Additional Plots and Stats with ggquickeda

Samer Mouksassi


In this vignette we will expand what we learned in the Introduction to ggquickeda vignette. We will again launch the app and select the built-in dataset. Then we will do the following actions:

cut a continuous variable to categorical


This illustrated how to use more than one y variable and how to generate a Median and a Ribbon showing a 95% Prediction interval (default) over the x variable (Time). We can see that Dose does not change over time and that the highest Age category is only present in the middle and third weight category (older subjects have higher weights). Next we will look at the Weight distributions in different ways first using a boxplot:


In the following part we will generate a descriptive stats table that reflect the plot that we just did. * But first let us fix the fact that Weight is repeated multiple time by subject as it does not change over time. Go to One Row by ID(s) and map it to ID.


To explore some of the univariate plots,remove all y variable(s) keeping Age as x variable gives:


Then selecting Weight as x variable gives:


As an exercise play with the options in the Histograms/Density/Bar to reproduce these plots.