---
title: 'Aurora Skewer Report'
output:
    html_document:
      highlight: pygments
---

```{r setup, include=FALSE, warning=FALSE, message=FALSE}
knitr::opts_chunk$set(error = TRUE, echo = FALSE)
```

```{css, echo=FALSE}
pre code, pre, code {
  white-space: pre !important;
  overflow-x: scroll !important;
  word-break: keep-all !important;
  word-wrap: initial !important;
}
```

```{r, echo=FALSE}
# to make the css theme to work, <link></link> tags cannot be added directly 
# as <script></script> tags as below.
# it has to be added using a code chunk with the htmltool functions!!!
css_link = tags$link()
css_link$attribs = list(rel="stylesheet", href="vakata-jstree-3.3.5/dist/themes/default/style.min.css")
css_link
```

```{r, eval=FALSE, echo=FALSE}
# this code chunk is purely for adding comments
# below is to add jQuery and jstree javascripts
```

<script src="vakata-jstree-3.3.5/dist/jstree.min.js"></script>


```{r, eval=FALSE, echo=FALSE}
# this code chunk is purely for adding comments
# javascript code below is to build the file tree interface
# see this for how to implement opening hyperlink: https://stackoverflow.com/questions/18611317/how-to-get-i-get-leaf-nodes-in-jstree-to-open-their-hyperlink-when-clicked-when
```
<script>
  jQuery(function () {
    // create an instance when the DOM is ready
    jQuery('#jstree').jstree().bind("select_node.jstree", function (e, data) {
     window.open( data.node.a_attr.href, data.node.a_attr.target )
    });
  });
</script>


```{r, eval=FALSE, echo=FALSE}
---
# ADD YOUR DATA ANALYSIS CODE AND MARKUP TEXT BELOW TO EXTEND THIS R MARKDOWN FILE
---
```

## Job script

```{bash echo=FALSE}
sh ${TOOL_INSTALL_DIR}/build-and-run-job-scripts.sh
```

```{r echo=FALSE,warning=FALSE,results='asis'}
# display content of the job-script.sh file.
cat('```bash\n')
cat(readLines(paste0(Sys.getenv('REPORT_FILES_PATH'), '/job-1-script.sh')), sep = '\n')
cat('\n```')
```

# Results summary

## Reads processing summary

```{r echo=TRUE}
log = readLines(paste0(Sys.getenv('REPORT_FILES_PATH'), '/trim-trimmed.log'))
start_line = grep('read.+processed; of these:', log)
end_line = grep('untrimmed.+available after processing', log)
processing_summary = gsub('(\\d+) ', '\\1\t', log[start_line:end_line])
processing_summary_df = do.call(rbind, strsplit(processing_summary, '\t'))
colnames(processing_summary_df) = c('Total reads:', processing_summary_df[1,1])
knitr::kable(processing_summary_df[-1, ])
```

## Length distribution of reads after trimming

```{r echo=TRUE, message=FALSE, warning=FALSE}
start_line = grep('length	count	percentage', log)
len_dist = log[(start_line):length(log)]
len_dist = do.call(rbind, strsplit(len_dist, '\t'))
columns = len_dist[1, ]
len_dist = as.data.frame(len_dist[-1, ])
colnames(len_dist) = columns

library(plotly)
library(ggplot2)
len_dist$count = as.numeric(len_dist$count)
labels = as.character(len_dist$length)
len_dist$length = 1:nrow(len_dist)
pp = ggplot(data = len_dist, aes(length, count)) +
  geom_line(color='red') +
  scale_x_continuous(name = 'Length',
                   breaks = 1:nrow(len_dist),
                   labels = labels) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  ylab('Count') +
  ggtitle('Length distribution')
ggplotly(pp)
```