How to Create Interactive Dashboards with DBPlot

How to Create Interactive Dashboards with dbplot

Overview

dbplot is an R package that leverages dplyr to compute aggregations inside databases and returns either ggplot objects or the SQL calculations. Use it to keep heavy aggregation in-database and render lightweight plots in R or Shiny dashboards.

When to use dbplot

Large datasets stored in SQL (Postgres, MySQL, SQLite, etc.)
Need to aggregate/ bin data before plotting to avoid pulling full tables
Building R/Shiny dashboards where responsiveness matters

Key concepts

dbplyr/tbl: connect to a remote table with dbplyr and treat it like a dplyr table.
In-database aggregation: use dbplot helpers to compute binning and summaries in SQL.
Output modes: dbplot can return a ggplot, the aggregated table (so you can collect() then plot), or the SQL/formula used for binning.

Minimal workflow (R + dbplot + ggplot2)

Connect to DB (DBI + dbplyr)

r
con <- DBI::dbConnect(RPostgres::Postgres(), dbname=“mydb”, host=”…”)
tbldb <- dplyr::tbl(con, “events”)

Use dbplot to aggregate and return a ggplot (example: time series)
r
library(dbplot); library(ggplot2) p <- tbl_db %>% dbplot_line(time_col = event_date, value = metric_col, summaryfun = mean) print(p)

For custom control, get aggregated data then plot
r
agg <- tbl_db %>% dbplot_histogram(db_col = value, bins = 30, return = “data”) %>% dplyr::collect() ggplot(agg, aes(x = bin, y = count)) + geomcol()

Integrating with Shiny for interactivity

Server: run dbplot aggregation when inputs change; collect() only the aggregated results.

UI: expose controls (date range, bin count, grouping, metrics).

Use plotOutput/plotly::ggplotly for zoom/hover or renderPlot for base ggplot.

Debounce inputs (e.g., shiny::debounce) to avoid repeated DB calls.

Cache recent query results (memoise or reactiveVal) for snappier UX.

Performance tips

Push as much aggregation and filtering into the database (WHERE, GROUP BY).

Limit returned rows: set sensible bin counts / summarizations.

Ensure indexes on filter/group columns.

For repeated queries, create indexed summary tables or materialized views.

Monitor query plans and optimize long-running SQL generated by dbplyr/dbplot.

Example dashboard components to include

Time-range selector (preset ranges + custom)

Granularity control (daily/weekly/monthly or bin count)

Metric selector (mean, sum, count)

Group-by selector (category / user segment)

Export button (CSV of aggregated data)

Quick example: Shiny server snippet

r
server <- function(input, output) { agg_data <- reactive({ tbl_db %>% filter(event_date >= input$start, event_date <= input$end) %>% dbplot_line(time_col = event_date, value = input$metric, summary_fun = input$agg, return = “data”) %>% collect() }) output$plot <- renderPlot({ ggplot(agg_data(), aes(x = time, y = value)) + geom_line() }) }

Resources

CRAN dbplot page (package docs and reference)

dbplot GitHub (examples and issues)

Posit guides on creating visualizations from databases

If you want, I can generate a full Shiny app template (ui + server) using dbplot for a specific schema — tell me your DB type and table column names.

How to Create Interactive Dashboards with DBPlot