usepa / tada Goto Github PK

The current code (rowwise and case_when functions) used to check data row by row is used in a number of functions (command+f in .R files to see which functions use it). But it takes a long time to run, especially for larger datasets. Research other methods of checking data row by row in R and compare the efficiency of those methods to the one being used.

Note that this may be the best way to do it; it could be that the process of looking at data row by row is cumbersome regardless of how it's code.

Set up dependency management system

• https://cran.r-project.org/web/packages/renv/vignettes/renv.html
• https://rstudio.github.io/packrat/

Add functions for filtering page to the utilities.R file or create a new filtering.R file to place these and the other specific filtering functions (continuous data, media not water, etc.).

Use functions from Jake's filtering vignette: https://usepa.sharepoint.com/:f:/r/sites/WQPDataAssessmentTeam/Shared%20Documents/General/TADA%20Dev/FirstTool_DataDiscoveryandCleaning/Logic-vignettes/Filtering?csf=1&web=1&e=bGWdEh

Fields for full dataset filtering
ActivityTypeCode
ActivityMediaName
ActivityMediaSubdivisionName
ActivityCommentText
MonitoringLocationTypeName
StateName
TribalLandName
OrganizationFormalName
CharacteristicName
HydrologicCondition
HydrologicEvent
BiologicalIntentName
MeasureQualifierCode
ActivityGroup
AssemblageSampledName
ProjectName
CharacteristicNameUserSupplied
DetectionQuantitationLimitTypeName
SampleTissueAnatomyName
LaboratoryName

Fields for characteristic level filtering
ActivityCommentText
ActivityTypeCode
ActivityMediaName
ActivityMediaSubdivisionName
MeasureQualifierCode
MonitoringLocationTypeName
HydrologicCondition
HydrologicEvent
ResultStatusIdentifier
MethodQualifierTypeName
ResultCommentText
ResultLaboratoryCommentText
ResultMeasure/MeasureUnitCode
ResultSampleFractionText
ResultTemperatureBasisText
ResultValueTypeName
ResultWeightBasisText
SampleCollectionEquipmentName
LaboratoryName
MethodDescriptionText
ResultParticleSizeBasisText
SampleCollectionMethod/MethodIdentifier
SampleCollectionMethod/MethodIdentifierContext
SampleCollectionMethod/MethodName
DataQuality/BiasValue
MethodSpeciationName
ResultAnalyticalMethod/MethodName
ResultAnalyticalMethod/MethodIdentifier
ResultAnalyticalMethod/MethodIdentifierContext
AssemblageSampledName
CharacteristicNameUserSupplied
DetectionQuantitationLimitTypeName

Page Requirements/Standards

All of the flag page functions should be consistent in the following ways:

• Clean argument indicates whether flag columns should be appended to the data (clean = FALSE), or flagged data is transformed/filtered from the dataset and no columns are appended (clean = TRUE).
• Default is clean = FALSE

Function Requirements

• Reference table required?
  No
• Include warning if flags are not applied?
  No
• Required columns:
  ActivityIdentifier
  ActivityConductingOrganizationText
  OrganizationFormalName
  OrganizationIdentifier
  ProjectIdentifier
  ResultCommentText
  ActivityCommentText

Development Notes

• ResultFlagsIndependent.R
• Required columns are those that are not included when checking for duplicates
• Use code from "auto clean" function
• Flag is unique identifier for each unique row (identical IDs = duplicate row)

Run all functions and test them, including the functions with open issues. Provide feedback.

We would like TADA to use the most up-to-date reference tables each time its used. Running the generate ref table functions automatically upon loading the package into R would allow us to do this. Do some research to learn how we can do that.

If it's not clear or there is no standard way to that, consider other approaches to achieving the goal of automatically refreshing tables

Discuss CCO vs. MIT license. Which is a better fit?

