Lint

.github/workflows/recheck.yml

@@ -0,0 +1,18 @@
+on:
+    workflow_dispatch:
+      inputs:
+        which:
+          type: choice
+          description: Which dependents to check
+          options:
+          - strong
+          - most
+
+name: Reverse dependency check
+
+jobs:
+    revdep_check:
+      name: Reverse check ${{ inputs.which }} dependents
+      uses: r-devel/recheck/.github/workflows/recheck.yml@v1
+      with:
+        which: ${{ inputs.which }}

.lintr

@@ -1,36 +1,31 @@
 linters: linters_with_tags(
     tags = c(
+        "best_practices", 
+        "common_mistakes", 
+        "consistency", 
+        "correctness", 
         "default", 
-        "best_practices",
-        "common_mistakes",
-        "correctness",
-        "executing"
-        ),
+        "efficiency", 
+        "executing",
+        "package_development",
+        "pkg_testthat",
+        "readability",
+        "regex", 
+        "robustness", 
+        "style", 
+        "tidy_design"
+    ),
     return_linter(),
     object_overwrite_linter(),
-    unnecessary_concatenation_linter(),
-    cyclocomp_linter(complexity_limit = 19),  # TODO small refactor
-    implicit_integer_linter = NULL,  # TODO refactor
-    library_call_linter = NULL, # TODO refactor
+    object_length_linter(),
+    line_length_linter(120),
+    todo_comment_linter = NULL, # TODO refactor
     implicit_assignment_linter = NULL, # TODO refactor
-    length_levels_linter = NULL, # TODO refactor
-    any_duplicated_linter = NULL, # TODO refactor
-    expect_length_linter = NULL, # TODO refactor
-    expect_comparison_linter = NULL, # TODO refactor
-    expect_null_linter = NULL, # TODO refactor
-    expect_not_linter = NULL, # TODO refactor
-    expect_named_linter = NULL, # TODO refactor
-    expect_type_linter = NULL, # TODO refactor
-    expect_s3_class_linter = NULL, # TODO refactor
-    fixed_regex_linter = NULL, # TODO refactor
-    redundant_ifelse_linter = NULL, #TODO refactor
+    expect_identical_linter = NULL, # TODO big refactor
     undesirable_function_linter = NULL, # TODO big refactor
-    line_length_linter(305),  # TODO change to 120 - big refactor
-    object_length_linter(length = 31),  # May or may not refactor
     nzchar_linter = NULL, # Whats the point of this lintr?
     condition_message_linter = NULL, # Whats the point of this lintr?
     condition_call_linter = NULL, # Whats the point of this lintr?
     paste_linter = NULL, # Probably never gonna remove this one
     object_name_linter = NULL # Probably never gonna remove this one
   )
-

R/S3GenericExtenstions.R

@@ -35,14 +35,15 @@
 #'
 c.caretList <- function(...) {
   new_model_list <- unlist(lapply(list(...), function(x) {
-    if (!inherits(x, "caretList")) {
-      if (!inherits(x, "train")) stop("class of modelList1 must be 'caretList' or 'train'")
-
-      # assuming this is a single train object
+    if (inherits(x, "caretList")) {
+      x
+    } else if (inherits(x, "train")) {
       x <- list(x)
-      names(x) <- x[[1]]$method
+      names(x) <- x[[1L]]$method
+      x
+    } else {
+      stop("class of modelList1 must be 'caretList' or 'train'")
     }
-    x
   }), recursive = FALSE)
 
   # Make sure names are unique
@@ -81,15 +82,15 @@ c.caretList <- function(...) {
 #'
 c.train <- function(...) {
   new_model_list <- unlist(lapply(list(...), function(x) {
-    if (!inherits(x, "caretList")) {
-      if (!inherits(x, "train")) stop("class of modelList1 must be 'caretList' or 'train'")
-
-      # assuming this is a single train object
+    if (inherits(x, "caretList")) {
+      x
+    } else if (inherits(x, "train")) {
       x <- list(x)
-      names(x) <- x[[1]]$method
+      names(x) <- x[[1L]]$method
       x
+    } else {
+      stop("class of modelList1 must be 'caretList' or 'train'")
     }
-    x
   }), recursive = FALSE)
 
