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ShinyによるSDKぽいツール #1

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74 changes: 74 additions & 0 deletions shiny_sdk_toos/server.R
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library(shiny)

data_rhc = read.csv("http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/rhc.csv")

shinyServer(function(input, output) {


data = reactive({
switch(input$select_data,
"1" = iris,
"2" = data_rhc,
"3" = iris,
"4" = data_rhc,
"5" = iris)
})

data_regression = reactive({
switch(input$select_data_regression,
"1" = iris,
"2" = data_rhc,
"3" = iris,
"4" = data_rhc,
"5" = iris)
})

data_clasificagtion = reactive({
switch(input$select_data_classfication,
"1" = iris,
"2" = data_rhc,
"3" = iris,
"4" = data_rhc,
"5" = iris)
})

output$distPlot_shiny <- renderPlot({
x <- faithful[, 2] # Old Faithful Geyser data
bins <- seq(min(x), max(x), length.out = input$bins_shiny + 1)

hist(x, breaks = bins, col = 'darkgray', border = 'white')
})

output$histPlot <- renderPlot({
x = data()[, input$select_columns_for_hist]
hist(x, col = 'darkgray', border = 'white')
})

output$scatterPlot <- renderPlot({
x = data_regression()[, input$select_columns_for_scatter_x]
y = data_regression()[, input$select_columns_for_scatter_y]
plot(x, y, col = 'darkgray', border = 'white')
})

output$table = DT::renderDataTable(data(), options= list(lengthMenu = c(5, 10, 20), pageLength = 5))

output$plot_regression <- renderPlot({
input$regressionButton

x = isolate(data_regression())[, isolate(input$select_columns_for_regression_x)]
y = isolate(data_regression())[, isolate(input$select_columns_for_regression_y)]

lm = lm(x ~ y)
plot(lm)
})

output$plot_classification <- renderPlot({
input$classificationButton

x = isolate(data_clasificagtion())[, isolate(input$select_columns_for_classification_x)]
y = isolate(data_clasificagtion())[, isolate(input$select_columns_for_classification_y)]

lm = lm(x ~ y)
plot(lm)
})
})
97 changes: 97 additions & 0 deletions shiny_sdk_toos/ui.R
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library(shiny)

shinyUI(navbarPage("Shinyサンプルアプリケーション",
tabPanel("Home",
headerPanel("『RとShinyで作るWebアプリケーション』のサンプルアプリケーション"),
h2("アプリケーション概要"),
p("オープンソースデータを用いて可視化と分析を行えるShinyアプリです。"),
helpText("サンプルなので、うまく動かない可能性もあるのでご注意ください。")),

tabPanel("Shinyとは?",
headerPanel("Shinyでは以下のようなアプリケーションが作成できます。"),
sidebarLayout(
sidebarPanel(
sliderInput("bins_shiny",
"Number of bins:",
min = 1,
max = 50,
value = 30)
),
mainPanel(
plotOutput("distPlot_shiny")
)
)),
tabPanel("可視化",sidebarLayout(
sidebarPanel(
selectInput("select_data", label = h3("データセットを選択してください。"),
choices = list("iris" = 1, "Right heart catheterization dataset" = 2,
"Data for Titanic passengers" = 3, "Very low birth weight infant" = 4,
"Boston neighborhood housing prices data" = 5), selected = "1"),
numericInput("select_columns_for_hist", label = h3("ヒストグラムプロットするデータ列を指定。"),
value = "1"),
h3("散布図プロットするデータ列を選択。"),
numericInput("select_columns_for_scatter_x", label = h4("X軸"),
value = "1"),
numericInput("select_columns_for_scatter_y", label = h4("Y軸"),
value = "1")
),
mainPanel(
tabsetPanel(type = "tabs",
tabPanel("Table", DT::dataTableOutput("table")),
tabPanel("ヒストグラム", plotOutput("histPlot")),
tabPanel("散布図", plotOutput("scatterPlot")),
tabPanel("みたいに他にも図を表示する")
)
))),
tabPanel("回帰", sidebarLayout(
sidebarPanel(
h2("回帰してみる"),
selectInput("select_data_regression", label = h3("データセットを選択してください。"),
choices = list("iris" = 1, "Right heart catheterization dataset" = 2,
"Data for Titanic passengers" = 3, "Very low birth weight infant" = 4,
"Boston neighborhood housing prices data" = 5), selected = "1"),
selectInput("select_regression", label = h3("回帰手法を選択する。"),
choices = list("重回帰分析" = 1, "ランダムフォレスト" = 2,
"3層ニューラルネット" = 3), selected = "1"),
h3("分析対象の列を選択"),
numericInput("select_columns_for_regression_x", label = h4("説明変数(「1:4」や「1, 2, 3」などの表記で複数選択可能)"),
value = "1"),
numericInput("select_columns_for_regression_y", label = h4("目的変数"),
value = "1"),
actionButton("regressionButton", "回帰！")
),
mainPanel(
tabsetPanel(type = "tabs",
tabPanel("プロットで結果を確認", plotOutput("plot_regression")),
tabPanel("みたいな要素を表示する")
)))),
tabPanel("分類", sidebarLayout(
sidebarPanel(
h2("分類してみる"),
selectInput("select_data_classfication", label = h3("データセットを選択してください。"),
choices = list("iris" = 1, "Right heart catheterization dataset" = 2,
"Data for Titanic passengers" = 3, "Very low birth weight infant" = 4,
"Boston neighborhood housing prices data" = 5), selected = "1"),
selectInput("select_classification", label = h3("分類手法を選択する。"),
choices = list("ロジスティック回帰" = 1, "ランダムフォレスト" = 2,
"3層ニューラルネット" = 3, "SVM" = 4), selected = "1"),
h3("分析対象の列を選択"),
numericInput("select_columns_for_classification_x", label = h4("説明変数(「1:4」や「1, 2, 3」などの表記で複数選択可能)"),
value = "1"),
numericInput("select_columns_for_classification_y", label = h4("目的変数"),
value = "1"),
actionButton("classificationButton", "分類！")
),
mainPanel(
tabsetPanel(type = "tabs",
tabPanel("プロットで結果を確認", plotOutput("plot_classification")),
tabPanel("みたいな要素を表示する")
)))),
navbarMenu("その他",
tabPanel("About",
h2("私の名前はNp-Urです。")
),
tabPanel("ソースコード",
a(href="https://github.com/chan-ume", p("https://github.com/chan-ume")))
)
))