R語言程式設計導論
林嶔 (Lin, Chin)
Lesson 13 R網頁App之基本介面函數介紹
第一節：讓你的App可以接受使用者上傳的檔案(1)
– 請在這裡下載練習用檔案
- 我們先練習讀取剛剛下載的範例檔(以下範例使用之路徑為“Example.txt”，請自行變更至適合的位置)
Data=read.table("Example.txt", header = TRUE) #讀取Example.txt，並存成"Data"這個物件(資料表格式)
head(Data, 10) #看"Data"這個資料表的前10個row
## Height Weight BMI Cholesterol Triglyceride
## 1 173.9095 69.30906 22.91623 196.8222 146.4670
## 2 171.1519 66.72796 22.77951 183.2731 152.1301
## 3 169.4500 69.74043 24.28855 193.6936 150.3699
## 4 164.4632 58.76008 21.72426 182.4610 140.6752
## 5 172.2093 64.85015 21.86743 186.5315 146.7873
## 6 166.4883 66.17726 23.87489 187.4913 165.1633
## 7 177.2201 92.62094 29.49057 211.4545 167.2411
## 8 177.6544 85.45003 27.07448 203.5222 166.3448
## 9 175.4248 89.43701 29.06267 208.3781 164.0171
## 10 163.2245 55.52598 20.84133 185.0684 149.5531
summary(Data) #看"Data"這個資料表的所有變項的基本資訊
## Height Weight BMI Cholesterol
## Min. :143.6 Min. : 35.15 Min. :13.66 Min. :161.7
## 1st Qu.:162.9 1st Qu.: 63.47 1st Qu.:22.54 1st Qu.:187.2
## Median :170.6 Median : 69.75 Median :24.62 Median :197.6
## Mean :168.7 Mean : 70.83 Mean :24.75 Mean :196.2
## 3rd Qu.:173.7 3rd Qu.: 80.53 3rd Qu.:27.21 3rd Qu.:204.8
## Max. :197.5 Max. :103.20 Max. :35.32 Max. :219.6
## Triglyceride
## Min. :114.0
## 1st Qu.:149.7
## Median :158.6
## Mean :158.6
## 3rd Qu.:167.0
## Max. :194.9
- 所以，我們將寫出一個簡單的App，只使用上面這些函數，讓使用者只要上傳檔案，就能看到他所上傳的資料之所有變項的基本資訊。
第一節：讓你的App可以接受使用者上傳的檔案(2)
library(shiny)
shinyUI(pageWithSidebar(
headerPanel("Summarize your data"),
sidebarPanel(
fileInput(inputId="files", label=h4("Upload your data file:"), multiple=FALSE, accept="text/plain"),
helpText("Note: you only can upload the .txt file."),
sliderInput("n", h4("Select the observed rows:"), min=5, max=20, value=10)
),
mainPanel(
verbatimTextOutput("summary"),
tableOutput("view")
)
))
library(shiny)
shinyServer(function(input, output) {
output$summary = renderPrint({
if (is.null(input$files)==TRUE) {return("You have to up load your data!!!")} else {
dat = read.table(input$files$datapath, header = TRUE)
summary(dat)
}
})
output$view = renderTable({
if (is.null(input$files)==TRUE) {return()} else {
dat = read.table(input$files$datapath, header = TRUE)
head(dat, input$n)
}
})
})
練習1：為你的App增加一張圖
- 還記得如何輸出圖形嗎? 你可能會用到下面三個函數:
– radioButtons() in ui.R(單選題)
– plotOutput() in ui.R(輸出圖形)
– renderPlot() in server.R(處理圖形)
- 我們可以利用plot()函數產生各變項之間的相關散佈圖(請在R Console之內試試看)
Data = read.table("Example.txt",header=TRUE) #讀取Example.txt，並存成"Data"這個物件(資料表格式)
head(Data, 10) #看"Data"這個資料表的前10個row
## Height Weight BMI Cholesterol Triglyceride
## 1 173.9095 69.30906 22.91623 196.8222 146.4670
## 2 171.1519 66.72796 22.77951 183.2731 152.1301
## 3 169.4500 69.74043 24.28855 193.6936 150.3699
## 4 164.4632 58.76008 21.72426 182.4610 140.6752
## 5 172.2093 64.85015 21.86743 186.5315 146.7873
## 6 166.4883 66.17726 23.87489 187.4913 165.1633
## 7 177.2201 92.62094 29.49057 211.4545 167.2411
## 8 177.6544 85.45003 27.07448 203.5222 166.3448
## 9 175.4248 89.43701 29.06267 208.3781 164.0171
## 10 163.2245 55.52598 20.84133 185.0684 149.5531
summary(Data) #看"Data"這個資料表的所有變項的基本資訊
## Height Weight BMI Cholesterol
## Min. :143.6 Min. : 35.15 Min. :13.66 Min. :161.7
## 1st Qu.:162.9 1st Qu.: 63.47 1st Qu.:22.54 1st Qu.:187.2
## Median :170.6 Median : 69.75 Median :24.62 Median :197.6
## Mean :168.7 Mean : 70.83 Mean :24.75 Mean :196.2
## 3rd Qu.:173.7 3rd Qu.: 80.53 3rd Qu.:27.21 3rd Qu.:204.8
## Max. :197.5 Max. :103.20 Max. :35.32 Max. :219.6
## Triglyceride
## Min. :114.0
## 1st Qu.:149.7
## Median :158.6
## Mean :158.6
## 3rd Qu.:167.0
## Max. :194.9
Color = "red"
