6 +206,7 @@ importFrom(pcaMethods,pca)
 importFrom(pcaMethods,scores)
 importFrom(proxy,dist)
 importFrom(proxy,pr_DB)
+importFrom(rlang,.data)
 importFrom(scales,colour_ramp)
 importFrom(scales,percent)
 importFrom(scales,rescale)

R/diffusionmap-plotting.r

@@ -47,7 +47,8 @@ NULL
 #' @importFrom stats setNames
 #' @importFrom grDevices palette hcl.colors
 #' @importFrom scatterplot3d scatterplot3d
-#' @importFrom ggplot2 ggplot aes aes_string
+#' @importFrom rlang .data
+#' @importFrom ggplot2 ggplot aes
 #' @importFrom ggplot2 geom_point
 #' @importFrom ggplot2 theme theme_minimal element_blank element_line element_text element_rect
 #' @importFrom ggplot2 scale_fill_identity scale_fill_manual scale_fill_gradientn scale_fill_identity
@@ -142,10 +143,10 @@ plot.DiffusionMap <- function(
 
 		use_mapping <- continuous || is_projection || !is.null(col_by)
 		p <-
-			ggplot(point_data, aes_string(d1, d2)) +
+			ggplot(point_data, aes(.data[[d1]], .data[[d2]])) +
 			theme_really_minimal() +
 			geom_point(
-				aes_string(fill = if (use_mapping) 'Colour' else 'ColourExpl'),
+				aes(fill = if (use_mapping) .data$Colour else .data$ColourExpl),
 				colour = I('#00000000'),
 				shape  = I(21))
 

R/diffusionmap.r

@@ -412,7 +412,7 @@ no_censoring <- function(dists, sigma, cb = invisible) {
 		exp(-d2@x / (2 * sigma ^ 2))
 	} else {
 		stopifnot(d2@uplo == 'U')
-		coords <- as(d2, 'dsTMatrix')
+		coords <- as(d2, 'TsparseMatrix')
 		i <- coords@i + 1L
 		j <- coords@j + 1L
 		sig2 <- sigma^2

R/dpt-plotting.r

@@ -36,7 +36,8 @@ NULL
 #' @importFrom methods is setMethod
 #' @importFrom scales colour_ramp rescale
 #' @importFrom utils capture.output
-#' @importFrom ggplot2 aes_string geom_path geom_point scale_colour_identity
+#' @importFrom rlang .data
+#' @importFrom ggplot2 geom_path geom_point scale_colour_identity
 #' @export
 plot.DPT <- function(
 	x, root = NULL,
@@ -144,7 +145,7 @@ get_plot_fn <- function(p) {
 		function(p2, dat, type = 'p', col, ...) {
 			xy <- colnames(dat)
 			geom <- switch(type, p = geom_point, l = geom_path, stop)
-			p2 + geom(aes_string(xy[[1L]], xy[[2L]], colour = 'Path'), data.frame(dat, Path = col))
+			p2 + geom(aes(.data[[xy[[1L]]]], .data[[xy[[2L]]]], colour = .data$Path), data.frame(dat, Path = col))
 		}
 	} else if (is.list(p) && 'points3d' %in% names(p)) {# scatterplot3d
 		function(p2, ...) {

R/gene-relevance-plotting-differential-map.r

@@ -66,7 +66,8 @@ differential_map <- function(relevance_map, genes = NULL, dims = 1:2, all = FALS
 	}))
 }
 
-#' @importFrom ggplot2 ggplot aes aes_string
+#' @importFrom rlang .data
+#' @importFrom ggplot2 ggplot aes
 #' @importFrom ggplot2 geom_point geom_spoke
 #' @importFrom ggplot2 scale_colour_gradientn
 #' @importFrom ggplot2 ggtitle facet_wrap
@@ -80,12 +81,12 @@ plot_differential_map_impl <- function(relevance_map, ..., genes, dims, pal, fac
 
 	d1 <- colnames(coords)[[1]]
 	d2 <- colnames(coords)[[2]]
-	gg <- ggplot(dtm, aes_string(d1, d2)) +
+	gg <- ggplot(dtm, aes(.data[[d1]], .data[[d2]])) +
 		geom_spoke(
-			aes_string(
-				angle = 'Angle', radius = 'Magnitude',
-				alpha = 'PartialsNorm',
-				colour = 'Expression'
+			aes(
+				angle = .data$Angle, radius = .data$Magnitude,
+				alpha = .data$PartialsNorm,
+				colour = .data$Expression
 			),
 			arrow = arrow(length = unit(.01, 'npc'))
 		) +

