程式碼
library(shiny)
library(ggplot2)
library(dplyr)
library(DT)
shinyUI(fluidPage(
titlePanel("Analysis Produce Price"),
sidebarLayout(
# Input(s)
sidebarPanel(width = 2,
fileInput("file1", "Choose CSV File",
accept = c("text/csv",
"text/comma-separated-values,text/plain",
".csv")),
# Select variable for x-axis
uiOutput("x.selector"),
# Select variable for y-axis
uiOutput("y.selector"),
# Select color
uiOutput("col.selector"),
# Select point size
sliderInput(inputId = "size",
label = "Size:",
min = 0, max = 5,
value = 2),
# Select which markets on to plot
uiOutput("market.selector"),
# Show data table
uiOutput("showdata.selector")
),
# Output(s)
mainPanel(width = 10,
tabsetPanel(id = "tabspanel", type = "tabs",
tabPanel(title = "Plot",
plotOutput(outputId = "scatterplot")),
tabPanel(title = "Data",
DT::dataTableOutput(outputId = "table"))
)
)
)
))
library(shiny)
library(ggplot2)
library(dplyr)
library(DT)
shinyServer(function(input, output, session) {
price <- reactive({
myfile <- input$file1
if(is.null(myfile))
{return()}
mydata <- read.table(myfile$datapath, header = TRUE, sep = ",")
mydata <- mydata[, 1:9]
colnames(mydata) <- c("date", "market", "product", "high", "medium", "low", "price_avg", "volume", "value")
mydata$date <- as.Date(paste(as.numeric(substr(mydata$date, 1, 3)) + 1911, substr(mydata$date, 4, 9), sep = ""))
mydata$volume <- as.numeric(mydata$volume)
mydata$market <- as.character(mydata$market)
mydata$product <- as.character(mydata$product)
mydata <- mydata[!is.na(mydata), ]
return(mydata)
})
#Create UI condition
# Select variable for x-axis
output$x.selector <- renderUI({
selectInput(inputId = "x",
label = "X-axis:",
choices = names(price()),
selected = names(price())[1])
})
# Select variable for y-axis
output$y.selector <- renderUI({
selectInput(inputId = "y",
label = "Y-axis:",
choices = names(price()),
selected = names(price())[7])
})
# Select color
output$col.selector <- renderUI({
selectInput(inputId = "z",
label = "Color by:",
choices = names(price()),
selected = names(price())[3])
})
# Select which markets on to plot
output$market.selector <- renderUI({
checkboxGroupInput(inputId = "selected_market",
label = "Select market(s):",
choices = levels(factor(price()$market)),
selected = levels(factor(price()$market)))
})
# Show data table
output$showdata.selector <- renderUI({
checkboxInput(inputId = "show_data",
label = "Show data table",
value = TRUE)
})
# Create a subset of data filtering for selected markets
price_subset <- reactive({
req(input$selected_market)
filter(price(), market %in% input$selected_market)
})
# Create scatterplot object the plotOutput function is expecting
output$scatterplot <- renderPlot({
ggplot(data = price_subset(), aes_string(x = input$x, y = input$y, color = input$z)) +
geom_point(size = input$size)
})
# Update code below to render data table regardless of current state of input$show_data
output$table <- DT::renderDataTable({
DT::datatable(data = price()[, 1:9],
options = list(pageLength = 10),
rownames = FALSE)
})
# Display data table tab only if show_data is checked
observeEvent(input$show_data, {
if(input$show_data){
showTab(inputId = "tabspanel", target = "Data", select = TRUE)
} else {
hideTab(inputId = "tabspanel", target = "Data")
}
})
})
成果
下篇文章再來說說這個 Shiny 的功能
已經撰寫完成:
