KPI dashboard
A Shiny dashboard with KPI value boxes, sidebar filters, a chart and a top-5 table, built with bslib on R’s airquality data. Free app.R to copy, edit and run.
Data explorer template for R Shiny
Pick a dataset and two columns to get a scatter plot, a searchable DT table and a summary, with axis choices that update per dataset. Free R Shiny template.
About this template
A small exploratory data analysis tool. Choose one of R’s built-in datasets, pick the columns for the X and Y axes, and switch between a scatter plot, an interactive table and a summary in tabs.
The axis menus are filled from the server with updateSelectInput(), so they always list the numeric columns of the chosen dataset. Add your own data frames to the list in the setup code.
What's inside
- Inputs that update each other with
observe()andupdateSelectInput() - A tabbed card (
navset_card_tab()) with plot, table and summary tabs - An interactive, searchable table with
DT::DTOutput() - An optional trend line from
lm(), switched with a checkbox req()to wait until the inputs are valid
The complete app.R
Download# Generated by DropToApp
library(shiny)
library(bslib)
# Datasets that come with R
datasets <- list(
"Cars (mtcars)" = mtcars,
"Flowers (iris)" = iris,
"Earthquakes (quakes)" = quakes,
"Air quality (airquality)" = airquality
)
ui <- page_sidebar(
title = "Data explorer",
sidebar = sidebar(
selectInput("dataset", "Dataset", choices = names(datasets)),
selectInput("x", "X axis", choices = NULL),
selectInput("y", "Y axis", choices = NULL),
checkboxInput("trend", "Add a trend line")
),
navset_card_tab(
nav_panel("Plot", plotOutput("scatter")),
nav_panel("Table", DT::DTOutput("table")),
nav_panel("Summary", verbatimTextOutput("summary"))
)
)
server <- function(input, output, session) {
# Inputs: input$dataset, input$x, input$y, input$trend
data <- reactive(datasets[[input$dataset]])
# Offer the numeric columns of the chosen dataset as axes
observe({
numeric_cols <- names(Filter(is.numeric, data()))
updateSelectInput(session, "x", choices = numeric_cols, selected = numeric_cols[1])
updateSelectInput(session, "y", choices = numeric_cols, selected = numeric_cols[2])
})
output$scatter <- renderPlot({
d <- data()
req(input$x %in% names(d), input$y %in% names(d))
plot(d[[input$x]], d[[input$y]], xlab = input$x, ylab = input$y,
pch = 19, col = "#007bc288")
if (input$trend) abline(lm(d[[input$y]] ~ d[[input$x]]), col = "#e74c3c", lwd = 2)
})
output$table <- DT::renderDT({
data()
})
output$summary <- renderPrint({
summary(data())
})
}
shinyApp(ui, server)
How to run it
- In your browser: run it with shinylive. R runs on your computer with webR; the first start downloads R and the packages, which can take a minute.
- In R or RStudio: save the code as
app.R, install the packages once withinstall.packages(c("shiny", "bslib", "DT")), then runshiny::runApp()in the folder with the file. - In DropToApp: open the template in the editor to change the layout by drag and drop or edit the code, and export your own
app.R.
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