QuickExplore: Interactive Dataset Explorer for SAS and Other Data Formats

Description

A 'Shiny' application that provides a SAS Studio-style interface for browsing, exploring, summarising, and converting datasets in SAS (.sas7bdat, .xpt), CSV, and R (.rds) formats.

The main entry point is run_app(). The package also exports several standalone helper functions useful outside the Shiny context:

• read_dataset() – read any supported format into a data frame.

• list_datasets() – enumerate datasets in a directory.

• get_variable_info() – variable-level metadata.

• compute_numeric_summary() / compute_categorical_summary() – tidy descriptive statistics.

• compute_crosstab() – wide-format contingency table with optional stratification (e.g. SEX × RACE by Treatment).

• format_file_size() – human-readable file size strings.

The Code Generator feature (code_generator_ui() / code_generator_server()) automatically produces a reproducible R script reflecting the current session state: dataset loading, filter/select expressions, summary statistics, and export format.

Author(s)

Maintainer: Ram Gaduputi ramsas88@gmail.com

Authors:

• Jagadish Katam Jagadish.katam@gmail.com

See Also

Useful links:

• https://github.com/ramsas88/QuickExplore

• Report bugs at https://github.com/ramsas88/QuickExplore/issues

Code Generator Module - Server

Description

Generates reproducible R code that mirrors the current QuickExplore session and provides clipboard-copy (via JS) and download-as-.R handlers.

Usage

code_generator_server(
  id,
  selected_dataset,
  filter_expr,
  selected_vars,
  group_var,
  summary_vars,
  output_format,
  csv_delim,
  json_pretty,
  crosstab_row = shiny::reactive(""),
  crosstab_col = shiny::reactive(""),
  crosstab_strat = shiny::reactive("")
)

Arguments

Value

NULL (invisibly). Called for side effects.

See Also

code_generator_ui()

Code Generator Module - UI

Description

Renders a panel displaying auto-generated R code that reproduces the current QuickExplore session (load dataset -> filter/select -> summarise -> export). Users can copy the code to the clipboard or download it as a .R script.

Usage

code_generator_ui(id)

Arguments

Value

A shiny::tagList() with the code generator UI.

See Also

code_generator_server()

Compute frequency statistics for categorical variables

Description

Returns value frequencies and percentages for each non-numeric variable in vars, optionally grouped by a second variable.

Usage

compute_categorical_summary(df, vars, group_var = NULL)

Arguments

Value

A data.frame with columns for the grouping variable (if any), the value, its frequency count, percentage, and the variable name. Returns NULL if there are no categorical variables in vars.

Examples

df <- data.frame(sex = c("M","F","M","F","M"), trt = c("A","A","B","B","A"))
compute_categorical_summary(df, c("sex", "trt"))

Compute a cross-tabulation of two categorical variables

Description

Produces a wide-format contingency table of row_var (rows) by col_var (columns), including row and column totals. When a strat_var is supplied the table is computed separately for each level of the stratification variable and the results are stacked with a leading Stratum column.

Usage

compute_crosstab(df, row_var, col_var, strat_var = NULL)

Arguments

Details

Missing values in any of the three variables are displayed as "(Missing)" rather than being silently dropped, so analysts can spot incomplete records.

Value

A data.frame in wide format:

• Column 1 (or 2 if stratified): row_var levels plus a "Total" row.

• Middle columns: one column per col_var level.

• Last column: Total (row sums).

• If strat_var is given, a leading Stratum column identifies each stratum. A grand-total block across all strata is not appended automatically — compute the unstratified table for that.

Examples

df <- data.frame(
  SEX  = c("M","F","M","F","M","F"),
  RACE = c("White","White","Black","Asian","Black","White"),
  TRT  = c("A","A","B","B","A","B")
)
compute_crosstab(df, "SEX", "RACE")
compute_crosstab(df, "SEX", "RACE", strat_var = "TRT")

Compute summary statistics for numeric variables

Description

Returns a tidy data frame with N, mean, median, standard deviation, minimum, and maximum for each numeric variable in vars.

Usage

compute_numeric_summary(df, vars, group_var = NULL)

Arguments

Value

A data.frame (one row per variable, or per variable × group level) or NULL if there are no numeric variables in vars.

Examples

df <- data.frame(x = rnorm(100), y = runif(100), g = rep(c("A", "B"), 50))
compute_numeric_summary(df, c("x", "y"))
compute_numeric_summary(df, c("x", "y"), group_var = "g")

Dataset Converter Module – Server

Description

Handles the dataset-conversion download for all supported output formats: .rds, .xlsx, .csv, .json, and SAS transport .xpt.

Usage

converter_server(id, loaded_data, selected_dataset)

Arguments

Value

A named list with three elements:

output_format

A shiny::reactive() returning the selected output format string.

csv_delim

A shiny::reactive() returning the CSV delimiter character.

json_pretty

A shiny::reactive() returning TRUE to pretty-print JSON output.

See Also

converter_ui()

Dataset Converter Module – UI

Description

Renders a two-column card layout: a conversion form on the left and a format-reference table plus output preview on the right.

