Package index • sampleDB

All functions

ArchiveAndDeleteControls(): Archive and Delete Wetlab EPPIcenter Samples

ArchiveAndDeleteSamples(): Archive and Delete Samples

Backup_SampleDB(): Backup Sample Database

ColumnData(): Create a ColumnData S3 object

DeleteEmptyContainer(): Delete Empty Wetlab Containers in the EPPIcenter SampleDB database

DeleteWholeBloodSamples(): Delete Whole Blood Controls

ErrorData: ErrorData class

MoveContainers(): Move Wetlab Containers in the EPPIcenter SampleDB database

MoveSpecimens(): Move Wetlab Samples

RenameContainers(): Rename Wetlab Containers in the EPPIcenter SampleDB database

Run_SampleDB(): Run sampleDB as a R Shiny App

SampleDB_Setup(): Set up and deploy the SampleDB environment, database, and Shiny server.

SearchByType(): Search for Wetlab Samples in the EPPIcenter SampleDB database

SearchControls(): Get all malaria blood controls

UpdateReferences(): Update References in the EPPIcenter SampleDB database

UploadSpecimens(): Upload Wetlab Samples

ValidationErrorCollection: ValidationErrorCollection class

add_bags_if_not_exist(): Add new bags to the database if they don't exist

add_malaria_blood_controls_if_not_exist(): Add malaria blood controls to the database if they don't exist

add_new_blood_spot_collections(): Add New Blood Spot Collections

add_new_dbs_control_sheets(): Add new DBS control sheets to the database

add_position_column(): Add a position column based on column attributes

add_row_numbers(): Add row numbers to a dataframe

add_to_errors(): Add an Error to the Error List

append_strains_to_db(): Append Strains to the Database Table

append_study_to_db(): Append Studies to the Database Table

append_whole_blood_tubes(): Add whole blood tubes to the database if they don't exist

bind_new_data(): Add Data to User Data

check_control_exists(): Check for Control Existence

check_cryovial_barcodes_exist(): Check Cryovial Barcodes in Database

check_cryovial_box_exists(): Check If Cryovial Box Exists in the Database

check_duplicated_rows(): Check for duplicated rows in a data frame

check_longitudinal_study_dates(): Check if collection dates are missing for samples in a longitudinal study

check_micronix_barcodes_exist(): Check Micronix Barcodes in Database

check_micronix_plate_exists(): Check If Micronix Plate Exists in the Database

check_missing_data(): Check for missing data in required fields

check_strain_percentage_match(): Validate that the strain and percentage columns have matching semicolon counts

check_unique_positions(): Check Unique Positions

collapse_and_nest_columns(): Collapse and Nest Strain and Percentage Columns

concat_position(): Concatenate Position from Column and Row Information

convert_density_representations(): Convert Density Representations to Real Numbers

convert_density_to_numeric(): Convert string values with 'k' or 'K' to numeric values

copy_database(): Copy SQLite Database

copy_if_not_exists(): Copy a file to a destination if it does not exist there.

create_control_label(): Create Control Label with Density and Composition Values

create_controls_for_batch(): Create Controls for the batch

create_flat_view_by_sample_type(): Flatten all database tables by sample type

create_flat_view_of_extractions(): View all extractions in the database

create_unique_id(): Create a unique identifier for compositions

create_unique_id_from_sorted(): Create a unique identifier from sorted strains and percentages

cryovial_na: Cryovial NA

denormalize_composition_ids(): Denormalize Composition IDs from User Data

display_app_url(): Display the URL of the deployed Shiny application.

.Backup(): Internal function to backup a SQLite database

.Compress(): Internal function to compress a file

.Uncompress(): Internal function to uncompress a file

execute_sql(): Execute SQL Queries on a Database

execute_upgrade_sql_script(): Execute sql

extract_search_criteria(): Extract Search Criteria from User CSV File

find_column_name(): Find First Matching Column Name

find_header(): Identify the row in a CSV file that matches the header based on required column names.

format_composition_types(): Format composition types

format_error(): Create Formatting Error Data Frame

format_labels(): Format labels for compositions in a dplyr pipeline

fuzzy_merge_unique_compositions(): Fuzzy Merge Unique Compositions

generate_upgrade_script_path(): Generate Path to Database Upgrade Script

get_all_control_types(): Get all control types

get_all_sample_types(): Get all sample types

get_batches(): Get all batches from the database

get_composition_types(): Get all composition type from the database

get_container_by_control(): Get containers by specified sample type

get_container_by_sample(): Get containers by specified sample type

get_container_table_name(): Get the Relevant Table Name Based on Container Class

get_control_action_types(): Get action types for a specific control

get_control_file_columns(): Retrieve ColumnData for a given control type and action

get_control_types(): Get control types from a JSON file

get_db_version(): Retrieve Current Database Version

get_densities(): Get all densities from the database

get_dereferenced_values(): Get dereferenced values using sample_values and shared_values

