diff --git a/NAMESPACE b/NAMESPACE
index 793f89f..686f070 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,221 +1,219 @@
 #exportPattern("^[[:alpha:]]+")
 
 export(
   "aggregate.bootstrap", "aggregate.bootstrap.list", "aggregate.challenge", "aggregate.ranked", "aggregate.ranked.list",
   "aggregateList", #?
   "aggregateThenRank",
   "as.challenge",
   "as.relation.challenge.incidence", "as.relation.ranked.list",
   "bootstrap", "bootstrap.ranked", "bootstrap.ranked.list", #"bootstrap.default",
   "boxplot.ranked.list", "boxplot.bootstrap.list", "boxplot.comparedRanks.list",
   #"check_strict_preference",
   "compareRanks",    "compareRanks.ranked",    "compareRanks.ranked.list", # "compareRanks.default",
   "consensus",       "consensus.ranked.list", #"consensus.default",
   "Decision", "decision.challenge",
   "blobplot","blobplot.ranked",
   "default_colors",
   "density.bootstrap.list",
   "extract.workflow",
   "kendall",    "kendall.bootstrap.list",
   #  "melt.aggregated.list",    "melt.ranked", "melt.ranked.list",
   # "merge.list",
   "lineplot.challenge",
   "methodsplot","methodsplot.challenge",
   "network", "plot.network",
   "podium", "podium.ranked","podium.ranked.list", "podium.challenge",#"podium.default",
   "print.aggregated", "print.aggregated.list", "print.comparedRanks",    "print.ranked", "print.ranked.list",
   #"quickmerge.list",
   "rank",    "rank.aggregated", "rank.aggregated.list", "rank.aggregatedRanks", "rank.aggregatedRanks.list", "rank.challenge", #"rank.default",
   "rankFrequencies", "rankFrequencies.bootstrap",    "rankFrequencies.bootstrap.list", #"rankFrequencies.default",
   #"rankNA2",
   "rankThenAggregate",
   "rankingHeatmap", "rankingHeatmap.ranked.list", "relation_dissimilarity.ranked.list",
   "report", "report.bootstrap.list",
   "second",
   "select.if", "select.if.aggregated.list", "select.if.comparedRanks.list", "select.if.list", "select.if.ranked.list",
   "significanceMap",
   "spearmansFootrule", "spearmansWeightedFootrule",
   "splitby",
   "stability", "stabilityByAlgorithm", "stabilityByAlgorithmStacked","stabilityByTask",
   "stability.ranked.list", "stability.bootstrap","relation_dissimilarity",
   "stabilityByAlgorithm.bootstrap.list",
   "stabilityByAlgorithmStacked.bootstrap.list",
   "stabilityByTask.bootstrap.list",
-  "subset.aggregated.list",    "subset.comparedRanks.list", "subset.list", "subset.ranked.list", "subset.bootstrap.list", "subset.bootstrap",#"which.top",
+  "subset.aggregated.list",    "subset.comparedRanks.list", "subset.list", "subset.ranked.list", "subset.bootstrap.list", #"which.top",
   "test",    "test.challenge", "test.default",
   "testThenRank",
   "violin", "violin.bootstrap.list",
   "winner", "winner.bootstrap",    "winner.bootstrap.list", "winner.default", "winner.ranked",    "winner.ranked.list",
   "winnerFrequencies",    "winnerFrequencies.bootstrap", "winnerFrequencies.bootstrap.list",    "winnerFrequencies.default"
   )
 
