Shiny基本介紹
一、參考資料
- 電子書：Mastering Shiny (Hadley Wickham著作)
- 網頁介紹：R 講題分享 – 利用 R 和 Shiny 製作網頁應用 (作者：Taiwan R User Group)
- Shiny作品集：Shiny Gallery
- Rstudio官網教學
- R Shiny cheat sheet： [網站頁面] [直接下載]
二、Shiny簡介
1. Shiny範例介紹
- Shiny簡單架構圖(出處:R 講題分享 – 利用 R 和 Shiny 製作網頁應用 (作者：Taiwan R User Group))
以R Shiny最基本範例來做介紹，在R程式中輸入:
library(shiny)
runExample("01_hello")
執行後會跳出Shiny範例：
以下為範例檔案的程式碼：
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")
)
)
)
# 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")
})
}
# Create Shiny app ----
shinyApp(ui = ui, server = server)
- faithful資料集說明
?faithful
Waiting time between eruptions and the duration of the eruption for the Old Faithful geyser in Yellowstone National Park, Wyoming, USA.
A data frame with 272 observations on 2 variables.
| 欄位名稱 | 資料類型 | 說明 |
|---|---|---|
| eruptions(噴發時間長度) | numeric | Eruption time in mins |
| waiting(下一次噴發等待時間) | numeric | Waiting time to next eruption (in mins) |
圖片來源: https://en.wikipedia.org/wiki/Old_Faithful
2. 小試身手
請繪製噴發時間長度(eruptions)的直方圖在Shiny上。
3. 呈現資料表
R shiny有提供呈現資料表的函數renderDataTable，但呈現表格的方式會不太好看，所以通常會使用DT套件，此套件說明可以參考此頁面。
接下來在剛剛的範例集中，呈現faithful的資料表給使用者觀看。
library(shiny)
library(DT)
# UI
ui <- fluidPage(
HTML('<center>'),
h3("Faithful資料表-DT版"),
HTML('</center>'),
dataTableOutput("DT_table"),
hr(),
HTML('<center>'),
h3("Faithful資料表-Shiny版"),
HTML('</center>'),
tableOutput("shiny_table")
)
# Server
server <- function(input, output) {
# DT版
output$DT_table <- renderDataTable({
datatable(faithful)
})
# Shiny版
output$shiny_table <- renderTable({
faithful
})
}
# Create Shiny app
shinyApp(ui = ui, server = server)
三、Shiny版面介紹
Shiny版面說明可參考Hadley Wickham的Mastering Shiny書籍。
以下介紹我自己常用的版型：
library(shiny)
library(shinythemes)
# UI
ui <- navbarPage("Shiny範例", id = "tabs",
# 主題設定
theme = shinytheme("flatly"),
#################### 主題1 ####################
tabPanel("主題1", value = "主題1",
tabsetPanel(type = "tabs",
tabPanel("頁籤1", value = "頁籤1",
h3("這裡是頁籤1頁面")
),
tabPanel("頁籤2", value = "頁籤2",
h3("這裡是頁籤2頁面")
)
)
),
#################### 主題2 ####################
tabPanel("主題2", value = "主題2",
h3("這裡是主題2頁面"),
sidebarLayout(
sidebarPanel(
h3("這裡是sidebarPanel")
),
mainPanel(
h3("這裡是mainPanel")
)
)
),
#################### 加入CSS修改字體 ####################
tags$head(tags$style(HTML("@import url('//fonts.googleapis.com/css?family=Noto+Sans+TC');
body, h2, h3, center {font-family: 'Noto Sans TC', sans-serif;}")))
)
# Server
server <- function(input, output) {
}
# Create Shiny app
shinyApp(ui = ui, server = server)
- 補充: Shiny的主題設定可到此頁面來挑選。
四、Shiny Widgets介紹
Shiny範例中有滑桿(Slider)可以選擇直方圖要畫的bin時外，Shiny也有提供其他常見的小工具，可以參考Shiny Widgets Gallery。
接下我們以Shiny範例來實作不同的Shiny Widgets功能。
1. 以選擇框(Select box)來調整bins
此範例是呈現如何透過selectInput來讓使用者挑選bins。
library(shiny)
# UI
ui <- fluidPage(
# 選擇框
selectInput("bins",
label = h3("Please select bins:"),
choices = list("15" = 15,
"20" = 20,
