.code to .val for engine argument in sparse data functions

Author: EmilHvitfeldt

R/sparsevctrs.R

@@ -9,7 +9,7 @@ to_sparse_data_frame <- function(x, object, call = rlang::caller_env()) {
 
       cli::cli_abort(
         "{.arg x} is a sparse matrix, but {.fn {class(object)[1]}} with
-        engine {.code {object$engine}} doesn't accept that.",
+        engine {.val {object$engine}} doesn't accept that.",
         call = call
       )
     }
@@ -35,7 +35,7 @@ materialize_sparse_tibble <- function(x, object, input) {
 
     cli::cli_warn(
       "{.arg {input}} is a sparse tibble, but {.fn {class(object)[1]}} with
-      engine {.code {object$engine}} doesn't accept that. Converting to 
+      engine {.val {object$engine}} doesn't accept that. Converting to 
       non-sparse."
     )
     for (i in seq_along(ncol(x))) {

tests/testthat/_snaps/sparsevctrs.md

@@ -4,23 +4,23 @@
       lm_fit <- fit(spec, avg_price_per_room ~ ., data = hotel_data[1:100, ])
     Condition
       Warning:
-      `data` is a sparse tibble, but `linear_reg()` with engine `lm` doesn't accept that. Converting to non-sparse.
+      `data` is a sparse tibble, but `linear_reg()` with engine "lm" doesn't accept that. Converting to non-sparse.
 
 # sparse tibble can be passed to `fit_xy()
 
     Code
       lm_fit <- fit_xy(spec, x = hotel_data[1:100, -1], y = hotel_data[1:100, 1])
     Condition
       Warning:
-      `x` is a sparse tibble, but `linear_reg()` with engine `lm` doesn't accept that. Converting to non-sparse.
+      `x` is a sparse tibble, but `linear_reg()` with engine "lm" doesn't accept that. Converting to non-sparse.
 
 # sparse matrices can be passed to `fit_xy()
 
     Code
       lm_fit <- fit_xy(spec, x = hotel_data[1:100, -1], y = hotel_data[1:100, 1])
     Condition
       Error in `fit_xy()`:
-      ! `x` is a sparse matrix, but `linear_reg()` with engine `lm` doesn't accept that.
+      ! `x` is a sparse matrix, but `linear_reg()` with engine "lm" doesn't accept that.
 
 # sparse matrices can not be passed to `fit()
 
@@ -37,15 +37,15 @@
       preds <- predict(lm_fit, sparse_mtcars)
     Condition
       Warning:
-      `x` is a sparse tibble, but `linear_reg()` with engine `lm` doesn't accept that. Converting to non-sparse.
+      `x` is a sparse tibble, but `linear_reg()` with engine "lm" doesn't accept that. Converting to non-sparse.
 
 # sparse matrices can be passed to `predict()
 
     Code
       predict(lm_fit, sparse_mtcars)
     Condition
       Error in `predict()`:
-      ! `x` is a sparse matrix, but `linear_reg()` with engine `lm` doesn't accept that.
+      ! `x` is a sparse matrix, but `linear_reg()` with engine "lm" doesn't accept that.
 
 # to_sparse_data_frame() is used correctly