plot(Data, col = Color)
練習-1 答案
library(shiny)
shinyUI(pageWithSidebar(
headerPanel("Summarize your data"),
sidebarPanel(
fileInput(inputId="files", label=h4("Upload your data file:"), multiple=FALSE, accept="text/plain"),
helpText("Note: you only can upload the .txt file."),
sliderInput("n", h4("Select the observed rows:"), min=5, max=20, value=10),
radioButtons("Color", "Select the color of histogram:", choices = c("Red" = "red", "Blue" = "blue", "Green" = "green"))
),
mainPanel(
verbatimTextOutput("summary"),
plotOutput("scatterPlot"),
tableOutput("view")
)
))
library(shiny)
shinyServer(function(input, output) {
DATA = reactive({
if (is.null(input$files)) {return()} else {
dat = read.table(input$files$datapath,header=T)
return(dat)
}
})
output$summary = renderPrint({
dat = DATA()
if (is.null(dat)) {return("You have to up load your data!!!")} else {
summary(dat)
}
})
output$scatterPlot = renderPlot({
dat = DATA()
if (is.null(dat)) {return()} else {
plot(dat,col=input$Color)
}
})
output$view = renderTable({
dat = DATA()
if (is.null(dat)) {return()} else {
head(dat,input$n)
}
})
})
第二節：完整的學習控制區的互動參數(1)
## Functions Inputs
## 1 checkboxGroupInput() selected values
## 2 checkboxInput() logical
## 3 dateInput() date
## 4 dateRangeInput() 2 dates
## 5 fileInput() file name
## 6 numericInput() numeric
## 7 radioButtons() selected value
## 8 selectInput() selected value(s)
## 9 sliderInput() numeric
## 10 submitButton() numeric
## 11 textInput() character
– 請在這裡下載基本物件範例
練習2：熟練地使用互動物件
– 請在這裡下載練習用檔案
- 可以允許使用者上傳範例檔案
- 在按下按鈕後可以畫出散布圖
- 讓使用者選擇散布圖的顏色
- 讓使用者能下載這張散布圖
- 有餘力的話，發揮你的創意增加控制bar
Data = read.table("Example.txt", header = TRUE) #讀取Example.txt，並存成"Data"這個物件(資料表格式)
Color = "red" #指定顏色
pdf("plot.pdf") #開啟一個pdf file，路徑為"plot.pdf"
plot(Data, col = Color) #將圖畫到開啟中的pdf file上去
dev.off() #關掉這個pdf file
練習2答案
library(shiny)
shinyUI(pageWithSidebar(
headerPanel("Ploting your data"),
sidebarPanel(
fileInput(inputId="files", label=h4("Upload your data file:"), multiple=FALSE, accept="text/plain"),
helpText("Note: you only can upload the .txt file."),
radioButtons("Color", "Select the color of histogram:", choices = c("Red" = "red", "Blue" = "blue", "Green" = "green"))
),
mainPanel(
plotOutput("scatterPlot"),
downloadButton("download", label = "Download plot", class = NULL)
)
))
library(shiny)
shinyServer(function(input, output) {
DATA = reactive({
if (is.null(input$files)) {return()} else {
dat = read.table(input$files$datapath,header=T)
return(dat)
}
})
output$scatterPlot = renderPlot({
dat = DATA()
if (is.null(dat)) {return()} else {
plot(dat,col=input$Color)
}
})
output$download = downloadHandler(
filename = function() {'plot.pdf'},
content = function(con) {
dat = DATA()
if (is.null(dat)) {return()} else {
pdf(con)
plot(dat,col=input$Color)
dev.off()
}
}
)
})
第三節：使控制區的參數隨著Server端的狀況而改變(1)
- 現在我們面臨一個問題，由於使用者上傳的檔案變數名稱是不特定的，假設我們想要做許多進階的分析將會受到變數名稱的限制。
– 所以，如果可以的話，我們希望左側的控制區可以出現一個選單，列出使用者所上傳的檔案的所有的變項名稱，並且讓使用者能選擇他想要分析的變項。
Data = read.table("Example.txt", header = TRUE)
head(Data, 10)