"30" = 30),
selected = 15),
# 呈現直方圖
plotOutput(outputId = "distPlot")
)
# Server
server <- function(input, output) {
output$distPlot <- renderPlot({
x <- faithful$waiting
bins <- seq(min(x), max(x), length.out = as.numeric(input$bins) + 1)
hist(x, breaks = bins, col = "#75AADB", border = "white",
xlab = "Waiting time to next eruption (in mins)",
main = "Histogram of waiting times")
})
}
# Create Shiny app
shinyApp(ui = ui, server = server)
2. 從後台控制選項內容
從上面的範例可以看到我們是在前端設定好可以調整的選項，但有時候我們希望選項能夠依據資料狀況自動呈現，例如說根據資料樣本數來決定bins可以選擇的大小。此時可以在server端透過updateSelectInput函數來更新ui的選擇項目。
library(shiny)
# UI
ui <- fluidPage(
# 選擇框
selectInput("bins",
label = h3("Please select bins:"),
choices = NULL,
selected = NULL),
# 呈現直方圖
plotOutput(outputId = "distPlot")
)
# Server
server <- function(input, output, session) {
# 更新選項內容
binsOption <- seq(5, 30, 5)
binsOption <- split(unname(binsOption), binsOption)
updateSelectInput(session, "bins",
choices = binsOption,
selected = binsOption[[1]])
output$distPlot <- renderPlot({
x <- faithful$waiting
bins <- seq(min(x), max(x), length.out = as.numeric(input$bins) + 1)
hist(x, breaks = bins, col = "#75AADB", border = "white",
xlab = "Waiting time to next eruption (in mins)",
main = "Histogram of waiting times")
})
}
# Create Shiny app
shinyApp(ui = ui, server = server)
3. 以輸入值形式(Text input)來調整bins
此範例是呈現如何透過textInput讓使用者自行輸入bins。
library(shiny)
# UI
ui <- fluidPage(
# 輸入對話框
textInput("bins", label = h3("請輸入bins:"), value = "5"),
# 呈現直方圖
plotOutput(outputId = "distPlot")
)
# Server
server <- function(input, output, session) {
output$distPlot <- renderPlot({
x <- faithful$waiting
bins <- seq(min(x), max(x), length.out = as.numeric(input$bins) + 1)
hist(x, breaks = bins, col = "#75AADB", border = "white",
xlab = "Waiting time to next eruption (in mins)",
main = "Histogram of waiting times")
})
}
# Create Shiny app
shinyApp(ui = ui, server = server)
五、觸發事件寫法
在以輸入值形式(Text input)來調整bins範例中，我們透過textInput讓使用者自行輸入bins來決定繪製直方圖，但可能會發生兩個問題：
- 使用者輸入不合適的值導致圖形無法繪製
- 使用者還沒輸入完馬上跑出圖形
為解決上述問題，我們可以設定一個按鈕，當使用者輸入完想要呈現的值後，按下按鈕圖才會繪製。此處需要加入actionButton函數做按鈕及observeEvent搭配isolate做觸發事件。
library(shiny)
# UI
ui <- fluidPage(
# 輸入對話框
textInput("bins", label = h3("請輸入bins:"), value = "5"),
# 建立繪製按鈕
actionButton("action", label = "繪製"),
# 呈現直方圖
plotOutput(outputId = "distPlot")
)
# Server
server <- function(input, output, session) {
observeEvent(input$action, {
output$distPlot <- renderPlot({
binNums <- as.numeric(isolate(input$bins))
x <- faithful$waiting
bins <- seq(min(x), max(x), length.out = binNums + 1)
hist(x, breaks = bins, col = "#75AADB", border = "white",
xlab = "Waiting time to next eruption (in mins)",
main = "Histogram of waiting times")
})
})
}
# Create Shiny app
shinyApp(ui = ui, server = server)
若使用者輸入錯誤的值，則提示輸入錯誤之寫法：
library(shiny)
# UI
ui <- fluidPage(
# 輸入對話框
textInput("bins", label = h3("請輸入bins(需介於5至30之間):"), value = "5"),
# 建立繪製按鈕
actionButton("action", label = "繪製"),
# 輸出警告
span(textOutput(outputId = "warningText"), style = "color:red; font-size:20px"),
# 呈現直方圖
plotOutput(outputId = "distPlot")
)
# Server
server <- function(input, output, session) {
observeEvent(input$action, {
# 讀取使用者輸入的值
binNums <- as.numeric(isolate(input$bins))
binNums <- ifelse(is.na(binNums), 0, binNums)
# 判斷是否在合理範圍
if((binNums >= 5) & (binNums <= 30)){
# 位在合理範圍內清空警告提醒並繪圖
output$warningText <- renderText({NULL})
output$distPlot <- renderPlot({
x <- faithful$waiting
bins <- seq(min(x), max(x), length.out = binNums + 1)
hist(x, breaks = bins, col = "#75AADB", border = "white",
xlab = "Waiting time to next eruption (in mins)",
main = "Histogram of waiting times")
})
}else{
# 沒有在合理範圍內則輸出警告提醒及清空圖形
output$warningText <- renderText({"請輸入正確的值域範圍唷!"})
output$distPlot <- renderPlot({NULL})
}
})
}
# Create Shiny app
shinyApp(ui = ui, server = server)
六、上傳Shiny
做好的Shiny如果想讓別人能夠觀看使用(即對外服務)，可以上傳到R Studio的Shinyapps空間上或者自己架設R Shiny Server。
1. R Shinyapps介紹
可參考下列頁面資訊：
在Shinyapps.io官網登入後，即可看到官網說明上傳步驟：
上述步驟接在R程式執行，步驟1及步驟2只要執行1次即可，要特別注意步驟2的token和secret資訊不能輕易給別人，否則他就可以操控你的帳戶。
步驟3是上傳Shiny的語法，將想要上傳的Shiny程式碼放在一個資料夾內，資料夾名稱即為專案名稱。Shiny程式碼的檔名請命名為app.r，一定要命名為這個，這樣才知道哪個是Shiny要執行的檔案。另外程式碼一定要是utf8編碼，否則上傳會出錯。
執行後即可在看到專案，點選網址即可看到剛製作的Shiny作品，其他人也可以透過此網址來觀賞你的Shiny作品。
如果想要刪除專案，先點選紅框左邊的盒子圖案(封存)，再按下右邊的垃圾桶圖案，即可刪除專案。
2. R Shiny Server
由於Shinyapps.io對於部分套件並不支援，所以如果想要使用其他套件的話，可以自己架設R Shiny Server來對外服務。
R Shiny Server架設只能夠在linux作業系統上，安裝流程可參考Shiny官網說明。
安裝完後記得要開啟防火牆3838的port(Shiny Server默認)，才能讓提供服務。
Shiny程式放在/srv/shiny-server資料夾路徑底下即可對外服務。
七、實作-股票技術分析圖形繪製網站
目標: 使用者輸入股票代碼後，呈現股票技術分析圖形給使用者觀看。
library(shiny)
library(lubridate)
library(DT)
library(shinythemes)
library(tidyquant)
library(tidyverse)
# UI
ui <- navbarPage("Shiny", id = "tabs",
theme = shinytheme("flatly"),
tabPanel("繪製技術分析圖形", value = "繪製技術分析圖形",
sidebarLayout(
# 側邊頁面
sidebarPanel(
# 輸入股票代碼
textInput("stockCode", label = h5("請輸入Yahoo Finance股票代碼:"),
value = "", placeholder = ""),
# 查詢日期範圍
dateRangeInput("dates", label = h5("請選擇繪製的日期範圍"),
start = Sys.Date()-days(90),
end = Sys.Date(),
min = Sys.Date()-years(5),
max = Sys.Date(),
separator = "至"),
# 查詢按鈕
actionButton("action", label = "查詢"),
# 輸出警告
br(),
span(textOutput(outputId = "warningText"), style = "color:red"),
# 說明資料來源
br(),
tags$div(
"本網站數據來源: ",
tags$a(href = "https://finance.yahoo.com/", "Yahoo Finance")
),
),
# 主頁面
mainPanel(
HTML("<center>"),
h3("技術分析圖形"),
HTML("</center>"),
plotOutput("stockPlot"),
hr(),
HTML("<center>"),
h3("股價資訊表"),
HTML("</center>"),
dataTableOutput("stockData")
)
)
),
# 字體設定:使用CSS語法
tags$head(
tags$style(HTML("@import url('//fonts.googleapis.com/css?family=Noto+Sans+TC');
body, h2, h3, h4, h5, center {font-family: 'Noto Sans TC', sans-serif;}")))
)
# Server
server <- function(input, output, session) {
# 啟動查詢
observeEvent(input$action, {
