How can I make a table which presents the results of these experimental models with robust standard errors

 # What was the callback rate for whites? Names_w <- Names %>% filter(black==0) Names_w %>% group_by(black,call_back) %>% summarise(N=n(),fraction=N/nrow(.)) Names_b <- Names %>% filter(black==1) Names_b %>% group_by(black,call_back) %>% summarise(N=n(),fraction=N/nrow(.)) t.test(Names_w$call_back,Names_b$call_back) #Is the African American/white callback rate differential different for men than for women?{use interaction} Names_w %>% group_by(black,female==0) %>% summarise_at(vars(call_back),funs("mean","sd")) #What is the difference in callback rates for high-quality versus low-quality resumes? resume_lq <- Names %>% filter(high==0) resume_hq <- Names %>% filter(high==1) t.test(resume_lq$call_back,resume_hq$call_back) #What is the high-quality/low-quality difference for white applicants? resume_w <- Names %>% filter(black==0) resume_lq <- resume_w %>% filter(high==0) resume_hq <- resume_w %>% filter(high==1) t.test(resume_lq$call_back,resume_hq$call_back) #For African American applicants? resume_b <- Names %>% filter(black==1) resume_lb <- resume_b %>% filter(high==0) resume_hb <- resume_b %>% filter(high==1) t.test(resume_lb$call_back,resume_b$call_back)