## Height Weight BMI Cholesterol Triglyceride
## 1 173.9095 69.30906 22.91623 196.8222 146.4670
## 2 171.1519 66.72796 22.77951 183.2731 152.1301
## 3 169.4500 69.74043 24.28855 193.6936 150.3699
## 4 164.4632 58.76008 21.72426 182.4610 140.6752
## 5 172.2093 64.85015 21.86743 186.5315 146.7873
## 6 166.4883 66.17726 23.87489 187.4913 165.1633
## 7 177.2201 92.62094 29.49057 211.4545 167.2411
## 8 177.6544 85.45003 27.07448 203.5222 166.3448
## 9 175.4248 89.43701 29.06267 208.3781 164.0171
## 10 163.2245 55.52598 20.84133 185.0684 149.5531
## Height Weight BMI Cholesterol
## Min. :143.6 Min. : 35.15 Min. :13.66 Min. :161.7
## 1st Qu.:162.9 1st Qu.: 63.47 1st Qu.:22.54 1st Qu.:187.2
## Median :170.6 Median : 69.75 Median :24.62 Median :197.6
## Mean :168.7 Mean : 70.83 Mean :24.75 Mean :196.2
## 3rd Qu.:173.7 3rd Qu.: 80.53 3rd Qu.:27.21 3rd Qu.:204.8
## Max. :197.5 Max. :103.20 Max. :35.32 Max. :219.6
## Triglyceride
## Min. :114.0
## 1st Qu.:149.7
## Median :158.6
## Mean :158.6
## 3rd Qu.:167.0
## Max. :194.9
– 還記得如何做linear regression吧？
Result = lm(Weight~Height, data = Data) #linear regression test
Result
##
## Call:
## lm(formula = Weight ~ Height, data = Data)
##
## Coefficients:
## (Intercept) Height
## -79.4208 0.8904
##
## Call:
## lm(formula = Weight ~ Height, data = Data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.2430 -6.6118 -0.7133 6.7464 25.6051
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -79.42076 15.28220 -5.197 1.11e-06 ***
## Height 0.89043 0.09039 9.851 2.55e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.552 on 98 degrees of freedom
## Multiple R-squared: 0.4975, Adjusted R-squared: 0.4924
## F-statistic: 97.04 on 1 and 98 DF, p-value: 2.552e-16
– 畫張散佈圖吧！
plot(Data$Height, Data$Weight, pch = 19)
abline(Result)
第三節：使控制區的參數隨著Server端的狀況而改變(2)
- 還記得之前學會的索引函數嗎?在Server端我們能呈現出所有的欄位名稱，並且利用這些名稱來建構迴歸分析
Data=read.table("Example.txt", header = TRUE)
colnames(Data) # 列出所有的欄位名稱
## [1] "Height" "Weight" "BMI" "Cholesterol" "Triglyceride"
colnames(Data)[1] # 列出第一個欄位名稱
## [1] "Height"
colnames(Data)[4] # 列出第四個欄位名稱(依此類推)
## [1] "Cholesterol"
Result1 = lm(Data[,"Weight"]~Data[,"Height"])
summary(Result1)
##
## Call:
## lm(formula = Data[, "Weight"] ~ Data[, "Height"])
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.2430 -6.6118 -0.7133 6.7464 25.6051
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -79.42076 15.28220 -5.197 1.11e-06 ***
## Data[, "Height"] 0.89043 0.09039 9.851 2.55e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.552 on 98 degrees of freedom
## Multiple R-squared: 0.4975, Adjusted R-squared: 0.4924
## F-statistic: 97.04 on 1 and 98 DF, p-value: 2.552e-16
- 接著我們要隆重介紹renderUI及uiOutput兩個函數，它們能讓我們先讀取檔案，再讓控制區出現檔案內的變數
第三節：使控制區的參數隨著Server端的狀況而改變(3)
library(shiny)
shinyUI(pageWithSidebar(
headerPanel("Linear regression for two continuous variables."),
sidebarPanel(
fileInput(inputId="files", label=h4("Upload your data file:"), multiple=FALSE, accept="text/plain"),
helpText("Note: you only can upload the .txt file."),
uiOutput("choose_columns1"), #這裡是關鍵
uiOutput("choose_columns2") #這裡是關鍵
),
mainPanel(
verbatimTextOutput("summary"),
plotOutput("plot",width = "500px", height = "500px")
)
))
library(shiny)
shinyServer(function(input, output) {
DATA <- reactive({
if (is.null(input$files)) {return()} else {
dat <- read.table(input$files$datapath,header=T)
return(dat)
}
})
output$choose_columns1 <- renderUI({ #這裡是關鍵
dat = DATA()
if (is.null(dat)) {return()} else {
colnames <- colnames(dat)
selectInput("Y", h4("Choose a dependence variable:"), choices = colnames)