   # Make sure names are unique

R/caretEnsemble.R

@@ -64,8 +64,8 @@ summary.caretEnsemble <- function(object, ...) {
   val <- getMetric.train(object$ens_model)
   cat(paste0("The following models were ensembled: ", types, " \n"))
   cat("They were weighted: \n")
-  cat(paste0(paste0(round(wghts, 4), collapse = " "), "\n"))
-  cat(paste0("The resulting ", metric, " is: ", round(val, 4), "\n"))
+  cat(paste0(paste0(round(wghts, 4L), collapse = " "), "\n"))
+  cat(paste0("The resulting ", metric, " is: ", round(val, 4L), "\n"))
 
   # Add code to compare ensemble to individual models
   cat(paste0("The fit for each individual model on the ", metric, " is: \n"))
@@ -96,7 +96,7 @@ extractModRes <- function(ensemble) {
     ),
     stringsAsFactors = FALSE
   )
-  names(modRes)[2:3] <- c(metric, paste0(metric, "SD"))
+  names(modRes)[2L:3L] <- c(metric, paste0(metric, "SD"))
   modRes
 }
 
@@ -163,12 +163,12 @@ varImp.caretEnsemble <- function(object, ...) {
 
   # Convert to data.frame
   dat <- varImpFrame(coef_importance)
-  dat[is.na(dat)] <- 0
+  dat[is.na(dat)] <- 0L
   names(dat) <- make.names(names(coef_importance))
 
   # Scale the importances
-  norm_to_100 <- function(d) d / sum(d) * 100
-  dat <- apply(dat, 2, norm_to_100)
+  norm_to_100 <- function(d) d / sum(d) * 100.0
+  dat <- apply(dat, 2L, norm_to_100)
 
   # Calculate overall importance
   model_weights <- coef(object$ens_model$finalModel)
@@ -179,7 +179,7 @@ varImp.caretEnsemble <- function(object, ...) {
   names(model_weights) <- names(object$models)
   model_weights <- model_weights[names(model_weights) %in% names(coef_importance)]
   model_weights <- abs(model_weights)
-  overall <- norm_to_100(apply(dat, 1, weighted.mean, w = model_weights))
+  overall <- norm_to_100(apply(dat, 1L, weighted.mean, w = model_weights))
   dat <- data.frame(overall = overall, dat)
 
   # Order by overall importance
@@ -191,7 +191,7 @@ varImp.caretEnsemble <- function(object, ...) {
 #' @keywords internal
 # This function only gets called once, in varImp.caretEnsemble
 clean_varImp <- function(x) {
-  names(x$importance)[1] <- "Overall"
+  names(x$importance)[1L] <- "Overall"
   x$importance <- x$importance[, "Overall", drop = FALSE]
   x$importance
 }
@@ -206,18 +206,18 @@ varImpFrame <- function(x) {
   dat$id <- row.names(dat)
   dat$model <- sub("\\.[^\n]*", "", dat$id)
   dat$var <- sub("^[^.]*", "", dat$id)
-  dat$var <- substr(dat$var, 2, nchar(dat$var))
+  dat$var <- substr(dat$var, 2L, nchar(dat$var))
 
   # Parse intercept variables
-  dat$var[grep("Inter", dat$var)] <- "Intercept"
+  dat$var[grep("Inter", dat$var, fixed = TRUE)] <- "Intercept"
   dat$id <- NULL
   row.names(dat) <- NULL
   dat <- reshape(dat,
     direction = "wide", v.names = "Overall",
     idvar = "var", timevar = "model"
   )
-  row.names(dat) <- dat[, 1]
-  dat[, -1]
+  row.names(dat) <- dat[, 1L]
+  dat[, -1L]
 }
 
