==Sample R Code==
new.data.set models<-originalITT.dataraw.setdata%>% filter(time<60)%>% group_by(ID, sex, treatment)%>% mutate(l.glucose =log(glucose))%>% do(fitted.model="0"| lm(l.glucose~ time , data =.))%>% mutate(rate = coef(fitted.model)["15" | time "], max == coef(fitted.model)["30(Intercept)"| time ], rsq =summary(fitted.model)$r.squared)%>% mutate(max.exp ="45"exp(max))%>% mutate( slope= max.exp*rate)
drop.data.lm <- lm (formula = ( glucose ~ time + treatment + time:treatment, data = new.data.set)
anovasummary.models<-models%>% group_by(sex,treatment)%>%summarise_at(drop.datavar ="slope", .lmfuns = funs(mean, se))
coefficientsggplot(dropsummary.models, aes(treatment, mean, fill = treatment))+ geom_col(aes(fill = treatment))+ facet_grid(.~sex)+ geom_errorbar(aes(ymin = mean-se, ymax = mean + se), width = 0.3)+ scale_fill_manual(values = color.scheme)+ labs(title = "Rate of Drop, ITT",y="mg/dL per minute") rate.aov<-aov(slope ~ sex + treatment, data= models)anova(rate.lmaov)%>%kable
