dummychapter1.Rmd

---
output:
  rmarkdown::html_document:
    highlight: pygments
    toc: true
    toc_depth: 3
    fig_width: 5
bibliography: "`r file.path(system.file(package='dummychapter1', 'vignettes'), 'bibliography.bib')`"
vignette: >
  %\VignetteIndexEntry{dummychapter1}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding[utf8]{inputenc}
---
# Insert Workshop Title

## Instructor(s) name(s) and contact information

Provide names and contact information for all instructors.

## Workshop Description

Along with the topic of your workshop, include how students can expect
to spend their time. For the description may also include information
about what type of workshop it is (e.g. instructor-led live demo, lab,
lecture + lab, etc.). Instructors are strongly recommended to provide
completely worked examples for lab sessions, and a set of stand-alone
notes that can be read and understood outside of the workshop.

## Pre-requisites

List any workshop prerequisites, for example:

* Basic knowledge of R syntax
* Familiarity with the GenomicRanges class
* Familiarity with xyz vignette (provide link)

List relevant background reading for the workshop, including any
theoretical background you expect students to have.

* List any textbooks, papers, or other reading that students should be
  familiar with. Include direct links where possible.

## Workshop Participation

Describe how students will be expected to participate in the workshop.

## _R_ / _Bioconductor_ packages used

List any _R_ / _Bioconductor_ packages that will be explicitly covered.

## Time outline

An example for a 45-minute workshop:

| Activity                     | Time |
|------------------------------|------|
| Packages                     | 15m  |
| Package Development          | 15m  |
| Contributing to Bioconductor | 5m   |
| Best Practices               | 10m  |

## Workshop goals and objectives

List "big picture" student-centered workshop goals and learning
objectives. Learning goals and objectives are related, but not the
same thing. These goals and objectives will help some people to decide
whether to attend the conference for training purposes, so please make
these as precise and accurate as possible.

*Learning goals* are high-level descriptions of what
participants will learn and be able to do after the workshop is
over. *Learning objectives*, on the other hand, describe in very
specific and measurable terms specific skills or knowledge
attained. The [Bloom's Taxonomy](#bloom) may be a useful framework
for defining and describing your goals and objectives, although there
are others.

## Learning goals

Some examples:

* describe how to...
* identify methods for...
* understand the difference between...

## Learning objectives

* analyze xyz data to produce...
* create xyz plots
* evaluate xyz data for artifacts

### A note about learning goals and objectives (#bloom)

While not a new or modern system for thinking about learning,
[Bloom's taxonomy][1] is one useful framework for understanding the
cognitive processes involved in learning. From lowest to highest
cognitive requirements:

1. Knowledge: Learners must be able to recall or remember the
   information.
2. Comprehension: Learners must be able to understand the information.
3. Application: Learners must be able to use the information they have
   learned at the same or different contexts.
4. Analysis: Learners must be able to analyze the information, by
   identifying its different components.
5. Synthesis: Learners must be able to create something new using
   different chunks of the information they have already mastered.
6. Evaluation: Learners must be able to present opinions, justify
   decisions, and make judgments about the information presented,
   based on previously acquired knowledge.

To use Bloom's taxonomy, consider the following sets of verbs and
descriptions for learning objectives:

1. Remember: Memorize, show, pick, spell, list, quote, recall, repeat,
   catalogue, cite, state, relate, record, name.
2. Understand: Explain, restate, alter, outline, discuss, expand,
   identify, locate, report, express, recognize, discuss, qualify,
   covert, review, infer.
3. Apply: Translate, interpret, explain, practice, illustrate,
   operate, demonstrate, dramatize, sketch, put into action, complete,
   model, utilize, experiment, schedule, use.
4. Analyze: Distinguish, differentiate, separate, take apart,
   appraise, calculate, criticize, compare, contrast, examine, test,
   relate, search, classify, experiment.
5. Evaluate: Decide, appraise, revise, score, recommend, select,
   measure, argue, value, estimate, choose, discuss, rate, assess,
   think.
6. Create: Compose, plan, propose, produce, predict, design, assemble,
   prepare, formulate, organize, manage, construct, generate, imagine,
   set-up.

[1]: https://cft.vanderbilt.edu/guides-sub-pages/blooms-taxonomy/ "Bloom's Taxonomy"

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

## How to add figures

`r knitr::include_graphics(file.path(system.file(package='dummychapter1', 'vignettes'), "fig.png"))`

## How to add citations

Cite like that: [@paper1]

## R Markdown

This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see <http://rmarkdown.rstudio.com>.

When you click the **Knit** button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:

```{r cars}
summary(cars)
```

## Including Plots

You can also embed plots, for example:

```{r pressure, echo=FALSE}
plot(pressure)
```

Note that the `echo = FALSE` parameter was added to the code chunk to prevent printing of the R code that generated the plot.