# 讀取使用者設定資訊
stockCode <- isolate(input$stockCode)
print(stockCode)
stockCode <- ifelse(stockCode == "", "NA", stockCode) # 防呆機制
fromDate <- isolate(input$dates[1])
toDate <- isolate(input$dates[2])
# 下載股票資料
stockPriceData <- tq_get(stockCode,
get = "stock.price",
from = fromDate,
to = toDate)
# 判斷是否有下載到資料
if(is.na(stockPriceData)){
# 若未載到資料則提示警告訊息給使用者
output$warningText <- renderText({"請輸入正確的Yahoo Finance股票代碼唷!"})
}else{
# 清空警告訊息
output$warningText <- renderText({NULL})
# 繪製技術分析圖形
output$stockPlot <- renderPlot({
stockPriceData <- stockPriceData %>%
mutate(date = ymd(date),
chg = ifelse(close > open, "up", "dn"), # 判斷當日上漲/下跌
flat_bar = (open == close), # 判斷當日開盤價是否等於收盤價(繪圖需額外繪製)
num = c(1:n())) # 樣本點編碼
plotCode <- unique(stockPriceData$symbol)
plotStartDate <- min(stockPriceData$date)
plotEndDate <- max(stockPriceData$date)
p <- ggplot(stockPriceData, aes(x = num)) + # 建立畫布
geom_linerange(aes(ymin = low, ymax = high)) + # 繪製上影線和下影線
geom_rect(aes(xmin = num-1/3 , xmax =num+1/3, ymin = pmin(open, close), # 繪製K棒實體部分
ymax = pmax(open, close), fill = chg)) +
scale_fill_manual(values = c("dn" = "forestgreen", "up" = "firebrick")) + # 改變K棒顏色，跌為綠色，漲為紅色
scale_color_manual(values=c("blue", "orange", "green")) + # 改變移動平均線顏色(此處參考奇摩股市網的顏色)
theme_bw() + # 將畫布底圖顏色改為白色
labs(title = paste0(plotCode), # 加入標題及改變X和Y軸名稱
subtitle = paste0("繪製期間: ", plotStartDate, " 至 ", plotEndDate),
x="", y="") +
theme(text = element_text(size = 16),
plot.title = element_text(hjust = 0.5), # 將標題置中
plot.subtitle = element_text(hjust = 0.5),
plot.margin = margin(0.5, 1.3, 0, 0, "cm"),
legend.position="bottom", # 把legend移到上面
legend.title=element_blank()) + # 將legend的title改掉
guides(fill = FALSE) # 將fill方式繪圖的legend拿掉
# 繪製垂直線位置 表示比對範圍
dataRowNum <- nrow(stockPriceData)
p <- p +
scale_x_continuous(breaks = seq(dataRowNum, 1,-ceiling(dataRowNum/5)), # 將x軸的標籤名稱更改為日期
labels = stockPriceData$date[seq(dataRowNum, 1,-ceiling(dataRowNum/5))], expand = c(0, 0))
# 若有開盤價=收盤價之交易日資料，因geom_rect無法繪製，要用geom_segment補繪
if(any(stockPriceData$flat_bar)){
p <- p +
geom_segment(data = stockPriceData[stockPriceData$flat_bar,],
aes(x = num-1/3, y = close, yend = close, xend = num+1/3))
}
return(p)
})
# 輸出表格
output$stockData <- DT::renderDataTable({
datatable(stockPriceData %>% arrange(desc(date)))
})
}
})
}
# Run the application
shinyApp(ui = ui, server = server)
- 補充：plotly套件有股票技術分析圖形範例程式碼candlestick，可替換程式碼內的ggplot2寫法。
八、建立Shiny帳密登入機制
我們可透過shinymanager套件來為Shiny建立帳密登入機制，但需注意此套件無法在Shinyapps.io上使用。
以下程式碼摘自套件Github範例:
# 建立帳密資訊
credentials <- data.frame(
user = c("shiny", "shinymanager"), # mandatory
password = c("azerty", "12345"), # mandatory
start = c("2019-04-15"), # optinal (all others)
expire = c(NA, "2019-12-31"),
admin = c(FALSE, TRUE),
comment = "Simple and secure authentification mechanism
for single ‘Shiny’ applications.",
stringsAsFactors = FALSE
)
library(shiny)
library(shinymanager)
