---
title: "Bayesian Statistics and Computing"
author: "Yanfei Kang"
date: "`r Sys.Date()`"
output:
  # pdf_document: default
  html_document: default
subtitle: 'Lecture 6: Reproducible learning via `rmarkdown`'
bibliography: refs.bib
---

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


```{r xaringanExtra-clipboard, echo=FALSE}
htmltools::tagList(
  xaringanExtra::use_clipboard(
    button_text = "<i class=\"fa fa-clipboard\"></i>",
    success_text = "<i class=\"fa fa-check\" style=\"color: #90BE6D\"></i>",
  ),
  rmarkdown::html_dependency_font_awesome()
)
```



## Why R markdown?

- Much **easier** *syntax* ~~syntax~~ than LaTex or Html
- Dynamic: easy to update and work with the R codes
- Multiple output formats
- Makes presentation easy
- Keep me (and my group) organized of weekly research progress reports, slides, papers etc.



## Header 1
### Header 2


## Creation of lists
### unordered
- Item 1
- Item 2

### ordered
1. Item 1
2. Item 2
    + item 2a
    + item 2b


## Inline codes
Write inline **R** code using backtick quotes: `forecast()`.


## Equations

- $x + y = z$ for inline equations
- $$ x^2 + \sqrt{(y)} = \epsilon $$ 


## Hyperlink

1. [R markdown website](http://rmarkdown.rstudio.com)

2. [R markdown guide](https://bookdown.org/yihui/rmarkdown/)

************************************

## R code with errors

```{r, error=TRUE}
x > 1
```

## R code chunks with plot 

See @hong2014global.

```{r RcodeDemoPlot, fig.width=8, fig.height=4, echo=TRUE, eval=TRUE, cache=TRUE}
library(fpp3)
# Load the Victorian electricity demand data from fpp3
data <- vic_elec

# Aggregate data by daily demand
daily_elec <- data %>%
  index_by(Date = as_date(Time)) %>%
  summarise(DailyDemand = sum(Demand))

# View the first few rows of the aggregated data
head(daily_elec)

autoplot(daily_elec, DailyDemand) +
  ggtitle("Daily Electricity Demand in Victoria") +
  xlab("Date") +
  ylab("Electricity Demand (MW)")
```


## Interactive plots

You can use interactive plots for if you output html documents.

```{r RcodeDemoPlotly, fig.width=8, fig.height=4, echo=FALSE, eval=TRUE}
# Create an interactive plot using Plotly
library(plotly)
p <- plot_ly(daily_elec, x = ~Date, y = ~DailyDemand, type = 'scatter', mode = 'lines') %>%
  layout(title = "Daily Electricity Demand in Victoria",
         xaxis = list(title = "Date"),
         yaxis = list(title = "Electricity Demand (MW)"))

# Display the plot
p
```


## R code chunks with table
```{r RcodeDemoTable}
knitr::kable(head(iris))
```



## Plain code block 

```
library(forecast)
library(ggplot2)
autoplot(AirPassengers)
```


## References