 
 importFrom("dplyr", "bind_rows","group_by","summarise","select_if","filter","mutate","right_join","anti_join","ungroup","arrange","desc")
 importFrom("rlang",":=",".data","!!")
 importFrom("reshape2","melt", "acast")
 importFrom("utils", "capture.output", "methods")
 importFrom("plyr", "llply")
 importFrom("knitr", "kable")
 importFrom("tidyr", "complete","expand")
 importFrom("purrr", "%>%")
 importFrom("rmarkdown", "render","word_document","pdf_document","html_document")
 importFrom("viridisLite", "viridis","cividis")
 importFrom("ggplot2", "aes","aes_string","geom_abline", "geom_bar", "geom_boxplot", "geom_count", "geom_density", "geom_jitter",
     "geom_line", "geom_point", "geom_raster", "geom_step", "geom_text", "geom_violin","annotate","guide_legend",
     "geom_vline", "ggplot", "ggtitle","vars","xlab","ylab","scale_size_area","theme_get","rel","geom_hline","ggplot_build","scale_fill_manual",
     "scale_y_continuous","coord_cartesian", "element_text", "facet_wrap", "position_jitter", "stat", "stat_summary", "theme", "unit","guides","scale_fill_viridis_c")
 importFrom("grDevices", "col2rgb", "gray", "rgb", "grey")
 importFrom("graphics", "abline", "axis", "barplot", "box", "layout",
                "legend", "par", "plot", "points", "segments","boxplot", "stripchart", "title", "grconvertX", "plot.new")
 importFrom("stats", "as.dist", "as.formula", "median", "p.adjust", "density",
                "quantile", "aggregate", "cor", "wilcox.test", "terms.formula", "complete.cases")
 importFrom("methods", "new")
 importFrom("relations","relation","as.relation",
     "relation_domain", "relation_incidence", "relation_is_asymmetric","relation_consensus","relation_ensemble",
     "relation_is_irreflexive", "relation_is_negatively_transitive",
     "relation_is_transitive", "relation_is_trichotomous", "relation_scores",
     "relation_violations","relation_dissimilarity")
 importFrom("graph", "addEdge")
 
 
 S3method(print, comparedRanks)
 S3method(print, aggregated)
 S3method(print, ranked)
 S3method(print, aggregated.list)
 S3method(print, ranked.list)
 
 S3method(aggregate, challenge)
 S3method(aggregate, ranked)
 S3method(aggregate, ranked.list)
 S3method(aggregate, bootstrap.list)
 S3method(aggregate, bootstrap)
 
 S3method(test, default)
 S3method(test, challenge)
 
 S3method(Aggregate, default)
 S3method(Aggregate, data.frame)
 S3method(Aggregate, list)
 
 S3method(Rank, default)
 S3method(Rank, data.frame)
 S3method(Rank, list)
 
 S3method(rank, default)
 S3method(rank, challenge)
 S3method(rank, aggregated)
 S3method(rank, aggregated.list)
 S3method(rank, aggregatedRanks)
 S3method(rank, aggregatedRanks.list)
 
 S3method(blobplot, default)
 S3method(blobplot, ranked)
 
 S3method(bootstrap, default)
 S3method(bootstrap, ranked)
 S3method(bootstrap, ranked.list)
 
 S3method(winner, default)
 S3method(winner, ranked)
 S3method(winner, ranked.list)
 S3method(winner, bootstrap)
 S3method(winner, bootstrap.list)
 
 S3method(rankFrequencies, default)
 S3method(rankFrequencies, bootstrap)
 S3method(rankFrequencies, bootstrap.list)
 
 S3method(winnerFrequencies, default)
 S3method(winnerFrequencies, bootstrap)
 S3method(winnerFrequencies, bootstrap.list)
 
 
 S3method(compareRanks,default)
 S3method(compareRanks,ranked)
 S3method(compareRanks,ranked.list)
 
 S3method(merge,list)
 
 S3method(melt,ranked.list)
 S3method(melt,ranked)
 S3method(melt,aggregated.list)
 
 S3method(boxplot,ranked.list)
 S3method(boxplot,comparedRanks.list)
 S3method(boxplot,bootstrap.list)
 
 S3method(select.if,default)
 S3method(select.if,list)
 S3method(select.if,aggregated.list)
 S3method(select.if,ranked.list)
 S3method(select.if,comparedRanks.list)
 
 S3method(subset,list)
 S3method(subset,bootstrap.list)
 S3method(subset,aggregated.list)
 S3method(subset,ranked.list)
 S3method(subset,comparedRanks.list)
-S3method(subset,bootstrap)
-
 
 S3method(podium,default)
 S3method(podium,challenge)
 S3method(podium,ranked)
 S3method(podium,ranked.list)
 
 S3method(network,default)
 S3method(network,ranked.list)
 S3method(network,dist)
 S3method(plot,network)
 
 S3method(density,bootstrap.list)
 
 S3method(as.relation,challenge.incidence)
 S3method(as.relation,ranked.list)
 
