transition remaining rlang warnings and errors to cli

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: parsnip
 Title: A Common API to Modeling and Analysis Functions
-Version: 1.2.1.9001
+Version: 1.2.1.9002
 Authors@R: c(
     person("Max", "Kuhn", , "[email protected]", role = c("aut", "cre")),
     person("Davis", "Vaughan", , "[email protected]", role = "aut"),

NEWS.md

@@ -2,7 +2,7 @@
 
 * Transitioned package errors and warnings to use cli (#1147 and #1148 by
   @shum461, #1153 by @RobLBaker and @wright13, #1154 by @JamesHWade, #1160, 
-  #1161).
+  #1161, #1081).
 
 * `fit_xy()` currently raises an error for `gen_additive_mod()` model specifications as the default engine (`"mgcv"`) specifies smoothing terms in model formulas. However, some engines specify smooths via additional arguments, in which case the restriction on `fit_xy()` is excessive. parsnip will now only raise an error when fitting a `gen_additive_mod()` with `fit_xy()` when using the `"mgcv"` engine (#775).
 

R/aaa_multi_predict.R

@@ -19,7 +19,7 @@
 #' @export
 multi_predict <- function(object, ...) {
   if (inherits(object$fit, "try-error")) {
-    rlang::warn("Model fit failed; cannot make predictions.")
+    cli::cli_warn("Model fit failed; cannot make predictions.")
     return(NULL)
   }
   check_for_newdata(...)
@@ -28,17 +28,19 @@ multi_predict <- function(object, ...) {
 
 #' @export
 #' @rdname multi_predict
-multi_predict.default <- function(object, ...)
-  rlang::abort(
-    glue::glue(
-      "No `multi_predict` method exists for objects with classes ",
-      glue::glue_collapse(glue::glue("'{class(object)}'"), sep = ", ")
-      )
-    )
+multi_predict.default <- function(object, ...) {
+  cli::cli_abort(
+    "No {.fun multi_predict} method exists for objects with classes
+     {.cls {class(object)}}."
+  )
+}
 
 #' @export
 predict.model_spec <- function(object, ...) {
-  rlang::abort("You must use `fit()` on your model specification before you can use `predict()`.")
+  cli::cli_abort(
+    "You must {.fun fit} your model specification
+     before you can use {.fun predict}."
+  )
 }
 
 #' Tools for models that predict on sub-models

R/adds.R

@@ -7,7 +7,7 @@
 #' @export
 add_rowindex <- function(x) {
   if (!is.data.frame(x)) {
-    rlang::abort("`x` should be a data frame.")
+    cli::cli_abort("{.arg x} should be a data frame.")
   }
   x <- dplyr::mutate(x, .row = seq_len(nrow(x)))
   x

R/arguments.R

@@ -22,8 +22,10 @@ check_eng_args <- function(args, obj, core_args) {
   if (length(common_args) > 0) {
     args <- args[!(names(args) %in% common_args)]
     common_args <- paste0(common_args, collapse = ", ")
-    rlang::warn(glue::glue("The following arguments cannot be manually modified ",
-                           "and were removed: {common_args}."))
+    cli::cli_warn(
+      "The argument{?s} {.arg {common_args}} cannot be manually
+       modified and {?was/were} removed."
+    )
   }
   args
 }
@@ -56,7 +58,7 @@ set_args <- function(object, ...) {
 set_args.model_spec <- function(object, ...) {
   the_dots <- enquos(...)
   if (length(the_dots) == 0)
-    rlang::abort("Please pass at least one named argument.")
+    cli::cli_abort("Please pass at least one named argument.")
   main_args <- names(object$args)
   new_args <- names(the_dots)
   for (i in new_args) {
@@ -262,7 +264,7 @@ make_xy_call <- function(object, target, env) {
       none = rlang::expr(x),
       data.frame = rlang::expr(maybe_data_frame(x)),
       matrix = rlang::expr(maybe_matrix(x)),
-      rlang::abort(glue::glue("Invalid data type target: {target}."))
+      cli::cli_abort("Invalid data type target: {target}.")
     )
   if (uses_weights) {
     object$method$fit$args[[ unname(data_args["weights"]) ]] <- rlang::expr(weights)
@@ -316,9 +318,13 @@ min_cols <- function(num_cols, source) {
     p <- ncol(source)
   }
   if (num_cols > p) {
-    msg <- paste0(num_cols, " columns were requested but there were ", p,
-                 " predictors in the data. ", p, " will be used.")
-    rlang::warn(msg)
+    cli::cli_warn(
+      c(
+        "!" = "{num_cols} column{?s} {?was/were} requested but there {cli::qty(p)} {?was/were}
+               {p} predictor{?s} in the data.",
+        "i" = "{p} predictor{?s} will be used."
+      )
+    )
     num_cols <- p
   }
 
