---
title: "Practical R: R Markdown"
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

# RMarkdown

---

![](../img/rmd1.png)
---

## R Markdown

.left-column30[
+ There are some choices you might need to make
  + Document is like a paper
  + Presentation is like PowerPoint
  + Shiny is an interactive web app developed in R. May talk about it towards the end
  + Various packages also provide templates for RMarkdown documents

]
.right-column30[
![](../img/rmd2.png)
]

---



.left-column30[
+ Elements on the right are output formats
  - Documents produce Word, PDF or HTML
  - Presentations produce PowerPoint, PDF, or HTML
]
.right-column30[
![](../img/rmd2.png)
]

---

```
---
title: "Untitled"
author: "Abhijit Dasgupta"
date: "9/11/2019"
output: html_document
---
```

This determines the title and author, and the output type. Typically don't modify the 
entry in `output` for now

--

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

This is a code chunk. RMarkdown evaluates this chunk of code and replaces the code 
with the results. This code chunk happens to be named "cars". The naming is optional but useful.

---

.pull-left[
### RMarkdown

````markdown
`r ''````{r cars}
summary(cars)
```
````
]
.pull-right[
### Results

```{r 01-intro-22}
summary(cars)
```
]

---

.pull-left[
### RMarkdown

````markdown
`r ''````{r}
library(tableone) # Use a package
kableone(CreateTableOne(data=airquality), 
         format='html')
```
````
]
.pull-right[
### Results

```{r 01-intro-23}
library(tableone) # Use a package
kableone(CreateTableOne(data=airquality), 
         format='html')
```

]

<br>
<br>
The code chunk on the left gets **replaced** by the table on the right in your document


---

![](../img/rmd5.png)

---

## Inline code evaluation

.pull-left[

### RMarkdown
The airquality data set has   
`` `r
nrow(airquality)` `` observations


The average ozone level is `` `r
mean(airquality$Ozone)` `` parts per billion
]
.pull-right[

### Results

The airquality data set has `r nrow(airquality)` observations

The average ozone level is   
`r mean(airquality$Ozone, na.rm=T)` `` parts per billion

]
