Simon Smart

@simonsmart.bsky.social

196 Followers 258 Following

✨ Research Software Engineer at University of Birmingham 🥔 Former spudologist 🌱 Grows plants 🚲 Rides bikes 🍺 Brews beer 🌐 https://github.com/simon-smart88

configured_data <- setup_load("my_data.csv", outcome = "continuous") |> setup_configure(reference_treatment = "Placebo", effects = "random", outcome_measure = "MD", ranking_option = "good", seed = 123) freq_forest(configured_data) |> write_plot("frequentist_forest_plot.pdf") bayes_model(configured_data) |> bayes_forest() |> write_plot("bayesian_forest_plot.pdf")
Example Quarto and html reports produced by the app
A screenshot of the app running show options in the sidebar and two forest plots displayed side by side
Poster for the seminar: MetaInsight v7: Making a comprehensive app for network meta analysis reproducible
f <- function(very_fast, incredibly_dangerous){ data.frame("very_fast" = very_fast, "incredibly_dangerous" = incredibly_dangerous) } df <- f(v = 1, i = 2) df$very * df$inc df$very_broken <- 3 df$very * df$inc
'<svg><text x="0" y="200" style="font-size: 100px;"> Hello world</text></svg>' |> charToRaw() |> rsvg::rsvg_svg(NULL) |> rawToChar()
==> devtools::test() ℹ Testing metainsight Error in load_imports(path) : The package "mirai" is required. Calls: <Anonymous> ... load_imports -> deps_check_installed -> check_installed Execution halted Exited with status 1.
Disaggregation regression produces high resolution predictions from data aggregated into polygons making it well suited to epidemiological modelling. Disagapp makes the process simple and easy! Join Simon Smart and Tim Lucas for this introductory workshop. On Teams 14:00-16:00 UTC 16 September 2025 See more details and sign up via the QR code
library(shiny) library(bslib) library(mirai) daemons(1) onStop(function() daemons(0)) ui <- page_fluid( p("The time is ", textOutput("current_time", inline = TRUE)), hr(), numericInput("n", "Sample size (n)", 100), numericInput("delay", "Seconds to take for plot", 5), actionButton("btn", "Go"), textOutput("progress_log"), plotOutput("plot"), ) my_func <- function(x, y, file){ writeLines(paste0("Starting..."), file) for (i in 1:y){ Sys.sleep(1) writeLines(paste0("Step ", i, " complete"), file) } writeLines(paste0("Finished"), file) runif(x) } server <- function(input, output, session) { progress_file <- tempfile() output$current_time <- renderText({ invalidateLater(1000) format(Sys.time(), "%H:%M:%S %p") }) task_var <- NULL task <- ExtendedTask$new( function(...) task_var <<- mirai(run(...), environment(), .args = list(run = my_func)) ) observeEvent(input$btn, { if (task$status() == "running"){ mirai::stop_mirai(task_var) } task$invoke(input$n, input$delay, progress_file)} ) output$plot <- renderPlot({ if (task$status() == "success"){ hist(task$result()) } }) progress_reader <- reactiveFileReader(100, session, progress_file, readFunc = function(file) { if (file.exists(file)) { readLines(file) } } ) output$progress_log <- renderPrint({ cat(paste(progress_reader(), collapse = "\n")) }) } shinyApp(ui = ui, server = server)
Flowchart of the suggested workflow for developing modules, prototyped in an rmarkwdown, transferred to a function, unit tests written, develop the shiny module, reproduce the module in rmarkdown and then write end-to-end tests.
A graph showing the proportion of academic apps published in 2023 that cite packages, are deployed, provide instructions, provide logging, are reproducible, can be saved and loaded, are open source, include functions, are modular, are packaged and include unit and end-to-end tests.
A screenshot of the module guidance documentation inside the demonstration app.
A screenshot of an rmarkdown document reproducing an analysis in shinyscholar
A screenshot of an R shiny application created by shinyscholar, with user inputs on the left sidebar, a tab panel to move between components and a main panel featuring a map, table and results.