 S3method(subset,bootstrap.list)
 S3method(subset,ranked.list)
 S3method(subset,list)
 S3method(subset,comparedRanks.list)
 S3method(subset,aggregated.list)
 
 S3method(decision,challenge)
 S3method(decision,default)
 
 S3method(lineplot,challenge)
 S3method(lineplot,default)
 
 S3method(methodsplot,challenge)
 S3method(methodsplot,default)
 
 S3method(significanceMap,data.frame)
 S3method(significanceMap,ranked.list)
 S3method(significanceMap,default)
 
 S3method(violin,bootstrap.list)
 S3method(violin,default)
 
 S3method(rankingHeatmap,ranked.list)
 S3method(rankingHeatmap,default)
 
 S3method(relation_dissimilarity,ranked.list)
 S3method(relation_dissimilarity,default)
 
 S3method(stabilityByTask,bootstrap.list)
 S3method(stabilityByTask,default)
 S3method(stability,bootstrap)
 S3method(stability,default)
 S3method(stability,ranked.list)
 
 S3method(stabilityByAlgorithm,bootstrap.list)
 S3method(stabilityByAlgorithm,default)
 
 S3method(stabilityByAlgorithmStacked,bootstrap.list)
 S3method(stabilityByAlgorithmStacked,default)
 
 S3method(consensus,ranked.list)
 S3method(consensus,default)
 
 S3method(report,bootstrap.list)
 S3method(report,ranked.list)
 S3method(report,default)
diff --git a/R/subset.R b/R/subset.R
index 238dfe4..463fc54 100644
--- a/R/subset.R
+++ b/R/subset.R
@@ -1,89 +1,73 @@
 subset.comparedRanks.list=function(x,
                                    tasks,...){
   res=x[tasks]
   class(res)="comparedRanks.list"
   res
 }
 
 subset.list=function(x,
                      tasks,...){
   x[tasks]
 }
 
-subset.bootstrap.list=function(x,
-                               tasks,...){
-  if (!is.null(as.list(match.call(expand.dots = T))$top)) stop("Subset of algorithms only sensible for single task challenges.")
-  res=list(bootsrappedRanks=x$bootsrappedRanks[tasks],
-           bootsrappedAggregate=x$bootsrappedAggregate[tasks],
-           matlist=x$matlist[tasks],
-           data=x$data[tasks],
-           FUN=x$FUN
-  )
-
-  attrib=attributes(x$data)
-  attrib$names=attr(res$data,"names")
-  attributes(res$data)=attrib
-  class(res)="bootstrap.list"
-  res
-
-}
-
 subset.aggregated.list=function(x,
                                 tasks,...){
   call=match.call(expand.dots = T)
   if (!is.null(as.list(call$top))) stop("Subset of algorithms only sensible for single task challenges.")
   matlist=x$matlist[tasks]
   res=list(matlist=matlist,
            call=list(x$call,call),
            data=x$data,
            FUN =  . %>% (x$FUN) %>%  (call)
   )
 
   class(res)=class(x)
   res
 
 }
 
 
 which.top=function(object,
                    top){
   mat=object$mat[object$mat$rank<=top,]
   rownames(mat)#[order(mat$rank)]
 }
 
 subset.ranked.list=function(x,
                             top,...) {
 
   if (length(x$matlist) != 1) {
     stop("Subset of algorithms only sensible for single-task challenges.")
   }
 
   taskMat <- x$matlist[[1]]
   taskData <- x$data[[1]]
   objectTop=x
   objectTop$matlist[[1]]=taskMat[taskMat$rank<=top,]
 
   taskMatRowNames <- rownames(objectTop$matlist[[1]])
   attribute <- attr(objectTop$data,"algorithm")
 
   selectedRowNames <- taskData[[attribute]] %in% taskMatRowNames
   objectTop$data[[1]] <- taskData[selectedRowNames,]
   objectTop$data[[1]][[attribute]] <- droplevels(objectTop$data[[1]][[attribute]])
 
