Title:	Quick Table Generation & Exploratory Analyses on ADaM-Ish Datasets
Version:	0.2.1
Description:	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.
License:	AGPL (≥ 3)
URL:	https://github.com/Biogen-Inc/tidyCDISC/, https://Biogen-Inc.github.io/tidyCDISC/
BugReports:	https://github.com/Biogen-Inc/tidyCDISC/issues
Depends:	R (≥ 2.10)
Imports:	cicerone, config, dplyr, DT, GGally, ggcorrplot, ggplot2, glue, golem, gt, haven, IDEAFilter, plotly, purrr, rlang, rmarkdown, shiny, shinyjs, shinyWidgets, sjlabelled, stringr, survival, tidyr, timevis, tippy (== 0.1.0)
Suggests:	knitr, spelling, testthat
VignetteBuilder:	knitr
Encoding:	UTF-8
Language:	en-US
LazyData:	true
RoxygenNote:	7.2.3
NeedsCompilation:	no
Packaged:	2023-03-16 13:49:10 UTC; aclark5
Author:	Aaron Clark [aut, cre], Jeff Thompson [aut], Teresa Wilson [aut], Nate Mockler [ccp, led], Maya Gans [aut], Robert Krajcik [ctb], Marly Gotti [ctb], Biogen Inc [cph]
Maintainer:	Aaron Clark <clark.aaronchris@gmail.com>
Repository:	CRAN
Date/Publication:	2023-03-16 14:20:02 UTC

ADAE

Description

Adverse Events Analysis Data from PHUSE Test Data Factory Project's GitHub.

Usage

adae

Format

Data frame with 32,139 features and 34 fields

Source

<https://github.com/phuse-org/TestDataFactory/blob/master/Updated/TDF_ADaM/adae.xpt>, downloaded 2020-06-17

ADLBC

Description

Laboratory Results Chemistry Analysis Data from PHUSE Test Data Factory Project's GitHub.

Usage

adlbc

Format

Data frame with 32,740 features and 58 fields

Source

<https://github.com/phuse-org/TestDataFactory/blob/master/Updated/TDF_ADaM/adlbc.xpt>, downloaded 2020-06-17

ADSL

Description

Subject Level Analysis Data from PHUSE Test Data Factory Project's GitHub.

Usage

adsl

Format

Data frame with 254 features and 51 fields

Source

<https://github.com/phuse-org/TestDataFactory/blob/master/Updated/TDF_ADaM/adsl.xpt>, downloaded 2020-06-17

ADTTE

Description

Time to Event Analysis Data from PHUSE Test Data Factory Project's GitHub.

Usage

adtte

Format

Data frame with 32,740 features and 58 fields

Source

<https://github.com/phuse-org/TestDataFactory/blob/master/Updated/TDF_ADaM/adtte.xpt>, downloaded 2021-01-26

ADVS

Description

Vital Signs Analysis Data from PHUSE Test Data Factory Project's GitHub.

Usage

advs

Format

Data frame with 32,139 features and 34 fields

Source

<https://github.com/phuse-org/TestDataFactory/blob/master/Updated/TDF_ADaM/advs.xpt>, downloaded 2020-06-17

Find the proper function to apply to each statistical and column block pairing and use the metadata associated with each column block for the function's arguments

Description

Find the proper function to apply to each statistical and column block pairing and use the metadata associated with each column block for the function's arguments

Usage

app_methods(agg, column, week, group, data, totals, filter = NA)

Arguments

Value

the table corresponding to the proper function to perform given the supplied column. This is used within a map to apply to all blocks inside the table generator module.

