---
title: "PKbioanalysis overview"
author: "Omar Elashkar"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{PKbioanalysis overview}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}

fig_width: 12
fig_height: 8
---

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



```{r}
library(PKbioanalysis)
```

## Plate generation, reusing and managing plates

```{r}
plate <- generate_96() 
plot(plate)
```

Calibration study examples

```{r}
plate <- generate_96("Calibration study") |> 
    add_cs_curve(c(1, 3, 5, 10, 20, 50, 100, 200, 500)) |>
    add_DB() |> 
    add_DB() |>
    add_blank() |>   # CS0IS1
    add_suitability(conc = 20) |>
    add_qcs(2, 200 ,400, qc_serial = FALSE) |> 
    add_DB() |> 
    add_blank(analyte = TRUE, IS = FALSE) |> # CS1IS0
    add_cs_curve(c(1, 3, 5, 10, 20, 50, 100, 200, 500)) |>
    add_qcs(lqc_conc = 2, mqc_conc =  180 , hqc_conc = 400) 

plot(plate)
plot(plate, color = "TYPE")
```

```{r eval = FALSE}
plate <- register_plate(plate)
plot(plate)
```

```{r eval = FALSE}
plate <- generate_96() |> 
    add_suitability(conc = 10) |>
    make_calibration_study(
        plate_std = c(1, 3, 5, 10, 20, 50, 100, 200, 500), 
        lqc_conc = 2, 
        mqc_conc = 40,
        hqc_conc = 100, 
        n_qc = 4, 
        qc_serial = TRUE, 
        n_CS0IS0 = 2,
        n_CS1IS0 = 2,
        n_CS0IS1 = 2)
plot(plate, color = "TYPE")
plot(plate, color  = "conc")

```

```{r eval = FALSE}
plate <- register_plate(plate)
```

```{r eval = FALSE}
plate_app()
```

## Adding samples

```{r}
plate <- generate_96() |> add_samples(1:20)

plot(plate)
```

### Adding samples manually or from external spreadsheet

```{r}
plate <- generate_96()

plate <- plate |> 
    add_samples("sample1") |> 
    add_samples("sample2") |>
    add_samples("sample3")

plot(plate)
```

### Define samples by attributes

```{r}
data(Indometh)
plate <- generate_96() |> 
    add_samples(samples = Indometh$Subject, 
                time = Indometh$time)
plot(plate, color = "time")
plot(plate, color = "samples")
```

### Automated crossover

```{r}
plate <- generate_96() |> 
    # add_samples_c(c("subject1", "subject2"), time = c(0, 10, 30, 60, 120), factor = "Male") |>
    add_samples_c("subject1", time = c(0, 10, 30, 60, 120), 
                  factor = "Male") |>
    add_samples_c("subject2", time =   c(0, 10, 30, 120), 
                  factor = "Female") 

plot(plate, color = "time")
plot(plate, color = "samples")
plot(plate, color = "factor")
plot(plate, color = "conc")
```

#### Food Effect Example


```{r}
plate <- generate_96() |> 
    add_samples_c(1:4, time = c(0, 10, 30, 60, 120), 
         factor = c("Fed", "Fast"))
plot(plate, color = "time")
plot(plate, color = "samples")
plot(plate, color = "factor")
plot(plate, color = "conc")
```

## Metabolic study (Multiple plates)

```{r}
plates <- make_metabolic_study(
    cmpd = c("NE", "DA", "5HT", "HVA", 
             "DOPAC", "MHPG", "5HIAA", "VMA"),
    time_points = c(0, 5, 10, 15, 30, 45, 60, 90, 120),
    n_NAD = 3, 
    n_noNAD = 2
)

```

```{r}
length(plates)
plot(plates[[1]], color = "samples", label_size = 9)
plot(plates[[2]], color = "samples", label_size = 9)
plot(plates[[3]], color = "samples", label_size = 9)
plot(plates[[4]], color = "samples", label_size = 9)
plot(plates[[5]], color = "samples", label_size = 9)
plot(plates[[6]], color = "samples", label_size = 9)


```

```{r eval = FALSE}
register_plate(plates)
```