   objectTop$fulldata=x$data
   objectTop
 }
 
 
-subset.bootstrap=function(x,
-                          top,...){
-  objectTop=x
-  objectTop$mat=objectTop$mat[objectTop$mat$rank<=top,]
-  objectTop$data=objectTop$data[objectTop$data[[attr(objectTop$data,"algorithm")]]%in% rownames(objectTop$mat),]
-  objectTop$data[[attr(objectTop$data,"algorithm")]]=droplevels(objectTop$data[[attr(objectTop$data,"algorithm")]])
-  objectTop$fulldata=x$data
-  objectTop$bootsrappedRanks=objectTop$bootsrappedRanks[rownames(objectTop$mat),]
-  objectTop$bootsrappedAggregate=objectTop$bootsrappedAggregate[rownames(objectTop$mat),]
+subset.bootstrap.list=function(x,
+                               top,...) {
+
+  if (length(x$matlist) != 1) {
+    stop("Subset of algorithms only sensible for single-task challenges.")
+  }
+
+  objectTop <- subset.ranked.list(x, top = top)
+
+  objectTop$bootsrappedRanks[[1]] <- objectTop$bootsrappedRanks[[1]][rownames(objectTop$matlist[[1]]),]
+  objectTop$bootsrappedAggregate[[1]] <- objectTop$bootsrappedAggregate[[1]][rownames(objectTop$matlist[[1]]),]
   objectTop
 }
 
diff --git a/tests/testthat/test-subset.R b/tests/testthat/test-subset.R
index d08316c..54822a1 100644
--- a/tests/testthat/test-subset.R
+++ b/tests/testthat/test-subset.R
@@ -1,102 +1,135 @@
 test_that("top 2 performing algorithms are extracted and data set is reduced respectively", {
   data <- rbind(
     data.frame(algo="A1", value=0.8, case="C1"),
     data.frame(algo="A2", value=0.6, case="C1"),
     data.frame(algo="A3", value=0.4, case="C1"),
     data.frame(algo="A1", value=0.2, case="C2"),
     data.frame(algo="A2", value=0.1, case="C2"),
     data.frame(algo="A3", value=0.0, case="C2"))
 
   challenge <- as.challenge(data, taskName="T1", algorithm="algo", case="case", value="value", smallBetter=FALSE)
 
   ranking <- challenge%>%aggregateThenRank(FUN=mean, ties.method="min")
 
   rankingSubset <- subset(ranking, top=2)
 
   expectedRankingSubset <- rbind(
     "A1" = data.frame(value_FUN = 0.5, rank = 1),
     "A2" = data.frame(value_FUN = 0.35, rank = 2))
 
   expect_equal(rankingSubset$matlist$T1, expectedRankingSubset)
 
   expect_equal(as.vector(rankingSubset$data$T1$algo), c("A1", "A2", "A1", "A2"))
   expect_equal(as.vector(rankingSubset$data$T1$value), c(0.8, 0.6, 0.2, 0.1))
   expect_equal(as.vector(rankingSubset$data$T1$case), c("C1", "C1", "C2", "C2"))
   expect_equal(as.vector(rankingSubset$data$T1$task), c("T1", "T1", "T1", "T1"))
 })
 
 test_that("extraction of subset raises error for multi-task data set", {
   dataTask1 <- cbind(task="T1",
                      rbind(
                        data.frame(algo="A1", value=0.8, case="C1"),
                        data.frame(algo="A2", value=0.6, case="C1"),
                        data.frame(algo="A3", value=0.4, case="C1"),
                        data.frame(algo="A1", value=0.2, case="C2"),
                        data.frame(algo="A2", value=0.1, case="C2"),
                        data.frame(algo="A3", value=0.0, case="C2")
                      ))
   dataTask2 <- cbind(task="T2",
                      rbind(
                        data.frame(algo="A1", value=0.2, case="C1"),
                        data.frame(algo="A2", value=0.3, case="C1"),
                        data.frame(algo="A3", value=0.4, case="C1"),
                        data.frame(algo="A1", value=0.7, case="C2"),
                        data.frame(algo="A2", value=0.8, case="C2"),
                        data.frame(algo="A3", value=0.9, case="C2")
                      ))
 
   data <- rbind(dataTask1, dataTask2)
 
   challenge <- as.challenge(data, by="task", algorithm="algo", case="case", value="value", smallBetter=FALSE)
 
   ranking <- challenge%>%aggregateThenRank(FUN=mean, ties.method="min")
 
   expect_error(subset(ranking, top=2),
                "Subset of algorithms only sensible for single-task challenges.", fixed=TRUE)
 })
 
