VizModules

R-CMD-check pkgdown

This package utilizes various viz packages (currently dittoViz and plotthis along with native plotting functions) to create interactivity-first Shiny modules for common plot types, designed to serve as building blocks for Shiny apps and as the basis for more complex/specialized modules.

These modules contain all possible functionality for each plot with some additional parameters that make use of the interactive features of plotly, e.g. interactive text annotations, arbitrary shape annotations, multiple download formats, etc.

The modules provide comprehensive plot control for app users, allowing for convenient aesthetic customizations and publication-quality images. They also provide developers a way to dramatically save time and reduce complexity of their plotting code or a flexible base to build more specialized Shiny modules upon.

Install

# CRAN
install.packages("VizModules")

# Development version
remotes::install_github("j-andrews7/VizModules")

Quick Start

Using Modules in Your Own App

To use a module in your own app, simply call the *InputsUI(), *OutputUI(), and *Server() functions for the module you want to use. For example, to use the ScatterPlot module from dittoViz, you would do something like this:

library(VizModules)

ui <- fluidPage(
    sidebarLayout(
        sidebarPanel(
            dittoViz_scatterPlotInputsUI(
                "cars",
                mtcars,
                defaults = list(
                    x.by = "wt",
                    y.by = "mpg",
                    color.by = "cyl"
                )
            )
        ),
        mainPanel(dittoViz_scatterPlotOutputUI("cars"))
    )
)

server <- function(input, output, session) {
    dittoViz_scatterPlotServer(
        "cars",
        data = reactive(mtcars)
    )
}

shinyApp(ui, server)

Every module uses the same trio of functions: *InputsUI() for controls, *OutputUI() for the plot, and *Server() for the logic. The separation of InputsUI and OutputUI allows you to place input controls and the actual plot wherever you’d like.

Use defaults to pre-fill inputs, and hide.inputs/hide.tabs to hide controls while keeping their values so you can enforce app-level defaults without exposing them.

Modules built on plotting functions from other packages expose most of the underlying arguments. The module input help pages (e.g., ?dittoViz_scatterPlotInputsUI, ?plotthis_AreaPlotInputsUI) list what is wired through and any omissions; cross-reference the underlying plot docs (?dittoViz::scatterPlot, ?plotthis::AreaPlot, etc.) to see the full parameter set.

Example Apps for Each Module

Every module has a corresponding *App() function that creates a complete Shiny app showcasing the module’s functionality with example data. For instance, plotthis_BarPlotApp() creates an app BarPlot module. You can run these apps directly to explore the module’s features and see how it works in a full Shiny context.

library(VizModules)
# Using built-in example data (or upload your own file in the app)
plotthis_BarPlotApp()

# Providing your own data
df <- data.frame(
    category = c("A", "B", "C"),
    value = c(10, 20, 15),
    group = c("X", "Y", "X")
)

plotthis_BarPlotApp(data = df)

App Factory

Every built-in *App() convenience function (e.g. plotthis_BarPlotApp(), linePlotApp()) is a thin wrapper around createModuleApp() with sensible default data. You can also pass your own custom wrapper module functions to createModuleApp() for rapid prototyping after defining the UI and server functions. All *App() wrappers and createModuleApp() accept defaults, hide.inputs, and hide.tabs so you can pre-fill or hide controls when testing a module in isolation; see vignette("defaults-and-hiding", package = "VizModules").

library(VizModules)

app <- createModuleApp(
    inputs_ui_fn = plotthis_BarPlotInputsUI,
    output_ui_fn = plotthis_BarPlotOutputUI,
    server_fn    = plotthis_BarPlotServer,
    data_list    = list("cars" = mtcars),
    title        = "My Bar Plot"
)

runApp(app)

Figure Builder App

The Figure Builder is now a fully reusable, namespaced Shiny module (figureBuilderUI() / figureBuilderServer()) that turns the plot modules into a free-form figure builder. It can be launched as a standalone app, embedded inside a larger app, or even instantiated more than once on a single page. Launch the standalone app with figureBuilderApp():

library(VizModules)

# Launch with the bundled example datasets and all modules
figureBuilderApp()

# Or seed it with your own datasets
figureBuilderApp(data_list = list("iris" = iris, "mtcars" = mtcars))

figureBuilderApp() accepts data_list to seed datasets, module_registry to add custom modules, and return_components = TRUE to get separate ui/server objects instead of a shinyApp(). See ?figureBuilderApp for details.

