working through final testthat

Still have issues with clonalNetwork

R/clonalBias.R

@@ -104,25 +104,29 @@ clonalBias <- function(sc.data,
     bias$cloneSize[i] <- as.vector(meta[which(meta[,cloneCall] == clone & meta[,split.by] == split),"cloneSize"])[1]
   }
 
-  bias$cloneSize <- factor(bias$cloneSize , rev(levels(meta[, "cloneSize"])))
+  bias$cloneSize <- factor(bias$cloneSize, rev(levels(meta[, "cloneSize"])))
 
   if (exportTable) {
     return(bias)
   }
-
+  bias$dotSize <- as.numeric(bias$cloneSize)
+
   #else, return the plot 
   ggplot(bias, aes(x=ncells,y=bias)) + 
-    geom_point(aes(fill=Top_state, size = cloneSize), shape = 21, stroke = 0.25) + 
+    geom_point(aes(fill=Top_state, size = dotSize), shape = 21, stroke = 0.25) + 
     stat_quantile(data=df_shuffle, 
                          quantiles = c(corrected_p), 
                          method = "rqss", 
-                         lambda=3, 
+                         lambda = 3, 
                          color = "black", 
                          lty = 2) + 
+    #This is ridiculous way to get around the internal ggplot "style"-based warnings
+    scale_size_continuous(breaks = as.vector(unique(bias$dotSize)), labels = as.vector(unique(bias$cloneSize))) + 
     scale_fill_manual(values = .colorizer(palette,  length(unique(bias[,"Top_state"])))) + 
     theme_classic() +
-    xlab("Clone Size") +
-    ylab("Clonotype Bias")
+    labs(size = "cloneSize", fill = "Group") + 
+    xlab("Clonal Size") +
+    ylab("Clonal Bias")
 }
 
 #Background summary of clones

R/clonalNetwork.R

@@ -176,7 +176,7 @@ clonalNetwork <- function(sc.data,
        }
     }
     if(is.null(edge.list)) {
-      stop("No shared clonotypes between the indicated identity")
+      stop("No shared clones between the indicated identity")
     }
 
     edge.list <- data.frame(edge.list)
@@ -207,7 +207,8 @@ clonalNetwork <- function(sc.data,
     if (exportTable) {
       return(edge.list1)
     }
-
+    #Warning from this is produced by geom_edge_bend and there is nothng that can be done
+    #until the authors of the package update it.
     plot <- ggraph(graph, layout = centers[match(names(V(graph)), rownames(centers)),]) + 
                   geom_point(data = coord, aes(x = coord[,1], y = coord[,2], color = group.by)) + 
                   geom_edge_bend(aes(edge_color = as.numeric(weight)), 

R/clonalOverlap.R

@@ -115,7 +115,8 @@ clonalOverlap <- function(input.data,
                 labs(fill = str_to_title(method)) +
                 geom_text(aes(label = round(value, digits = 3), 
                               color = ifelse(value <= mean_value,
-                                             "white", "black"))) +
+                                             "white", "black")), 
+                          na.rm = TRUE) +
                 scale_fill_gradientn(colors = .colorizer(palette, 7), na.value = "white") +
                 scale_color_identity() +
                 theme_classic() + 

R/startracDiversity.R

@@ -106,7 +106,7 @@ StartracDiversity <- function(sc.data,
     col <- length(unique(mat_melt[,"majorCluster"]))
 
     plot <- ggplot(mat_melt, aes(x=majorCluster, y=value)) +
-                geom_boxplot(aes(fill = majorCluster), outlier.alpha = 0) +
+                geom_boxplot(aes(fill = majorCluster), outlier.alpha = 0, na.rm = TRUE) +
                 facet_grid(variable ~.) +
                 theme_classic() +
                 ylab("Index Score") +