Error while trying glmnet() in R: "Error in storage.mode(xd) <- "double" : 'list' object cannot be coerced to type 'double'"

I'm trying to create a logistic regression model using Ridge, this is the code:

glmnet(X_Train, Y_Train, family='binomial', alpha=0, type.measure='auc')

And this is the error message I'm getting:

Error in storage.mode(xd) - double : 'list' object cannot be coerced to type 'double'

I tried converting all the variables into numeric but still doesn't work.

I'm going to post the code for those two datasets so you can reproduce it:

libraries:

library(dplyr)
library(fastDummies)
library(missForest)
library(glmnet)

Data:

url - 'https://archive.ics.uci.edu/ml/machine-learning-databases/credit-screening/crx.data'
crx - read.csv(url, sep = ,, header = F)

Getting rid of null-values:

crx[crx == ?] - NA
crx - type.convert(crx, as.is=FALSE)
crx.i - missForest(as.data.frame(crx))
crx - crx.i$ximp

Data transformations:

crx - crx %% 
rename(Gender = V1,
         Age = V2,
         Debt =  V3,
         Married = V4,
         BankCustomer = V5,
         EducationLevel = V6,
         Ethnicity = V7,
         YearsEmployed = V8,
         PriorDefault = V9,
         Employed = V10,
         CreditScore = V11,
         DriversLicense = V12,
         Citizen = V13,
         ZipCode = V14,
         Income = V15,
         ApprovalStatus = V16)

crx = subset(crx, select = -ZipCode)

crx - crx %% 
mutate(ApprovalStatus = recode(ApprovalStatus, 
                  + = 1, 
                  - = 0)) 

# Normalizing numeric variables:
crx$Age - scale(crx$Age)
crx$Debt - scale(crx$Debt)
crx$YearsEmployed - scale(crx$YearsEmployed)
crx$CreditScore - scale(crx$CreditScore)
crx$Income - scale(crx$Income)

crx$Gender - NULL
crx$DriversLicense - NULL

Creation of dummy variables:

df - dummy_cols(crx, remove_selected_columns = T)

df$ApprovalStatus_0 - NULL
df$ApprovalStatus_1 - NULL
df$Married_l - NULL
df$BankCustomer_gg - NULL

df$ApprovalStatus - crx$ApprovalStatus

Creation of Training datasets and Test datasets:

X - df %% dplyr::select(-ApprovalStatus)
Y - df$ApprovalStatus

X_Train - X[0:590, ]
Y_Train - Y[0:590]

X_Test - X[591:nrow(X), ]
Y_Test - Y[591:length(Y)]

And trying to use the glmnet:

glmnet(X_Train, Y_Train, family='binomial', alpha=0, type.measure='auc')

I did some research and I found an article saying that you have to convert everything into numeric class, so I tried converting everything into numeric variables like this:

Y_Train - as.numeric(Y_Train)
X_Train - as.data.frame(apply(X_Train, 2, as.numeric))

And still doesn't work. What am I doing wrong exactly?