Examples

if(interactive()){
  data(example_dat1, package = "tidyCDISC")

  # Create non-missing table section
  app_methods("NON_MISSING", 
             structure("USUBJID", class = c("character", "ADSL")), NA, 
             "TRT01P", example_dat1$AE, example_dat1$totals)
            
  # Create ANOVA table section
  app_methods("ANOVA", 
            structure("TEMP", class = c("character", "BDS")), "Week 2", 
            "TRT01P", example_dat1$BDS, example_dat1$totals)

  # Create change table section
  app_methods("CHG", 
            structure("WEIGHT", class = c("character", "BDS")), "Week 12", 
            "TRT01P", example_dat1$BDS, example_dat1$totals)

  # Create mean table section
  app_methods("MEAN", 
            structure("PULSE", class = c("character", "BDS")), "Baseline", 
            "TRT01P", example_dat1$BDS, example_dat1$totals)
}

GT Column Names

Description

The function creates the labels for each column using the total function so the columns are now NAME N= X

Usage

col_for_list_expr(col_names, col_total)

Arguments

Value

A character object of class from_markdown.

Examples

data(example_dat2, package = "tidyCDISC")

labels <- col_for_list_expr(example_dat2$col_names, example_dat2$col_totals)
labels

if (interactive()) {
# TG table without nice column labels or totals
example_dat2$TG_table

# TG table with nice column labels and totals
gt::cols_label(example_dat2$TG_table, .list = labels)
}

Identify Names of Columns

Description

A function to transform the gt row names from generics to the column name and the total N of each column

Usage

common_rownames(data, group)

Arguments

Value

A character vector

Examples

data(adsl, package = "tidyCDISC")

# Values of TRT01P
unique(adsl$TRT01P)

# Common row names based on TRT01P
common_rownames(adsl, "TRT01P")

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

Description

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

Usage

data_to_filter(datafile, input_filter_df)

Arguments

Value

A 'data.frame' object based on the reduction of 'datafile' from 'input_filter_df'.

Examples

if(interactive()) {
  datalist <- list(ADSL = tidyCDISC::adsl, ADAE = tidyCDISC::adae, 
                   ADVS = tidyCDISC::advs, ADLBC = tidyCDISC::adlbc, 
                   ADTTE = tidyCDISC::adtte)

  # Returns combined dataset
  data_to_filter(datalist, c("ADSL", "ADAE"))
}

Function to clean and combine ADAE dataset with ADSL

Description

Function to clean and combine ADAE dataset with ADSL

Usage

data_to_use_str(x, ae_data, bds_data)

Arguments

Value

A 'data.frame' object containing data of the AE variety if 'x == "ADAE"' or one of the BDS variety if not.

Examples

if(interactive()) {
  datalist <- list(ADSL = tidyCDISC::adsl, ADVS = tidyCDISC::advs, 
                   ADAE = tidyCDISC::adae, ADLBC = tidyCDISC::adlbc)
                 
  pre_adsl <- prep_adsl(datalist$ADSL, input_recipe = 'NONE')
  pre_adae <- prep_adae(datalist, pre_adsl$data, 'NONE')
  ae_data <- pre_adae$data
  bds_data <- prep_bds(datalist, ADSL = pre_adsl$data)
  
  all.equal(data_to_use_str("ADAE", ae_data, bds_data), ae_data)
  all.equal(data_to_use_str("ADSL", ae_data, bds_data), bds_data)
}

Example Data Set 1

Description

Pre-processed data for purposes of demonstrating app_methods.

Usage

example_dat1

Format

A list with 3 elements:

AE

data frame, pre-processed AE dataset

BDS

data frame, pre-processed BDS dataset

totals

data frame, contains totals by grouping variable for pre-processed data

Example Data Set 2

Description

Pre-processed data for the purposes of demonstrating col_for_list_expr.

Usage

example_dat2

Format

A list with 3 elements:

TG_table

data frame, pre-processed gt table object with basic column names

col_names

vector, the column names

col_totals

vector, totals corresponding to each column

Get Factor Levels

Description

Extracts the factor levels of a vector or returns the unique values if the vector is not a factor.

