diff --git a/R/rankingHeatmap.R b/R/rankingHeatmap.R
index ee290cd..59f3ee5 100644
--- a/R/rankingHeatmap.R
+++ b/R/rankingHeatmap.R
@@ -1,75 +1,72 @@
 rankingHeatmap <- function(x,...) UseMethod("rankingHeatmap")
 rankingHeatmap.default <- function(x, ...) stop("not implemented for this class")
 
 rankingHeatmap.ranked=function (x,ties.method="min",...) {
   ordering=rownames(x$mat)[order(x$mat$rank)]
-  #dd=x$data  
+  #dd=x$data
   # dd will be same as x$data, except that na.treat is handled if aggregateThenRank
   dd=as.challenge(x$data,
-                  value=attr(x$data,"value"), 
+                  value=attr(x$data,"value"),
                   algorithm=attr(x$data,"algorithm") ,
                   case=attr(x$data,"case"),
                   annotator = attr(x$data,"annotator"),
                   smallBetter = !attr(x$data,"largeBetter"),
                   na.treat=x$call[[1]][[1]]$na.treat)
-  
-  rankingHeatmap(dd,                 
+
+  rankingHeatmap(dd,
                  ordering=ordering,
                  ties.method=ties.method,...)
 }
 
 
 rankingHeatmap.ranked.list=function (x,ties.method="min",...) {
-  
+
   xx=x$data
-  
+
   a=lapply(names(x$matlist),function(subt){
     ordering=rownames(x$matlist[[subt]])[order(x$matlist[[subt]]$rank)]
-    
-    dd=as.challenge(xx[[subt]],value=attr(xx,"value"), 
+
+    dd=as.challenge(xx[[subt]],
+                    value=attr(xx,"value"),
                     algorithm=attr(xx,"algorithm") ,
                     case=attr(xx,"case"),
+                    by=attr(xx, "by"),
                     annotator = attr(xx,"annotator"),
                     smallBetter = !attr(xx,"largeBetter"),
                     na.treat=x$call[[1]][[1]]$na.treat)
-    
+
     rankingHeatmap(dd,
                    ordering=ordering,
                    ties.method=ties.method,...)
   })
   a
 }
 
 
 rankingHeatmap.challenge=function(x,
                                   ordering,
-                                  ties.method="min",...){
+                                  ties.method="min",...) {
   ranking=x%>%rank( ties.method = ties.method )
-  
-  dat=as.data.frame(table(ranking$mat[[attr(x,"algorithm")]],
-                          ranking$mat$rank,
+
+  task <- ranking$matlist[[1]]
+
+  dat=as.data.frame(table(task[[attr(x,"algorithm")]],
+                          task$rank,
                           dnn=c("algorithm","rank")),
                     responseName = "Count")
   dat$algorithm=factor(dat$algorithm, levels=ordering)
-  # dat$Count=as.factor(dat$Count)
-  # dat$Count[dat$Count==0]=NA
-  ncases=length(unique(x[[attr(x,"case")]]))
+  ncases=length(unique(task[[attr(x,"case")]]))
   ggplot(dat)+
-    geom_raster(aes(algorithm,rank, fill= Count))+
-    geom_hline(yintercept = seq(1.5,max(ranking$mat$rank)-.5,by=1),
+    geom_raster(aes(algorithm, rank, fill= Count))+
+    geom_hline(yintercept = seq(1.5,max(task$rank)-.5,by=1),
                color=grey(.8),size=.3)+
     geom_vline(xintercept = seq(1.5,length(unique(dat$algorithm))-.5,by=1),
                color=grey(.8),size=.3)+
     scale_fill_viridis_c(direction = -1,
-                         limits=c(0,ncases),
-                         # limits=c(1,ncases),
-                         # breaks=function(a) round(seq(a[1],a[2],length.out=5)),
-                         # na.value = "white"
+                         limits=c(0,ncases)
     )+
     theme(axis.text.x = element_text(angle = 90),
           aspect.ratio=1)+
     xlab("Algorithm")+
     ylab("Rank")
-  #scale_y_discrete(name="Rank",breaks=rev(ranking$matlist[[subt]]$rank))
-  
 }