@@ -335,9 +341,14 @@ min_rows <- function(num_rows, source, offset = 0) {
   }
 
   if (num_rows > n - offset) {
-    msg <- paste0(num_rows, " samples were requested but there were ", n,
-                  " rows in the data. ", n - offset, " will be used.")
-    rlang::warn(msg)
+    cli::cli_warn(
+      c(
+        "!" = "{num_rows} sample{?s} {?was/were} requested but there were
+               {n} rows in the data.",
+        "i" = "{n - offset} sample{?s} will be used."
+      )
+    )
+
     num_rows <- n - offset
   }
 

R/augment.R

@@ -86,7 +86,12 @@ augment.model_fit <- function(x, new_data, eval_time = NULL, ...) {
       "regression"          = augment_regression(x, new_data),
       "classification"      = augment_classification(x, new_data),
       "censored regression" = augment_censored(x, new_data, eval_time = eval_time),
-      rlang::abort(paste("Unknown mode:", x$spec$mode))
+      cli::cli_abort(
+        c(
+          "Unknown mode {.val {x$spec$mode}}.",
+          "i" = "Model mode should be one of {.or {.val {all_modes}}}."
+        )
+      )
     )
   tibble::new_tibble(res)
 }

R/autoplot.R

@@ -44,7 +44,9 @@ map_glmnet_coefs <- function(x) {
   # work. If an object is loaded from a new session, they will need to load the
   # package.
   if (is.null(coefs)) {
-    rlang::abort("Please load the glmnet package before running `autoplot()`.")
+    cli::cli_abort(
+      "Please load the {.pkg glmnet} package before running {.fun autoplot}."
+    )
   }
   p <- x$dim[1]
   if (is.list(coefs)) {
@@ -161,8 +163,7 @@ check_penalty_value <- function(x) {
   cl <- match.call()
   arg_val <- as.character(cl$x)
   if (!is.vector(x) || length(x) != 1 || !is.numeric(x) || x < 0) {
-    msg <- paste0("Argument '", arg_val, "' should be a single, non-negative value.")
-    rlang::abort(msg)
+    cli::cli_abort("{.arg {arg_val}} should be a single, non-negative value.")
   }
   invisible(x)
 }

R/case_weights.R

@@ -87,10 +87,9 @@ case_weights_allowed <- function(spec) {
     get_from_env(paste0(mod_type, "_fit")) %>%
     dplyr::filter(engine == mod_eng & mode == mod_mode)
   if (nrow(model_info) != 1) {
-    rlang::abort(
-      glue::glue(
-        "Error in getting model information for model {mod_type} with engine {mod_eng} and mode {mod_mode}."
-      )
+    cli::cli_abort(
+      "Error in getting model information for model {mod_type} with
+       engine {mod_eng} and mode {mod_mode}."
     )
   }
   # If weights are used, they are protected data arguments with the canonical

