This vignette shows how to render interactive plots and tabular results in Shiny using the visOmopResults package and functions that build on it. Specifically, we will demonstrate:
Small/static tables →
gt, for compact, fixed results such as cohort counts or characteristics.Large/dynamic tables →
DTorreactable, for large results or those that require sorting/filtering to interpret.Plots → build with
ggplot2and (optionally) wrap withplotly::ggplotly()for interactivity in shiny.
Set up
Load packages and mock data.
library(shiny)
library(bslib)
library(sortable)
library(shinyWidgets)
library(gt)
library(DT)
library(reactable)
library(plotly)
library(dplyr)
library(visOmopResults)
library(IncidencePrevalence)
library(CohortCharacteristics)
library(shinycssloaders)
# Mock results in visOmopResults
data <- visOmopResults::data
# Remove global options (just in case we have them from previous work)
setGlobalPlotOptions(style = NULL, type = NULL)
setGlobalTableOptions(style = NULL, type = NULL)Shiny App: User Inteface
The Shiny app has three panels, one for each result. All allow filtering by sex strata and provide panel-specific visualization options:
- Baseline characteristics: shows a gt
table with controls for headers, groups, and hidden columns..
- Large Scale characteristics: renders as a
datatable or reactable, with options to group
or hide columns.
- Incidence: displays a static ggplot
or interactive plotly plot, with options for colouring,
faceting, and ribbons.
Example UI Code
ui <- bslib::page_navbar(
title = "visOmopResults for Shiny",
window_title = "visOmopResults • Shiny",
collapsible = TRUE,
# Baseline Characteristics (GT table)
bslib::nav_panel(
title = "Baseline Characteristics",
icon = icon("users-gear"),
bslib::layout_sidebar(
sidebar = bslib::sidebar(
title = "Filters",
shinyWidgets::pickerInput(
inputId = "summarise_characteristics_sex",
label = "Sex",
choices = c("overall", "Male", "Female"),
selected = "overall",
multiple = TRUE
),
width = 320,
position = "left",
open = TRUE
),
bslib::card(
full_screen = TRUE,
bslib::card_header("Table layout"),
bslib::layout_sidebar(
sidebar = bslib::sidebar(
title = "Arrange columns",
sortable::bucket_list(
header = NULL,
group_name = "col-buckets",
orientation = "horizontal",
add_rank_list(
text = "None",
labels = c("variable_name", "variable_level", "estimate_name"),
input_id = "summarise_characteristics_table_none"
),
add_rank_list(
text = "Header",
labels = c("sex"),
input_id = "summarise_characteristics_table_header"
),
add_rank_list(
text = "Group columns",
labels = c("cdm_name", "cohort_name"),
input_id = "summarise_characteristics_table_group_column"
),
add_rank_list(
text = "Hide",
labels = "table_name",
input_id = "summarise_characteristics_table_hide"
)
),
position = "right",
width = 400,
open = FALSE
),
# GT output
gt::gt_output("summarise_characteristics_table") |>
shinycssloaders::withSpinner(type = 4)
)
)
)
),
# Large Scale Characterisation (DT / reactable)
bslib::nav_panel(
title = "Large Scale Characterisation",
icon = icon("table"),
bslib::layout_sidebar(
sidebar = bslib::sidebar(
# title = "Display options",
shinyWidgets::pickerInput(
inputId = "large_scale_sex",
label = "Sex",
choices = c("overall", "Male", "Female"),
selected = "overall",
multiple = TRUE
),
radioButtons(
"large_engine",
"Renderer",
choices = c("DT", "reactable"),
inline = TRUE
),
sortable::bucket_list(
header = NULL,
group_name = "col-buckets",
orientation = "horizontal",
add_rank_list(
text = "None",
labels = c("variable_name", "variable_level", "estimate_name"),
input_id = "large_scale_none"
),
add_rank_list(
text = "Group columns",
labels = c("cdm_name", "cohort_name"),
input_id = "large_scale_group_column"
),
add_rank_list(
text = "Hide",
labels = character(),
input_id = "large_scale_hide"
)
),
width = 320
),
bslib::card(
full_screen = TRUE,
bslib::card_header("Cohort characteristics (large-scale)"),
conditionalPanel(
"input.large_engine == 'DT'",
DTOutput("large_dt") |> shinycssloaders::withSpinner(type = 4)
),
conditionalPanel(
"input.large_engine == 'reactable'",
reactableOutput("large_reactable") |> shinycssloaders::withSpinner(type = 4)
)
)
)
),
# Incidence (ggplot → plotly)
bslib::nav_panel(
title = "Incidence",
icon = icon("chart-line"),
bslib::layout_sidebar(
sidebar = bslib::sidebar(
title = "Plot options",
shinyWidgets::pickerInput(
"incidence_sex",
"Sex strata",
choices = c("overall", "Male", "Female"),
selected = "overall",
multiple = TRUE
),
shinyWidgets::pickerInput(
inputId = "facet",
label = "Facet",
selected = "sex",
multiple = TRUE,
choices = c("cdm_name", "incidence_start_date", "sex", "outcome_cohort_name"),
),
shinyWidgets::pickerInput(
inputId = "colour",
label = "Colour",
selected = "outcome_cohort_name",
multiple = TRUE,
choices = c("cdm_name", "incidence_start_date", "sex", "outcome_cohort_name")
),
checkboxInput("inc_ribbon", "Show ribbon (CI)", TRUE),
checkboxInput("interactive", "Interactive Plot", TRUE),
width = 320
),
bslib::card(
full_screen = TRUE,
bslib::card_header("Incidence over time"),
uiOutput("incidence_plot", height = "520px") |> shinycssloaders::withSpinner(type = 4)
)
)
)
)Shiny App: Server
1) Baseline characteristics
The server filters results by the selected sex and creates a
gt table using the tableCharacteristics()
function from the CohortCharacteristics package. This
function is built on visOmopResults, which ensures
consistent styling and supports arguments to define headers, group
columns, and hide columns.
