R Markdown: The Definitive Guide A PreTeXt Edition

output:
  flexdashboard::flex_dashboard:
    orientation: rows

A narrow column {data-width=350}
--------------------------------

Two tabs {.tabset}
------------------

### Tab A

### Tab B

---
title: "Multiple Pages"
output: flexdashboard::flex_dashboard
---

Visualizations {data-icon="fa-signal"}
===================================== 
    
### Chart 1
    
```{r}
```
    
### Chart 2

```{r}
```
   
Tables {data-icon="fa-table"}
=====================================     

### Table 1
    
```{r}
```
    
### Table 2

```{r}
```

---
title: "Storyboard Commentary"
output: 
  flexdashboard::flex_dashboard:
    storyboard: true
---

### A nice scatterplot here

```{r}
plot(cars, pch = 20)
grid()
```

---

Some commentary about Frame 1.

### A beautiful histogram on this board

```{r}
hist(faithful$eruptions, col = 'gray', border = 'white', main = '')
```

---

Some commentary about Frame 2.

---
title: "Dashboard Value Boxes"
output:
  flexdashboard::flex_dashboard:
    orientation: rows
---

```{r setup, include=FALSE}
library(flexdashboard)
# these computing functions are only toy examples
computeArticles = function(...) return(45)
computeComments = function(...) return(126)
computeSpam = function(...) return(15)
```

### Articles per Day

```{r}
articles = computeArticles()
valueBox(articles, icon = "fa-pencil")
```

### Comments per Day

```{r}
comments = computeComments()
valueBox(comments, icon = "fa-comments")
```

### Spam per Day

```{r}
spam = computeSpam()
valueBox(
  spam, icon = "fa-trash",
  color = ifelse(spam > 10, "warning", "primary")
)
```

---
title: "Dashboard Gauges"
output:
  flexdashboard::flex_dashboard:
    orientation: rows
---

```{r setup, include=FALSE}
library(flexdashboard)
```

### Contact Rate

```{r}
gauge(91, min = 0, max = 100, symbol = '%', gaugeSectors(
  success = c(80, 100), warning = c(40, 79), danger = c(0, 39)
))
```

### Average Rating 

```{r}
gauge(37.4, min = 0, max = 50, gaugeSectors(
  success = c(41, 50), warning = c(21, 40), danger = c(0, 20)
))
```

### Cancellations

```{r}
gauge(7, min = 0, max = 10, gaugeSectors(
  success = c(0, 2), warning = c(3, 6), danger = c(7, 10)
))
```

---
title: "Text Annotations"
output:
  flexdashboard::flex_dashboard:
    orientation: rows
---

Monthly deaths from bronchitis, emphysema and asthma in the
UK, 1974–1979 (Source: P. J. Diggle, 1990, Time Series: A
Biostatistical Introduction. Oxford, table A.3)

```{r setup, include=FALSE}
library(dygraphs)
```

Row {data-height=600}
-------------------------------------

### All Lung Deaths

```{r}
dygraph(ldeaths)
```

Row {data-height=400}
-------------------------------------

### Male Deaths

```{r}
dygraph(mdeaths)
```

> Monthly deaths from lung disease in the UK, 1974–1979

### About dygraphs

This example makes use of the dygraphs R package. The dygraphs
package provides rich facilities for charting time-series data 
in R. You can use dygraphs at the R console, within R Markdown
documents, and within Shiny applications.

---
title: "Navigation Bar"
output:
  flexdashboard::flex_dashboard:
    navbar:
      - { title: "About", href: "https://example.com/about" }
---

---
title: "Social Links"
output:
  flexdashboard::flex_dashboard:
    social: [ "twitter", "facebook", "menu" ]
---

---
title: "Old Faithful Eruptions"
output: flexdashboard::flex_dashboard
runtime: shiny
---

```{r global, include=FALSE}
# load data in 'global' chunk so it can be shared
# by all users of the dashboard
library(datasets)
data(faithful)
```

Column {.sidebar}
--------------------------------------------------

Waiting time between eruptions and the duration of the eruption
for the Old Faithful geyser in Yellowstone National Park,
Wyoming, USA.

```{r}
selectInput(
  "n_breaks", label = "Number of bins:",
  choices = c(10, 20, 35, 50), selected = 20
)

sliderInput(
  "bw_adjust", label = "Bandwidth adjustment:",
  min = 0.2, max = 2, value = 1, step = 0.2
)
```

Column
--------------------------------------------------

### Geyser Eruption Duration

```{r}
renderPlot({
  erpt = faithful$eruptions
  hist(
    erpt, probability = TRUE, breaks = as.integer(input$n_breaks),
    xlab = "Duration (minutes)", main = "Geyser Eruption Duration",
    col = 'gray', border = 'white'
  )
  
  dens = density(erpt, adjust = input$bw_adjust)
  lines(dens, col = "blue", lwd = 2)
})
```