Chi -Square test in R using dplyr

Question

I would like to perform a chi-square test in R using dpylr. Specifically, I would like to investigate whether there is a difference in customer churn between male and female customers. Here a short example of my data.

  sex   churn 
  <fct> <lgl>
 1 W     FALSE
 2 W     FALSE
 3 W     FALSE
 4 W     FALSE
 5 W     FALSE
 6 W     FALSE 
 7 W     FALSE
 8 W     FALSE
 9 W     FALSE
10 W     FALSE
11 W     FALSE
12 W     FALSE
13 M     FALSE
14 W     FALSE
15 W     FALSE
16 W     FALSE
17 W     FALSE
18 M     FALSE
19 W     FALSE
20 W     TRUE 
21 W     TRUE 
22 M     FALSE
23 M     FALSE
24 W     TRUE 
25 W     FALSE

With the summarise and spread function I already get a nice summary table.

churn_latest %>% 
group_by(sex, churn) %>% 
summarise(n = n()) %>% 
spread(key = sex, value =  n)

Now I would like to apply a chi-square test to it, but I always get the following error: 'x' and 'y' must have at least 2 levels. This is of course the case for me, so I must have an error in the syntax.

churn_latest %>% 
group_by(sex, churn) %>% 
summarise(chi = chisq.test(sex, churn))

I would be very happy if someone had a solution to my problem. Many thanks in advance!