tidyCDISC0.2.2 package

Quick Table Generation & Exploratory Analyses on ADaM-Ish Datasets

app_methods

Find the proper function to apply to each statistical and column block...

col_for_list_expr

GT Column Names

common_rownames

Identify Names of Columns

data_to_filter

The smallest possible data set we could filter to semi-join later

data_to_use_str

Function to clean and combine ADAE dataset with ADSL

get_levels

Get Factor Levels

prep_adae

Function to pre-filter the ADAE depending on the stan table selected

prep_adsl

Function to pre-filter the ADSL depending on the stan table selected

prep_bds

Combine BDS Data Frames

pretty_IDs

Create Pretty IDs for TG Table

run_app

Run the Shiny Application

show_adamish_alert

Show ADaM Compatibility Alert

std_footnote

Create Standard Footnotes for TG Table

suggest_adam_column_mapping

Suggest Column Mappings for ADaM

suggest_adam_domain

Suggest ADaM Domain

tg_gt

Prepare the table generator data for output

validate_adamish

Validate ADaM Structure

varN_fctr_reorder

Re-order Factor Levels by VARN

Download source packageRead PDF manual

Provides users a quick exploratory dive into common visualizations without writing a single line of code given the users data follows the Analysis Data Model (ADaM) standards put forth by the Clinical Data Interchange Standards Consortium (CDISC) <https://www.cdisc.org>. Prominent modules/ features of the application are the Table Generator, Population Explorer, and the Individual Explorer. The Table Generator allows users to drag and drop variables and desired statistics (frequencies, means, ANOVA, t-test, and other summary statistics) into bins that automagically create stunning tables with validated information. The Population Explorer offers various plots to visualize general trends in the population from various vantage points. Plot modules currently include scatter plot, spaghetti plot, box plot, histogram, means plot, and bar plot. Each plot type allows the user to plot uploaded variables against one another, and dissect the population by filtering out certain subjects. Last, the Individual Explorer establishes a cohesive patient narrative, allowing the user to interact with patient metrics (params) by visit or plotting important patient events on a timeline. All modules allow for concise filtering & downloading bulk outputs into html or pdf formats to save for later.

  • Maintainer: Aaron Clark
  • License: AGPL (>= 3)
  • Last published: 2026-02-05

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