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:
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:
Change the mapped y variable(s) to Weight and x variable to Age and remove Weight from Recode into Quantile Categories and select Age instead.
Go to Plot types, Points, Lines(?) and increase Point Size to 2.4 and make the transparency of the points equal to 0.58
Explore the jitter position including the custom one
Go to Color/Group/Split/Size/Fill Mappings (?) and map Color By:, Group By: Fill By: and Column Split: to Gender
Go To Median PI(?) and uncheck Ignore Mapped Group so the Median PI uses the mapped Gender Group By:.
Try to experiment what Label Values? and Label N? do Keep Label N? checked.
Apply all the selected options in the screenshot
Go to Boxplots and check the Add a Boxplot? checkbox.
Explore how you can resize outliers and remove the legend.
Next go to the Mean (CI) menu select Mean and check Show points and Force Mean(s) Shape
Try to play with the various shapes options and or the size of the mean point(s).
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.