MIT License

Copyright (c) 2022 Environmental Protection Agency

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

toupper function is in the test_data.R file (base R function)

add true duplicate rows function as well

How can we make TADA download dataRetrieval from the remote, not CRAN automatically?

remotes::install_github("USGS-R/dataRetrieval")

Page Requirements/Standards

All of the flag page functions should be consistent in the following ways:

• Clean argument indicates whether flag columns should be appended to the data (clean = FALSE), or flagged data is transformed/filtered from the dataset and no columns are appended (clean = TRUE).
• Default is clean = FALSE

Function Requirements

• Reference table required?
  No
• Include warning if flags are not applied?
  No
• Required columns:
  ProjectFileUrl

Development Notes

• Consider using this logic:
  ProjectAttachedBinaryObject is populated, QAPPavailable = Y (when clean = TRUE, these columns are retained)
  ProjectAttachedBinaryObject is not populated, QAPPavailable = N (when clean = TRUE, these columns are not retained)

Page Requirements/Standards

All of the generate reference file functions should be consistent in the following ways:

• No argument is included. Similar to the check() or dcoument() functions in devtools
• Where possible, read data in via URL (not from a static, downloaded file) to maintain up-to-date records
• Finish each function with UpdateInternalData(x), which is a function unique to TADA (at the top of GenerateRefTables.R) that updates sysdata.rda without overwriting other data.

Development Notes

• Raw data only has "inches" as the target unit for units of type (Description column) "Length Distance." This function adds data for "m" and "ft" target units

Page Requirements/Standards

All of the flag page functions should be consistent in the following ways:

• Clean argument indicates whether flag columns should be appended to the data (clean = FALSE), or flagged data is transformed/filtered from the dataset and no columns are appended (clean = TRUE).
• Default is clean = FALSE

Function Requirements

• Reference table required?
  Yes, sourced from WQX QAQC Characteristic Validation table
• Include warning if flags are not applied?
  Yes, Metadata transformations may be affected
• Required columns:
  CharacteristicName
  MethodSpeciationName

Development Notes

None

Check List

• Review how to use reference table
• Create function to add/update reference table
• Create InvalidSpeciation function

Publish package with "pre-release" status.

See:

• https://docs.github.com/en/repositories/releasing-projects-on-github/managing-releases-in-a-repository
• https://cran.r-project.org/web/packages/remotes/vignettes/dependencies.html
• https://cran.r-project.org/web/packages/remotes/vignettes/dependencies.html

WQXInvalidResultUnit
WQXInvalidFraction
WQXInvalidSpeciation

ALWAYS: Run all site and result flag functions and generate summary table (drafted in mock ups).

Flag=TRUE: append all flag columns to dataset
Flag=FALSE: do not append all flag columns to dataset

Clean=TRUE: remove all data that has been flagged AND remove flag columns if there
Clean=FALSE: do not remove all data that has been flagged

Page Requirements/Standards

All of the flag page functions should be consistent in the following ways:

• Clean argument indicates whether flag columns should be appended to the data (clean = FALSE), or flagged data is transformed/filtered from the dataset and no columns are appended (clean = TRUE).
• Default is clean = FALSE

Function Requirements

• Reference table required?
  No
• Include warning if flags are not applied?
  Yes, "Data summaries and calculations may be affected by choosing to retain special characters in the ResultValue field. In order to ensure transformation functions will run properly, set clean = TRUE."
• Required columns:
  ResultMeasureValue

Development Notes

ResultFlagsDependent.R

Add a column with the unique identifiers for each comparable data combination that has a unique identifiers included in the harmonization template. For combinations that do not already have a specific identifier in the reference file, generate a unique identifier.

Function title: CensoredDataSubstitutions or TransformCensorData?

Generate DetectionQuantitationLimitTypeName (and associated DetectionQuantitationLimitMeasure/MeasureValue and DetectionQuantitationLimitMeasure/MeasureUnitCode) and ResultDetectionConditionText from Result Value where needed

Depends on ResultsSpecialChars Function:

• Convert Result Values that start with "<" into an appropriate Detection Condition and Detection Limit Value. That is: a Result Value of "<0.25" would be converted into a ResultDetectionConditionText of "Present Below Quantification Limit", a Nondetect Result Value of "0.25", and a DetectionQuantitationLimitTypeName of "Lower Quantitation Limit"

Two options (best for when <70% of data are censored):

• Robust ROS [Regression on Order Statistics (ROS)] (for lower limits, use random number between detection limit and 0)
• x times the detection limit

devtools has a function to add vignette folder

Page Requirements/Standards
All of the flag page functions should be consistent in the following ways

• Clean argument indicates whether flag columns should be appended to the data (clean = FALSE), or flagged data is transformed/filtered from the dataset and no columns are appended (clean = TRUE).
• Default is clean = FALSE

Function Requirements

• Reference table required?
  No
• Include warning if flags are not applied?
  No

Required columns:
ActivityMediaName

Development Notes:
None

When clean = FALSE, append column titled "InvalidCoordinates" with the following:

1. If the LAT is outside of the 0 to 90 range and longitude is outside of the -180 to 0 range, flag row as "NotInNorthAmerica".