# UI
ui <- fluidPage(
tags$h2("My secure application"),
verbatimTextOutput("auth_output")
)
# Wrap your UI with secure_app
ui <- secure_app(ui)
# Server
server <- function(input, output, session) {
# call the server part
# check_credentials returns a function to authenticate users
res_auth <- secure_server(
check_credentials = check_credentials(credentials)
)
output$auth_output <- renderPrint({
reactiveValuesToList(res_auth)
})
# your classic server logic
}
shinyApp(ui, server)
補充
補充範例1
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 = "waiting_distPlot"),
plotOutput(outputId = "eruptions_distPlot")
)
)
)
# 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$bins) change
# 2. Its output type is a plot
output$waiting_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")
})
output$eruptions_distPlot <- renderPlot({
x <- faithful$eruptions
bins <- seq(min(x), max(x), length.out = input$bins + 1)
hist(x, breaks = bins, col = "#75AADB", border = "white",
xlab = "Waiting time to next eruption (in mins)",
main = "Histogram of waiting times")
})
}
# Create Shiny app ----
shinyApp(ui = ui, server = server)
補充範例2
library(shiny)
library(lubridate)
library(DT)
library(shinythemes)
library(plotly)
library(tidyquant)
library(tidyverse)
library(shinymanager)
credentials <- data.frame(
user = c("nsysu", "shiny", "shinymanager"), # mandatory
password = c("nsysu", "azerty", "12345"), # mandatory
start = c("2019-04-15"), # optinal (all others)
expire = c(NA, NA, "2020-12-31"),
admin = c(FALSE, FALSE, TRUE),
comment = "Simple and secure authentification mechanism
for single ‘Shiny’ applications.",
stringsAsFactors = FALSE
)
# UI
ui <- navbarPage("Shiny", id = "tabs",
theme = shinytheme("flatly"),
tabPanel("繪製技術分析圖形", value = "繪製技術分析圖形",
sidebarLayout(
# 側邊頁面
sidebarPanel(
# 輸入股票代碼
textInput("stockCode", label = h5("請輸入Yahoo Finance股票代碼:"),
value = "", placeholder = ""),
# 查詢日期範圍
dateRangeInput("dates", label = h5("請選擇繪製的日期範圍"),
start = Sys.Date()-days(90),
end = Sys.Date(),
min = Sys.Date()-years(5),
max = Sys.Date(),
separator = "至"),
# 查詢按鈕
actionButton("action", label = "查詢"),
# 輸出警告
br(),
span(textOutput(outputId = "warningText"), style = "color:red"),
# 說明資料來源
br(),
tags$div(
"本網站數據來源: ",
tags$a(href = "https://finance.yahoo.com/", "Yahoo Finance")
),
),
# 主頁面
mainPanel(
HTML("<center>"),
h3("技術分析圖形"),
HTML("</center>"),
plotlyOutput("stockPlot"),
hr(),
HTML("<center>"),
h3("股價資訊表"),
HTML("</center>"),
dataTableOutput("stockData")
)
)
),
# 字體設定:使用CSS語法
tags$head(
tags$style(HTML("@import url('//fonts.googleapis.com/css?family=Noto+Sans+TC');
body, h2, h3, h4, h5, center {font-family: 'Noto Sans TC', sans-serif;}")))
)
ui <- secure_app(ui, theme = shinytheme("flatly"))
# Server
server <- function(input, output, session) {
# call the server part
# check_credentials returns a function to authenticate users
res_auth <- secure_server(
