Getting Started with QuickExplore

Overview

QuickExplore provides a point-and-click Shiny interface modelled after SAS Studio’s library/dataset browser. It supports SAS (.sas7bdat, .xpt), CSV, and R (.rds) files and lets you explore, filter, summarise, and export datasets without writing any code.

The package also exposes a set of standalone R functions for use in scripts or other Shiny applications.

Launching the Application

library(QuickExplore)
run_app()

The app opens in your default browser. If you are running inside RStudio it opens in the Viewer pane.

First Steps

Click Add Library in the left sidebar.
Give the library a short name (e.g. MYLIB) and enter the path to a folder that contains your data files.
Click a dataset name in the list to load it.
Use the Data, Summary, and Convert tabs to explore and export.

Standalone Utility Functions

All helper functions used internally by the app are exported and can be called directly.

Reading a Dataset

df <- read_dataset("/path/to/data/demog.sas7bdat")
df <- read_dataset("/path/to/data/analysis.csv")
df <- read_dataset("/path/to/data/model_output.rds")

Listing Datasets in a Directory

datasets <- list_datasets("/path/to/data/")
print(datasets)

Variable Metadata

info <- get_variable_info(df)
head(info)

Descriptive Statistics

df <- data.frame(
  age  = c(25, 34, 45, 52, 28, NA),
  sex  = c("M", "F", "M", "F", "M", "F"),
  dose = c(10, 20, 10, 30, 20, 10)
)

# Numeric summary
library(QuickExplore)
compute_numeric_summary(df, c("age", "dose"))
#>   Variable N Missing  Mean Median    SD Min Max
#> 1      age 5       1 36.80     34 11.43  25  52
#> 2     dose 6       0 16.67     15  8.16  10  30

# Categorical summary
compute_categorical_summary(df, "sex")
#>   sex Frequency Percentage Variable
#> 1   F         3         50      sex
#> 2   M         3         50      sex

Grouped Summaries

compute_numeric_summary(df, c("age", "dose"), group_var = "sex")

Shiny Module API

Each tab in the application is implemented as a reusable Shiny module. You can embed any of these modules in your own Shiny apps:

library(shiny)
library(QuickExplore)

ui <- fluidPage(
  data_viewer_ui("viewer")
)

server <- function(input, output, session) {
  my_data <- reactiveVal(mtcars)
  my_path <- reactiveVal("mtcars")    # or a real file path
  data_viewer_server("viewer", my_data, my_path)
}

shinyApp(ui, server)

Available module pairs:

UI function	Server function	Purpose
`dataset_browser_ui()`	`dataset_browser_server()`	Library + dataset sidebar
`data_viewer_ui()`	`data_viewer_server()`	Interactive table with filters
`summary_panel_ui()`	`summary_panel_server()`	Descriptive statistics
`converter_ui()`	`converter_server()`	Multi-format export

Session Info

sessionInfo()
#> R version 4.5.0 (2025-04-11)
#> Platform: aarch64-apple-darwin20
#> Running under: macOS 26.4.1
#> 
#> Matrix products: default
#> BLAS:   /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRblas.0.dylib 
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1
#> 
#> locale:
#> [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#> 
#> time zone: Europe/Zurich
#> tzcode source: internal
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] QuickExplore_0.1.0
#> 
#> loaded via a namespace (and not attached):
#>  [1] vctrs_0.7.2       cli_3.6.5         knitr_1.51        rlang_1.2.0      
#>  [5] xfun_0.57         otel_0.2.0        generics_0.1.4    jsonlite_2.0.0   
#>  [9] glue_1.8.0        htmltools_0.5.9   sass_0.4.10       rmarkdown_2.31   
#> [13] evaluate_1.0.5    jquerylib_0.1.4   tibble_3.3.1      fastmap_1.2.0    
#> [17] yaml_2.3.12       lifecycle_1.0.5   compiler_4.5.0    dplyr_1.2.1      
#> [21] pkgconfig_2.0.3   rstudioapi_0.18.0 digest_0.6.39     R6_2.6.1         
#> [25] tidyselect_1.2.1  pillar_1.11.1     magrittr_2.0.5    bslib_0.10.0     
#> [29] tools_4.5.0       cachem_1.1.0