# Supplematal Table 1 - missings by sex and year --------------------------
## levels of bmi according to skinfold comments
sB(bmi~sexc+tsf.com, FUN=quantile, probs=c(0.05,0.50,0.95),data=d) %>% roundc(.,1)
## sexc tsf.com bmi.5. bmi.50. bmi.95.
## 1 Men 0 20.3 26.8 36.3
## 2 Men 1 21.0 30.1 44.5
## 3 Men 2 33.1 43.2 62.1
## 4 Women 0 19.4 26.7 38.6
## 5 Women 1 20.1 30.6 48.1
## 6 Women 2 30.2 39.6 53.8
sB(bmi~sexc+ssf.com, FUN=quantile, probs=c(0.05,0.50,0.95),data=d) %>% roundc(.,1)
## sexc ssf.com bmi.5. bmi.50. bmi.95.
## 1 Men 0 20.1 26.4 34.4
## 2 Men 1 23.2 32.4 44.7
## 3 Men 2 28.3 37.1 52.7
## 4 Women 0 19.3 26.2 37.7
## 5 Women 1 22.0 33.1 48.2
## 6 Women 2 29.6 38.9 53.6
dx=mutate(d, tsf.com=factor(tsf.com,
labels=c('Valid', 'Could Not\nObtain', 'Exceeds\nJaw')),
bmi=pmin(bmi,75), ageg=factor(ageg,labels=c('20_30','40_59','60_99')))
ggplot(dx, aes(tsf.com, bmi)) + geom_violin() + theme_df() +
facet_grid(sexc~ageg) + labs(x='\nTSF comment code\n', y='BMI\n') +
geom_boxplot(width=0.1)
