Workflow Tidymodels Formula Object - r

Question

I am fairly new using R and I am following a guide and learning to build an Expected Goals Model for my hockey league. When I run the code below, I get the error at the bottom. Is there something simple that I am missing?
Seems like its trying to use a formula in the model portion of the workflow but I already have a recipe in there. Thanks in advance for any help anyone can offer me! The guide is here https://www.thesignificantgame.com/portfolio/expected-goals-model-with-tidymodels/
library(tidymodels)
library(tidyverse)
library(dplyr)
set.seed(1972)
train_test_split <- initial_split(data = EXPECTED_GOALS_MODEL, prop = 0.80)
train_data <- train_test_split %>% training()
test_data <- train_test_split %>% testing()
xg_recipe <- recipe(Goal ~ DistanceC + Angle + Home + Hand + AgeDec31 + GoalieAgeDec31 + NewX + NewY, data = train_data) %>% update_role(NewX, NewY, new_role = "ID")
model <- logistic_reg() %>% set_engine("glm")
xg_wflow <- workflow() %>% add_model(model) %>% add_recipe(xg_recipe)
xg_wflow
xg_fit <- xg_wflow %>% fit(data = train_data)
Error in validObject(.Object) :
invalid class “model” object: invalid object for slot "formula" in class "model": got class "workflow", should be or extend class "formula"
In addition: Warning message:
In fit(., data = train_data) :
fit failed: Error in as.matrix(y) : argument "y" is missing, with no default
fit(x = ., data = train_data)

Answer 1

It's difficult to tell exactly what the issue is without a reproducible example, though this error brings up a few questions up for me:
Does the EXPECTED_GOALS_MODEL data indeed have a column called Goal in it, with two unique levels? Have you also spelled the remainder of the column names correctly?
Are your tidymodels package installs up to date?
Does this error persist if you run specifically generics::fit(data = train_data) instead of fit(data = train_data)? This almost looks like a different fit() is being dispatched to.
Here's a place to start with a reprex:
library(tidymodels)
data(ames)
set.seed(1972)
ames <- ames %>% rowid_to_column()
train_test_split <- initial_split(data = ames, prop = 0.80)
train_data <- train_test_split %>% training()
test_data <- train_test_split %>% testing()
xg_recipe <- recipe(Sale_Price ~ ., data = train_data) %>% update_role(rowid, new_role = "ID")
model <- linear_reg() %>% set_engine("glm")
xg_wflow <- workflow() %>% add_model(model) %>% add_recipe(xg_recipe)
xg_fit <- xg_wflow %>% fit(data = train_data)
xg_fit
#> ══ Workflow [trained] ══════════════════════════════════════════════════════════
#> Preprocessor: Recipe
#> Model: linear_reg()
#>
#> ── Preprocessor ────────────────────────────────────────────────────────────────
#> 0 Recipe Steps
#>
#> ── Model ───────────────────────────────────────────────────────────────────────
#>
#> Call: stats::glm(formula = ..y ~ ., family = stats::gaussian, data = data)
#>
#> Coefficients:
#> (Intercept)
#> -2.583e+07
#> MS_SubClassOne_Story_1945_and_Older
#> 7.419e+03
#> MS_SubClassOne_Story_with_Finished_Attic_All_Ages
#> 1.562e+04
#> MS_SubClassOne_and_Half_Story_Unfinished_All_Ages
#> 1.060e+04
#> MS_SubClassOne_and_Half_Story_Finished_All_Ages
#> 8.413e+03
#> MS_SubClassTwo_Story_1946_and_Newer
#> 3.007e+03
#> MS_SubClassTwo_Story_1945_and_Older
#> 1.793e+04
#> MS_SubClassTwo_and_Half_Story_All_Ages
#> -3.909e+03
#> MS_SubClassSplit_or_Multilevel
#> -1.098e+04
#> MS_SubClassSplit_Foyer
#> -4.038e+03
#> MS_SubClassDuplex_All_Styles_and_Ages
#> -2.004e+04
#> MS_SubClassOne_Story_PUD_1946_and_Newer
#> -2.335e+04
#> MS_SubClassOne_and_Half_Story_PUD_All_Ages
#> -2.482e+04
#> MS_SubClassTwo_Story_PUD_1946_and_Newer
#> -1.794e+04
#> MS_SubClassPUD_Multilevel_Split_Level_Foyer
#> -2.098e+04
#> MS_SubClassTwo_Family_conversion_All_Styles_and_Ages
#> 6.903e+03
#> MS_ZoningResidential_High_Density
#> -3.853e+03
#> MS_ZoningResidential_Low_Density
#> -3.661e+03
#> MS_ZoningResidential_Medium_Density
#> -8.240e+03
#> MS_ZoningA_agr
#> -3.824e+03
#> MS_ZoningC_all
#> -1.800e+04
#> MS_ZoningI_all
#> -3.299e+04
#> Lot_Frontage
#> 1.336e+01
#>
#> ...
#> and 506 more lines.
Created on 2022-09-28 by the reprex package (v2.0.1)
Hope this helps!
Simon, tidymodels team