If you have your own <summarised_result> table,
which don’t has a dedicated table function, you can instead use
visOmopTable() to generate a gt table in
Shiny. This allows you to group estimates and configure header, group,
and hidden column options in a similar way.
2) Large Scale characteristics
These results are not in <summarised_result>
format, as shown below:
data$large_scale_characteristics
#> # A tibble: 952 × 8
#> cdm_name cohort_name sex concept_name window concept_id count percentage
#> <chr> <chr> <chr> <chr> <chr> <chr> <int> <dbl>
#> 1 my_duckdb_… denominato… over… Acute aller… -inf … 4084167 113 4.41
#> 2 my_duckdb_… denominato… over… Acute bacte… -inf … 4294548 607 23.7
#> 3 my_duckdb_… denominato… over… Acute bronc… -inf … 260139 2303 89.8
#> 4 my_duckdb_… denominato… over… Acute chole… -inf … 198809 29 1.13
#> 5 my_duckdb_… denominato… over… Acute viral… -inf … 4112343 2388 93.1
#> 6 my_duckdb_… denominato… over… Alzheimer's… -inf … 378419 15 0.59
#> 7 my_duckdb_… denominato… over… Anemia -inf … 439777 73 2.85
#> 8 my_duckdb_… denominato… over… Angiodyspla… -inf … 4310024 281 11.0
#> 9 my_duckdb_… denominato… over… Appendicitis -inf … 440448 125 4.88
#> 10 my_duckdb_… denominato… over… Atopic derm… -inf … 133834 54 2.11
#> # ℹ 942 more rowsIn this case, we use visTable() to generate tables as
either a datatable or a reactable, depending
on the user’s choice in the UI. The table type is specified with the
type argument.
For both table types, we pass the UI-selected columns to
groupColumn and hide. We do not generate a
header for this result, as it would require restructuring the estimates
into a single “estimate_value” column.
The look and behaviour of the tables can be customised through the style argument. Available options can be explored with:
tableStyle("datatable")tableStyle("reactable")
In this vignette, we modify the datatable style in the
server code so filters appear at the top of the table instead of the
default bottom.
3) Incidence
For incidence results, we use the plotIncidence()
function from the IncidencePrevalence package. This
function creates a ggplot object, which can be rendered as
a static plot with plotOutput or as an interactive plot
with plotlyOutput. Users can also select which columns to
use for colouring and faceting, and whether to display confidence
interval ribbons.
For other results—whether <summarised_reuslt>
class or not—you can generate plots in a similar way by using the
plotting functions available in visOmopResults.
Example Server Code
Note: Both
CohortCharacteristicsandIncidencePrevalencefunctions for plotting and tabulation are built onvisOmopResults, which means they share a consistent interface and style.
server <- function(input, output, session) {
# Baseline (GT)
output$summarise_characteristics_table <- gt::render_gt({
data$summarised_characteristics |>
# filter results by sex
filterStrata(sex %in% input$summarise_characteristics_sex) |>
# create GT table
CohortCharacteristics::tableCharacteristics(
header = input$summarise_characteristics_table_header,
groupColumn = input$summarise_characteristics_table_group_column,
hide = input$summarise_characteristics_table_hide,
type = "gt"
)
})
# Large scale characteristics
getLargeScaleResults <- reactive({
data$large_scale_characteristics |>
filter(.data$sex %in% input$large_scale_sex)
})
# To render as DT
output$large_dt <- renderDT({
getLargeScaleResults() |>
visTable(
hide = input$large_scale_hide,
groupColumn = input$large_scale_group_column,
type = "datatable",
style = list(
filter = "top",
searchHighlight = TRUE,
rownames = FALSE
)
)
})
# To render as reactable
output$large_reactable <- reactable::renderReactable({
getLargeScaleResults() |>
visTable(
hide = input$large_scale_hide,
groupColumn = input$large_scale_group_column,
type = "reactable",
style = "default"
)
})
# Incidence
getIncidencePlot <- reactive({
data$incidence |>
filterStrata(sex %in% input$incidence_sex) |>
plotIncidence(
colour = input$colour,
facet = input$facet,
ribbon = input$inc_ribbon
) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))
})
output$incidence_plot <- renderUI({
plt <- getIncidencePlot()
if (input$interactive) {
ggplotly(plt)
} else {
renderPlot(plt)
}
})
}