Or try the hosted example.

The Figure Builder is also a self-contained Shiny module, so you can embed it in a larger app (and even use more than one instance on a page) with figureBuilderUI() / figureBuilderServer(), just like the plot modules:

library(VizModules)

ui <- fluidPage(
    figureBuilderUI("figure_builder")
)

server <- function(input, output, session) {
    figureBuilderServer("figure_builder")
}

shinyApp(ui, server)

It allows you to interactively compose complicated figures using the modules in a single page:

Building Custom Wrapper Modules

The modules in VizModules are designed to be composed and extended. You can build higher-level modules that add custom logic while reusing the full functionality of the base modules. Many internal helpers for axes, faceting, and layouts are now exported to support this, along with the defaults/hide.inputs/hide.tabs controls covered in vignette("defaults-and-hiding", package = "VizModules").

For more details, see vignette("custom-modules", package = "VizModules").

Custom Shiny Inputs

Beyond the standard shiny::*Input widgets, VizModules ships two reusable custom Shiny inputs that are used throughout the package and are available for your own apps: multiColorPicker() and multiDynamicInput(). See vignette("custom-shiny-inputs", package = "VizModules") for full details.

multiColorPicker() assigns a color to each level of a discrete variable, either by applying a named palette to every group at once or by fine-tuning individual groups with a color picker and an editable hex field. It returns a named character vector of hex colors keyed by group and can be updated from the server with updateMultiColorPicker().

multiDynamicInput() is a general-purpose widget that lets users dynamically add and remove rows of heterogeneous inputs (e.g. a color, a numeric, and a select per row). It returns a named list of rows and can be updated from the server with updateMultiDynamicInput().

Modules Provided

Currently, VizModules contains a functional Shiny module for the following visualization functions:

dittoViz

plotthis

Plotting Functions Defined in VizModules

Via direct implementation with plotly.

Statistical Testing

The BoxPlot, ViolinPlot, and yPlot modules include a Stats tab that adds pairwise statistical testing with bracket annotations directly on the plotly figure. The underlying helpers (compute_pairwise_stats(), create_stat_annotations(), apply_stat_annotations(), generate_pair_strings(), parse_pair_strings()) are exported so you can add the same bracket annotations to any custom plotly figure. See vignette("statistical-testing", package = "VizModules").

Export Summary Data

collect_source_data() collects the interactive plot as HTML, its plot data, pairwise testing statistics (if applied), and UI input values into a single list, and create_source_download_handler() turns that into a compact zip folder of summary data for the output plot. create_source_download_handler() also accepts a named list of summaries (one per plot), which is how the Figure Builder bundles every plot on the canvas into one download.

Supported Tests

Features

Data Format for Paired Tests

When using paired tests (Wilcoxon signed-rank or paired t-test), each group must have the same number of observations in corresponding order. Data should be sorted so that paired samples align row-by-row within each group.

Modules Planned

dittoViz

dittoViz is under active development, so additional modules may be added as more visualization functions are added.

Contributing a New Module

To contribute a new module to the package, see the vignette for clear guidelines: vignette("adding-a-new-module", package = "VizModules")

Available Modules

linePlot:

plotthis_AreaPlot:

(Source Plotting Function)

plotthis_BoxPlot:

(Source Plotting Function)

plotthis_DensityPlot:

(Source Plotting Function)

dumbbellPlot:

plotthis_Histogram:

(Source Plotting Function)

parallelCoordinatesPlot:

piePlot:

radarPlot:

dittoViz_ScatterPlot:

(Source Plotting Function)

plotthis_SplitBarPlot:

(Source Plotting Function)

plotthis_ViolinPlot:

(Source Plotting Function)

dittoViz_yPlot:

(Source Plotting Function)

plotthis_DotPlot:

(Source Plotting Function)

figureBuilder:

AI Usage Statement

The developers made use of AI tools (e.g. GitHub Copilot, Claude Code) for code generation, documentation writing, and test creation. AI assistance was used to accelerate development after the initial module scaffolding and structure was in place, but all AI-generated content was reviewed and edited by human eyeballs/hands to ensure accuracy and quality. Our own hands are all over this project, and we are invested in it. Any inaccuracies, bugs, or issues are attributable to us, and we welcome contributions to help improve the package.