Usage

get_levels(x)

Arguments

Value

x vector

References

A character vector containing the levels of the factor/vector

Examples

data(adae, package = "tidyCDISC")

# Create levels based on VARN
varN_fctr_adae <- varN_fctr_reorder(adae)

# `adae` does not have factor but `varN_fctr_adae` does
levels(adae$RACE)
levels(varN_fctr_adae$RACE)

# `get_levels()` either creates the factor or retrieves it
get_levels(adae$RACE)
get_levels(varN_fctr_adae$RACE)

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

Description

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

Usage

prep_adae(datafile, ADSL, input_recipe)

Arguments

Value

A 'list' containing a 'data.frame' object and character vector specifying the pre-filter applied.

Examples

if(interactive()) {
  datalist <- list(ADSL = tidyCDISC::adsl, ADVS = tidyCDISC::advs, 
                   ADAE = tidyCDISC::adae, ADLBC = tidyCDISC::adlbc)
                 
  pre_adsl <- prep_adsl(datalist$ADSL, input_recipe = 'NONE')

  # Create AE data set
  prep_adae(datalist, pre_adsl$data, input_recipe = 'NONE')
}

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

Description

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

Usage

prep_adsl(ADSL, input_recipe)

Arguments

Value

A 'list' containing a 'data.frame' object and character vector specifying the pre-filter applied.

Examples

data(adsl, package = "tidyCDISC")

# Process ADSL data for STAN table

prep_adsl(adsl, input_recipe = 'Table 3: Accounting of Subjects')

# Return ADSL data if no STAN table selected

prep_adsl(adsl, input_recipe = "NONE")

Combine BDS Data Frames

Description

A function to combine all BDS data frames into one large data set.

Usage

prep_bds(datafile, ADSL)

Arguments

Value

A data frame containing the BDS data bound by rows.

Examples

if(interactive()) {
  datalist <- list(ADSL = tidyCDISC::adsl, ADVS = tidyCDISC::advs, 
                   ADAE = tidyCDISC::adae, ADLBC = tidyCDISC::adlbc)
                 
  pre_adsl <- prep_adsl(datalist$ADSL, input_recipe = 'NONE')

  prep_bds(datalist, ADSL = pre_adsl$data)
}

Create Pretty IDs for TG Table

Description

Replaces ugly ID patterns of a stat block with pretty replacements for display purposes (e.g. NON_MISSING becomes Subject Count for those with Non Missing values)

Usage

pretty_IDs(ID)

Arguments

Value

A character vector of pretty IDs.

Examples

# List of patterns that can be replaced
patterns <- c("MEAN", "FREQ", "CHG", "Y_FREQ", "MAX_FREQ", "NON_MISSING", 
              "NESTED_FREQ_DSC", "NESTED_FREQ_ABC")
IDs <- paste(patterns, "of VAR")

IDs
pretty_IDs(IDs)

Run the Shiny Application

Description

Run the Shiny Application

Usage

run_app(...)

Arguments

Value

No return value, called to run the application.

Create Standard Footnotes for TG Table

Description

Creates a footnote with a source on the left and date run on the right.

Usage

std_footnote(data, source)

Arguments

Value

a 'gt' object

Prepare the table generator data for output

Description

Prepare the data.frame so that it's ready for output via 'gt' or other

Usage

tg_gt(tg_datalist, blockData, total_df, group)

Arguments

Value

a data.frame containing output polished for presentation in 'gt'

Re-order Factor Levels by VARN

Description

Function to that looks for VARN counterparts to any character or factor VAR variables in any dataframe and re-orders there factor levels, taking the lead from VARN's numeric guide.

Usage

varN_fctr_reorder(data)

Arguments

Value

The data frame after having factor levels re-ordered by VARN

Examples

data(adae, package = "tidyCDISC")

varN_fctr_adae <- varN_fctr_reorder(adae)

unique(adae[,c("AGEGR1", "AGEGR1N")])
levels(adae$AGEGR1)
levels(varN_fctr_adae$AGEGR1)

unique(adae[,c("RACE", "RACEN")])
levels(adae$RACE)
levels(varN_fctr_adae$RACE)