Data explorer
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KPI dashboard template for R Shiny
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.
About this template
A classic dashboard layout: filters in a sidebar, three key numbers in value boxes across the top, and a chart and a table underneath. It uses the airquality dataset that comes with R (daily measurements in New York, May to September 1973), so it runs as is.
Every number updates as you change the filters. Swap in your own data in the setup code and the reactive filter in the server, and the layout keeps working.
What's inside
- Value boxes whose values come from the server with
textOutput() - A
reactive()that filters the data once, shared by every output - A checkbox group and a slider as filters, with all months ticked to start
- A full-screen card with a base R plot, and a table of the hottest days
- The Flatly Bootswatch theme via
bs_theme()
The complete app.R
Download# Generated by DropToApp
library(shiny)
library(bslib)
# Daily air quality in New York, May to September 1973 (built into R)
aq <- airquality
aq$Date <- as.Date(paste(1973, aq$Month, aq$Day, sep = "-"))
# Average of x, rounded, or "-" when there is nothing to average
avg <- function(x, unit = "") {
if (length(x) == 0 || all(is.na(x))) return("-")
paste0(round(mean(x, na.rm = TRUE), 1), unit)
}
ui <- page_sidebar(
title = "Air quality dashboard",
sidebar = sidebar(
title = "Filters",
checkboxGroupInput(
"months",
"Months",
choices = c("May", "June", "July", "August", "September"),
selected = c("May", "June", "July", "August", "September")
),
sliderInput(
"min_temp",
"Minimum temperature (F)",
min = 56,
max = 97,
value = 56
)
),
theme = bs_theme(bootswatch = "flatly"),
layout_columns(
value_box(
title = "Days",
value = textOutput("n_days"),
theme = "primary"
),
value_box(
title = "Average temperature",
value = textOutput("avg_temp"),
theme = "warning"
),
value_box(
title = "Average ozone (ppb)",
value = textOutput("avg_ozone"),
theme = "success"
)
),
card(
full_screen = TRUE,
card_header("Daily maximum temperature"),
plotOutput("temp_plot", height = "300px")
),
card(card_header("Hottest days"), tableOutput("hottest"))
)
server <- function(input, output, session) {
# Inputs: input$months, input$min_temp
filtered <- reactive({
keep <- aq$Month %in% match(input$months, month.name) & aq$Temp >= input$min_temp
aq[keep, ]
})
output$n_days <- renderText(nrow(filtered()))
output$avg_temp <- renderText(avg(filtered()$Temp, " F"))
output$avg_ozone <- renderText(avg(filtered()$Ozone))
output$temp_plot <- renderPlot({
d <- filtered()
validate(need(nrow(d) > 0, "No days match the filters."))
plot(d$Date, d$Temp, type = "h", lwd = 3, col = "#2c3e50",
xlab = "", ylab = "Temperature (F)")
})
output$hottest <- renderTable({
d <- filtered()
d <- d[order(-d$Temp), c("Date", "Temp", "Ozone", "Wind")]
d$Date <- format(d$Date, "%d %B")
head(d, 5)
})
}
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")), 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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