diff --git a/tests/testthat/test-aggregateThenRank.R b/tests/testthat/test-aggregateThenRank.R
index 3de82e8..31b039d 100644
--- a/tests/testthat/test-aggregateThenRank.R
+++ b/tests/testthat/test-aggregateThenRank.R
@@ -1,126 +1,145 @@
 test_that("aggregate-than-rank by mean works with two algorithms for one case, small values are better", {
   data <- rbind(
     data.frame(algo="A1", value=0.6, case="C1"),
     data.frame(algo="A2", value=0.8, case="C1"))
 
   challenge <- as.challenge(data, algorithm="algo", case="case", value="value", smallBetter = TRUE)
 
   ranking <- challenge%>%aggregateThenRank(FUN = mean)
 
   expectedRanking <- rbind(
     "A1" = data.frame(value_FUN = 0.6, rank = 1),
     "A2" = data.frame(value_FUN = 0.8, rank = 2))
 
   expect_equal(ranking$mat, expectedRanking)
 })
 
 test_that("aggregate-than-rank by mean works with two algorithms (reverse order) for one case, small values are better", {
   data <- rbind(
             data.frame(algo = "A2", value = 0.8, case = "C1"),
             data.frame(algo = "A1", value = 0.6, case = "C1"))
 
   challenge <- as.challenge(data, algorithm = "algo", case = "case", value = "value", smallBetter = TRUE)
 
   ranking <- challenge%>%aggregateThenRank(FUN = mean)
 
   expectedRanking <- rbind("A2" = data.frame(value_FUN = 0.8, rank = 2),
                            "A1" = data.frame(value_FUN = 0.6, rank = 1))
 
   expect_equal(ranking$mat, expectedRanking)
 })
 
 test_that("aggregate-than-rank by mean works with two algorithms for one case, large values are better", {
   data <- rbind(
     data.frame(algo="A1", value=0.6, case="C1"),
     data.frame(algo="A2", value=0.8, case="C1"))
 
   challenge <- as.challenge(data, algorithm="algo", case="case", value="value", smallBetter = FALSE)
 
   ranking <- challenge%>%aggregateThenRank(FUN = mean)
 
   expectedRanking <- rbind(
     "A1" = data.frame(value_FUN = 0.6, rank = 2),
     "A2" = data.frame(value_FUN = 0.8, rank = 1))
 
   expect_equal(ranking$mat, expectedRanking)
 })
 
 test_that("aggregate-than-rank by mean works with two algorithms (reverse order) for one case, large values are better", {
   data <- rbind(
     data.frame(algo = "A2", value = 0.8, case = "C1"),
     data.frame(algo = "A1", value = 0.6, case = "C1"))
 
   challenge <- as.challenge(data, algorithm = "algo", case = "case", value = "value", smallBetter = FALSE)
 
   ranking <- challenge%>%aggregateThenRank(FUN = mean)
 
   expectedRanking <- rbind("A2" = data.frame(value_FUN = 0.8, rank = 1),
                            "A1" = data.frame(value_FUN = 0.6, rank = 2))
 
   expect_equal(ranking$mat, expectedRanking)
 })
 
 test_that("aggregate-than-rank raises error for invalid aggregation function", {
   data <- rbind(
     data.frame(algo="A1", value=0.6, case="C1"),
     data.frame(algo="A2", value=0.6, case="C1"))
 
   challenge <- as.challenge(data, algorithm="algo", case="case", value="value", smallBetter = TRUE)
 
   expect_error(challenge%>%aggregateThenRank(FUN = meanx),
                "object 'meanx' not found", fixed = TRUE)
 })
 
 test_that("aggregate-than-rank by mean works with two algorithms for one case and 'min' as ties method", {
   data <- rbind(
     data.frame(algo="A1", value=0.6, case="C1"),
     data.frame(algo="A2", value=0.6, case="C1"))
 
   challenge <- as.challenge(data, algorithm="algo", case="case", value="value", smallBetter = TRUE)
 
   ranking <- challenge%>%aggregateThenRank(FUN = mean, ties.method = "min")
 
   expectedRanking <- rbind(
     "A1" = data.frame(value_FUN = 0.6, rank = 1),
     "A2" = data.frame(value_FUN = 0.6, rank = 1))
 
   expect_equal(ranking$mat, expectedRanking)
 })
 
 test_that("aggregate-than-rank by mean works with two algorithms for one case and 'max' as ties method", {
   data <- rbind(
     data.frame(algo="A1", value=0.6, case="C1"),
     data.frame(algo="A2", value=0.6, case="C1"))
 
   challenge <- as.challenge(data, algorithm="algo", case="case", value="value", smallBetter = TRUE)
 
   ranking <- challenge%>%aggregateThenRank(FUN = mean, ties.method = "max")
 
   expectedRanking <- rbind(
     "A1" = data.frame(value_FUN = 0.6, rank = 2),
     "A2" = data.frame(value_FUN = 0.6, rank = 2))
 
   expect_equal(ranking$mat, expectedRanking)
 })
 
 test_that("aggregate-than-rank raises error for invalid ties method", {
   data <- rbind(
     data.frame(algo="A1", value=0.6, case="C1"),
     data.frame(algo="A2", value=0.6, case="C1"))
 
   challenge <- as.challenge(data, algorithm="algo", case="case", value="value", smallBetter = TRUE)
 
   expect_error(challenge%>%aggregateThenRank(FUN = mean, ties.method = "maxx"),
                "'arg' should be one of \"average\", \"first\", \"last\", \"random\", \"max\", \"min\"", fixed = TRUE)
 })
 
 test_that("aggregate-than-rank raises error for invalid ties method even when no ties present", {
   data <- rbind(
     data.frame(algo="A1", value=0.6, case="C1"),
     data.frame(algo="A2", value=0.8, case="C1"))
 
   challenge <- as.challenge(data, algorithm="algo", case="case", value="value", smallBetter = TRUE)
 
   expect_error(challenge%>%aggregateThenRank(FUN = mean, ties.method = "maxx"),
                "'arg' should be one of \"average\", \"first\", \"last\", \"random\", \"max\", \"min\"", fixed = TRUE)
 })
+
+test_that("aggregate-than-rank by mean works with two algorithms for two case", {
+  data <- rbind(
+    data.frame(algo="A1", value=0.6, case="C1"),
+    data.frame(algo="A1", value=0.4, case="C2"),
+    data.frame(algo="A2", value=0.8, case="C1"),
+    data.frame(algo="A2", value=1.0, case="C2"))
+
+  challenge <- as.challenge(data, algorithm="algo", case="case", value="value", smallBetter = TRUE)
+
+  ranking <- challenge%>%aggregateThenRank(FUN = mean)
+
+  expectedRanking <- rbind(
+    "A1" = data.frame(value_FUN = 0.5, rank = 1),
+    "A2" = data.frame(value_FUN = 0.9, rank = 2))
+
+  expect_equal(ranking$mat, expectedRanking)
+})
+