Improving test coverage

R/data_classes.R

@@ -554,7 +554,7 @@ check_triple_omic <- function(triple_omic, fast_check = TRUE) {
 
     if (nrow(samples_not_unique) != 0) {
       stop(glue::glue(
-        "{nrow(samples_not_unique)} features were present multiple times with
+        "{nrow(samples_not_unique)} samples were present multiple times with
         the same sample primary key"
       ))
     }

R/hclust.R

@@ -1,11 +1,13 @@
 hclust_tidy_omic <- function(
-    tidy_omic,
-    feature_var,
-    sample_var,
-    value_var,
-    cluster_dim,
-    distance_measure = "dist",
-    hclust_method = "ward.D2") {
+  tidy_omic,
+  feature_var,
+  sample_var,
+  value_var,
+  cluster_dim,
+  distance_measure = "dist",
+  hclust_method = "ward.D2"
+  ) {
+
   check_tidy_omic(tidy_omic)
 
   checkmate::assertChoice(feature_var, tidy_omic$design$features$variable)
@@ -37,7 +39,7 @@ hclust_tidy_omic <- function(
   cluster_orders$columns <-
     coerce_to_classes(
       cluster_orders$columns,
-      tidy_omic$data[[tidy_omic$design$feature_pk]]
+      tidy_omic$data[[tidy_omic$design$sample_pk]]
     )
 
   # order rows and columns
@@ -49,7 +51,7 @@ hclust_tidy_omic <- function(
     )
 
   if (cluster_dim == "columns") {
-    # order by factor or alpha-numerically
+    # order features by factor or alpha-numerically
 
     if (
       any(class(distinct_features[[feature_var]]) %in% c("factor", "ordered"))
@@ -93,7 +95,7 @@ hclust_tidy_omic <- function(
     )
 
   if (cluster_dim == "rows") {
-    # order by factor or alpha-numerically
+    # order samples by factor or alpha-numerically
 
     if (any(class(distinct_samples[[sample_var]]) %in% c("factor", "ordered"))) {
       # retain previous ordering
@@ -208,13 +210,15 @@ hclust_tidy_omic <- function(
 #' hclust_order(df, "letters", "numbers", "noise", "rows")
 #' @export
 hclust_order <- function(
-    df,
-    feature_pk,
-    sample_pk,
-    value_var,
-    cluster_dim,
-    distance_measure = "dist",
-    hclust_method = "ward.D2") {
+  df,
+  feature_pk,
+  sample_pk,
+  value_var,
+  cluster_dim,
+  distance_measure = "dist",
+  hclust_method = "ward.D2"
+  ) {
+
   checkmate::assertDataFrame(df)
   checkmate::assertChoice(feature_pk, colnames(df))
   checkmate::assertChoice(sample_pk, colnames(df))

R/mutates.R

@@ -150,6 +150,9 @@ center <- function(x) {
 #'   mutate(new_sample_var = "foo") %>%
 #'   select(-DR)
 #' new_variable_tables <- c("new_sample_var" = "samples")
+#'
+#' update_tidy_omic(tidy_omic, updated_tidy_data, new_variable_tables)
+#'
 #' @export
 update_tidy_omic <- function(
     tidy_omic,

R/utils.R

@@ -24,19 +24,18 @@ format_names_for_plotting <- function(chars, width = 40, truncate_at = 80) {
 }
 
 coerce_to_classes <- function(obj, reference_obj) {
-  reference_obj_class <- class(reference_obj)
+  reference_obj_class <- class(reference_obj)[1]
 
   if (any(reference_obj_class %in% "glue")) {
     out <- glue::as_glue(obj)
   } else if (any(reference_obj_class %in% c("factor", "ordered"))) {
-    out <-
-      do.call(
-        reference_obj_class,
-        list(
-          x = obj,
-          levels = levels(reference_obj)
-        )
+    out <- do.call(
+      reference_obj_class,
+      list(
+        x = obj,
+        levels = levels(reference_obj)
       )
+    )
   } else if (reference_obj_class == "character") {
     out <- as.character(obj)
   } else if (reference_obj_class == "numeric") {

tests/testthat/_snaps/data_classes.md

@@ -51,6 +51,79 @@
       Error in `create_tidy_omic()`:
       ! measurement were assigned to multiple classes of variables each variable should only belong to one class
 
