Is R's glm function useless in a big data / machine learning setting? I am surprised that R’s glm will “break” (not converge with default setting) for the following “toy” example (binary classification with ~50k data, ~10 features), but glmnet returns results in seconds.
Am I using glm incorrectly (for example, should I set max iteration, etc.), or is R’s glm not good for big data setting? Does adding regularization make a problem easy to solve?
d=ggplot2::diamonds
d$price_c=d$price>2500
d=d[,!names(d) %in% c("price")]

lg_glm_fit=glm(price_c~.,data=d,family = binomial())

library(glmnet)
x=model.matrix(price_c~.,d)
y=d$price_c
lg_glmnet_fit=glmnet(x = x,y=y,family="binomial", alpha=0)

Warning messages:
1: glm.fit: algorithm did not converge 
2: glm.fit: fitted probabilities numerically 0 or 1 occurred 

EDIT:
Thanks for Matthew Drury and Jake Westfall's answer. I understand the perfect separation issue which is which is already addressed.  How to deal with perfect separation in logistic regression?
And in my original code, I do have the third line which drops the column that derives the label.
The reason I mention about "big data" is because in many "big data" / "machine learning" settings, people may not carefully test assumptions or know if data can be perfectly separated. But glm seems to be easily broken with "unfriendly" messages, and there is not a easy way to add the regularization to fix it.
 A: This has nothing to do with glm, you simply created a problem with an artificial perfect separation:
df <- data.frame(x = rnorm(100), y = rnorm(100))
df$y_c = df$y > 0

glm(y_c~., data=df, family=binomial())


Warning messages:
  1: glm.fit: algorithm did not converge 
  2: glm.fit: fitted probabilities numerically 0 or 1 occurred 

y is a perfect predictor of y_c.
A: The unregularized model is suffering from complete separation because you are trying to predict the dichotomized variable price_c from the continuous variable price from which it is derived.
The regularized model avoids the problem of complete separation by imposing a penalty that keeps the coefficient for the price predictor from going off to $\infty$ or $-\infty$. So it manages to converge fine and work well.
You should remove the continuous price predictor from the design matrix in this toy example.
Edit: As @Erik points out, the continuous price predictor is already removed from the design matrix, which I somehow missed. So the complete separation arises from some other predictor or combination of predictors.
It's also worth adding that, of course, none of these issues have anything to do with the particular implementation of logistic regression in R's glm() function. It is simply about regularized vs. unregularized logistic regression.