Usage

converter_ui(id)

Arguments

Value

A shiny::tagList() with the converter UI.

See Also

converter_server()

Data Viewer Module – Server

Description

Handles data display, filtering, variable inspection, and download for the Data Viewer tab.

Usage

data_viewer_server(id, loaded_data, selected_dataset)

Arguments

Value

A named list with three elements:

filtered_data

A shiny::reactiveVal() with the current filtered data.frame.

filter_expr

A shiny::reactive() returning the raw filter expression string.

selected_vars

A shiny::reactive() returning the selected variable names.

See Also

data_viewer_ui()

Data Viewer Module – UI

Description

Creates a tabbed panel with three sub-tabs: an interactive data table (Data Viewer), a filter/subset interface (Explore Data), and a variable metadata explorer (Variables).

Usage

data_viewer_ui(id)

Arguments

Value

A shiny::tagList() with the viewer UI.

See Also

data_viewer_server()

Dataset Browser Module – Server

Description

Handles library registration, dataset listing, and dataset loading for the sidebar browser panel.

Usage

dataset_browser_server(id, selected_dataset, loaded_data)

Arguments

Value

A list of reactive values: libraries (named list of library-path pairs) and selected_library (the currently active library name).

See Also

dataset_browser_ui()

Dataset Browser Module – UI

Description

Renders the sidebar panel that lets users add/remove directory-based libraries and select a dataset to load.

Usage

dataset_browser_ui(id)

Arguments

Value

A shiny::tagList() containing the sidebar UI elements.

See Also

dataset_browser_server()

Format a file size in bytes as a human-readable string

Description

Format a file size in bytes as a human-readable string

Usage

format_file_size(size)

Arguments

Value

A character string such as "1.4 MB" or "340 KB".

Examples

format_file_size(1048576)   # "1 MB"
format_file_size(512)       # "512 B"

Get metadata for a loaded dataset

Description

Returns file-level metadata including the number of rows and columns, file size, and timestamps.

Usage

get_dataset_metadata(df, filepath)

Arguments

Value

A named list with elements: filename, filepath, format, n_rows, n_cols, file_size, modified, and created.

Examples

df <- read_dataset("/data/demog.csv")
meta <- get_dataset_metadata(df, "/data/demog.csv")
meta$n_rows

Extract variable-level metadata from a dataset

Description

Returns a data frame describing each variable: its type, SAS label, SAS format, missing value counts, and number of unique values.

Usage

get_variable_info(df)

Arguments

Value

A data.frame with columns Variable, Type, Label, Format, Missing_Count, Missing_Pct, and N_Unique.

Examples

df <- data.frame(x = 1:5, y = letters[1:5])
get_variable_info(df)

List supported dataset files in a directory

Description

Scans a directory for files with extensions .sas7bdat, .xpt, .csv, or .rds (case-insensitive) and returns a summary data frame.

Usage

list_datasets(dirpath)

Arguments

Value

A data.frame with columns Name, Format, Size, Modified, and Path. Returns an empty data frame if no supported files are found.

Examples

datasets <- list_datasets("/data/mylib")

Read a dataset based on its file extension

Description

Dispatches to the appropriate reader based on the file extension. Supported formats: .sas7bdat, .xpt, .csv, .rds.

Usage

read_dataset(filepath)

Arguments

Details

For SAS formats (.sas7bdat, .xpt), blank strings are automatically converted to NA after loading. This matches SAS behaviour where a blank character value is treated as a system-missing value, not as a valid empty string.

Value

A data.frame (or tibble) with the dataset contents. For SAS formats, all-whitespace character values are coerced to NA_character_.

Examples

df <- read_dataset("/data/mylib/demog.sas7bdat")
df <- read_dataset("/data/exports/study.csv")

Launch the Dataset Explorer Shiny Application

Description

Opens the interactive Dataset Explorer in your default web browser (or the RStudio Viewer pane when called from within RStudio). The application provides a SAS Studio-style interface for browsing libraries, exploring datasets, computing summary statistics, and converting between data formats.

Usage

run_app(...)

Arguments

Value

Called for its side effect of launching a Shiny application. Returns NULL invisibly.

Examples

# Launch with default settings
run_app()

# Launch on a specific port without opening a browser
run_app(port = 4321, launch.browser = FALSE)

Summary Panel Module – Server

Description

Computes and renders descriptive statistics for numeric and categorical variables, plus a missing-value summary table.

Usage

summary_panel_server(id, loaded_data)

Arguments

Value

A named list with two elements:

summary_vars

A shiny::reactive() returning the selected variable names.

group_var

A shiny::reactive() returning the grouping variable name ("" = none).

See Also

summary_panel_ui()

Summary Panel Module – UI

Description

Renders the summary statistics panel with dataset overview cards plus tables for numeric, categorical, and missing-value statistics.

Usage

summary_panel_ui(id)

Arguments

Value

A shiny::tagList() with the summary UI elements.

See Also

summary_panel_server()