<style type="text/css">
div#TOC li {
    list-style:none;
    background-image:none;
    background-repeat:none;
    background-position:0;
}

h1.title {
  font-size: 20px;
  color: DarkRed;
  text-align: center;
}
h4.author { /* Header 4 - and the author and data headers use this too  */
    font-size: 18px;
  font-family: "Times New Roman", Times, serif;
  color: DarkRed;
  text-align: center;
}
h4.date { /* Header 4 - and the author and data headers use this too  */
  font-size: 18px;
  font-family: "Times New Roman", Times, serif;
  color: DarkBlue;
  text-align: center;
}
h1 { /* Header 3 - and the author and data headers use this too  */
    font-size: 22px;
    font-family: "Times New Roman", Times, serif;
    color: darkred;
    text-align: center;
}
h2 { /* Header 3 - and the author and data headers use this too  */
    font-size: 18px;
    font-family: "Times New Roman", Times, serif;
    color: navy;
    text-align: left;
}

h3 { /* Header 3 - and the author and data headers use this too  */
    font-size: 15px;
    font-family: "Times New Roman", Times, serif;
    color: navy;
    text-align: left;
}

h4 { /* Header 4 - and the author and data headers use this too  */
    font-size: 18px;
    font-family: "Times New Roman", Times, serif;
    color: darkred;
    text-align: left;
}
</style>

```{r setup, include=FALSE}
if (!require("knitr")) {
   install.packages("knitr")
   library(knitr)
}
if (!require("shiny")) {
   install.packages("shiny")
   library(shiny)
}
if (!require("ggplot2")) {
   install.packages("ggplot2")
   library(ggplot2)
}
if (!require("tidyverse")) {
   install.packages("tidyverse")
   library(tidyverse)
}
if (!require("dplyr")) {
   install.packages("dplyr")
   library(dplyr)
}
knitr::opts_chunk$set(echo = TRUE,       
                      warning = FALSE,   
                      result = TRUE,   
                      message = FALSE,
                      fig.align='center', 
                      fig.pos = 'ht')
```




# Introduction

Shiny is a powerful and popular web framework for R programmers to elevate the way people consume analytics for both technical and non-technical decision makers. It is used in many organizations from start-ups to top-trafficked web sites. 

You can see from the last lecture that we can create a highly interactive shiny app to engage users in the applications in simulations. This lecture focuses on creating shiny apps for data analysis. The benefits of shiny in business analytics obvious



# Naming Conventions Revisited

Most programming languages seem to have their own take on how user should name something. This is not the case in R. Actually, R does not have an official naming convention. R community uses naming conventions in many other languages. Below is a list of some of the most common conventions.

* **myVariableName** - lower camel case. This naming convention is used in Java and JavaScript.

* **MyVariableName** - upper camel case / PascalCase. This naming convention is used for class names in many languages including
Java, Python and JavaScript.

* **my_variable_name** - underscore_separated (also called snake case). This naming convention is used for function and variable names in many languages including C++, Perl and Ruby.

* **my.variable.name** - Period.separated. This naming convention is unique to R and used in many core functions such as `as.numeric` or `read.table`.

* **myvariancename** - all lower case. This naming convention is common in MATLAB. Note that a single lowercase name, such as mean, conforms to all conventions but UpperCamelCase.

In shiny library, the lowerCamelCase conventions is used. In the back-end coding, you can use any of the above naming conventions or a hybrid of different naming conventions. 



# Working Data Set 

This note shows the steps of creating an R shiny application for data analysis.

R Shiny applications are effective tool for exploratory visual analytics. As other analysis, it is essential to
understand the data, know the critical information, and use appropriate tools and effective approaches to convey the information to your audience.

Creating A shiny applications is a combination of arts, technology and science. 

In this note, I use the well-known `iris data set` to illustrate how to create shiny applications in data analysis.

We create two code chunks to explain the two modules (ui and server) separately.


# UI Module: Front-end Design


**Graphical Analysis Plan* ()**

  1. Scatter plot
  
  2. Linear regression -inferential table
  
  3. Prediction

**User input information (UI)**

  1. Species
  
  2. Selection of X and Y variable
  
  3. input value of X for prediction 

I use three different input control widgets in this case study. A list of commonly used input control widgets as well as the code to create the widget can be found at <https://shiny.rstudio.com/gallery/widget-gallery.html>.

