diff --git a/tests/testthat/test-aggregateThenRank.R b/tests/testthat/test-aggregateThenRank.R
new file mode 100644
index 0000000..f42b50e
--- /dev/null
+++ b/tests/testthat/test-aggregateThenRank.R
@@ -0,0 +1,61 @@
+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)
+})