add unit test to track local vars/values issue

tests/testthat/test_filterFeatures.R

@@ -1,9 +1,9 @@
 test_that("filterFeatures", {
     ## Prepare data
-    data(feat1)    
+    data(feat1)
     feat1 <- aggregateFeatures(feat1, 1, fcol = "Sequence", name = "peptides", fun = colMedians)
     feat1 <- aggregateFeatures(feat1, 2, fcol = "Protein", name = "proteins", fun = colMedians)
-    
+
     ## Test character filters
     filter1 <- expect_message(filterFeatures(feat1, ~  location == "Mitochondrion"))
     filter2 <- expect_message(filterFeatures(feat1, ~startsWith(location, "Mito")))
@@ -19,7 +19,7 @@ test_that("filterFeatures", {
     expect_equal(filter1, filter6)
     expect_equal(filter1, filter7)
     expect_identical(lengths(filter1), c(6L, 2L, 1L))
-    
+
     ## Test filter stored in variable
     target <- "Mitochondrion"
     filter8 <- expect_message(filterFeatures(feat1, ~  location == target))
@@ -32,35 +32,35 @@ test_that("filterFeatures", {
         expect_message(filterFeatures(feat1, ~  location == target2))
     }
     expect_equal(filter8, runfilter())
-    
+
     ## Test numerical filters
     filter1 <- expect_message(filterFeatures(feat1, VariableFilter("pval", 0.03, "<=")))
     filter2 <- expect_message(filterFeatures(feat1, ~ pval <= 0.03))
     expect_equal(filter1, filter2)
-    
+
     ## Test no match filters
     expect_true(all(dims(filterFeatures(feat1, VariableFilter("location", "not")))[1, ] == 0))
     expect_true(all(dims(filterFeatures(feat1, ~ location == "not"))[1, ] == 0))
     expect_true(all(dims(filterFeatures(feat1, ~ is.na(pval)))[1, ] == 0))
-    
+
     ## Test fraud filters
     expect_error(VariableFilter("pval", TRUE, "<="))
     expect_error(VariableFilter("location", TRUE, "!="))
-})  
+})
 
 
 test_that("filterFeatures with NAs", {
     ## Prepare data
     data(feat1)
     rowData(feat1[[1]])[1, "location"] <- NA
     flt <- VariableFilter("location", "Mitochondrion")
-    
+
     ## Default is na.rm = FALSE
     res1 <-  expect_message(filterFeatures(feat1, flt))
     res2 <-  expect_message(filterFeatures(feat1, flt, na.rm = FALSE))
     expect_identical(res1, res2)
     expect_true(is.na(rowData(res1[[1]])[1, "location"]))
-    
+
     ## Removing the row with the NA
     res3 <-  expect_message(filterFeatures(feat1, flt, na.rm = TRUE))
     expect_identical(nrow(assay(res1, 1)), nrow(assay(res3, 1)) + 1L)
@@ -80,17 +80,17 @@ test_that("filterFeatures with missing filter variables", {
     data(feat1)
     feat1 <- aggregateFeatures(feat1, 1, fcol = "Sequence", name = "peptides", fun = colMedians)
     feat1 <- aggregateFeatures(feat1, 2, fcol = "Protein", name = "proteins", fun = colMedians)
-    
+
     ## Using Variable filter
-    
-    ## Test: 1 variable, missing in some assays, remove features for 
+
+    ## Test: 1 variable, missing in some assays, remove features for
     ## missing variables
     expect_message(
         filt <- filterFeatures(feat1, VariableFilter("pval", 0.03, "<=")),
         regexp = "pval.*1 out of 3.*keep['] argument"
     )
     expect_identical(lengths(filt), c(3L, 0L, 0L))
-    ## Test: 1 variable, missing in some assays, keep features for 
+    ## Test: 1 variable, missing in some assays, keep features for
     ## missing variables
     expect_message(
         filt <- filterFeatures(feat1, VariableFilter("pval", 0.03, "<="), keep = TRUE),
@@ -101,17 +101,17 @@ test_that("filterFeatures with missing filter variables", {
     expect_error(filterFeatures(feat1, ~ foo),
                  regexp = "foo.*absent")
     ## Variable filter only allows filtering 1 variable at a time
-    
+
     ## Using formula filter
-    
-    ## Test: 1 variable, missing in some assays, remove features for 
+
+    ## Test: 1 variable, missing in some assays, remove features for
     ## missing variables
     expect_message(
         filt <- filterFeatures(feat1, ~ pval <= 0.03),
         regexp = "pval.*1 out of 3.*keep['] argument"
     )
     expect_identical(lengths(filt), c(3L, 0L, 0L))
-    ## Test: 1 variable, missing in some assays, keep features for 
+    ## Test: 1 variable, missing in some assays, keep features for
     ## missing variables
     expect_message(
         filt <- filterFeatures(feat1, ~ pval <= 0.03, keep = TRUE),
@@ -121,7 +121,7 @@ test_that("filterFeatures with missing filter variables", {
     ## Test: 1 variable, missing in all assays
     expect_error(filterFeatures(feat1, ~ foo),
                  regexp = "foo.*absent")
-    
+
     ## Test: 2 variables, 1 present in all assays and 1 in some, remove
     ## features for missing variables
     expect_message(
@@ -142,7 +142,7 @@ test_that("filterFeatures with missing filter variables", {
     ## Test: 2 variable, 2 missing in all assays
     expect_error(filterFeatures(feat1, ~ foo & bar),
                  regexp = "foo['][,] [']bar.*absent")
-})  
+})
 
