Tutorial6b_shiny.Rmd

---
title: "R Notebook"
output:
  html_document:
    toc: true
    toc_float: true
    theme: spacelab
runtime: shiny
---

This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code. 

# Step 1: Getting ready
## Install `Shiny`.
```{r, eval=F}
install.packages("shiny")
```

## Load the libraries.
```{r}
library(shiny)
```

## Setting up your folder
R `shiny` provides a platform for making a web application from Rstudio. You need to select a working folder in your computer and set up an `app` folder, where you need to initiate two files: `ui.R` and `server.R`. 
![set up an app folder](http://shiny.rstudio.com/tutorial/lesson1/images/example1-folder.png)

## Initiate app files

An example app with the pair of files: 

```{r, echo=F}
library(shiny)
shinyApp(
  ui=fluidPage(
    sidebarPanel(
      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)
    ),
    mainPanel(
      plotOutput("plot")
    )
  ),
  server<-function(input, output) {
    output$plot=renderPlot({
    hist(faithful$eruptions, 
         probability = TRUE, 
         breaks = as.numeric(input$n_breaks),
         xlab = "Duration (minutes)", 
         main = "Geyser eruption duration")
    
    dens <- density(faithful$eruptions, 
                    adjust = input$bw_adjust)
    lines(dens, col = "blue")
    })
  }
)
```

### An example of `server.R`
```{r, eval=F}
library(shiny)

shinyServer(function(input, output) {

    output$plot=renderPlot({
    hist(faithful$eruptions, 
         probability = TRUE, 
         breaks = as.numeric(input$n_breaks),
         xlab = "Duration (minutes)", 
         main = "Geyser eruption duration")
    
    dens <- density(faithful$eruptions, 
                    adjust = input$bw_adjust)
    lines(dens, col = "blue")
  })
})
```

### An example of `ui.R`
```{r, eval=F}
shinyUI(
fluidPage(
  sidebarPanel(
    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)
  ),
  mainPanel(
    plotOutput("plot")
  )
))
```

# Step 2: Use a Shiny theme
You can slightly change the look of your shiny app by choosing a [theme](https://rstudio.github.io/shinythemes/).

```{r, eval=F}
install.packages("shinythemes")
```

```{r, eval=F}
## ui.R ##
library(shinythemes)

fluidPage(theme = shinythemes("cerulean"),
  ...
)
```

```{r, echo=F}
library(shiny)
library(shinythemes)
shinyApp(
  ui=fluidPage(theme = shinytheme("spacelab"),
    sidebarPanel(
      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)
    ),
    mainPanel(
      plotOutput("plot")
    )
  ),
  server<-function(input, output) {
    output$plot=renderPlot({
    hist(faithful$eruptions, 
         probability = TRUE, 
         breaks = as.numeric(input$n_breaks),
         xlab = "Duration (minutes)", 
         main = "Geyser eruption duration")
    
    dens <- density(faithful$eruptions, 
                    adjust = input$bw_adjust)
    lines(dens, col = "blue")
    })
  }
)
```

# Step 3: Choose a data set to use
For the purpose of this exercise, we will use the `airquality` data set in R. 

```{r}
library(shiny)
library(DT)
shinyApp(
  ui=fluidPage(
    headerPanel('Air Quality Data'),
    sidebarPanel(
      title = 'Air Quality Data',
      selectInput('xcol', 'X Variable', names(airquality)[-c(5,6)],
                  selected=names(airquality)[[1]]),
      selectInput('ycol', 'Y Variable', names(airquality)[-c(5,6)],
                  selected=names(airquality)[[2]]),
     # actionButton('select2', 'Select the above variables.'),
      sliderInput("subsample", label = "Size of random samples",
                  min = 5, max = 50, value = 10, step = 1),
      actionButton('resetSelection',
                   label = "Click to reset row selection"
      ) # end of action button
    ),
    mainPanel(
      
      fluidRow(
        column(6,  
               h1('select rows'), 
               DT::dataTableOutput('x1')),
        column(6, 
               plotOutput('x2', height = 400))
      ),
      verbatimTextOutput('info')
    )
  ),
  server<-function(input, output) {
    
    selectedData <- 
    #airquality[,c(input$xcol,input$ycol)]
    reactive({
        airquality[, c(input$xcol, input$ycol)]
      })
    nn <- nrow(airquality)
    
    output$x1 = DT::renderDataTable(airquality[,-c(5,6)], 
                                    options = list(
                                      lengthMenu = list(c(3, 5, 10), c('3', '5', '10')),
                                      pageLength = 5
                                    ),
                                    server = FALSE,
                                    selection = list(target = 'row+column'))
    
    proxy = dataTableProxy('x1')
    
    observeEvent(input$resetSelection, {
      proxy %>% selectRows(sample(1:nn, input$subsample, replace=F))
    })
    
    
    # highlight selected rows in the scatterplot
    output$x2 = renderPlot(height = 400, {
      par(mar = c(4, 4, 1, .1))
      plot(airquality[, c(input$xcol, input$ycol)])
      s = input$x1_rows_selected
      if (length(s)) {
        points(airquality[s, c(input$xcol, input$ycol), drop = FALSE], 
                            pch = 19, cex = 2)
        abline(lsfit(airquality[s,input$xcol], 
                     airquality[s,input$ycol])$coef, col=2)
      }
    })
    
    output$info = renderPrint({
      s = input$x1_rows_selected
      cor.sel=NA
      if(length(s)) cor.sel=cor(airquality[s,input$xcol], 
                                airquality[s,input$ycol],
                                use="pairwise.complete.obs")
      list(xcol=input$xcol, ycol=input$ycol, 
           cor.all=cor(airquality[,input$xcol], 
                       airquality[,input$ycol],
                       use="pairwise.complete.obs"),
           cor.sel=cor.sel)
    })
    
  }
)
```

# Step 4: make a R flex dashboard

```{r, eval=F}
install.packages("flexdashboard", type = "source")
```

When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the *Preview* button or press *Cmd+Shift+K* to preview the HTML file).