}
})
output$choose_columns2 <- renderUI({ #這裡是關鍵
dat = DATA()
if (is.null(dat)|is.null(input$Y)) {return()} else {
colnames <- colnames(dat)
selectInput("X", h4("Choose a independence variable:"), choices = colnames[which(colnames!=input$Y)])
}
})
output$summary <- renderPrint({
dat = DATA()
if (is.null(dat)|is.null(input$Y)|is.null(input$X)) {return()} else {
X <- dat[,input$X] #這裡是關鍵
Y <- dat[,input$Y] #這裡是關鍵
Result=lm(Y~X)
return(summary(Result))
}
})
output$plot <- renderPlot({
dat = DATA()
if (is.null(dat)|is.null(input$Y)|is.null(input$X)) {return()} else {
X <- dat[,input$X] #這裡是關鍵
Y <- dat[,input$Y] #這裡是關鍵
plot(X,Y,pch=19)
abline(lm(Y~X),col="black")
}
})
})
練習3：換個分析方法
- 兩個連續變項，除了能做linear regression之外，還有Correlation可以做。
Result1 = cor.test(Data[,"Weight"], Data[,"Height"], method = "pearson") # Pearson correlation
Result1
##
## Pearson's product-moment correlation
##
## data: Data[, "Weight"] and Data[, "Height"]
## t = 9.8507, df = 98, p-value = 2.552e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.5907925 0.7920433
## sample estimates:
## cor
## 0.7053591
Result2 = cor.test(Data[,"Weight"], Data[,"Height"], method = "spearman") # Spearman correlation
Result2
##
## Spearman's rank correlation rho
##
## data: Data[, "Weight"] and Data[, "Height"]
## S = 49490, p-value < 2.2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.7030303
– 請依照剛剛的範例，設計一個選單，讓使用者能指定要使用Pearson correlation還是Spearman correlation。
– 當然，能讓使用者改個散布圖的顏色也是必須的…
練習3答案
library(shiny)
shinyUI(pageWithSidebar(
headerPanel("Linear regression for two continuous variables."),
sidebarPanel(
fileInput(inputId="files", label=h4("Upload your data file:"), multiple=FALSE, accept="text/plain"),
helpText("Note: you only can upload the .txt file."),
uiOutput("choose_columns1"), #這裡是關鍵
uiOutput("choose_columns2"), #這裡是關鍵
radioButtons("method", "What is the method to analysis?", choices = c("Pearson correlation" = "pearson", "Spearman correlation" = "spearman")),
radioButtons("Color", "Select the color of histogram:", choices = c("Red" = "red", "Blue" = "blue", "Green" = "green"))
),
mainPanel(
verbatimTextOutput("summary"),
plotOutput("plot",width = "500px", height = "500px")
)
))
library(shiny)
shinyServer(function(input, output) {
DATA <- reactive({
if (is.null(input$files)) {return()} else {
dat <- read.table(input$files$datapath,header=T)
return(dat)
}
})
output$choose_columns1 <- renderUI({ #這裡是關鍵
dat = DATA()
if (is.null(dat)) {return()} else {
colnames <- colnames(dat)
selectInput("Y", h4("Choose a dependence variable:"), choices = colnames)
}
})
output$choose_columns2 <- renderUI({ #這裡是關鍵
dat = DATA()
if (is.null(dat)|is.null(input$Y)) {return()} else {
colnames <- colnames(dat)
selectInput("X", h4("Choose a independence variable:"), choices = colnames[which(colnames!=input$Y)])
}
})
output$summary <- renderPrint({
dat = DATA()
if (is.null(dat)|is.null(input$Y)|is.null(input$X)) {return()} else {
X <- dat[,input$X] #這裡是關鍵
Y <- dat[,input$Y] #這裡是關鍵
Result=cor.test(X,Y,method=input$method)
return(Result)
}
})
output$plot <- renderPlot({
dat = DATA()
if (is.null(dat)|is.null(input$Y)|is.null(input$X)) {return()} else {
X <- dat[,input$X] #這裡是關鍵
Y <- dat[,input$Y] #這裡是關鍵
plot(X,Y,pch=19,col=input$Color)
abline(lm(Y~X),col="black")
}
})
})
小結
- 控制區的基本互動參數應已熟悉
- 更熟悉上傳/下載檔案的功能
- renderUI及uiOutput
- 認識了更多UI模組
- 了解如何做UI的優化
- 之後的上課，我們將加強server端的運算效率，以及更多物件的呈現。