Workshop.qmd

---
title: "Advanced exploratory visualization techniques using ggplot2, plotly and purrr"
subtitle: "@rinpharma 2023"
author: Omar Elashkar
date: "10/27/2023"
format: html
---


## Loading Libraries 

```{r}
library(nlmixr2)
library(tidyverse)
library(pmxTools)
```


## Data Exploration

```{r}
data(warfarin)
# load pk/pd data
warfarin <- warfarin |> 
  mutate(id = as.factor(id)) |> 
  mutate(dvid = as.factor(dvid)) |> 
  mutate(wt_cat = cut( wt, c(min(wt)-1, median(wt), max(wt)+1), labels = c("low wt", "high wt"))) |> 
  mutate(age_cat = cut( age, c(min(age)-1, median(age), max(age)+1), labels = c("young age", "old age")))  |>
  mutate(amt = ifelse(amt == 0, NA, amt)) |> 
  fill(amt, .direction = "down") |> 
  filter(evid == 0) 
```


```{r}
war_pk <- warfarin |> filter(dvid == "cp")

war_pd <- warfarin |> filter(dvid == "pca")

war_pkpd <- warfarin |> pivot_wider(names_from = dvid, values_from = dv, values_fn = median)

war_clin <- war_pk |> 
  select(id, wt, age, sex, wt_cat, age_cat) |> distinct()

war_exposure <- get_auc(war_pk, time = "time", id = "id", dv = "dv")  |> left_join(war_clin)
```


## Exercise 1 

-   Use `war_pk` data
-   map `time` on x-axis, `dv` on y-axis and color to `id`
-   try both `facet_wrap` and `facet grid` for `wt_cat~sex`
  -   which once is better in this case?
  
```{r}

```

  

## Exercise 2

- Use `war_pd` data
- map `time` on x-axis, `dv` on y-axis and `color` to id
- `facet_grid` by wt_cat on y-axis and combination of sex and age_cat on x-axis
- In facet, pass `margin = TRUE`


```{r}

```


## Exercise 3 
- Use the provided dataframe `points` to create the following figure. 
- You will need `geom_polygon` for that.  Map `x`and `y`. choose the appropriate color and fill.
- Use `annotate` to create the annotation text and curve.

```{r}
filter <- war_pk |> filter(id == 1)
cmax <- max(filter$dv) # 10.8
tmax <- filter$time[filter$dv == cmax] # 9
points <- data.frame(x = c(8, 10, 10, 8), y = c(10, 10, 11, 11))

## answer here


```

## Exercise 4 

Create PD spaghetti plot colore by subject

- data `war_pkpd`
- map x to `cp` and y to `pca`
- modify the text trace to include all relevant information; age, wt ...etc.

```{r}

```



## Exercise 5
Create interactive boxplot of PK data `war_pk` for each: sex, age_cat, wt_cat


```{r}

```


## Exercise 6 

- Create interactive grid plots for PK data `war_pk` split by ID using `map()`


```{r}

```