R/condense_control.R

@@ -23,17 +23,12 @@
 condense_control <- function(x, ref) {
   mismatch <- setdiff(names(ref), names(x))
   if (length(mismatch)) {
-    rlang::abort(
+    cli::cli_abort(
       c(
-        glue::glue(
-          "Object of class `{class(x)[1]}` cannot be coerced to ",
-          "object of class `{class(ref)[1]}`."
-        ),
-        "The following arguments are missing:",
-        glue::glue_collapse(
-          glue::single_quote(mismatch),
-          sep = ", ", last = ", and "
-        )
+        "Object of class {.cls class(x)[1]} cannot be coerced to
+         object of class {.cls class(ref)[1]}.",
+        "i" = "{cli::qty(mismatch)} The argument{?s} {.arg {mismatch}}
+               {?is/are} missing."
       )
     )
   }

R/contr_one_hot.R

@@ -16,11 +16,11 @@
 #' @export
 contr_one_hot <- function(n, contrasts = TRUE, sparse = FALSE) {
   if (sparse) {
-    rlang::warn("`sparse = TRUE` not implemented for `contr_one_hot()`.")
+    cli::cli_warn("{.code sparse = TRUE} not implemented for {.fun contr_one_hot}.")
   }
 
   if (!contrasts) {
-    rlang::warn("`contrasts = FALSE` not implemented for `contr_one_hot()`.")
+    cli::cli_warn("{.code contrasts = FALSE} not implemented for {.fun contr_one_hot}.")
   }
 
   if (is.character(n)) {
@@ -30,12 +30,12 @@ contr_one_hot <- function(n, contrasts = TRUE, sparse = FALSE) {
     n <- as.integer(n)
 
     if (length(n) != 1L) {
-      rlang::abort("`n` must have length 1 when an integer is provided.")
+      cli::cli_abort("{.arg n} must have length 1 when an integer is provided.")
     }
 
     names <- as.character(seq_len(n))
   } else {
-    rlang::abort("`n` must be a character vector or an integer of size 1.")
+    cli::cli_abort("{.arg n} must be a character vector or an integer of size 1.")
   }
 
   out <- diag(n)

R/control_parsnip.R

@@ -28,17 +28,21 @@ control_parsnip <- function(verbosity = 1L, catch = FALSE) {
   res
 }
 
-check_control <- function(x) {
+check_control <- function(x, call = rlang::caller_env()) {
   if (!is.list(x))
-    rlang::abort("control should be a named list.")
+    cli::cli_abort("{.arg control} should be a named list.", call = call)
   if (!isTRUE(all.equal(sort(names(x)), c("catch", "verbosity"))))
-    rlang::abort("control should be a named list with elements 'verbosity' and 'catch'.")
+    cli::cli_abort(
+      "{.arg control} should be a named list with elements {.field verbosity}
+       and {.field catch}.",
+      call = call
+    )
   # based on ?is.integer
   int_check <- function(x, tol = .Machine$double.eps^0.5) abs(x - round(x)) < tol
   if (!int_check(x$verbosity))
-    rlang::abort("verbosity should be an integer.")
+    cli::cli_abort("{.arg verbosity} should be an integer.", call = call)
   if (!is.logical(x$catch))
-    rlang::abort("catch should be a logical.")
+    cli::cli_abort("{.arg catch} should be a logical.", call = call)
   x
 }
 

R/convert_data.R

@@ -42,7 +42,9 @@
                                     composition = "data.frame",
                                     remove_intercept = TRUE) {
   if (!(composition %in% c("data.frame", "matrix"))) {
-    rlang::abort("`composition` should be either 'data.frame' or 'matrix'.")
+    cli::cli_abort(
+      "{.arg composition} should be either {.val data.frame} or {.val matrix}."
+    )
   }
 
   if (remove_intercept) {
@@ -74,7 +76,7 @@
 
   w <- as.vector(model.weights(mod_frame))
   if (!is.null(w) && !is.numeric(w)) {
-    rlang::abort("`weights` must be a numeric vector")
+    cli::cli_abort("{.arg weights} must be a numeric vector.")
   }
 