 test_that("extraction of subset returns all algorithms even when more are requested", {
   data <- rbind(
     data.frame(algo="A1", value=0.8, case="C1"),
     data.frame(algo="A2", value=0.6, case="C1"),
     data.frame(algo="A3", value=0.4, case="C1"),
     data.frame(algo="A1", value=0.2, case="C2"),
     data.frame(algo="A2", value=0.1, case="C2"),
     data.frame(algo="A3", value=0.0, case="C2"))
 
   challenge <- as.challenge(data, taskName="T1", algorithm="algo", case="case", value="value", smallBetter=FALSE)
 
   ranking <- challenge%>%aggregateThenRank(FUN=mean, ties.method="min")
 
   rankingSubset <- subset(ranking, top=4)
 
   expectedRankingSubset <- rbind(
     "A1" = data.frame(value_FUN = 0.5, rank = 1),
     "A2" = data.frame(value_FUN = 0.35, rank = 2),
     "A3" = data.frame(value_FUN = 0.2, rank = 3))
 
   expect_equal(rankingSubset$matlist$T1, expectedRankingSubset)
 })
 
 test_that("extraction of subset returns more algorithms then requested when ties are present", {
   data <- rbind(
     data.frame(algo="A1", value=0.8, case="C1"),
     data.frame(algo="A2", value=0.8, case="C1"),
     data.frame(algo="A3", value=0.8, case="C1"),
     data.frame(algo="A1", value=0.2, case="C2"),
     data.frame(algo="A2", value=0.2, case="C2"),
     data.frame(algo="A3", value=0.2, case="C2"))
 
   challenge <- as.challenge(data, taskName="T1", algorithm="algo", case="case", value="value", smallBetter=FALSE)
 
   ranking <- challenge%>%aggregateThenRank(FUN=mean, ties.method="min")
 
   rankingSubset <- subset(ranking, top=2)
 
   expectedRankingSubset <- rbind(
     "A1" = data.frame(value_FUN = 0.5, rank = 1),
     "A2" = data.frame(value_FUN = 0.5, rank = 1),
     "A3" = data.frame(value_FUN = 0.5, rank = 1))
 
   expect_equal(rankingSubset$matlist$T1, expectedRankingSubset)
 })
+
+test_that("top 2 performing algorithms are extracted from bootstrap ranking and data set is reduced respectively", {
+  data <- rbind(
+    data.frame(algo="A1", value=0.8, case="C1"),
+    data.frame(algo="A2", value=0.6, case="C1"),
+    data.frame(algo="A3", value=0.4, case="C1"),
+    data.frame(algo="A1", value=0.2, case="C2"),
+    data.frame(algo="A2", value=0.1, case="C2"),
+    data.frame(algo="A3", value=0.0, case="C2"))
+
+  challenge <- as.challenge(data, taskName="T1", algorithm="algo", case="case", value="value", smallBetter=FALSE)
+
+  ranking <- challenge%>%aggregateThenRank(FUN=mean, ties.method="min")
+
+  set.seed(1)
+  rankingBootstrapped <- ranking%>%bootstrap(nboot=10)
+
+  rankingBootstrappedSubset <- subset(rankingBootstrapped, top=2)
+
+  expectedRankingSubset <- rbind(
+    "A1" = data.frame(value_FUN = 0.5, rank = 1),
+    "A2" = data.frame(value_FUN = 0.35, rank = 2))
+
+  expect_equal(rankingBootstrappedSubset$matlist$T1, expectedRankingSubset)
+
+  expect_equal(as.vector(rankingBootstrappedSubset$data$T1$algo), c("A1", "A2", "A1", "A2"))
+  expect_equal(as.vector(rankingBootstrappedSubset$data$T1$value), c(0.8, 0.6, 0.2, 0.1))
+  expect_equal(as.vector(rankingBootstrappedSubset$data$T1$case), c("C1", "C1", "C2", "C2"))
+  expect_equal(as.vector(rankingBootstrappedSubset$data$T1$task), c("T1", "T1", "T1", "T1"))
+
+  expect_equal(dim(rankingBootstrappedSubset$bootsrappedRanks$T1), c(2, 10))
+  expect_equal(dim(rankingBootstrappedSubset$bootsrappedAggregate$T1), c(2, 10))
+})