Generative AI tools (GitHub Copilot, ChatGPT, Claude, Gemini, Cursor, etc.) are explicitly welcome for building Shiny apps with these modules in addition to creating new modules. To do so, we recommend prefixing prompts with the below to aid LLM usage (or adding it to a file and attaching it directly).

LLM Instructions

Copy the prompt below into your LLM or save it in a file (Copilot, ChatGPT, Claude, Gemini, Cursor, etc.) before asking it to build a Shiny app with VizModules. It points the model to the authoritative, locally-installed sources of truth so it can use the package correctly.

You are helping me build a Shiny application using the installed R package VizModules, which provides interactivity-first, plotly-based Shiny modules for common plot types. Before writing code, ground yourself in the package’s own documentation rather than guessing at the API.

Core concept. Every module is a trio of functions that share an id: *InputsUI(id, ...) renders the controls, *OutputUI(id) renders the plotly output, and *Server(id, data, ...) holds the logic. InputsUI and OutputUI are separate so controls and plot can be placed anywhere in the layout. data is passed to the server as a reactive(). Use the defaults argument to pre-fill inputs and hide.inputs/hide.tabs to lock values while hiding their controls.

Where to look (all available after install.packages/remotes::install_github): - vignette("quick-start", package = "VizModules") — start here: end-to-end walkthrough of wiring *InputsUI(), *OutputUI(), and *Server() into an app, using defaults, and the example *App() functions. - vignette("custom-modules", package = "VizModules") — how to extend existing modules by building wrapper modules (adding custom logic/inputs while reusing a base module). Follow the namespace pattern: process namespaced inputs inside moduleServer(), then call the base *Server() outside it with the bare id to avoid double-namespacing. - vignette("adding-a-new-module", package = "VizModules") — how to author a brand-new module from scratch (the InputsUI/OutputUI/Server contract, conventions, and helpers). - vignette("defaults-and-hiding", package = "VizModules") — using defaults, hide.inputs, and hide.tabs to pre-fill or hide controls. - vignette("statistical-testing", package = "VizModules") — the Stats tab and the exported compute_pairwise_stats() / create_stat_annotations() / apply_stat_annotations() helpers. - vignette("custom-model-lines", package = "VizModules") — the pluggable model-line backend registry (register_model_backend()). - vignette("custom-shiny-inputs", package = "VizModules") — the reusable multiDynamicInput() widget. - The README — overview, install, the full list of available modules, the App Factory (createModuleApp()), statistical-testing features, and summary-data export. - Per-function help pages via ? — e.g. ?dittoViz_scatterPlotInputsUI, ?plotthis_BarPlotServer, ?createModuleApp. Module help pages document exactly which underlying arguments are wired through and any omissions. Cross-reference the underlying plotting docs (?dittoViz::scatterPlot, ?plotthis::AreaPlot, etc.) for the complete parameter set. Browse all docs with help(package = "VizModules") or the pkgdown site: https://j-andrews7.github.io/VizModules/reference/. - NEWS.md (news(package = "VizModules")) — newest features and changes.

Available modules: dittoViz_scatterPlot, dittoViz_yPlot, plotthis_AreaPlot, plotthis_ViolinPlot, plotthis_BoxPlot, plotthis_BarPlot, plotthis_SplitBarPlot, plotthis_DensityPlot, plotthis_DotPlot, plotthis_Histogram, plus the natively-implemented linePlot, piePlot, radarPlot, parallelCoordinatesPlot, and dumbbellPlot. Each has a matching *App() function (e.g. plotthis_BarPlotApp()) you can run to see it in action.

Optional building blocks (inspect their source/help in the installed package’s R/ directory or via ?): - Data table / filtering module — ?dataFilterUI, ?dataFilterServer. - Statistical testing helpers (pairwise + omnibus brackets on plotly figures) — see ?compute_pairwise_stats, ?apply_stat_annotations, and the README “Statistical Testing” section; supported by the BoxPlot, ViolinPlot, and yPlot modules. - Summary-data export — ?collect_source_data and ?create_source_download_handler. - App factory — ?createModuleApp (every *App() is a thin wrapper around it).

Rules: All plots are plotly-based; prefer the documented module arguments over hand-rolled plotting. Verify function signatures against the installed help pages before using them, and tell me explicitly if a feature you need is not exposed by a module.