get_destination_container_by_control(): Get destination container by specified control type

get_destination_location_by_control(): Get destination location by specified control type

get_duplicated_rows(): Find duplicate rows in a data frame

get_environ_file_path(): Get environ file path

get_expected_versions(): Retrieve Expected Versions from JSON File

get_extractions(): Get extractions

get_file_types_for_control(): Get file types for a specific control

get_file_types_for_sample(): Get file types for a specific sample

get_location_by_sample(): Get locations by specified sample type

get_normalized_path(): Get normalized path

get_percentages(): Get all batches from the database

get_position_column_by_sample(): Get the expected position column for a sample type

get_reference_file_columns(): Retrieve ColumnData for a given reference type.

get_sample_action_types(): Get action types for a specific sample

get_sample_file_columns(): Retrieve ColumnData for a given sample type and action.

get_sample_types(): Get sample types from a JSON file

get_sample_types_by_action(): Get sample types from a JSON file by action

get_storage_container_by_control(): Get source container by specified control type

get_storage_location_by_control(): Get source location by specified control type

get_strains(): Get all strains from the database

get_studies(): Get all studies from the database

get_unique_compositions_from_database(): Extract unique compositions from the database

get_unique_compositions_from_user_data(): Normalize Percentages by Group

get_values(): Get values with priority to sample_values over shared_values.

handle_unknown_date_tokens(): Handle Unknown Date Tokens

handle_upgrade_error(): Handle any errors during the database upgrade

init_and_copy_to_db(): Initialize Database Connection and Copy User Data

init_db_conn(): Initialize a Database Connection

initialize_database_with_base_version(): Initialize Database with Base Version

is_linux_platform(): Check if the system platform is Linux.

is_shiny_server_installed(): Check if Shiny server is installed.

join_blood_spot_collections(): Join Blood Spot Collections

join_composition_ids(): Process user data to handle malaria blood control details

join_dbs_control_sheets(): Join DBS control sheets based on bag ID and label

join_locations_and_bags(): Join locations and bags from user data to the database

join_locations_and_boxes(): Process Whole Blood Box Data

join_malaria_controls(): Join malaria controls based on density and study subject ID

merge_configs(): Merge Configuration Lists

message_not_site_install(): Display a message indicating the need for a site install.

message_not_supported_platform(): Display a message indicating Shiny server isn't supported.

message_shiny_not_installed(): Display a message indicating Shiny server isn't installed.

micronix_na: Micronix NA

normalize_composition_ids(): Normalize Composition IDs from User Data

perform_batch_db_validations(): Perform Database-Related Validations for Batches

perform_composition_db_validations(): Perform Database-Related Validations for Compositions

perform_cryovial_db_validations(): Perform Database-Related Validations for Cryovials

perform_dbs_sample_db_validations(): Perform Database-Related Validations for DBS Samples

perform_dbs_sheet_db_validations(): Perform Database-Related Validations for DBS Sheets

perform_micronix_db_validations(): Perform Database-Related Validations for Micronix

perform_strain_db_validations(): Perform Database-Related Validations for Strains

perform_whole_blood_db_validations(): Perform Database-Related Validations for Whole Blood Controls

prepare_control_data_for_validation(): Prepare DataFrame for Control Validation

prepare_control_for_upload(): Prepare Control for Upload

prepare_new_compositions(): Prepare new compositions for database upload

prepare_reference_data_for_validation(): Prepare DataFrame for Reference Validation

prepare_specimen_data_for_validation(): Prepare DataFrame for Specimen Validation

print(<ColumnData>): Print method for ColumnData objects

process_and_append_compositions(): Process and append new compositions to the database

process_bag_data(): Process user data to handle bag details

process_blood_spot_collection_data(): Process Blood Spot Collection Data

process_control_csv(): Process and Validate Control CSV File

process_dbs_control_sheet_data(): Process user data to handle DBS control sheet details

process_malaria_blood_control_data(): Process user data to handle malaria blood control details

process_reference_csv(): Process and Validate Reference CSV File

process_specimen_csv(): Process and Validate Specimen User CSV File

process_whole_blood_location_container(): Process Whole Blood Box Data

read_and_preprocess_csv(): Read and Preprocess User CSV File

rejoin_dbs_control_sheet_ids(): Rejoin to Get New DBS Control Sheet IDs

rejoin_malaria_controls(): Rejoin malaria controls based on density and study subject ID

rejoin_to_get_bag_ids(): Rejoin the payload with the database to retrieve bag IDs

remove_na_columns_in_row(): Remove columns that have NA in a row

reshape_identifier_to_long_form(): Reshape Identifier Data to Long Form

retrieve_compositions_by_identifier(): Retrieve compositions from the database by identifier

retrieve_compositions_by_label(): Retrieve compositions from the database by label

search_by_study_subject_file_upload(): Search by Study Subject (and other longitudinal study criteria)

search_compositions(): Search for Compositions in Database

search_micronix_tube(): Search Micronix Tube Samples

select_relevant_columns(): Select relevant columns from the user CSV file

set_header_row(): Set a Specific Row as the Header

set_user_file_header(): Set the header row for the user CSV file

setup_database(): Setup or upgrade the database

setup_directory(): Setup a directory if it does not exist.