 #' @title Plot Diagnostics for an caretEnsemble Object
@@ -252,9 +252,9 @@ plot.caretEnsemble <- function(x, ...) {
     theme_bw() +
     labs(x = "Individual Model Method", y = metricLab)
 
-  if (nrow(x$error) > 0) {
+  if (nrow(x$error) > 0L) {
     plt <- plt +
-      geom_hline(linetype = 2, linewidth = 0.2, yintercept = min(x$error[[metricLab]]), color = I("red"))
+      geom_hline(linetype = 2L, linewidth = 0.2, yintercept = min(x$error[[metricLab]]), color = I("red"))
   }
   plt
 }
@@ -274,7 +274,7 @@ extractPredObsResid <- function(object, show_class_id = 2L) {
   obs <- predobs$obs
   id <- predobs$rowIndex
   if (type == "Regression") {
-    pred <- pred[[1]]
+    pred <- pred[[1L]]
   } else {
     show_class <- levels(object)[show_class_id]
     pred <- pred[[show_class]]
@@ -361,11 +361,11 @@ autoplot.caretEnsemble <- function(object, xvars = NULL, show_class_id = 2L, ...
       ymin = .data[["ymin"]],
       ymax = .data[["ymax"]]
     )) +
-    ggplot2::geom_point(size = I(3), alpha = I(0.8)) +
+    ggplot2::geom_point(size = I(3L), alpha = I(0.8)) +
     ggplot2::theme_bw() +
     ggplot2::geom_smooth(
       method = "lm", se = FALSE,
-      linewidth = I(1.1), color = I("red"), linetype = 2
+      linewidth = I(1.1), color = I("red"), linetype = 2L
     ) +
     ggplot2::labs(
       x = "Fitted Values", y = "Range of Resid.",
@@ -377,24 +377,24 @@ autoplot.caretEnsemble <- function(object, xvars = NULL, show_class_id = 2L, ...
   if (is.null(xvars)) {
     xvars <- names(x_data)
     xvars <- setdiff(xvars, c(".outcome", ".weights", "(Intercept)"))
-    xvars <- sample(xvars, 2)
+    xvars <- sample(xvars, 2L)
   }
   data.table::set(x_data, j = "id", value = seq_len(nrow(x_data)))
   plotdf <- merge(ensemble_data, x_data, by = "id")
   g5 <- ggplot2::ggplot(plotdf, ggplot2::aes(.data[[xvars[1]]], .data[["resid"]])) +
     ggplot2::geom_point() +
     ggplot2::geom_smooth(se = FALSE) +
-    ggplot2::scale_x_continuous(xvars[1]) +
+    ggplot2::scale_x_continuous(xvars[1L]) +
     ggplot2::scale_y_continuous("Residuals") +
-    ggplot2::labs(title = paste0("Residuals Against ", xvars[1])) +
+    ggplot2::labs(title = paste0("Residuals Against ", xvars[1L])) +
     ggplot2::theme_bw()
-  g6 <- ggplot2::ggplot(plotdf, ggplot2::aes(.data[[xvars[2]]], .data[["resid"]])) +
+  g6 <- ggplot2::ggplot(plotdf, ggplot2::aes(.data[[xvars[2L]]], .data[["resid"]])) +
     ggplot2::geom_point() +
     ggplot2::geom_smooth(se = FALSE) +
     ggplot2::scale_x_continuous(xvars[2]) +
     ggplot2::scale_y_continuous("Residuals") +
-    ggplot2::labs(title = paste0("Residuals Against ", xvars[2])) +
+    ggplot2::labs(title = paste0("Residuals Against ", xvars[2L])) +
     ggplot2::theme_bw()
   # nolint end: object_usage_linter
-  suppressMessages(gridExtra::grid.arrange(g1, g2, g3, g4, g5, g6, ncol = 2))
+  suppressMessages(gridExtra::grid.arrange(g1, g2, g3, g4, g5, g6, ncol = 2L))
 }