Experience with ggiraphExtra

Reading data

df = read.csv("~/Dropbox/VOS Study/Diabetes Incidence/Diabetes Incidence Data.csv")
df$diab = ifelse(df$v2.diab1=="Diabetes", 1, 0)

Bar plots

# Percent of diabets 
ggSpine(data=df, aes(x=sex, fill=v2.diab1), addlabel=TRUE, width=0.5, interactive=F)

# Actual frequency 
ggBar(data=df, aes(x=sex, fill=v2.diab1), addlabel=TRUE, horizontal=TRUE, width=0.5, interactive=F)

Visualization of linear regression using ggigraphExtra

# fitting a  model 
fit1 = lm(wbbmc ~ pcfat, data=df)

# Visualization of the model without interactive 
ggPredict(fit1, se=T, interactive=F)

# New model with sex 
fit2 = lm(wbbmc ~ sex + pcfat, data=df)
ggPredict(fit2, se=T, interactive=F) + labs(x="Percent body fat", y="Whole body BMC") + theme(legend.position="top")

# Effect plot
ggEffect(wbbmc ~ age*pcfat, data=df, interactive=F)

# ANOVA
fit4 = aov(pcfat ~ v2.diab1, data=df)
res = TukeyHSD(fit4, ordered = TRUE)
ggHSD(res, interactive=F)

Visualization of logistic regression

fit3 = glm(diab ~ sex + v1.HbA1c, family=binomial, data=df)
ggPredict(fit3, se=TRUE, interactive=F, digits=3)

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!