We next design the UI in the following code chunk. 

```{r}
library(shiny)
##  Working data set
##  The iris data set will be used in this app.
iris0 = read.table("https://raw.githubusercontent.com/pengdsci/sta553/main/shiny/iris0.txt", header = TRUE)
x.names = names(iris0)[-c(1,6)]
y.names =names(iris0)[-1]
##
# Define UI for random distribution app ----
ui <- fluidPage(
  # App title ----
    titlePanel("Analysis of Iris Data"),
  ############
  # Sidebar layout with input and output definitions ----
    sidebarLayout(
    # Sidebar panel for inputs ----
      sidebarPanel(
         # Input: Select the random distribution type ----
         radioButtons("species", "Species",
                   c("setosa",
                     "versicolor",
                     "virginica",
                     "all"),
                   inline = FALSE,
                   selected = "all"),

      # br() element to introduce extra vertical spacing ----
      hr(),

      # Input: Slider for the number of observations to generate ----
      selectInput("Y",
                  "Response Variable: Y",
                  y.names),

      selectInput("X",
                  "Predictor Variable: X",
                  x.names),
      ####
       hr(),
      ###
        sliderInput("newX", "New Value for Prediction:", 2.5, min = 0, max = 20, step = 0.1)
       ),
    # Main panel for displaying outputs ----
    mainPanel(
      # Output: Tabset w/ plot, summary, and table ----
      tabsetPanel(type = "tabs",
                  tabPanel("Scatter Plot", plotOutput("plot")),
                  tabPanel("Regression Coefficients", tableOutput("table")),
                  tabPanel("Diagnostics",plotOutput("diagnosis")),
                  tabPanel("Prediction", plotOutput("predPlt"))
                  
      )

    )
  )
)
##
## Server function
server <- function(input, output) {}
## 
shinyApp(ui = ui, server = server)
```



# Server Module: Back-end Coding

In the next code chunk, I copy the UI function and draft the server function. I use species a filter variable. I also allow users to choose response and predictor variables to build a linear regression model and use the model to make prediction by providing new values of the predictor variable.