+# Catch corner cases when reading wide data
+
+    Code
+      convert_wide_to_tidy_omic(wide_df_nonunique_feature_id, feature_pk = "name")
+    Condition
+      Warning in `convert_wide_to_tidy_omic()`:
+      4 rows did not contain a unique name; adding extra variables 'unique_name' & 'entry_number' to distinguish them
+      Warning:
+      `mutate_()` was deprecated in dplyr 0.7.0.
+      i Please use `mutate()` instead.
+      i See vignette('programming') for more help
+    Message
+      1 measurement variables were defined as the
+      left overs from the specified feature and sample varaibles:
+      abundance
+    Output
+      $data
+      # A tibble: 19,500 x 5
+         name    entry_number unique_name sample abundance                            
+         <chr>          <int> <chr>       <chr>  <chr>                                
+       1 YOL029C            1 YOL029C-1   BP     biological process unknown           
+       2 YOL029C            2 YOL029C-2   BP     cytokinesis, completion of separation
+       3 YOL029C            3 YOL029C-3   BP     biological process unknown           
+       4 YOL029C            4 YOL029C-4   BP     cell wall organization and biogenesis
+       5 YOL029C            5 YOL029C-5   BP     cell wall organization and biogenesi~
+       6 FKH1               1 FKH1        BP     pseudohyphal growth*                 
+       7 HOC1               1 HOC1        BP     cell wall mannoprotein biosynthesis* 
+       8 CSN12              1 CSN12       BP     adaptation to pheromone during conju~
+       9 YAL046C            1 YAL046C     BP     biological process unknown           
+      10 SLG1               1 SLG1        BP     cell wall organization and biogenesi~
+      # i 19,490 more rows
+
+      $design
+      $design$features
+      # A tibble: 3 x 2
+        variable     type               
+        <chr>        <chr>              
+      1 unique_name  feature_primary_key
+      2 name         character          
+      3 entry_number integer            
+
+      $design$samples
+      # A tibble: 1 x 2
+        variable type              
+        <chr>    <chr>             
+      1 sample   sample_primary_key
+
+      $design$measurements
+      # A tibble: 3 x 2
+        variable    type               
+        <chr>       <chr>              
+      1 unique_name feature_primary_key
+      2 sample      sample_primary_key 
+      3 abundance   character          
+
+      $design$feature_pk
+      [1] "unique_name"
+
+      $design$sample_pk
+      [1] "sample"
+
+
+      attr(,"class")
+      [1] "tidy_omic" "tomic"     "general"  
+
+# Find primary or foreign keys in tomic table
+
+    Code
+      get_identifying_keys(brauer_2008_triple, "foo")
+    Condition
+      Error in `get_identifying_keys()`:
+      ! Assertion on 'table' failed: Must be element of set {'features','samples','measurements'}, but is 'foo'.
+
 # Test that get_tomic_table() can retrieve various tables
 
     Code
@@ -60,3 +133,19 @@
       ! based on the "tomic" primary keys, tomic_table doesn't appear to
              be features, samples or measurements
 
+# Catch corner cases when interconverting tomics
+
+    Code
+      check_tomic(mtcars)
+    Condition
+      Error in `check_tomic()`:
+      ! Assertion on 'tomic' failed: Must inherit from class 'tomic', but has class 'data.frame'.
+
+---
+
+    Code
+      tomic_to(romic::brauer_2008_tidy, "foo")
+    Condition
+      Error in `tomic_to()`:
+      ! Assertion on 'to_class' failed: Must be element of set {'tidy_omic','triple_omic'}, but is 'foo'.
+

tests/testthat/_snaps/design.md

@@ -17,3 +17,19 @@
        9 sample          sample_primary_key  measurements
       10 expression      numeric             measurements
 
