我使用 RODBC 处理来自R中SQL Server的数据,在获得我的结果后,我创建 ShinyApp 来部署我的结果但是我想从我的SQL获取数据直接查询而不将我的结果导出到Excel,然后将其导入闪亮,我该怎么做?
Test <- odbcDriverConnect("driver={SQL Server};server=localhost;database=Fakahany;trusted_connection=true")
Orders<- sqlQuery(Test,"
SELECT
WHWorkOrderHeaderId
, OtherLangDescription
FROM Warehouse.WHWorkOrderDetails
INNER JOIN Warehouse.WHWorkOrderHeader AS WHH
ON Warehouse.WHWorkOrderDetails.WHWorkOrderHeaderId = WHH.ID
INNER JOIN Warehouse.StockItems
ON Warehouse.WHWorkOrderDetails.StockItemId = Warehouse.StockItems.Id
WHERE Type = 'IO'
ORDER BY OtherLangDescription ASC")
#Creating the correlations
Orders$OtherLangDescription <- as.factor(Orders$OtherLangDescription)
orderList <- unique(Orders$OtherLangDescription)
ListId <- lapply(orderList, function(x) subset(Orders, OtherLangDescription == x)$WHWorkOrderHeaderId)
Initial_Tab <- lapply(ListId, function(x) subset(Orders, WHWorkOrderHeaderId %in% x)$OtherLangDescription)
Correlation_Tab <- mapply(function(Product, ID) table(Product)/length(ID),
Initial_Tab, ListId)
colnames(Correlation_Tab) <- orderList
cor_per<- round(Correlation_Tab*100,2)
DF<-data.frame(row=rownames(cor_per)[row(cor_per)], col=colnames(cor_per)[col(cor_per)], corr=c(cor_per))
这是我的应用代码:
#loading Packages
library(RODBC)
library(shiny)
library(rsconnect)
ui <- fluidPage(
titlePanel("Item Correlation"),
sidebarPanel(
selectInput("Item2","Select Item",choices= DF$FirstItem),
h6("Powerd By:"),
img(src='edrak.png',height='50px',width='110px')
# ,selectInput("Item","SelectItem",choices= DF$col)
),
mainPanel(
tableOutput("Itemcorr")
)
)
server <- function(input,output){
output$Itemcorr <- renderTable({
subset(DF, DF$FirstItem == input$Item2)
})
}
shinyApp(ui, server)
答案 0 :(得分:0)
这应该做你想要的。
library(RODBCext)
library(shiny)
ui <- shinyUI(
pageWithSidebar(
headerPanel("Hide Side Bar example"),
sidebarPanel(
textInput("CATEGORY", "Enter CATEGORY below"),
submitButton(text="Submit")
),
mainPanel(
tabsetPanel(
tabPanel("Data", tableOutput("tbTable"))
)
)
)
)
server <- function(input, output, session)
{ # NOTE THE BRACE HERE
myData <- reactive({
req(input$CATEGORY)
#connect to database
dbhandle = odbcDriverConnect('driver={SQL Server};server=Server_Name;database=Database_Name;trusted_connection=true')
#build query
query = "SELECT * FROM [Your_Table] where [CATEGORY] = ?"
#store results
res <- sqlExecute(channel = dbhandle,
query = query,
data = list(input$CATEGORY),
fetch = TRUE,
stringsAsFactors = FALSE)
#close the connection
odbcClose(dbhandle)
#return results
res
})
output$tbTable <-
renderTable(myData())
} # AND NOTE THE CLOSING BRACE HERE
shinyApp(ui = ui, server = server)
您可能也想考虑这一点。
library(shiny)
library(RODBCext)
shinyApp(
ui =
shinyUI(
fluidPage(
uiOutput("select_category"),
tableOutput("display_data")
# plotOutput("plot_data")
)
),
# server needs the function; looks ok
server = shinyServer(function(input, output, session)
{
# A reactive object to get the query. This lets you use
# the data in multiple locations (plots, tables, etc) without
# having to perform the query in each output slot.
QueriedData <- reactive({
req(input$showDrop)
ch <- odbcDriverConnect("driver={SQL Server};server=Server_Name;database=DATABASE_NAME;trusted_connection=true")
showList <- sqlExecute(ch, "SELECT * FROM [Your_Table] WHERE Category = ?",
data = list(Category = input$showDrop),
fetch = TRUE,
stringsAsFactors = FALSE)
odbcClose(ch)
showList
})
# The select input control. These can be managed dynamically
# from the server, and then the control send back to the UI
# using `renderUI`
output$select_category <- renderUI({
ch <- odbcDriverConnect("driver={SQL Server};server=Server_Name;database=DATABASE_NAME;trusted_connection=true")
showList <- sqlExecute(ch, "Select Distinct Category From [Your_Table] Order by Category",
fetch = TRUE,
stringsAsFactors = FALSE)
odbcClose(ch)
selectInput(inputId = "showDrop",
label = "Select Asset",
showList$Category)
})
# Display the data in a table
output$display_data <- renderTable({
QueriedData()
})
# Display a plot
# output$plot_data <-
# renderPlot({
# plot(QueriedData()) # fill in the plot code you want to use.
# })
})
)