One JSON will be written for each session of the application.
Value
A list that can be used in track_usage().
Examples
library(shiny)
library(shinylogs)
# temp directory for writing logs
tmp <- tempdir()
# when app stop,
# navigate to the directory containing logs
onStop(function() {
browseURL(url = tmp)
})
#> function ()
#> {
#> .callbacks$remove(id)
#> }
#> <bytecode: 0x563eb7717eb0>
#> <environment: 0x563eb8dbadd8>
# Classic Iris clustering with Shiny
ui <- fluidPage(
headerPanel("Iris k-means clustering"),
sidebarLayout(
sidebarPanel(
selectInput(
inputId = "xcol",
label = "X Variable",
choices = names(iris)
),
selectInput(
inputId = "ycol",
label = "Y Variable",
choices = names(iris),
selected = names(iris)[[2]]
),
numericInput(
inputId = "clusters",
label = "Cluster count",
value = 3,
min = 1,
max = 9
)
),
mainPanel(
plotOutput("plot1")
)
)
)
server <- function(input, output, session) {
# Store JSON with logs in the temp dir
track_usage(
storage_mode = store_json(path = tmp)
)
# classic server logic
selectedData <- reactive({
iris[, c(input$xcol, input$ycol)]
})
clusters <- reactive({
kmeans(selectedData(), input$clusters)
})
output$plot1 <- renderPlot({
palette(c("#E41A1C", "#377EB8", "#4DAF4A", "#984EA3",
"#FF7F00", "#FFFF33", "#A65628", "#F781BF", "#999999"))
par(mar = c(5.1, 4.1, 0, 1))
plot(selectedData(),
col = clusters()$cluster,
pch = 20, cex = 3)
points(clusters()$centers, pch = 4, cex = 4, lwd = 4)
})
}
if (interactive())
shinyApp(ui, server)