 test_that("filterFeatures on selected assays", {
     data("feat2")
@@ -169,7 +169,7 @@ test_that("filterFeatures on selected assays", {
     ## --- One doesn't contain variable
     ## ---- keep = FALSE
     expect_message(
-        filt <- filterFeatures(feat2, VariableFilter("y", 0, "<"), 
+        filt <- filterFeatures(feat2, VariableFilter("y", 0, "<"),
                                i = 1:2),
         regexp = "2 out of 3"
     )
@@ -179,14 +179,14 @@ test_that("filterFeatures on selected assays", {
     expect_identical(exp, dims(filt))
     ## ---- keep = TRUE
     expect_message(
-        filt <- filterFeatures(feat2, VariableFilter("y", 0, "<"), 
+        filt <- filterFeatures(feat2, VariableFilter("y", 0, "<"),
                                i = 1:2, keep = TRUE),
         regexp = "2 out of 3"
     )
     exp <- init
     exp[1, 2] <- 1L
     expect_identical(exp, dims(filt))
-    
+
     ## - With formulat
     ## -- Filter on 1 selected assay
     expect_message(
@@ -225,4 +225,25 @@ test_that("filterFeatures on selected assays", {
     exp <- init
     exp[1, 2] <- 1L
     expect_identical(exp, dims(filt))
-})
+})
+
+
+test_that("test filterFeatures with value and vars in GlobalEnv", {
+    data(feat1)
+    feat1 <- aggregateFeatures(feat1, 1, fcol = "Sequence", name = "peptides", fun = colMedians)
+    feat1 <- aggregateFeatures(feat1, 2, fcol = "Protein", name = "proteins", fun = colMedians)
+    ##
+    expect_error(filterFeatures(feat1, ~  location == target),
+                 "'target' is/are absent from all rowData.")
+    target <- "Mitochondrion"
+    expect_type(filterFeatures(feat1, ~  location == target), "S4")
+    location <- 1
+    expect_error(filterFeatures(feat1, ~  location == target),
+                 "No vars left.")
+    ##
+    rm(location)
+    res1 <- filterFeatures(feat1, ~ pval <= 0.03 & grepl("Mito", location))
+    res2 <- filterFeatures(feat1, ~ pval <= 0.03) |>
+        filterFeatures(~ location == "Mitochondrion")
+    expect_identical(res1, res2)
+})