Reindent code to use just two spaces

R/methods.R

@@ -1,47 +1,47 @@
 # Construct UI for the methods editor.
 methods_ui <- function(id) {
-    verticalLayout(
-        h3("Methods"),
-        selectInput(
-            NS(id, "optimization_genes"),
-            "Genes to optimize for",
-            choices = list(
-                "Reference genes" = "reference",
-                "Comparison genes" = "comparison"
-            )
+  verticalLayout(
+    h3("Methods"),
+    selectInput(
+      NS(id, "optimization_genes"),
+      "Genes to optimize for",
+      choices = list(
+        "Reference genes" = "reference",
+        "Comparison genes" = "comparison"
+      )
+    ),
+    selectInput(
+      NS(id, "optimization_target"),
+      "Optimization target",
+      choices = list(
+        "Mean rank" = "mean",
+        "Median rank" = "median",
+        "First rank" = "min",
+        "Last rank" = "max",
+        "Customize weights" = "custom"
+      )
+    ),
+    lapply(methods, function(method) {
+      verticalLayout(
+        checkboxInput(
+          NS(id, method$id),
+          span(
+            method$description,
+            class = "control-label"
+          ),
+          value = TRUE
         ),
-        selectInput(
-            NS(id, "optimization_target"),
-            "Optimization target",
-            choices = list(
-                "Mean rank" = "mean",
-                "Median rank" = "median",
-                "First rank" = "min",
-                "Last rank" = "max",
-                "Customize weights" = "custom"
-            )
-        ),
-        lapply(methods, function(method) {
-            verticalLayout(
-                checkboxInput(
-                    NS(id, method$id),
-                    span(
-                        method$description,
-                        class = "control-label"
-                    ),
-                    value = TRUE
-                ),
-                sliderInput(
-                    NS(id, sprintf("%s_weight", method$id)),
-                    NULL,
-                    min = -1.0,
-                    max = 1.0,
-                    step = 0.01,
-                    value = 1.0
-                )
-            )
-        })
-    )
+        sliderInput(
+          NS(id, sprintf("%s_weight", method$id)),
+          NULL,
+          min = -1.0,
+          max = 1.0,
+          step = 0.01,
+          value = 1.0
+        )
+      )
+    })
+  )
 }
 
 # Construct server for the methods editor.
@@ -50,89 +50,89 @@ methods_ui <- function(id) {
 #
 # @return A reactive containing the weighted results.
 methods_server <- function(id, analysis, comparison_gene_ids) {
-    moduleServer(id, function(input, output, session) {
-        # Observe each method's enable button and synchronise the slider state.
-        lapply(methods, function(method) {
-            observeEvent(input[[method$id]], {
-                shinyjs::toggleState(
-                    sprintf("%s_weight", method$id),
-                    condition = input[[method$id]]
-                )
-            })
-
-            shinyjs::onclick(sprintf("%s_weight", method$id), {
-                updateSelectInput(
-                    session,
-                    "optimization_target",
-                    selected = "custom"
-                )
-            })
-        })
-
-        # This reactive will always contain the currently selected optimization
-        # gene IDs in a normalized form.
-        optimization_gene_ids <- reactive({
-            gene_ids <- if (input$optimization_genes == "comparison") {
-                comparison_gene_ids()
-            } else {
-                analysis()$preset$reference_gene_ids
-            }
-
-            sort(unique(gene_ids))
-        })
-
-        # This reactive will always contain the optimal weights according to
-        # the selected parameters.
-        optimal_weights <- reactive({
-            withProgress(message = "Optimizing weights", {
-                setProgress(0.2)
-
-                included_methods <- NULL
-
-                for (method in methods) {
-                    if (input[[method$id]]) {
-                        included_methods <- c(included_methods, method$id)
-                    }
-                }
-
-                geposan::optimal_weights(
-                    analysis(),
-                    included_methods,
-                    optimization_gene_ids(),
-                    target = input$optimization_target
-                )
-            })
-        }) |> bindCache(
-            analysis(),
-            optimization_gene_ids(),
-            sapply(methods, function(method) input[[method$id]]),
-            input$optimization_target
+  moduleServer(id, function(input, output, session) {
+    # Observe each method's enable button and synchronise the slider state.
+    lapply(methods, function(method) {
+      observeEvent(input[[method$id]], {
+        shinyjs::toggleState(
+          sprintf("%s_weight", method$id),
+          condition = input[[method$id]]
         )
+      })
 
-        reactive({
-            weights <- NULL
-
-            if (length(optimization_gene_ids()) < 1 |
-                input$optimization_target == "custom") {
-                for (method in methods) {
-                    if (input[[method$id]]) {
-                        weight <- input[[sprintf("%s_weight", method$id)]]
-                        weights[[method$id]] <- weight
-                    }
-                }
-            } else {
-                weights <- optimal_weights()
-
-                for (method_id in names(weights)) {
-                    updateSliderInput(
-                        session,
-                        sprintf("%s_weight", method_id),
-                        value = weights[[method_id]]
-                    )
-                }
-            }
-
-            geposan::ranking(analysis(), weights)
-        })
+      shinyjs::onclick(sprintf("%s_weight", method$id), {
+        updateSelectInput(
+          session,
+          "optimization_target",
+          selected = "custom"
+        )
+      })
     })
+
+    # This reactive will always contain the currently selected optimization
+    # gene IDs in a normalized form.
+    optimization_gene_ids <- reactive({
+      gene_ids <- if (input$optimization_genes == "comparison") {
+        comparison_gene_ids()
+      } else {
+        analysis()$preset$reference_gene_ids
+      }
+
+      sort(unique(gene_ids))
+    })
+
+    # This reactive will always contain the optimal weights according to
+    # the selected parameters.
+    optimal_weights <- reactive({
+      withProgress(message = "Optimizing weights", {
+        setProgress(0.2)
+
+        included_methods <- NULL
+
+        for (method in methods) {
+          if (input[[method$id]]) {
+            included_methods <- c(included_methods, method$id)
+          }
+        }
+
+        geposan::optimal_weights(
+          analysis(),
+          included_methods,
+          optimization_gene_ids(),
+          target = input$optimization_target
+        )
+      })
+    }) |> bindCache(
+      analysis(),
+      optimization_gene_ids(),
+      sapply(methods, function(method) input[[method$id]]),
+      input$optimization_target
+    )
+
+    reactive({
+      weights <- NULL
+
+      if (length(optimization_gene_ids()) < 1 |
+        input$optimization_target == "custom") {
+        for (method in methods) {
+          if (input[[method$id]]) {
+            weight <- input[[sprintf("%s_weight", method$id)]]
+            weights[[method$id]] <- weight
+          }
+        }
+      } else {
+        weights <- optimal_weights()
+
+        for (method_id in names(weights)) {
+          updateSliderInput(
+            session,
+            sprintf("%s_weight", method_id),
+            value = weights[[method_id]]
+          )
+        }
+      }
+
+      geposan::ranking(analysis(), weights)
+    })
+  })
 }