check_credentials = check_credentials(credentials)
)
output$auth_output <- renderPrint({
reactiveValuesToList(res_auth)
})
# 啟動查詢
observeEvent(input$action, {
# 讀取使用者設定資訊
stockCode <- isolate(input$stockCode)
print(stockCode)
stockCode <- ifelse(stockCode == "", "NA", stockCode) # 防呆機制
fromDate <- isolate(input$dates[1])
toDate <- isolate(input$dates[2])
# 下載股票資料
stockPriceData <- tq_get(stockCode,
get = "stock.price",
from = fromDate,
to = toDate)
# 判斷是否有下載到資料
if(is.na(stockPriceData)){
# 若未載到資料則提示警告訊息給使用者
output$warningText <- renderText({"請輸入正確的Yahoo Finance股票代碼唷!"})
}else{
# 清空警告訊息
output$warningText <- renderText({NULL})
output$stockPlot <- renderPlotly({
fig <- stockPriceData %>% plot_ly(x = ~date, type="candlestick",
open = ~open, close = ~close,
high = ~high, low = ~low)
fig <- fig %>% layout(title = "Basic Candlestick Chart")
})
# # 繪製技術分析圖形
# output$stockPlot <- renderPlot({
#
# stockPriceData <- stockPriceData %>%
# mutate(date = ymd(date),
# chg = ifelse(close > open, "up", "dn"), # 判斷當日上漲/下跌
# flat_bar = (open == close), # 判斷當日開盤價是否等於收盤價(繪圖需額外繪製)
# num = c(1:n())) # 樣本點編碼
#
# plotCode <- unique(stockPriceData$symbol)
# plotStartDate <- min(stockPriceData$date)
# plotEndDate <- max(stockPriceData$date)
#
# p <- ggplot(stockPriceData, aes(x = num)) + # 建立畫布
# geom_linerange(aes(ymin = low, ymax = high)) + # 繪製上影線和下影線
# geom_rect(aes(xmin = num-1/3 , xmax =num+1/3, ymin = pmin(open, close), # 繪製K棒實體部分
# ymax = pmax(open, close), fill = chg)) +
# scale_fill_manual(values = c("dn" = "forestgreen", "up" = "firebrick")) + # 改變K棒顏色，跌為綠色，漲為紅色
# scale_color_manual(values=c("blue", "orange", "green")) + # 改變移動平均線顏色(此處參考奇摩股市網的顏色)
# theme_bw() + # 將畫布底圖顏色改為白色
# labs(title = paste0(plotCode), # 加入標題及改變X和Y軸名稱
# subtitle = paste0("繪製期間: ", plotStartDate, " 至 ", plotEndDate),
# x="", y="") +
# theme(text = element_text(size = 16),
# plot.title = element_text(hjust = 0.5), # 將標題置中
# plot.subtitle = element_text(hjust = 0.5),
# plot.margin = margin(0.5, 1.3, 0, 0, "cm"),
# legend.position="bottom", # 把legend移到上面
# legend.title=element_blank()) + # 將legend的title改掉
# guides(fill = FALSE) # 將fill方式繪圖的legend拿掉
#
#
# # 繪製垂直線位置 表示比對範圍
# dataRowNum <- nrow(stockPriceData)
# p <- p +
# scale_x_continuous(breaks = seq(dataRowNum, 1,-ceiling(dataRowNum/5)), # 將x軸的標籤名稱更改為日期
# labels = stockPriceData$date[seq(dataRowNum, 1,-ceiling(dataRowNum/5))], expand = c(0, 0))
#
# # 若有開盤價=收盤價之交易日資料，因geom_rect無法繪製，要用geom_segment補繪
# if(any(stockPriceData$flat_bar)){
# p <- p +
# geom_segment(data = stockPriceData[stockPriceData$flat_bar,],
# aes(x = num-1/3, y = close, yend = close, xend = num+1/3))
# }
#
# return(p)
# })
# 輸出表格
output$stockData <- DT::renderDataTable({
datatable(stockPriceData %>% arrange(desc(date)))
})
}
})
}
# Run the application
shinyApp(ui = ui, server = server)
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