   # TODO: Do we actually use the offset when fitting?
@@ -151,7 +153,9 @@
                                     na.action = na.pass,
                                     composition = "data.frame") {
   if (!(composition %in% c("data.frame", "matrix"))) {
-    rlang::abort("`composition` should be either 'data.frame' or 'matrix'.")
+    cli::cli_abort(
+      "{.arg composition} should be either {.val data.frame} or {.val matrix}."
+    )
   }
 
   mod_terms <- object$terms
@@ -218,7 +222,7 @@
                                     y_name = "..y",
                                     remove_intercept = TRUE) {
   if (is.vector(x)) {
-    rlang::abort("`x` cannot be a vector.")
+    cli::cli_abort("{.arg x} cannot be a vector.")
   }
 
   if (remove_intercept) {
@@ -251,10 +255,10 @@
 
   if (!is.null(weights)) {
     if (!is.numeric(weights)) {
-      rlang::abort("`weights` must be a numeric vector")
+      cli::cli_abort("{.arg weights} must be a numeric vector.")
     }
     if (length(weights) != nrow(x)) {
-      rlang::abort(glue::glue("`weights` should have {nrow(x)} elements"))
+      cli::cli_abort("{.arg weights} should have {nrow(x)} elements.")
     }
 
     form <- patch_formula_environment_with_case_weights(
@@ -294,17 +298,17 @@ local_one_hot_contrasts <- function(frame = rlang::caller_env()) {
   rlang::local_options(contrasts = contrasts, .frame = frame)
 }
 
-check_form_dots <- function(x) {
+check_form_dots <- function(x, call = rlang::caller_env()) {
   good_args <- c("subset", "weights")
   good_names <- names(x) %in% good_args
   if (any(!good_names)) {
-    rlang::abort(
-      glue::glue(
-        "These argument(s) cannot be used to create the data: ",
-        glue::glue_collapse(glue::glue("`{names(x)[!good_names]}`"), sep = ", "),
-        ". Possible arguments are: ",
-        glue::glue_collapse(glue::glue("`{good_args}`"), sep = ", ")
-      )
+    cli::cli_abort(
+      c(
+        "The argument{?s} {.arg {names(x)[!good_names]}} cannot be used to create
+         the data.",
+        "Possible arguments are {.arg {.or {good_args}}."
+      ),
+      call = call
     )
   }
   invisible(NULL)
@@ -339,18 +343,21 @@ will_make_matrix <- function(y) {
   all(can_convert)
 }
 
-check_dup_names <- function(x, y) {
+check_dup_names <- function(x, y, call = rlang::caller_env()) {
   if (is.vector(y))
     return(invisible(NULL))
 
   common_names <- intersect(colnames(x), colnames(y))
-  if (length(common_names) > 0)
-    rlang::abort(
-      glue::glue(
-        "`x` and `y` have at least one name in common: ",
-        glue::glue_collapse(glue::glue("'{common_names}'"), sep = ", ")
-      )
+  if (length(common_names) > 0) {
+    cli::cli_abort(
+      c(
+        "{.arg x} and {.arg y} have the name{?s} {.val {common_names}} in common.",
+        "i" = "Please ensure that {.arg x} and {.arg y} don't share any column names."
+      ),
+      call = call
     )
+  }
+
   invisible(NULL)
 }
 
@@ -369,10 +376,11 @@ maybe_matrix <- function(x) {
     non_num_cols <- vapply(x, function(x) !is.numeric(x), logical(1))
     if (any(non_num_cols)) {
       non_num_cols <- names(non_num_cols)[non_num_cols]
-      non_num_cols <- glue::glue_collapse(glue::single_quote(non_num_cols), sep = ", ")
-      msg <- glue::glue("Some columns are non-numeric. The data cannot be ",
-                        "converted to numeric matrix: {non_num_cols}.")
-      rlang::abort(msg)
+
+      cli::cli_abort(
+        "The column{?s} {.val {non_num_cols}} {?is/are} non-numeric, so the
+         data cannot be converted to a numeric matrix."
+      )
     }
     x <- as.matrix(x)
   }