make_html_plots.Rmd

---
author: "Prashant Kalvapalle"
date: '`r format(Sys.time(), "%d %B, %Y")`'
output: 
  html_document:
    theme: flatly
    toc: TRUE
    toc_float: TRUE
---

---
title: `r title_name`
---

```{r setup, include=FALSE}
library(knitr)
knitr::opts_chunk$set(echo = F) #, fig.width = 10, fig.height = 5)
```

## Goal of the analysis

Analyzing qPCR data. Using Cq values from quantstudio application. Convert to absolute quantification using standard curves  

``` {r stdcurvename}
if(exists('std_to_retrieve')) str_c('Using Standard curve : ', std_to_retrieve)

```

## Cq Plots

```{r cq_plt, warning=FALSE}
# Horizontal orientation : for label readability
print(horz.cq)

# interactive plots : ggplotly
plotly::ggplotly(horz.cq)

# plotly::ggplotly(plt.cq) # enable if vertical plot is required

if(exists('plt.cq_w.std')) print(plt.cq_w.std)

```

## Copies plot

```{r copies1, warning = FALSE}

if(plot_mode != 'absolute_quantification') {"### plotting `2^40-Cq` as an estimate : not representative copy number"}


if(exists('plt.copies_w.mean')) 
{
  # Horizontal orientation - logscale : for label readability
  print(horz.copies_w.mean)
  
  # interactive plots : ggplotly
  plotly::ggplotly(horz.copies_w.mean)
  
  } else {

    # plot plain copies without mean
    if(exists('plt.copies')) 
    {
      print(plt.copies) # this is vertical version ; didn't see the point of this anyway
      
      format_logscale_y(plt.copies)
    }
  }

```

## Melting temp

### Tm1

Largest melting temperature peak
```{r tm1}

if(exists('plt.tm1'))
{
  plt.tm1
  
  plotly::ggplotly(plt.tm1)
}

if(exists('plt.tm1_w.std')) plt.tm1_w.std

```

### All Tm peaks

All peaks of melting temperature
```{r alltm}
if(exists('plt.alltm')) plt.alltm

if(exists('plt.alltm_2')) plt.alltm_2

```

## Data output

```{r datadisplay}

if(exists('absolute_dat')) # Prints output only if absolute_dat exists
  {absolute_dat %>% 
  
  # Select only mean copies
  select(Target_name, assay_var.label, mean_Copies.per.ul.template, Sample_category) %>% 
    distinct() %>% # Remove replicates
  mutate(across(assay_var.label, ~ str_replace(., '\n', ' '))) %>% # replace new lines with space
  mutate_if(is.numeric, format, digits = 3, scientific = T) %>% # format scientific with 2 decimal plates
kable(., caption = 'Mean data') # format table for clean output
}

```


## Vertically oriented plots
for legacy use if someone prefers these. _labels are not very readable_

```{r vertplots}
# vertical orientation : for legacy use
plt.cq

if(exists('plt.copies_w.mean')) 
{
  # regular plot
  plt.copies_w.mean %>% print()
  # logscale
  log.copies_w.mean <- format_logscale_y(plt.copies_w.mean)
}

```