I choose `tabset` layout to design this shiny app.

```{r}
library(shiny)
##  Working data set
##  The iris data set will be used in this app.
iris0 = read.table("https://raw.githubusercontent.com/pengdsci/sta553/main/shiny/iris0.txt", header = TRUE)
x.names = names(iris0)[-c(1,6)]
y.names =names(iris0)[-1]
##
# Define UI for random distribution app ----
ui <- fluidPage(

  # App title ----
        titlePanel(
           h4("Analysis of Iris Data", 
                  align = "left", style = "color:navy", br(), br())),
  ############
  # Sidebar layout with input and output definitions ----
    sidebarLayout(
    # Sidebar panel for inputs ----
      sidebarPanel(

            tags$head(
               tags$style("body {background-color: white }")),
              
         # Input: Select the random distribution type ----
         radioButtons("species", "Species",
                   c("setosa",
                     "versicolor",
                     "virginica",
                     "all"),
                   inline = FALSE,
                   selected = "all"),

      # br() element to introduce extra vertical spacing ----
      br(),
      # Input: Slider for the number of observations to generate ----
       selectInput("Y",
                  "Response Variable: Y",
                  y.names),

       selectInput("X",
                  "Predictor Variable: X",
                  x.names,
                  selected = x.names[2]),
        ####
       hr(),
      ###
      sliderInput("newX", "New Value for Prediction:", 2.5, min = 0, max = 20, step = 0.1),
      ###
       hr(),
       HTML('<p><center><img src="https://github.com/pengdsci/sta553/blob/main/image/goldenRamLogo.png?raw=true"  
                           width="80" height="80"></center></p>'),
       HTML('<p style="font-family:Courier; color:Red; font-size: 20px;"><center>
                 <font size =2> <a href="mailto:cpeng@wcupa.edu">
                 <font color="purple">Report bugs to C. Peng </font></a> </font></center></p>'),
          ),   # close sidebarPanel
    # Main panel for displaying outputs ----
    mainPanel(
      # Output: Tabset w/ plot, summary, and table ----
      tabsetPanel(type = "tabs",
                  tabPanel("Scatter Plot", plotOutput("plot")),
                  tabPanel("Regression Coefficients", tableOutput("table")),
                  tabPanel("Diagnostics",plotOutput("diagnosis")),
                  tabPanel("Prediction", plotOutput("predPlt"))
              )
          )      # close mainPanel
      )          # close sideLayout
  )              # close fluidPage

###################################
###################################
##       Server function
###################################
###################################
server <- function(input, output) {
####################################
##     Some R functions 
####################################
#### Subsetting data based on Species
  workDat = function(){
      if (input$species == "setosa") {
          workingData = iris0[which(iris0$Species == "setosa"),]
      } else if (input$species == "versicolor") {
          workingData = iris0[which(iris0$Species == "versicolor"),]
      } else if (input$species == "virginica") {
          workingData = iris0[which(iris0$Species == "virginica"),]
      } else {
          workingData = iris0
      }
   workingData 
  }
  ######################################
  #######   Scatter Plots
  ######################################
  output$plot <- renderPlot({
    dataset = workDat()[,-1]   # define the working data set
    #####
    plot(dataset[,input$X], dataset[,input$Y], 
         xlab = input$X,
         ylab = input$Y,
         main = paste("Relationship between", input$Y, "and", input$X)
        )
    ## adding a regression line to the plot
     abline(lm(dataset[,input$Y] ~ dataset[,input$X]),
            col = "blue",
            lwd = 2)
  })
  
   ######################################
  #######   Scatter Plots
  ######################################
  output$table <- renderTable({
    br()
    br()
    dataset = workDat()[,-1]
    # define the working data set
    m0 = lm(dataset[,input$Y] ~ dataset[,input$X])
    #summary(m0)
    regcoef = data.frame(coef(summary(m0)))
    
    ##
    regcoef$Pvalue = regcoef[,names(regcoef)[4]]
    ###
    regcoef$Variable = c("Intercept", input$X)
    regcoef[,c(6, 1:3, 5)]
    
  })
  ######################################
  #######   Diagnostics
  ######################################
  output$diagnosis <- renderPlot({
    dataset = workDat()[,-1]   # define the working data set
    #####
    m1=lm(dataset[,input$Y] ~ dataset[,input$X])
    par(mfrow=c(2,2))
    plot(m1)
  })   

  ######################################
  #######       Scatter Plots
  ######################################
  output$predPlt <- renderPlot({
    dataset = workDat()[,-1]   # define the working data set

    ###
    m3 = lm(dataset[,input$Y] ~ dataset[,input$X])

    pred.y = coef(m3)[1] + coef(m3)[2]*input$newX
         #####
    plot(dataset[,input$X], dataset[,input$Y], 
    #    xlab = input$X,
         ylab = input$Y,
         main = paste("Relationship between", input$Y, "and", input$X)
       )
        ## adding a regression line to the plot
     abline(m3,
            col = "red",
            lwd = 1,
            lty=2)
    points(input$newX, pred.y, pch = 19, col = "red", cex = 2)
  })
}

## 
shinyApp(ui = ui, server = server)
```


# File Uploading

Sometimes, you want want use a file up-loading widget to load the file to R shiny apps. 
You can run the the shiny app demo to get a sample code.


```{r}
source("https://raw.githubusercontent.com/pengdsci/sta553/main/shiny/shinyCodeExtractor.txt")
#runExample2("01_hello")        # a histogram
#runExample("02_text")         # tables and data frames
#runExample("03_reactivity")   # a reactive expression
#runExample("04_mpg")          # global variables
#runExample("05_sliders")      # slider bars
#runExample("06_tabsets")      # tabbed panels
#runExample("07_widgets")      # help text and submit buttons
#runExample("08_html")         # Shiny app built from HTML
runExample("09_upload")       # file upload wizard
#runExample("10_download")     # file download wizard
#runExample("11_timer")        # an automated timer
```












