Add predict

DESCRIPTION

@@ -28,4 +28,4 @@ Suggests:
 Config/testthat/edition: 3
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.3.1.9000
+RoxygenNote: 7.3.2

NAMESPACE

@@ -16,12 +16,12 @@ S3method(orbital,step_scale)
 S3method(orbital,step_select)
 S3method(orbital,step_zv)
 S3method(orbital,workflow)
+S3method(predict,orbital_class)
 S3method(print,orbital_class)
 export(orbital)
 export(orbital_dt)
 export(orbital_inline)
 export(orbital_json_read)
 export(orbital_json_write)
-export(orbital_predict)
 export(orbital_sql)
 export(to_r_fun)

NEWS.md

@@ -1,5 +1,7 @@
 # orbital (development version)
 
+* `orbital_predict()` has been removed and replaced with the more idiomatic `predict()` method. (#10)
+
 # orbital 0.1.0
 
 * Initial CRAN submission.

R/predict.R

@@ -1,7 +1,8 @@
-#' Use orbital in a mutate way
+#' Prediction using orbital objects
 #'
-#' @param .data A data frame that can be used with mutate.
-#' @param x A orbital object.
+#' @param object A orbital object.
+#' @param new_data A data frame to predict with.
+#' @param ... Not currently used.
 #'
 #' @returns A modified data frame.
 #'
@@ -21,13 +22,13 @@
 #'
 #' orbital_obj <- orbital(wf_fit)
 #'
-#' mtcars %>%
-#'   orbital_predict(orbital_obj)
+#' predict(orbital_obj, mtcars)
 #' @export
-orbital_predict <- function(.data, x) {
-  res <- dplyr::mutate(.data, !!!orbital_inline(x))
+predict.orbital_class <- function(object, new_data, ...) {
+  rlang::check_dots_empty()
+  res <- dplyr::mutate(new_data, !!!orbital_inline(object))
 
-  pred_name <- names(x)[length(x)]
+  pred_name <- names(object)[length(object)]
   res <- dplyr::select(res, dplyr::any_of(pred_name))
 
   res

README.Rmd

@@ -40,6 +40,7 @@ devtools::install_github("tidymodels/orbital")
 Given a fitted workflow
 
 ```{r}
+#| message: false
 library(tidymodels)
 
 rec_spec <- recipe(mpg ~ ., data = mtcars) %>%
@@ -66,10 +67,8 @@ orbital_obj <- orbital(wf_fit)
 orbital_obj
 ```
 
-and then "predicting" with it using `orbital_predict()` to get the same results
+and then "predicting" with it using `predict()` to get the same results
 
 ```{r}
-mtcars %>%
-  as_tibble() %>%
-  orbital_predict(orbital_obj)
+predict(orbital_obj, as_tibble(mtcars))
 ```

README.md

@@ -31,24 +31,6 @@ Given a fitted workflow
 
 ``` r
 library(tidymodels)
-#> ── Attaching packages ────────────────────────────────────── tidymodels 1.2.0 ──
-#> ✔ broom        1.0.6      ✔ recipes      1.0.10
-#> ✔ dials        1.2.1      ✔ rsample      1.2.1 
-#> ✔ dplyr        1.1.4      ✔ tibble       3.2.1 
-#> ✔ ggplot2      3.5.1      ✔ tidyr        1.3.1 
-#> ✔ infer        1.0.7      ✔ tune         1.2.1 
-#> ✔ modeldata    1.4.0      ✔ workflows    1.1.4 
-#> ✔ parsnip      1.2.1      ✔ workflowsets 1.1.0 
-#> ✔ purrr        1.0.2      ✔ yardstick    1.3.1
-#> ── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ──
-#> ✖ purrr::discard() masks scales::discard()
-#> ✖ dplyr::filter()  masks stats::filter()
-#> ✖ dplyr::lag()     masks stats::lag()
-#> ✖ recipes::step()  masks stats::step()
-#> • Dig deeper into tidy modeling with R at https://www.tmwr.org
-```
-
-``` r
 
 rec_spec <- recipe(mpg ~ ., data = mtcars) %>%
   step_normalize(all_numeric_predictors())
@@ -103,13 +85,10 @@ orbital_obj
 #> 11 equations in total.
 ```
 
-and then “predicting” with it using `orbital_predict()` to get the same
-results
+and then “predicting” with it using `predict()` to get the same results
 
 ``` r
-mtcars %>%
-  as_tibble() %>%
-  orbital_predict(orbital_obj)
+predict(orbital_obj, as_tibble(mtcars))
 #> # A tibble: 32 × 1
 #>    .pred
 #>    <dbl>

tests/testthat/test-predict.R

@@ -16,7 +16,7 @@ test_that("orbital works with workflows - recipe", {
   obj <- orbital(wf_fit)
 
   expect_identical(
-    mtcars %>% orbital_predict(obj),
+    predict(obj, mtcars),
     mtcars %>% dplyr::mutate(!!!orbital_inline(obj)) %>% dplyr::select(.pred)
   )
 })