---
title: "Additional Plots and Stats with ggquickeda"
author: "Samer Mouksassi"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Additional Plots and Stats with ggquickeda}
  %\VignetteEngine{knitr::rmarkdown_notangle}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

In this vignette we will expand what we have learned in the Introduction to ggquickeda vignette.

### Multiple Y variables, recoding continuous variables to categories and Medina/PI:
This first section will illustrate 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).

Using the built-in demo dataset:

* Change the mapped y variable(s) to Conc and Dose
(ProTip: you can drag and drop the y variables to change their order)
* In the **Categorize/cut** subtab select Age to be cut into three binned categories
* In the **Categorize/cut** subtab select Weight to be cut into three quantiles categories
* Go to **Color/Group/Split/Size/Fill Mappings** and map Colour By and Fill By to Age and Column Split to Weight the Extra Row split is automatically set to yvars since you have selected more than one y variable. 

![cut a continuous variable to categorical](./intro_7.png){width=100%}

* Go to **Median PIs** and select Median/PI and then you this get this plot:

![MedianPI](./intro_8.png){width=100%}

We can see that Dose does not change over time and that the highest Age category is only present in the second and third weight categories (older subjects have higher weights).

### Boxplots, Median/PI, Mean:

* Remove all previous mappings from column split, colour by etc.
* 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 **Points, Lines** and increase Point Size to 6 and make the transparency of the points equal to 0.1
* Explore the jitter position including the custom one
* Go to **Color/Group/Split/Size/Fill** and map Color By:, Group By: Fill By: and Column Split: to Gender
* Go to **One Row by ID(s)** and select ID so we keep one row by ID  

![MedianPI](./intro_9.png){width=100%}

* 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

![MedianPI](./intro_10.png){width=100%}


* Go to **Boxplots** and check the Add a Boxplot? checkbox.
* Go back to **Median PI** and choose the PI to be 50% so it will be at the boxplot box edges.
* Go to **Boxplots** try to change the size of outliers and to remove the boxplot legend.

![Boxplots](./intro_11.png){width=100%}

* Next go to the **Mean (CI)** menu select Mean and check Show points and Force Mean(s) Shape (Diamond will be used by default)
* Try to play with the various shapes options and or the size of the mean point(s).
* Check Label Values? and Check Ignore Mapped Color and choose Mean Label Geom as auto text repel.

![MEANDIAMOND](./intro_12.png){width=100%}


### Continuous and categorical variables descriptive stats:

In the following part we will generate a descriptive stats table that reflect the plot that we just did and then add Race.

* Click on the **Descriptive Stats** Tab
* Map Extra Column Split to Gender and explore with the Flip the order of the columns checkbox
* Try to add more statistics in the Statistics to display for continuous variables
* Add Race to the y variable(s) to see the statistics for a categorical variable

![DescStats](./intro_13.png){width=100%}

### Univariate Plots:

Remove all y variable(s) and any column splits keeping Age as x variable gives a barplot since Age has been categorized.

![barplots](./intro_14.png){width=100%}

Remove Age from Recode into Quantile Categories so it goes back to a numeric variable and the generated distribution will be a density plot instead of a barplot.
Reapply the ID in **One Row by ID(s)** as the data manipulation steps are sequential and changing something in the first tab will reset the steps in the subsequent ones.

![distribution](./intro_15.png){width=100%}

Play with the options in the **Histograms/Density/Bar** to see how they affect the generated plots.