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---
title: "Using RMarkdown to create scientific manuscripts"
author: "Jörn Alexander Quent"
date: "5 December 2017"
output: ioslides_presentation
css: styles.css
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
```
## Introduction
- R is an open source programming language for statistics and data visualisation.
- Markdown is a lightweight markup language.
##RMarkdown workflow
##Pros
* Easy to share whole code used for analysis with the public and journals.
* Helps to achieve higher reproducibility with respect to analysis.
* Many manuscripts contain erroneous statistics (see statcheck; Nuijten et al., 2016).
* RMarkdown helps to track and avoid mistakes:
* Rounding _p_-value,
* Copy & paste errors,
* Forgetting to up update statistic.
##Cons
* Sometimes things might go wrong when you try to switch between output formats (e.g. from .pdf to .html).
* You will frequently find yourself that you need to ask for help for more intricate stuff for instance on [https://stackoverflow.com](https://stackoverflow.com).
* List of references can only be generated at the end of the script.
##YAML header
## R Chunks
##Analysis
##Reporting statistics
##Graphics
##Graphics
##Tables
##Formulas
##Citation
##Citation
##Citation
## Thank you
- If you need help, send me an e-mail Alex.Quent at mrc-cbu.cam.ac.uk.
- Scripts, presentation etc. available on [https://github.com/JAQuent/methodsDay2017](https://github.com/JAQuent/methodsDay2017).
# Questions?