---
title: "Practical R: About the class"
author: Abhijit Dasgupta
date: BIOF 339

---

```{r setup, include=FALSE, child=here::here('slides/templates/setup.Rmd')}
```
```{r setup1, include=FALSE}
library(pander)
library(emo)
```

layout: true

<div class="my-header">
<span>BIOF339</span></div>
</div>

---

class: middle, center, inverse
name: about
# About this class

---

## Learning Objectives

- Run R and RStudio, making use of inherent R features
- Find and make use of the extensive packages (R add-ons) available for analyzing biological and other forms of data
- Load, manipulate, and combine data to make it amenable to further analyses
- Visualize data with extensive graphics capabilities of R (including ggplot)
- Use R to run statistical models and hypothesis tests and report results conforming to standards expected in scientific journals
- Write reports using the powerful `rmarkdown` package and its derivatives

---

# Plan

```{r outline, echo = FALSE, warning=F, message=F}

dts <- paste('Week', 1:7)
topics <- c(
  'Introduction to R: Working environmnent and data structures',
  'Using packages to enhance data ingestion, munging, and reporting',
  'Data visualization for exploration and reporting',
  'Statistical analyses using R',
  'Statistical learning using R',
  'Designing and analyzing experiments, with a sprinkling of bioinformatics',
  'Reproducible documents for analytic reporting'
)

D <- tibble( Week = dts, Topic=topics)
knitr::kable(D)

```

---

## Teaching materials

1. The main ideas for the week will be developed through videos, screencasts and slides
1. I will assign tutorials where you can interactively work with R to improve your understanding
    + [RStudio Primers](https://rstudio.cloud/learn/primers)
    + I will create and periodically update a R package of R tutorials, that will be called `BIOF339Tutorials`. Instructions are forthcoming


---

# Grading rubric

1. Homeworks for each week are due Sunday at 11:59pm (50%)
    - No late homeworks
    - We'll have 6 homeworks, I'll score the top 4 for grade
1. Final project: A RMarkdown report/presentation demonstrating an end-to-end data analysis in R using your own data, from data ingestion to munging to analyses and graphics, with a brief introduction and conclusion (30%)
1. Class participation (20%): Discussion topics each week

---

# Submitting assignments

## Homework

+ All homework will be submitted via Canvas
+ You must submit your homework using R Markdown
    - The submission will consist of 2 files: A Rmd file and the corresponding HTML file. Both are required for full credit. 
+ I will initially provide templates for the homework, but you will be expected to create your own R Markdown documents by week 4. 

---

# Communication

- Primarily via `r fontawesome('slack')` [Slack](http://www.slack.com). 
  - Please join the BIOF339 Slack channel using [this link](https://join.slack.com/t/biof339/shared_invite/zt-hczp2mg1-Yh0yqms52wAA8H445jkBUg).
  - You will see a channel `#fall2020-a`. Please join this channel
- Slack for broadcasting messages, answering questions and the like. 
  - If you have a question, you can directly message me on Slack. Expect an answer within 24 hours.
- Office hours by appointment

---

# Class project

- Create a R Markdown document or presentation
- Use your own data, or data available on the web (legally)
- Show me that you can 
  - import data into R
  - manipulate (munge) the data
  - perform some analysis on the data
  - create a visualization
  - create a report in R Markdown
- 5 minute _lightning talks_ that can be recorded using Quicktime or [Screencastify](https://www.screencastify.com/)