H_0: \beta_1 = 0 \qquad versus \qquad H_A: \beta_1 \neq 0
looks like it might be formula behind a glm binomial model. And, of course, its something that might be used in writing up the results.
But it's not code.
Let's say you have an outcome Y that can take on either a 1 or 0 value (a binary outcome) and some number of independent covariates, X_1 ... X_n. So, a model might look like this
fit <- glm(Y ~ X1 + X2 ..., data = mydata)
and
summary(fit)
will give the fit statistics, along with a p-value that you can use to decide whether to reject or fail to reject H_0. Just decide \alpha before hand.