2. If the LAT or LONG includes the specific strings, 000 or 999, or if the LAT is outside of the -90 to 90 range and longitude is outside of the -180 to 180 range, flag row as "Invalid".

3. Precision can be measured by the number of decimal places in the latitude and longitude provided. If the LAT or LONG does not have any numbers to the right of the decimal point, flag row as "Imprecise". Precision can be measured by the number of decimal places in the latitude and longitude provided.

When clean = TRUE, append column titled "InvalidCoordinates with the following:"

1. If NotInNorthAmerica: LAT has a - sign, autoclean and change it to +; if LONG is +, autoclean and change it to -; include "ChangedLatLongSign" in the "InvalidCoordinates" column

2. If the LAT or LONG includes the specific strings, 00 or 999, or if the LAT is outside of the -90 to 90 range and longitude is outside of the -180 to 180 range, flag row as "Invalid".

3. If the LAT or LONG does not have any numbers to the right of the decimal point, still only flag row as "Imprecise". Do not remove from dataset.

Include additional Boolean argument for imprecise lat/longs:
When imprecise=TRUE, if the LAT or LONG does not have any numbers to the right of the decimal point, remove from dataset

When imprecise=FALSE, if the LAT or LONG does not have any numbers to the right of the decimal point, do not remove from dataset

Suggest to focus on nutrients to start

Page Requirements/Standards

All of the flag page functions should be consistent in the following ways:

• Clean argument indicates whether flag columns should be appended to the data (clean = FALSE), or flagged data is transformed/filtered from the dataset and no columns are appended (clean = TRUE).
• Default is clean = FALSE

Function Requirements

• Reference table required?
  Yes, sourced from WQX QAQC Characteristic Validation table
• Include warning if flags are not applied?
  Yes, Metadata transformations may be affected
• Required columns:
  CharacteristicName
  ResultSampleFractionText

Development Notes

None

Check List

• Create function to add/update reference table
• Create InvalidFraction function

Function Logic
Always: Generate "TADAHarmonizationTable"
Flag=TRUE: Append all yellow columns pulled from the "TADAHarmonizationTemplate" to the master TADA data profile as well.
Flag=FALSE: DO NOT append all yellow columns pulled from the "TADAHarmonizationTemplate" to the master TADA data profile
Clean=FALSE: Do not transform or convert yet.
Clean=TRUE: Perform all transformations and conversions.

Dependent on "TADAHarmonizationReferenceFile"
"TADAHarmonizationTable" is generated for the specific dataset using the "TADAHarmonizationReferenceFile". The "TADAHarmonizationReferenceFile" includes logic for harmonizing synonyms and units

Dependent on other functions
The WQXInvalidResultUnit function (clean=TRUE) is required to run this function
The WQXInvalidFraction function (clean=TRUE) is required to run this function
The WQXInvalidSpeciation function (clean=TRUE) is required to run this function
The WQXTargetUnits function is required to run this function

Notes
Suggest to focus on nutrients to start

Include the following functions in the Utilities.R file:

• AutoFilter (media not water, biological data, etc.)
• TrueDuplicate
• RemoveColumnsWithNAs (do not do this because we require columns for some of the functions, remove columns at very of process end instead = final cleaning & output function; alternatively we could say remove all NAs except for the TADA critical columns)

Page Requirements/Standards
All of the flag page functions should be consistent in the following ways

Clean argument indicates whether flag columns should be appended to the data (clean = FALSE), or flagged data is transformed/filtered from the dataset and no columns are appended (clean = TRUE).
Default is clean = FALSE
Function Requirements

Reference table required?
No
Include warning if flags are not applied?
No
Required columns:
ActivityMediaName

Development Notes:

• Consider auto filter for assemblage and media subdivision fields as well - to assist with simplifying the dataset (autofiltering) and ensuring results are comparable
• May be part of a larger function instead of being a standalone function........could autoclean remove duplicate rows, remove blank colums, remove medianotwater data, and remove any other data our tool cannot support (TBD )

And move to DataDiscoveryRetrieval.R

Generate map using WQP station metadata. May can be static but include colors, size, and shapes to provide useful information about the WQP monitoring sites. Interactivity is a plus, but not a requirement for the MVP.

