• Framework developed by 
• take R code and make a web "app" quickly
  • ui.R
    • simple input and output setup
  • server.R
    • reactivity for R 

What

• can easily extend/expand

• multiple tabs
• control reactivity
• easy deployment with shinyapps.io, but can run shiny server on web service of choice

Capabilities

Building with Shiny

taking R to the web

ui.R

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server.R

# ui.R

## Libraries ----
library(shiny)
library(plotly)
library(leaflet)
library(maps)

shinyUI(fluidPage(theme = "bootstrap.css",
                  # Google Analytics
                  tags$head(includeScript("www/google-analytics.js")),
                  # Application title
                  HTML('<img src="logo.svg" style = "max-height:130px" width = "100%"/>'),
                  fluidRow(column(12, align ="center",
                                  h3("A Clean Power Plan Evaluation Tool")
                  )),
# ...

                  fluidRow(
                    selectizeInput("stateInput", #inputID
                                   label = "State", #label
                                   choices = NULL,
                                   selected = "Alabama",
                                   multiple = FALSE,
                                   options = list(placeholder = 'select a state name'))
                  ),
# ...
))

# server.R

## Libraries ----
library(shiny)
library(plotly)
library(leaflet)
library(maps)

## Non-Reactive ---

# ... 
### Plant Location Data
geodata <- read.csv("data/plantgeodata.csv")

# ...

## Reactive ---
shinyServer(function(input, output, session) {
  
  updateSelectizeInput(session,
                       'stateInput',
                       choices = statenames, #must be a character vector!!! http://shiny.rstudio.com/articles/selectize.html
                       selected = "Alabama",
                       server = TRUE)
  
  output$NGCCSlider <- renderUI({
    sliderInput("NGCCCap",
                "NGCC Average Capacity Factor (%)",
                min = 0,
                max = 90,
                value = generationDataCleaned[input$stateInput, "NGCCcapFactor"] * 100,
                step = 1,
                width = '1000px')
    
  })
# ...
  output$ratePlotly <- renderPlotly({
    r <- plot_ly(result(),
                 type = "bar",       # all "bar" attributes: https://plot.ly/r/reference/#bar
                 orientation = "h",
                 x = Rate,               # more about bar's "x": /r/reference/#bar-x
                 y = Name,                # more about bar's "y": /r/reference/#bar-y
                 name = "lbsCO2/MWh",
                 opacity = 0.7,    # more about bar's "name": /r/reference/#bar-name
                 marker = list(         # marker is a named list, valid keys: /r/reference/#bar-marker
                   color=c("517C96","FF8200")     # more about marker's "color" attribute: /r/reference/#bar-marker-color
                 ))
    r <- add_trace(x = c(result()[1,5],result()[1,5]), type = "line",
                   marker = list(
                     color="red",
                     size = 0
                   ),
                   line = list(          # marker is a named list, valid keys: /r/reference/#bar-marker
                     color="red",
                     width = 6
                     
                   ),
                   name = "Goal"
    )
    r <- layout(r,              # all of layout's properties: /r/reference/#layout
                xaxis = list(           # layout's xaxis is a named list. List of valid keys: /r/reference/#layout-xaxis
                  title = ""     # xaxis's title: /r/reference/#layout-xaxis-title
                ),
                yaxis = list(           # layout's yaxis is a named list. List of valid keys: /r/reference/#layout-yaxis
                  title = "lbsCO2/MWh"      # yaxis's title: /r/reference/#layout-yaxis-title
                ),
                margin = list(
                  l = 120
                )
                
    )
    
  })


}) /end reactive

Building with Shiny

taking R to the web

• Leveraging R with Shiny framework, while collaborating with a team
  • opportunity to introduce version control
  • introduce team to open source community

Goal

• too many things introduced at a time
  • learning R
  • using RStudio
  • learning Shiny
  • learning git, github
• "version control" :: emails
  • zip files
  • merge was fun...

Reality

Building with Shiny

taking R to the web

Clean Power Plan

• Effort to cut US power plant emissions by 32% from 2005 levels in 2030 with state-specific plans
• Implementation begins in 2022
• Provide states flexibility in energy options to meet plan

What is it?

Timeline

Building with Shiny

taking R to the web

• June 2014, proposed
• Aug  2015, announced
• Oct  2015, 24 states sue
• Feb 9, 2016, Supreme Court stayed plan pending review
• June 2016 - suit to be heard in DC District Court

States' Views on Clean Power Plan

New!

• Shiny v0.13.0 20 Jan 2016 released a bombshell of updates
  • Gadgets
    • interactive locally-run data analysis

Shiny Modules

• Aimed to help us reduce complexity within server.R script - "abstract", as well as easily reuse between projects

Building with Shiny

taking R to the web

library(shiny)

source("linked_scatter.R")

ui <- fixedPage(
  h2("Module example"),
  linkedScatterUI("scatters"),
  textOutput("summary")
)

server <- function(input, output, session) {
  df <- callModule(linkedScatter, "scatters", reactive(mpg),
    left = reactive(c("cty", "hwy")),
    right = reactive(c("drv", "hwy"))
  )

  output$summary <- renderText({
    sprintf("%d observation(s) selected", nrow(dplyr::filter(df(), selected_)))
  })
}

shinyApp(ui, server)

example from RStudio

app.R

["helper"].R

library(shiny)
library(ggplot2)

linkedScatterUI <- function(id) {
  ns <- NS(id)

  fluidRow(
    column(6, plotOutput(ns("plot1"), brush = ns("brush"))),
    column(6, plotOutput(ns("plot2"), brush = ns("brush")))
  )
}

linkedScatter <- function(input, output, session, data, left, right) {
  # Yields the data frame with an additional column "selected_"
  # that indicates whether that observation is brushed
  dataWithSelection <- reactive({
    brushedPoints(data(), input$brush, allRows = TRUE)
  })

  output$plot1 <- renderPlot({
    scatterPlot(dataWithSelection(), left())
  })

  output$plot2 <- renderPlot({
    scatterPlot(dataWithSelection(), right())
  })

  return(dataWithSelection)
}

scatterPlot <- function(data, cols) {
  ggplot(data, aes_string(x = cols[1], y = cols[2])) +
    geom_point(aes(color = selected_)) +
    scale_color_manual(values = c("black", "#66D65C"), guide = FALSE)
}

Building with Shiny

taking R to the web

Building with Shiny

taking R to the web