diff --git a/inst/appdir/characterizationOfTasksBootstrapping.Rmd b/inst/appdir/characterizationOfTasksBootstrapping.Rmd
index c761603..51f6438 100644
--- a/inst/appdir/characterizationOfTasksBootstrapping.Rmd
+++ b/inst/appdir/characterizationOfTasksBootstrapping.Rmd
@@ -1,49 +1,49 @@
 ### Visualizing bootstrap results
 To investigate which tasks separate algorithms well (i.e., lead to a stable ranking), a blob plot is recommended.
 
 Bootstrap results can be shown in a blob plot showing one plot for each
 task. In this view, the spread of the blobs for each algorithm
 can be compared across tasks. Deviations from the diagonal indicate deviations
 from the consensus ranking (over tasks). Specifically, if rank
 distribution of an algorithm is consistently below the diagonal,
 the algorithm performed better in this task than on average
 across tasks, while if the rank distribution of an algorithm
 is consistently above the diagonal, the algorithm performed
 worse in this task than on average across tasks. At the bottom
 of each panel, ranks for each algorithm in the tasks is provided.
 
 
 <!-- Shows which subtask leads to stable ranking and in which subtask ranking is more uncertain. -->
 
 
 Same as in Section \ref{blobByTask} but now ordered according to consensus.
 
 \bigskip
 
 ```{r blobplot_bootstrap_byTask,fig.width=9, fig.height=9}
 #stabilityByTask.bootstrap.list
 if (length(boot_object$matlist)<=6 &nrow((boot_object$matlist[[1]]))<=10 ){
   stabilityByTask(boot_object,
                   ordering=ordering_consensus,
                   max_size = 9,
                   size=4,
                   shape=4) + scale_color_manual(values=cols)
 } else {
   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 = 9,
                                ordering=ordering_consensus,
                                size.ranks=.25*theme_get()$text$size,
                                size=4,
                                shape=4) + scale_color_manual(values=cols) + ggtitle(subt)
   }
-  for (i in 1:length(pl)) print(pl[[i]])
+  print(pl)
 }
 ```
\ No newline at end of file
diff --git a/inst/appdir/visualizationBlobPlots.Rmd b/inst/appdir/visualizationBlobPlots.Rmd
index b730e57..bb36915 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}
 showLabelForSingleTask <- FALSE
 
 if (length(names(boot_object$bootsrappedRanks)) > 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 {
-  for (i in 1:length(pl)) print(pl[[i]])
+  print(pl)
 #}
 
 ```