Option 1: Generate blank TADADataProfile
Option2: Read in filled out TADADataProfile and check the fields (column headers), not field values (done)

Page Requirements/Standards

All of the flag page functions should be consistent in the following ways:

• Clean argument indicates whether flag columns should be appended to the data (clean = FALSE), or flagged data is transformed/filtered from the dataset and no columns are appended (clean = TRUE).
• Default is clean = FALSE

Function Requirements

• Reference table required?
  Yes, sourced from WQX QAQC Characteristic Validation table
• Include warning if flags are not applied?
  No
• Required columns:
  CharacteristicName
  ResultMeasureValue
  ResultMeasure.MeasureUnitCode

Development Notes

ResultFlagsIndependent.R
Filter Type == "CharacteristicUnit"
Required columns to join the reference table and the TADA profile:

• Characteristic, CharacteristicName
• Source, AcitivtyMediaName
• Value, ResultMeasure.MeasureUnitCode
  This one is a little tricky because Maximum values pertain to the "Value Unit" (target unit) column, not the "Value" (original unit) column. Therefore, units must be converted before checking if a value is outside the range. Here's some logic to get started:
• Join Maximum and Conversion Factor columns to the input dataset (by Characteristic, Source, and Value)
• Create a new ResultMeasureValue column (e.g. ConvertedValue), which is ResultMeasureValue / Conversion Factor
• Create flag column, add flag when Maximum >= ConvertedValue

Page Requirements/Standards

All of the flag page functions should be consistent in the following ways:

• Clean argument indicates whether flag columns should be appended to the data (clean = FALSE), or flagged data is transformed/filtered from the dataset and no columns are appended (clean = TRUE).
• Default is clean = FALSE

Function Requirements

• Reference table required?
  Yes, Unit conversion reference table
• Include warning if flags are not applied?
  Yes, Data summaries and calculations may be affected (show only if convert = FALSE)
• Required columns:
  Activity(Top/Bottom)DepthHeight fields
  ResultDepthHeightMeasure fields

Development Notes

• HOLD on development- finalize components of this function with discussion
• This function will do 2 things: 1) Flag rows with depth profile data, 2) optionally convert depth profile data to a uniform unit

Develop two functions to create a histogram and box plot:

1. histogram
2. boxplot

Function inputs/required columns

• Group rows for plots by: TADA.ComparableDataIdentifier from Harmonization Template (https://github.com/USEPA/TADA/blob/develop/inst/extdata/HarmonizationTemplate.csv)
• Time Period
• Unit
• Result Values

Leverage NADA2 R package or other packages if possible:
https://cran.r-project.org/web/packages/NADA2/index.html

Import data using web service for the full physical chemical profile directly (not dataRetrieval)

In the vignette, generate two files that are used throughout process and available to a user to view at any time (.csv?). Generate "TADAProfileClean" file and "TADAProfileOriginal".

For function, simply import the data (do not write to global environment) --like what dataRetrieval does

Function title: CensoredDataSummary
Some result values are either below or above the detection limit of the equipment used to collect data. Users may want substitute values for this data to make it more useful for assessment. This function will provide a summary of the censored data in this dataset.

Always: generate censored data stats table and provide it to function users as a .csv (or in environment?)