+# Catch malformed design objects
+
+    Code
+      check_design(malformed_design)
+    Condition
+      Error in `check_design()`:
+      ! The following unexpected attributes were found in the design: foo
+
+---
+
+    Code
+      check_design(malformed_design)
+    Condition
+      Error in `check_design()`:
+      ! The following attributes were missing in the design: feature_pk
+

tests/testthat/_snaps/dim_reduction.md

@@ -0,0 +1,20 @@
+# Sample mahalanobis distances are calculated
+
+    Code
+      pc_distances
+    Output
+      # A tibble: 36 x 4
+         sample nutrient    DR pc_distance
+         <chr>  <chr>    <dbl>       <dbl>
+       1 G0.05  G         0.05       188. 
+       2 G0.1   G         0.1        125. 
+       3 G0.15  G         0.15       128. 
+       4 G0.2   G         0.2        125. 
+       5 G0.25  G         0.25        83.4
+       6 G0.3   G         0.3        101. 
+       7 N0.05  N         0.05       371. 
+       8 N0.1   N         0.1        226. 
+       9 N0.15  N         0.15       123. 
+      10 N0.2   N         0.2        100. 
+      # i 26 more rows
+

tests/testthat/_snaps/mutates.md

@@ -36,3 +36,55 @@
       ! bar is not present in measurements, valid value_vars include:
       expression
 
+# Factor levels can be updated using a list of factor orders
+
+    Code
+      brauer_w_mystery_nutrients <- update_sample_factors(brauer_2008_tidy_w_NAs,
+        list(nutrient = NUTRIENT_ORDER))
+    Message
+      ! NA was present in the sample metadata's nutrient field but did not have a corresponding factor level in the `factor_levels` list. They will be added to the end of the specified factor levels
+      ! The nutrient field in the sample metadata contains 2 NA values. These entries will be replaced with an "unspecified" level.
+
+---
+
+    Code
+      reordered_tidy <- update_sample_factors(brauer_2008_tidy, list(nutrient = CONFUSED_NUTRIENT_ORDER))
+    Message
+      ! "G" was present in the sample metadata's nutrient field but did not have a corresponding factor level in the `factor_levels` list. They will be added to the end of the specified factor levels
+      ! "C" was present in `factor_levels` for nutrient but did not have a corresponding entry in the sample metadata.
+
+---
+
+    Code
+      update_sample_factors(brauer_2008_tidy, list(nutrient = 1:5))
+    Condition
+      Error in `set_factor_levels()`:
+      ! The factor levels for nutrient were "integer". This should be a character vector.
+
+---
+
+    Code
+      update_sample_factors(brauer_2008_tidy, list(nutrient = c("G", "G", "N", "L")))
+    Condition
+      Error in `set_factor_levels()`:
+      ! 1 factor levels was duplicated in the `factor_levels` specification for "nutrient": G
+
+---
+
+    Code
+      update_sample_factors(brauer_2008_tidy, list(DR = seq(0.05, 0.3, by = 0.05)))
+    Condition
+      Error in `set_factor_levels()`:
+      ! The factor levels for DR were "numeric". This should be a character vector.
+
+# Update tidy omics with new added variables
+
+    Code
+      update_tidy_omic(tidy_omic, updated_tidy_data, c())
+    Condition
+      Error in `update_tidy_omic()`:
+      ! updated_tidy_data contains 1
+      - new fields: new_sample_var.
+      - Add these to "new_variable_tables" so that romic know how to
+      - use them.
+

tests/testthat/helper.R

@@ -7,7 +7,7 @@ three_col_df <- tidyr:::expand_grid(
   samples = 1:10
 ) %>%
   dplyr::mutate(
-    measurement = 1
+    measurement = 1:100
   )
 
 simple_tidy <- create_tidy_omic(