dmcognigen

Overview

dmcognigen provides functions for data management tasks at the Clinical Pharmacology and Pharmacometrics (CPP) business unit of Simulations Plus, Inc.

Install it with:

remotes::install_github("simulations-plus/dmcognigen")

Load it with:

library(dmcognigen)

Functionality

Count Observations and Unique Values by Group

• The function expected to be used most frequently in this package is the cnt() function.
• cnt() is an extension of dplyr::count() intended to count the number of distinct occurrences of variables within some group. For example, we commonly cnt(.data, STUDYID, n_distinct_vars = USUBJID) to count the number of records within each STUDYID along with the number of unique subjects (USUBJID) within each STUDYID.

Calculate Standard Variables

• See full details in the Calculations vignette.
• Use the calculate_*() family of functions to apply standard equations.

Read and Leverage Data Requirements

• See full details in the Data Requirements vignette.
• Check which data requirement files are available with available_requirements_table() and import data requirements with read_requirements().
• Use attributes of requirements to apply characteristics defined in data requirements to a dataset.
  • The "decode_tbls" attribute can be utilized within join_decode_labels() or join_decode_levels().
  • The "labels_named_list" attribute can be utilized within set_labels().

Interact with Decodes

• See full details in the Decode Tables vignette.
• Extract decodes from vectors with extract_decode_tbls().
• Extract decodes from a dataset with extract_decode_tbls_from_data().
• Merge decodes with join_decode_labels() or join_decode_levels().
• Create factor variables with set_decode_factors().

Search Datasets

• Find datasets where variables exist with in_which().
• Search for patterns in variable names, variable labels, and variable content across all datasets in an environment with search_environment_data() then summarize the results with cnt_search_result().