SPpolyriboseq.Rmd

---
title: SPpolyriboseq
author: "Author: FirstName LastName"
date: "Last update: `r format(Sys.time(), '%d %B, %Y')`" 
output:
  BiocStyle::html_document:
    toc_float: true
    code_folding: show
  BiocStyle::pdf_document: default
package: systemPipeR
vignette: |
  %\VignetteIndexEntry{RIBO-Seq Workflow Template}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
fontsize: 14pt
bibliography: bibtex.bib
---

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```{css, echo=FALSE}
pre code {
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overflow-x: scroll !important;
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```{r style, echo = FALSE, results = 'asis'}
BiocStyle::markdown()
options(width=60, max.print=1000)
knitr::opts_chunk$set(
    eval=as.logical(Sys.getenv("KNITR_EVAL", "TRUE")),
    cache=as.logical(Sys.getenv("KNITR_CACHE", "TRUE")), 
    tidy.opts=list(width.cutoff=60), tidy=TRUE)
```

```{r setup, echo=FALSE, message=FALSE, warning=FALSE, eval=FALSE}
suppressPackageStartupMessages({
    library(systemPipeR)
})
```

# Workflow environment

_`systemPipeR`_ workflows can be designed and built from start to finish with a 
single command, importing from an R Markdown file or stepwise in interactive 
mode from the R console. 

This tutorial will demonstrate how to build the workflow in an interactive mode, 
appending each step. The workflow is constructed by connecting each step via 
`appendStep` method. Each `SYSargsList` instance contains instructions needed 
for processing a set of input files with a specific command-line or R software 
and the paths to the corresponding outfiles generated by a particular tool/step. 

To create a Workflow within _`systemPipeR`_, we can start by defining an empty
container and checking the directory structure:

```{r create_workflow, message=FALSE, eval=FALSE}
library(systemPipeR)
sal <- SPRproject()
sal
```
## Load packages 

This is an empty template that contains only one demo step. 
Refer to our [website](https://systempipe.org/sp/spr/spr_run/) for how to 
add more steps. If you prefer a more enriched template, 
[read this page](https://systempipe.org/sp/spr/templates/) for other 
pre-configured templates.

```{r load_systempiper, eval=TRUE, spr=TRUE}
appendStep(sal) <- LineWise(
    code = {
        library(systemPipeR)
    }, 
    step_name = "load_SPR"
)
```

## Version Information

```{r sessionInfo, eval=FALSE, spr=TRUE}
appendStep(sal) <- LineWise(
    code = {
        sessionInfo()
        }, 
    step_name = "sessionInfo", 
    dependency = "load_SPR")
```

# Running workflow

## Interactive job submissions in a single machine

For running the workflow, `runWF` function will execute all the steps store in 
the workflow container. The execution will be on a single machine without 
submitting to a queuing system of a computer cluster. 

```{r runWF, eval=FALSE}
sal <- runWF(sal)
```

## Visualize workflow

_`systemPipeR`_ workflows instances can be visualized with the `plotWF` function.

```{r plotWF, eval=FALSE}
plotWF(sal, rstudio = TRUE)
```

## Checking workflow status

To check the summary of the workflow, we can use:

```{r statusWF, eval=FALSE}
sal
statusWF(sal)
```

## Accessing logs report

_`systemPipeR`_ compiles all the workflow execution logs in one central location,
making it easier to check any standard output (`stdout`) or standard error
(`stderr`) for any command-line tools used on the workflow or the R code stdout.

```{r logsWF, eval=FALSE}
sal <- renderLogs(sal)
```

# References