--- title: "Bayesian Statistics and Computing" author: "Yanfei Kang" date: 'r Sys.Date()' # output: slidy_presentation output: html_document: default # pdf_document: default # word_document: default subtitle: 'Lecture 6: Reproducible learning via rmarkdown' bibliography: refs.bib --- {r setup, include=FALSE} knitr::opts_chunk$set(tidy = TRUE, echo = TRUE, warning = FALSE, message = FALSE)  ## Why R markdown? - Much **easier** *syntax* ~~syntax~~ than LaTex or Html - Dynamic: easy to update and work with the R codes - Multiple output formats - Makes presentation easy - Keep me (and my group) organized of weekly research progress reports, slides, papers etc. ## Header 1 ### Header 2 ## Creation of lists ### unordered - Item 1 - Item 2 ### ordered 1. Item 1 2. Item 2 + item 2a + item 2b ## Inline codes Write inline **R** code using backtick quotes: forecast(). ## Equations -$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=FALSE, cache=TRUE} library(tscompdata) library(ggplot2) library(forecast) autoplot(gefcom2012_temp$1) + ggtitle('Temperature') + xlab('Time')  ## 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} # library(tscompdata) # library(ggplot2) # library(forecast) # library(plotly) # p <- autoplot(gefcom2012_temp\$1) + # ggtitle('Temperature') + # xlab('Time') # ggplotly(p)  ## R code chunks with table {r RcodeDemoTable} knitr::kable(head(iris))  ## Plain code block  library(forecast) library(ggplot2) autoplot(AirPassengers)  ## References