laser-beam-doc-example.qmd

---
title: "Getting Started Code-Along" 
subtitle: "LASER Quarto document example"
author: "The LASER Team"
date: last-modified
format:
  html:
    toc: true
    toc-title: Contents
    toc-location: left
    theme: 
      - united
      - "css/quarto-laser-html.scss"
resources:
  - demo.pdf
---

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

library(datasets)
library(tidyverse)
library(ggplot2)
```
## Quarto Markdown

Let's get familiar with the Quarto Markdown layout and syntax.

### YAML Heading

Yaml is a data serialization language. It contains things like title, author,
dates, type of document etc. YAML is picky about its syntax and spacing!

Check out this site on[YAML
headings](https://zsmith27.github.io/rmarkdown_crash-course/lesson-4-yaml-headers.html)
by @Smith.

### Quarto Markdown

This is a Quarto Markdown document. Markdown is a simple formatting syntax
for authoring HTML, PDF, webpage and MS Word documents. For more details
on using Quarto Markdown see <https://quarto.org/>.

When you click the **Render** 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 also press `ctrl`+`shift`+`k`

### Markdown Syntax

Quarto Markdowns syntax is based off of [Pandoc's
Markdown](https://pandoc.org/MANUAL.html).

##### Headers

      # Level One (largest)
      ## Level Two
      ### Level Three
      #### Level Four
      ##### Level Five (smallest)

##### Bullet points

Bullet points can be used with a `single dash` or a `plus sign`

> Outcomes:

-   Learn R Markdown.
    -   Learn about YAML heading.
    -   Learn Quarto Markdown Syntax.
-   Learn programming basics in R

### Add a Code Chunk

You can add code chunks in two ways:

1.  Menu Bar \> 'Code' \> 'insert chunk'

2.  Add a code chunk by holding down `Ctrl` + `Alt` + `I`


## Programming Basics


### Functions 
```{r, eval=FALSE}
mean()
max()
filter()
class()
```


```{r}
round(3.14)
#' In the space below, use the round() function to 
#' round 3.14 to the nearest whole number.
#' Run your code to see the result. 

```

### Arguments 

`args()` function 

```{r}
?mean()
?class()

```

Take a look at the arguments for the round() function. 

```{r}
?round()
```


In the code chunk below:

1. Add an argument to the round() function 
2. Round pi to 1 decimal place. 
3. Remember to add a comma between arguments


```{r}

```

### Objects 


```{r}
twenty <-20
twenty
thirty <- 20
thirty
forty <- 20
forty
new_data <- 20
new_data


```

Try to run the following in a code chunk...

13 <- 20 * 4

```{r}

```

**What happens?**

Now run the following in a code chunk...

x <- 20 * 4

```{r}

```


**What happens?**

Don't ever have names like TRUE/FALSE or NaN, inf, NA

###  Assignment Operator: `<-`

In the chunk below

1. Assign a number between 1 and 10 to a new object called my_number.
2. Multiply my_number by 2 and save as my_product. 
3. Type my_product and run to examine the contents. 

```{r}

```


## Pipe operator

There are several pipe operators available in R. Pipes are a powerful tool for combining a sequence of functions or processes. 

Remember how you rounded pi earlier? We can use piping to do something similar:

```{r}
pi_sixdigits <- pi %>%  
  round(6)

pi_sixdigits
```


### The Native R Pipe

 R has a native pipe operator, shown below
 
 `|>`

### The {magrittr} package Pipe

The pipe from the {magrittr} package is written as "percent greater than percent", as below

 `%>%`

The original pipe operator, `%>%`, comes from the {magrittr} package which is part of the tidyverse. All packages in the tidyverse load `%>%` for you automatically, so you don’t usually load the {magrittr} package explicitly.


### The {ggplot2} Pipe

The {ggplot2} package uses the plus sign as a pipe. We'll learn more about this later, but you can 
see an example below:

```{r}
mpg %>% 
  group_by(class) %>%  
  summarise(mean_cty = mean(cty), mean_hwy = mean(hwy)) %>%  
  ggplot() +
    geom_point(mapping = aes(x = mean_cty, y = mean_hwy)) +
    geom_smooth(mapping = aes(x = mean_cty, y = mean_hwy), method = lm) 

```


## Packages 

### Installing

You should do this in the console...no worries we will show you how! :)


`install.packages("tidyverse")`


```{r}
library(tidyverse)
?tidyverse

```

Run the following code to install the {readr} package. 


**Did it work?**

If not, maybe you need to install first. You can also check if your package is loaded in the package pane.

Now use the `library()` function to load the {readr} package into R.

```{r}
library(readr)

```

### Getting Help

Use `?` to check the help document for the {readr} package.

```{r}

```