diff --git a/inst/appdir/visualizationBlobPlots.Rmd b/inst/appdir/visualizationBlobPlots.Rmd
index 7dd012f..2b9e95d 100644
--- a/inst/appdir/visualizationBlobPlots.Rmd
+++ b/inst/appdir/visualizationBlobPlots.Rmd
@@ -1,39 +1,39 @@
 ## *Blob plot* for visualizing ranking stability based on bootstrap sampling \label{blobByTask}
 
 Algorithms are color-coded, and the area of each blob at position $\left( A_i, \text{rank } j \right)$ is proportional to the relative frequency $A_i$ achieved rank $j$ across $b=$ `r ncol(boot_object$bootsrappedRanks[[1]])` bootstrap samples. The median rank for each algorithm is indicated by a black cross. 95\% bootstrap intervals across bootstrap samples are indicated by black lines. 
 
 
 \bigskip
 
 ```{r blobplot_bootstrap,fig.width=9, fig.height=9, results='hide'}
 showLabelForSingleTask <- FALSE
 
-if (length(names(boot_object$bootsrappedRanks)) > 1) {
+if (n.tasks > 1) {
   showLabelForSingleTask <- TRUE
 }
 
 pl=list()
 for (subt in names(boot_object$bootsrappedRanks)){
   a=list(bootsrappedRanks=list(boot_object$bootsrappedRanks[[subt]]),
          matlist=list(boot_object$matlist[[subt]]))
   names(a$bootsrappedRanks)=names(a$matlist)=subt
   class(a)="bootstrap.list"
   r=boot_object$matlist[[subt]]
 
   pl[[subt]]=stabilityByTask(a,
                              max_size =8,
                              ordering=rownames(r[order(r$rank),]),
                              size.ranks=.25*theme_get()$text$size,
                              size=8,
                              shape=4,
                              showLabelForSingleTask=showLabelForSingleTask) + scale_color_manual(values=cols)
 
 }
 
 # if (length(boot_object$matlist)<=6 &nrow((boot_object$matlist[[1]]))<=10 ){
 #   ggpubr::ggarrange(plotlist = pl)
 # } else {
   print(pl)
 #}
 
 ```