drop check_pname in favor just just check_name

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: epipredict
 Title: Basic epidemiology forecasting methods
-Version: 0.1.12
+Version: 0.1.13
 Authors@R: c(
     person("Daniel J.", "McDonald", , "daniel@stat.ubc.ca", role = c("aut", "cre")),
     person("Ryan", "Tibshirani", , "ryantibs@cmu.edu", role = "aut"),

NEWS.md

@@ -42,6 +42,7 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
 - adjust default quantiles throughout so that they match.
 - force `layer_residual_quantiles()` to always include `0.5`.
 - Rename `recipes:::check_training_set()` to `recipes:::validate_training_data()`, as it changed in recipes 1.1.0.
+- A new column name duplicating an existing column name results in an error instead of a random name.
 
 # epipredict 0.1
 

R/layer_cdc_flatline_quantiles.R

@@ -227,7 +227,6 @@ slather.layer_cdc_flatline_quantiles <-
       ) %>%
       select(all_of(c(avail_grps, ".pred_distn_all")))
 
-    # res <- check_pname(res, components$predictions, object)
     components$predictions <- left_join(
       components$predictions,
       res,

R/layer_point_from_distn.R

@@ -93,7 +93,12 @@ slather.layer_point_from_distn <-
       components$predictions$.pred <- dstn
     } else {
       dstn <- tibble(dstn = dstn)
-      dstn <- check_pname(dstn, components$predictions, object)
+      dstn <- check_name(
+        dstn,
+        components$predictions,
+        object,
+        newname = object$name
+      )
       components$predictions <- mutate(components$predictions, !!!dstn)
     }
     components

R/layer_quantile_distn.R

@@ -96,7 +96,12 @@ slather.layer_quantile_distn <-
       dstn <- snap(dstn, truncate[1], truncate[2])
     }
     dstn <- tibble(dstn = dstn)
-    dstn <- check_pname(dstn, components$predictions, object)
+    dstn <- check_name(
+      dstn,
+      components$predictions,
+      object,
+      newname = object$name
+    )
     components$predictions <- mutate(components$predictions, !!!dstn)
     components
   }

R/layer_residual_quantiles.R

@@ -139,7 +139,12 @@ slather.layer_residual_quantiles <-
 
     estimate <- components$predictions$.pred
     res <- tibble(.pred_distn = r$dstn + estimate)
-    res <- check_pname(res, components$predictions, object)
+    res <- check_name(
+      res,
+      components$predictions,
+      object,
+      newname = object$name
+    )
     components$predictions <- mutate(components$predictions, !!!res)
     components
   }

R/utils-misc.R

@@ -1,36 +1,3 @@
-#' Check that newly created variable names don't overlap
-#'
-#' `check_pname` is to be used in a slather method to ensure that
-#'   newly created variable names don't overlap with existing names.
-#'   Throws an warning if check fails, and creates a random string.
-#' @param res A data frame or tibble of the newly created variables.
-#' @param preds An epi_df or tibble containing predictions.
-#' @param object A layer object passed to [slather()].
-#' @param newname A string of variable names if the object doesn't contain a
-#'   $name element
-#'
-#' @keywords internal
-check_pname <- function(res, preds, object, newname = NULL) {
-  if (is.null(newname)) newname <- object$name
-  new_preds_names <- colnames(preds)
-  intersection <- new_preds_names %in% newname
-  if (any(intersection)) {
-    newname <- rand_id(newname)
-    rlang::warn(
-      paste0(
-        "Name collision occured in `",
-        class(object)[1],
-        "`. The following variable names already exists: ",
-        paste0(new_preds_names[intersection], collapse = ", "),
-        ". Result instead has randomly generated string `",
-        newname, "`."
-      )
-    )
-  }
-  names(res) <- newname
-  res
-}
-
 # Copied from `epiprocess`:
 
 #' "Format" a character vector of column/variable names for cli interpolation

tests/testthat/test-layer_residual_quantiles.R

@@ -28,6 +28,23 @@ test_that("Returns expected number or rows and columns", {
   expect_equal(unique(unnested$.pred_distn_quantile_level), c(.0275, 0.5, .8, .95))
 })
 
+test_that("new name works correctly", {
+  f <- frosting() %>%
+    layer_predict() %>%
+    layer_naomit(.pred) %>%
+    layer_residual_quantiles(name = "foo")
+
+  wf1 <- wf %>% add_frosting(f)
+  expect_equal(names(forecast(wf1)), c("geo_value", "time_value", ".pred", "foo"))
+
+  f <- frosting() %>%
+    layer_predict() %>%
+    layer_naomit(.pred) %>%
+    layer_residual_quantiles(name = "geo_value")
+
+  wf1 <- wf %>% add_frosting(f)
+  expect_error(forecast(wf1))
+})
 
 test_that("Errors when used with a classifier", {
   tib <- tibble(