split_and_sort(): Split and sort strains and percentages

split_and_unnest_columns(): Split and Unnest Strain and Percentage Columns

stop_app_error(): Stop with an application error

stop_formatting_error(): Stop with a formatting error

stop_validation_error(): Stop with a validation error

update_configuration_file(): Update Configuration File

update_env_variable(): Update environment variable

upgrade_database(): Upgrade Database

upload_compositions(): Upload compositions to the database

upload_controls(): Upload Controls to the Database

upload_extracted_dna(): Upload Extracted DNA Data to SampleDB

validate_and_format_control_file(): Check if the user CSV file for control meets the requirements

validate_and_format_reference_file(): Check if the user CSV file for control meets the requirements

validate_and_format_specimen_file(): Check if the user CSV file meets the requirements

validate_batch(): Validate Batch Data

validate_box_uniqueness(): Check if boxes have enough unique cryovial barcodes to be easily identifiable.

validate_composition(): Validate Compositions Data

validate_composition_sum(): Validate if control compositions sum correctly

validate_control_uid_in_batch(): Validate Control UID exists within a batch

validate_controls(): Validate Control Specimens

validate_cryovial(): Validate Cryovial Specimen Data

validate_cryovial_collection_dates(): Confirm that Cryovials with no barcodes have collection dates if there's already a sample from that study subject.

validate_cryovial_moves(): Validate Cryovial Moves

validate_cryovial_position(): Validate Cryovial-specific position rules

validate_cryovial_uploads(): Validate Cryovial Uploads

validate_date_format(): Validate Date Format

validate_dates_with_tokens(): Validate Dates with Tokens

validate_dbs_bag_label_is_unique(): Validate DBS Bag Label is unique at a location

validate_dbs_box_bag_exists(): Check DBS Specimen Box and Bag exist

validate_dbs_sample(): Validate DBS Specimen Data

validate_dbs_sample_label_uniqueness(): DBS Specimen paper identifier uniqueness check

validate_dbs_sample_moves(): Validate DBS Specimen Moves

validate_dbs_sample_uploads(): Validate Micronix Uploads

validate_dbs_sheet(): Validate DBS Sheet Control Data

validate_dbs_sheet_create(): Validate DBS Sheet Create

validate_dbs_sheet_extraction(): Validate DBS Sheet Extraction

validate_dbs_sheet_label_maps_to_uids(): Validate that a sheet name for a batch consistently contains the same densities and compositions. Sheet names (or labels) should be descriptive of the contents that are on the sheet. This validation check is in place to enforce sheet name specificity by only allowing a sheet name to link to composition types and densities that we're used with the first named sheet on upload.

validate_dbs_sheet_move(): Validate DBS Sheet Move

validate_empty_cryovial_well_upload(): Cryovial Empty Well Validation

validate_empty_micronix_well_upload(): Micronix Empty Well Validation

validate_existing_barcodes_by_study(): Validate if barcodes already exist for a given study

validate_extraction_counts_with_totals(): Validate Extraction Counts Against Blood Spot Collection Totals

validate_location_reference_db(): Validate location references (Database Version)

validate_longitudinal_study(): Ensure uniqueness of study subjects and collection dates within longitudinal studies.

validate_matrix_container(): Validate Matrix Container position

validate_micronix(): Validate Micronix Specimen Data

validate_micronix_barcode_length(): Validate Micronix-specific barcode length rules

validate_micronix_moves(): Validate Micronix Moves

validate_micronix_position(): Validate Micronix-specific position rules

validate_micronix_uploads(): Validate Micronix Uploads

validate_non_longitudinal_study_subjects(): Check uniqueness of StudySubject within non-longitudinal studies.

validate_references(): Validate References

validate_specimen_type_db(): Validate specimen type references (Database Version)

validate_specimens(): Validate Specimens Main Function

validate_strain(): Validate Strains Data

validate_strains_in_database(): Validate strains recorded in the database

validate_study_reference_db(): Validate study references (Database Version)

validate_study_short_code(): Validate that study does not have a date for it's short code

validate_whole_blood(): Validate Whole Blood Control Data

validate_whole_blood_create(): Validate Whole Blood Create

validate_whole_blood_extraction(): Validate Whole Blood Extraction

validate_whole_blood_move(): Validate Whole Blood Move

validate_whole_blood_tube_exists(): Validate Cryovial Tube Exists