Depends on ResultsSpecialChars Function

See summary table here:
https://usepa.sharepoint.com/sites/WQPDataAssessmentTeam/Shared%20Documents/Forms/AllItems.aspx?id=%2Fsites%2FWQPDataAssessmentTeam%2FShared%20Documents%2FGeneral%2FTADA%20Dev%2FFirstTool%5FDataDiscoveryandCleaning%2FTemplates%2FDraft%20Templates&viewid=deb12f0b%2D3d2c%2D4694%2Dbe22%2D7d262f785cce

Page Requirements/Standards

All of the flag page functions should be consistent in the following ways

• Clean argument indicates whether flag columns should be appended to the data (clean = FALSE), or flagged data is transformed/filtered from the dataset and no columns are appended (clean = TRUE).
• Default is clean = FALSE

Function Requirements

• Reference table required?
  Yes, sourced from WQX QAQC Characteristic Validation table
• Include warning if flags are not applied?
  No
• Required columns:
  CharacteristicName
  ResultAnalyticalMethod.MethodIdentifier
  ResultAnalyticalMethod.MethodIdentifierContext

Development Notes

None

Check List

• Create function to add/update reference table
• Create UncommonAnalyticalMethodID function

Page Requirements/Standards

All of the flag page functions should be consistent in the following ways:

• Clean argument indicates whether flag columns should be appended to the data (clean = FALSE), or flagged data is transformed/filtered from the dataset and no columns are appended (clean = TRUE).
• Default is clean = FALSE

Function Requirements

• Reference table required?
  Yes, sourced from WQX QAQC Characteristic Validation table
• Include warning if flags are not applied?
  No
• Required columns:
  CharacteristicName
  ResultMeasureValue
  ResultMeasure.MeasureUnitCode

Development Notes

ResultFlagsIndependent.R
Filter Type == "CharacteristicUnit"
Required columns to join the reference table and the TADA profile:

• Characteristic, CharacteristicName
• Source, AcitivtyMediaName
• Value, ResultMeasure.MeasureUnitCode
  This one is a little tricky because Minimum values pertain to the "Value Unit" (target unit) column, not the "Value" (original unit) column. Therefore, units must be converted before checking if a value is outside the range. Here's some logic to get started:
• Join Minimum and Conversion Factor columns to the input dataset (by Characteristic, Source, and Value)
• Create a new ResultMeasureValue column (e.g. ConvertedValue), which is ResultMeasureValue / Conversion Factor
• Create flag column, add flag when Minimum <= ConvertedValue

Consider including TADA at the beginning of all columns that TADA appends to the dataset.

For example, from the Harmonization Template:

• TADA Suggested CharacteristicName
• TADA CharacteristicName assumptions
• TADA Suggested sample fraction
• TADA Fraction assumptions
• TADA Suggested speciation
• TADA Speciation Conversion Factor
• TADA Speciation Assumptions
• TADA Suggested result unit
• TADA UnitConversionFactor
• TADA UnitConversionCoefficient

Consider adding outlier information to TADA stats function.

Append one or two additional columns to the dataset flagging outliers at the individual station/char level and/or at the all stations/char level.

Add new function input for stats to flag outliers across single station (input ID) or all stations:
Scale = AllStations
Scale = IndividualStations

Option 1: Generate Blank TADARetrieval Template
Option 2: Upload filled in TADARetrieval template and download WQP data

Use data retrieval package for this

Upon data retrieval, generate two files that are used throughout process and available to a user to view at any time (.csv?). Generate "TADAProfileClean" file and "TADAProfileOriginal".

Page Requirements/Standards

All of the flag page functions should be consistent in the following ways:

• Clean argument indicates whether flag columns should be appended to the data (clean = FALSE), or flagged data is transformed/filtered from the dataset and no columns are appended (clean = TRUE).
• Default is clean = FALSE

Function Requirements

• Reference table required?
  No
• Include warning if flags are not applied?
  No
• Required columns:
  ResultDetectionConditionText

Development Notes

ResultFlagsIndependent.R
Use code from "auto clean" function

Double check and complete any missing documentation

Page Requirements/Standards

All of the flag page functions should be consistent in the following ways

• Clean argument indicates whether flag columns should be appended to the data (clean = FALSE), or flagged data is transformed/filtered from the dataset and no columns are appended (clean = TRUE).
• Default is clean = FALSE

Function Requirements

• Reference table required?
  Yes, sourced from WQX QAQC Characteristic Validation table
• Include warning if flags are not applied?
  Yes, Unit conversions, data summaries, and data calculations may be affected
• Required columns:
  CharacteristicName
  ResultMeasure.MeasureUnitCode
  ActivityMediaName

Development Notes

None

Check List

• Create function to add/update reference table
• Create InvalidResultUnit function

Calculate = Yes
Calculate = No

GenerateHarmonizationTable = Yes
GenreateTable = No

LogicColumn = Yes
LogicColumn = No

See Total Nitrogen Summations page for more detailed requirements

The following logic can be hard coded:
Multiple forms of nitrogen can be summed to calculate TN. Here is the logic TADA uses for this calculation:
If available, use the total N result for a given day, even if there are other constitutes available
If total N is not available, use the sum of multiple constituents available for the same day as TN. If only 1 constituent is available for a given day, use that as total N or P.

