Interactive Dataset Explorer for 'R' and 'SAS' and Other Data Formats

A 'Shiny' application that provides nice interface for browsing, exploring, summarising, and converting datasets stored in 'SAS' (.sas7bdat, .xpt), CSV (.csv), and 'R' (.rds) formats. Users can register multiple directory-based libraries, interactively filter data using 'dplyr' expressions, inspect per-variable statistics, and export datasets to Excel, JSON, CSV, 'R' data, or 'SAS' transport formats.

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.

install package from GitHub

pak::pak("Ramsas88/QuickExplore")

Launching the Application

r{eval = FALSE} 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

1. Click Add Library in the left sidebar.
2. Give the library a short name (e.g. MYLIB) and enter the path to a folder that contains your data files.
3. Click a dataset name in the list to load it.
4. 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"))

# Categorical summary
compute_categorical_summary(df, "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: