Shiny 是RStudio公司开发的新包，利用Shiny 可以轻松构建交互式Web应用程序(App)。
安装Shiny包
> install.packages("shiny")
Shiny包中内置了11个例子来展示Shiny的使用。
Example 1: Hello Shiny
Hello Shiny Screenshot
Hello Shiny 使用faithful数据画了一个直方图，这个直方图可以调节bin的个数。
Hello Shiny运行
> library(shiny)
> runExample("01_hello")
Shiny App的架构
Shiny App 本质上基于一个R脚本(app.R).
app.R 有三个组成成分：
- 一个用户界面
- 一个服务器功能
- 调用
shinyApp函数
用户界面(ui)包含了App的的布局和外观。
服务器功能(server)包含了电脑构建app需要的指令。
shinyApp 函数根据UI/server创建Shiny app。
注：版本0.10.2以前，Shiny 不支持一个文件构建App，ui 和server各自需要一个单独的文件：i.R and server.R。
下面研究Hello Shiny的组成
用户界面：ui
library(shiny)
# Define UI for app that draws a histogram ----
ui <- fluidPage(
# App title ----
titlePanel("Hello Shiny!"),
# Sidebar layout with input and output definitions ----
sidebarLayout(
# Sidebar panel for inputs ----
sidebarPanel(
# Input: Slider for the number of bins ----
sliderInput(inputId = "bins",
label = "Number of bins:",
min = 1,
max = 50,
value = 30)
),
# Main panel for displaying outputs ----
mainPanel(
# Output: Histogram ----
plotOutput(outputId = "distPlot")
)
)
)
服务器功能：server
# Define server logic required to draw a histogram ----
server <- function(input, output) {
# Histogram of the Old Faithful Geyser Data ----
# with requested number of bins
# This expression that generates a histogram is wrapped in a call
# to renderPlot to indicate that:
#
# 1. It is "reactive" and therefore should be automatically
# re-executed when inputs (input$bins) change
# 2. Its output type is a plot
output$distPlot <- renderPlot({
x <- faithful$waiting
bins <- seq(min(x), max(x), length.out = input$bins + 1)
hist(x, breaks = bins, col = "#75AADB", border = "white",
xlab = "Waiting time to next eruption (in mins)",
main = "Histogram of waiting times")
})
}
shinyApp
shinyApp函数根据上面构建的UI/server创建Shiny app
shinyApp(ui, server)
Example 2: Shiny Text
Tabsets Screenshot
library(shiny)
runExample("02_text")
ui
# Define UI for dataset viewer app ----
ui <- fluidPage(
# App title ----
titlePanel("Shiny Text"),
# Sidebar layout with a input and output definitions ----
sidebarLayout(
# Sidebar panel for inputs ----
sidebarPanel(
# Input: Selector for choosing dataset ----
selectInput(inputId = "dataset",
label = "Choose a dataset:",
choices = c("rock", "pressure", "cars")),
# Input: Numeric entry for number of obs to view ----
numericInput(inputId = "obs",
label = "Number of observations to view:",
value = 10)
),
# Main panel for displaying outputs ----
mainPanel(
# Output: Verbatim text for data summary ----
verbatimTextOutput("summary"),
# Output: HTML table with requested number of observations ----
tableOutput("view")
)
)
)
server
# Define server logic to summarize and view selected dataset ----
server <- function(input, output) {
# Return the requested dataset ----
datasetInput <- reactive({
switch(input$dataset,
"rock" = rock,
"pressure" = pressure,
"cars" = cars)
})
# Generate a summary of the dataset ----
output$summary <- renderPrint({
dataset <- datasetInput()
summary(dataset)
})
# Show the first "n" observations ----
output$view <- renderTable({
head(datasetInput(), n = input$obs)
})
}
Example 3: Reactivity
[站外图片上传中...(image-930865-1560176024515)]
library(shiny)
runExample("03_reactivity")
- 响应式编程
响应式编程是一种面向数据流和变化传播的编程范式。
input values => R code => output values
input values变化时，R code会自动执行，同时更新结果
使用reactive()可以创建响应式表达式
datasetInput <- reactive({
switch(input$dataset,
"rock" = rock,
"pressure" = pressure,
"cars" = cars)
})
改变input或者input$obs, 下面的表达式都会重新运行。
output$view <- renderTable({
head(datasetInput(), n = input$obs)
})
ui
# Define UI for dataset viewer app ----
ui <- fluidPage(
# App title ----
titlePanel("Reactivity"),
# Sidebar layout with input and output definitions ----
sidebarLayout(
# Sidebar panel for inputs ----
sidebarPanel(
# Input: Text for providing a caption ----
# Note: Changes made to the caption in the textInput control
# are updated in the output area immediately as you type
textInput(inputId = "caption",
label = "Caption:",
value = "Data Summary"),
# Input: Selector for choosing dataset ----
selectInput(inputId = "dataset",
label = "Choose a dataset:",
choices = c("rock", "pressure", "cars")),
# Input: Numeric entry for number of obs to view ----
numericInput(inputId = "obs",
label = "Number of observations to view:",
value = 10)
),
# Main panel for displaying outputs ----
mainPanel(
# Output: Formatted text for caption ----
h3(textOutput("caption", container = span)),
# Output: Verbatim text for data summary ----
verbatimTextOutput("summary"),
# Output: HTML table with requested number of observations ----
tableOutput("view")
)
)
)
server
# Define server logic to summarize and view selected dataset ----
server <- function(input, output) {
# Return the requested dataset ----
# By declaring datasetInput as a reactive expression we ensure
# that:
#
# 1. It is only called when the inputs it depends on changes
# 2. The computation and result are shared by all the callers,
# i.e. it only executes a single time
datasetInput <- reactive({
switch(input$dataset,
"rock" = rock,
"pressure" = pressure,
"cars" = cars)
})
# Create caption ----
# The output$caption is computed based on a reactive expression
# that returns input$caption. When the user changes the
# "caption" field:
#
# 1. This function is automatically called to recompute the output
# 2. New caption is pushed back to the browser for re-display
#
# Note that because the data-oriented reactive expressions
# below don't depend on input$caption, those expressions are
# NOT called when input$caption changes
output$caption <- renderText({
input$caption
})
# Generate a summary of the dataset ----
# The output$summary depends on the datasetInput reactive
# expression, so will be re-executed whenever datasetInput is
# invalidated, i.e. whenever the input$dataset changes
output$summary <- renderPrint({
dataset <- datasetInput()
summary(dataset)
})
# Show the first "n" observations ----
# The output$view depends on both the databaseInput reactive
# expression and input$obs, so it will be re-executed whenever
# input$dataset or input$obs is changed
output$view <- renderTable({
head(datasetInput(), n = input$obs)
})
}
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