Use HarmonizationTemplate
https://usepa.sharepoint.com/:x:/r/sites/WQPDataAssessmentTeam/_layouts/15/Doc.aspx?sourcedoc=%7B756FBEA4-399E-4B40-BB15-4E52041151D0%7D&file=HarmonizationTemplate.xlsx&action=default&mobileredirect=true

Generate summary statistics table using HarmonizationIDs

Create function to remove true duplicates
Create filtering functions

Fields for full dataset filtering
ActivityTypeCode
ActivityMediaName
ActivityMediaSubdivisionName
ActivityCommentText
MonitoringLocationTypeName
StateName
TribalLandName
OrganizationFormalName
CharacteristicName
HydrologicCondition
HydrologicEvent
BiologicalIntentName
MeasureQualifierCode
ActivityGroup
AssemblageSampledName
ProjectName
CharacteristicNameUserSupplied
DetectionQuantitationLimitTypeName
SampleTissueAnatomyName
LaboratoryName

Fields for characteristic level filtering
ActivityCommentText
ActivityTypeCode
ActivityMediaName
ActivityMediaSubdivisionName
MeasureQualifierCode
MonitoringLocationTypeName
HydrologicCondition
HydrologicEvent
ResultStatusIdentifier
MethodQualifierTypeName
ResultCommentText
ResultLaboratoryCommentText
ResultMeasure/MeasureUnitCode
ResultSampleFractionText
ResultTemperatureBasisText
ResultValueTypeName
ResultWeightBasisText
SampleCollectionEquipmentName
LaboratoryName
MethodDescriptionText
ResultParticleSizeBasisText
SampleCollectionMethod/MethodIdentifier
SampleCollectionMethod/MethodIdentifierContext
SampleCollectionMethod/MethodName
DataQuality/BiasValue
MethodSpeciationName
ResultAnalyticalMethod/MethodName
ResultAnalyticalMethod/MethodIdentifier
ResultAnalyticalMethod/MethodIdentifierContext
AssemblageSampledName
CharacteristicNameUserSupplied
DetectionQuantitationLimitTypeName

Convert all units using MeasureUnit (CSV): https://cdx2.epa.gov/wqx/download/DomainValues/MeasureUnit.CSV

If clean=FALSE, append target unit and conversion columns only

• If unit is not recognizable or able to be converted, flag as "manual conversion required" or for some specific ones, include flag "UnitIncludesMetadata".

If clean=TRUE, append target unit and conversion columns AND convert units in dataset

• If unit is not recognizable or able to be converted, flag as "manual conversion required" or for some specific ones, include flag "UnitIncludesMetadata".

Generate record summary. May fit well in Utilities.R folder because it can be used throughout the assessment process.

This function relies on initial data retrieval generation of a "clean" file and a "raw file"

Summary
Total Records in Raw File: 544
Total Records Removed: 48
Total Records in Clean File: 496

Total Sites in Raw File: 20
Total Sites Removed: 4
Total Sites in Clean File: 16

Apply more advanced methods and/or different methods depending on characteristics being assessed and proportion of censored data for each

Additional options
KaplanMeier
Other methods (TBD)

Must download and upload template

Page Requirements/Standards

All of the flag page functions should be consistent in the following ways:

• Clean argument indicates whether flag columns should be appended to the data (clean = FALSE), or flagged data is transformed/filtered from the dataset and no columns are appended (clean = TRUE).
• Default is clean = FALSE

Function Requirements

• Reference table required?
  No
• Include warning if flags are not applied?
  No
• Required columns:
  QAPPApprovedIndicator

Development Notes

• Consider using this logic:
  QAPPApprovedIndicator is populated, QAPPApproved = Y (when clean = TRUE, these columns are retained)
  QAPPApprovedIndicator is not populated, QAPPApproved = N (when clean = TRUE, these columns are not retained)