R/gene-relevance-plotting-gr-map.r

@@ -22,7 +22,8 @@ setMethod('plot_gene_relevance', c('GeneRelevance', 'missing'), function(coords,
 	plot_gene_relevance_impl(coords, iter_smooth = iter_smooth, n_top = n_top, genes = genes, dims = dims, pal = pal, col_na = col_na, limit = limit, ...)
 })
 
-#' @importFrom ggplot2 ggplot aes_string
+#' @importFrom rlang .data
+#' @importFrom ggplot2 ggplot
 #' @importFrom ggplot2 geom_point
 #' @importFrom ggplot2 scale_color_manual
 #' @importFrom ggplot2 ggtitle
@@ -109,7 +110,7 @@ plot_gene_relevance_impl <- function(relevance_map, ..., iter_smooth, n_top, gen
 
 	d1 <- colnames(coords)[[1]]
 	d2 <- colnames(coords)[[2]]
-	rel_map <- ggplot(rel_map_data, aes_string(x = d1, y = d2, colour = 'Gene', text = 'TopN')) +
+	rel_map <- ggplot(rel_map_data, aes(x = .data[[d1]], y = .data[[d2]], colour = .data$Gene, text = .data$TopN)) +
 		geom_point(alpha = .8) +
 		geom_rangeframe(colour = par('col')) +
 		scale_color_manual(values = pal, na.value = col_na) +

R/gene-relevance-plotting-rank.r

@@ -25,7 +25,8 @@ setMethod('plot_gene_relevance_rank', c('GeneRelevance', 'missing'), function(co
 #' @importFrom tidyr gather
 #' @importFrom tidyselect one_of
 #' @importFrom scales percent
-#' @importFrom ggplot2 ggplot aes_string stat
+#' @importFrom rlang .data
+#' @importFrom ggplot2 ggplot after_stat
 #' @importFrom ggplot2 scale_fill_gradientn scale_alpha_continuous
 #' @importFrom ggplot.multistats stat_summaries_hex
 plot_gene_relevance_rank_impl <- function(relevance_map, ..., genes, dims, n_top, pal, bins, faceter) {
@@ -49,9 +50,9 @@ plot_gene_relevance_rank_impl <- function(relevance_map, ..., genes, dims, n_top
 
 	d1 <- colnames(coords)[[1]]
 	d2 <- colnames(coords)[[2]]
-	gg <- ggplot(d, aes_string(d1, d2, z = 'Rank')) +
+	gg <- ggplot(d, aes(.data[[d1]], .data[[d2]], z = .data$Rank)) +
 		stat_summaries_hex(
-			aes_string(fill = 'stat(top10)', alpha  = 'stat(size)'),
+			aes(fill = after_stat(.data$top10), alpha = after_stat(.data$size)),
 			funs = list(top10 = top10, size = 'length', 'median'),
 			bins = bins
 		) +

R/methods-coercion.r

@@ -60,7 +60,7 @@ setMethod('as.data.frame', 'DiffusionMap', function(x, row.names = NULL, optiona
 #' @importFrom Biobase as.data.frame.ExpressionSet
 #' @importFrom ggplot2 fortify
 #' @rdname coercions
-#' @exportS3Method fortify
+#' @exportS3Method fortify DiffusionMap
 #' @export fortify.DiffusionMap
 fortify.DiffusionMap <- function(model, data, ...) as.data.frame(model, ...)
 setAs('DiffusionMap', 'data.frame', function(from) as.data.frame(from))
@@ -83,7 +83,7 @@ setMethod('as.data.frame', 'DPT', function(x, row.names = NULL, optional = FALSE
 #' @importFrom Biobase as.data.frame.ExpressionSet
 #' @importFrom ggplot2 fortify
 #' @rdname coercions
-#' @exportS3Method fortify
+#' @exportS3Method fortify DPT
 #' @export fortify.DPT
 fortify.DPT <- function(model, data, ...) as.data.frame(model, ...)
 setAs('DPT', 'data.frame', function(from) as.data.frame(from))