scale pies and add legend in emapplot, #304

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: enrichplot
 Title: Visualization of Functional Enrichment Result
-Version: 1.27.2.001
+Version: 1.27.2.002
 Authors@R: c(
     person(given = "Guangchuang", family = "Yu", email = "[email protected]",   role  = c("aut", "cre"), comment = c(ORCID = "0000-0002-6485-8781")),
     person(given = "Chun-Hui", family = "Gao", email = "[email protected]", role = "ctb", comment = c(ORCID = "0000-0002-1445-7939")))

NEWS.md

@@ -1,6 +1,7 @@
-# enrichplot 1.27.2.001
+# enrichplot 1.27.2.002
 
-+ a safe way to extract gene sets in `ridgeplot()` (2024-12-12, Thu)
++ scale pies and add pie legend in `emapplot()` (2024-12-12, Thu, #304)
++ a safe way to extract gene sets in `ridgeplot()` (2024-12-12, Thu, #303)
 
 # enrichplot 1.27.2
 

R/cnetplot.R

@@ -107,14 +107,20 @@ cnetplot.compareClusterResult <- function(
 #' @importFrom ggplot2 coord_fixed
 add_node_pie <- function(p, d, pie = "equal", pie_scale = 1) {
     dd <- d[,c('Cluster', 'Description', 'Count')]
+    pathway_size <- sapply(split(dd$Count, dd$Description), sum)
     if (pie == "equal") dd$Count <- 1
-    dd <- tidyr::pivot_wider(dd, names_from=.data$Cluster, values_from=.data$Count, values_fill=0)
-
+    dd <- tidyr::pivot_wider(dd, names_from="Cluster", values_from="Count", values_fill=0)
+    dd$pathway_size <- sqrt(pathway_size[dd$Description]/sum(pathway_size))
+
+
     p <- p %<+% dd +
-        scatterpie::geom_scatterpie(cols=as.character(unique(d$Cluster)), 
+        scatterpie::geom_scatterpie(aes(x=.data$x, y=.data$y, r=.data$pathway_size), 
+            cols=as.character(unique(d$Cluster)), 
             legend_name = "Cluster", color=NA, pie_scale = pie_scale) +
+        scatterpie::geom_scatterpie_legend(dd$pathway_size, x=min(p$data$x), y=min(p$data$y), n=3,
+            labeller=function(x) round(sum(pathway_size) * x^2)) +
         coord_fixed() +
-        guides(size = "none")
+        guides(size = "none") 
 
     return(p)
 }

R/emapplot_utilities.R

@@ -557,27 +557,27 @@ add_ellipse <- function(p, group_legend, label_style,
 
 
 
-##' Get data for pie plot 
-##'
-##' @param enrichDf A data.frame of enrichment result.
-##' @param pie proportion of clusters in the pie chart, one of 'equal' (default) and 'Count'
-##' @param mergedEnrichDf A data.frame of merged enrichment result.
-##' @param cex_pie2axis It is used to adjust the relative size of the pie chart on the coordinate axis.
-##' @param p a ggraph object.
-##' @param cex_category Number indicating the amount by which plotting category
-##' nodes should be scaled relative to the default.
-##' @noRd
-get_pie_data <- function(enrichDf, pie, mergedEnrichDf, cex_pie2axis, p, cex_category) {
-    ggData <- p$data
-    ID_Cluster_mat <- prepare_pie_category(enrichDf = enrichDf, pie=pie) 
-    desc <- mergedEnrichDf$Description[match(rownames(ID_Cluster_mat),
-                                      mergedEnrichDf$Description)]
-    i <- match(desc, ggData$name)
-    ID_Cluster_mat$x <- ggData$x[i]
-    ID_Cluster_mat$y <- ggData$y[i]
-    ID_Cluster_mat$radius <- sqrt(ggData$size[i] / sum(ggData$size) * cex_category * cex_pie2axis)
-    return(ID_Cluster_mat)
-}
+# ##' Get data for pie plot 
+# ##'
+# ##' @param enrichDf A data.frame of enrichment result.
+# ##' @param pie proportion of clusters in the pie chart, one of 'equal' (default) and 'Count'
+# ##' @param mergedEnrichDf A data.frame of merged enrichment result.
+# ##' @param cex_pie2axis It is used to adjust the relative size of the pie chart on the coordinate axis.
+# ##' @param p a ggraph object.
+# ##' @param cex_category Number indicating the amount by which plotting category
+# ##' nodes should be scaled relative to the default.
+# ##' @noRd
+# get_pie_data <- function(enrichDf, pie, mergedEnrichDf, cex_pie2axis=1, p, cex_category=1) {
+#     ggData <- p$data
+#     ID_Cluster_mat <- prepare_pie_category(enrichDf = enrichDf, pie=pie) 
+#     desc <- mergedEnrichDf$Description[match(rownames(ID_Cluster_mat),
+#                                       mergedEnrichDf$Description)]
+#     i <- match(desc, ggData$name)
+#     ID_Cluster_mat$x <- ggData$x[i]
+#     ID_Cluster_mat$y <- ggData$y[i]
+#     ID_Cluster_mat$radius <- sqrt(ggData$size[i] / sum(ggData$size) * cex_category * cex_pie2axis)
+#     return(ID_Cluster_mat)
+# }
 
 # ##' Add category node(pie plot) 
 # ##'