Sandbox::https://github.com/jbousquin/EPATADA/blob/develop/R/autoFilter.R

Repository navigation

History

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

188

189

190

191

192

193

194

195

196

197

198

199

200

201

202

203

204

205

206

207

208

209

210

211

212

213

214

215

216

217

218

219

220

221

222

223

224

225

226

227

228

229

230

231

232

233

234

235

236

237

238

239

240

241

242

243

244

245

246

247

248

249

250

251

252

253

254

255

256

257

258

259

260

261

262

263

264

265

266

267

268

269

270

271

272

273

274

275

276

277

278

279

280

281

282

283

284

285

286

287

288

289

290

291

292

293

294

295

296

297

298

299

300

301

302

303

304

305

306

307

308

309

310

311

312

313

314

315

316

317

318

319

320

321

322

323

324

325

326

327

328

329

330

331

332

333

334

335

336

337

338

339

340

341

342

343

344

345

346

347

348

349

350

351

352

353

354

355

356

357

358

359

360

361

362

363

364

365

366

367

368

369

370

371

372

373

374

375

376

377

378

379

380

381

382

383

384

385

386

387

388

389

390

391

392

393

394

395

396

397

398

399

400

401

402

403

404

405

406

407

408

409

410

411

412

413

414

415

416

417

418

419

420

421

422

423

424

425

426

427

428

429

430

431

432

433

434

435

436

437

438

439

440

441

442

443

444

445

446

447

448

449

450

451

452

453

454

455

456

457

458

459

460

461

462

463

464

465

466

467

468

469

470

471

472

473

474

475

476

477

478

479

480

481

482

483

484

485

486

487

488

489

490

491

492

493

494

495

496

497

498

499

500

501

502

503

504

505

506

507

508

509

510

511

512

513

514

515

516

517

518

519

520

521

522

523

524

525

526

527

528

529

530

531

532

533

534

535

536

537

538

539

540

541

542

543

544

545

546

547

548

549

550

551

552

553

554

555

556

557

558

559

560

561

562

563

564

565

566

567

568

569

570

571

572

573

574

575

576

577

578

579

580

581

582

583

584

585

586

587

588

589

590

591

592

593

594

595

596

597

598

599

600

601

602

603

604

605

606

607

608

609

610

611

612

613

614

615

616

617

618

619

620

621

622

623

624

625

626

627

628

629

630

631

632

633

634

635

636

637

638

639

640

641

642

643

644

645

646

647

648

649

650

651

652

653

654

655

656

657

658

659

660

661

662

663

664

665

666

#' Unique Field Values Table

#' Function creates a table yielding the number of unique values in each field (column) returned.

#' The first column of the summary table holds the field name and the second column holds the

#' unique value count.

#' @param .data TADA dataframe

#' @param display A character string denoting what fields to return in the summary table. Defaults to "key". "all" will return all fields in the dataset, "most" will return most field names except those holding numeric values or units, and "key" returns the most important columns to review. Note that if a field is completely NA, it will not be shown on the summary table.

#' @param characteristicName Optional. Defaults to "null". A vector of TADA-converted (all caps) WQP characteristics a user may provide to filter the results to one or more characteristics of interest. "null" will show a summary table for the whole dataset.

#' @return A summary table yielding the number of unique values in each field.

#' @export

#' @examples

#' # Load example dataset:

#' utils::data(Data_Nutrients_UT)

#' # Count table of key fields in Data_Nutrients_UT dataset

#' fieldCountUT <- TADA_FieldCounts(Data_Nutrients_UT)

#' # Count table of most fields in Data_Nutrients_UT, filtered to only

#' # AMMONIA results.

#' fieldCountUTAmmonia <- TADA_FieldCounts(Data_Nutrients_UT,

#' display = "most", characteristicName = "AMMONIA"

#' )

TADA_FieldCounts <- function(

.data,

display = c("key", "most", "all"),

characteristicName = "null"

) {

# check .data is data.frame

TADA_CheckType(.data, "data.frame", "Input object")

# Check if the input data frame is empty

if (nrow(.data) == 0) {

message("The entered data frame is empty. The function will not run.")

return(NULL) # Exit the function early

}

display <- match.arg(display)

# filter to characteristic if provided

if (!characteristicName %in% c("null")) {

.data <- subset(

.data,

.data$TADA.CharacteristicName %in% c(characteristicName)

)

if (dim(.data)[1] < 1) {

stop(

"Characteristic name(s) provided are not contained within the input dataset. Note that TADA converts characteristic names to ALL CAPS for easier harmonization of synonyms from different WQP data providers (USGS's NWIS and EPA's WQX)."

)

}

# remove fields with only NAs from df

df <- .data |> dplyr::select(where(~ !all(is.na(.x))))

if (display == "key") {

cols <- c(

"TADA.ActivityType.Flag",

"TADA.Media.Flag",

"TADA.ActivityMediaName",

"ActivityMediaSubdivisionName",

"TADA.MonitoringLocationTypeName",

"OrganizationFormalName",

"TADA.CharacteristicName",

"SubjectTaxonomicName",

"TADA.MeasureQualifierCode.Def",

"HydrologicCondition",

"HydrologicEvent",

"BiologicalIntentName",

"AssemblageSampledName",

"CharacteristicNameUserSupplied",

"SampleTissueAnatomyName",

"CharacteristicNameUserSupplied",

"TADA.ComparableDataIdentifier",

"ActivityRelativeDepthName",

"ResultStatusIdentifier",

"ResultValueTypeName"

)

}

if (display == "most") {

cols <- c(

"ActivityTypeCode",

"TADA.ActivityType.Flag",

"TADA.Media.Flag",

"ActivityGroup",

"OrganizationIdentifier",

"OrganizationFormalName",

"ActivityTypeCode",

"ActivityMediaSubdivisionName",

"ActivityRelativeDepthName",

"ProjectIdentifier",

"ProjectName",

"TADA.MonitoringLocationIdentifier",

"MonitoringLocationName",

"ActivityCommentText",

"SampleAquifer",

"HydrologicCondition",

"HydrologicEvent",

"SampleCollectionMethod.MethodIdentifier",

"SampleCollectionMethod.MethodIdentifierContext",

"SampleCollectionMethod.MethodName",

"SampleCollectionMethod.MethodDescriptionText",

"SampleCollectionEquipmentName",

"ResultDetectionConditionText",

"ResultStatusIdentifier",

"ResultValueTypeName",

"DataQuality.PrecisionValue",

"DataQuality.BiasValue",

"DataQuality.ConfidenceIntervalValue",

"DataQuality.UpperConfidenceLimitValue",

"DataQuality.LowerConfidenceLimitValue",

"ResultCommentText",

"USGSPCode",

"SubjectTaxonomicName",

"SampleTissueAnatomyName",

"ResultAnalyticalMethod.MethodIdentifier",

"ResultAnalyticalMethod.MethodIdentifierContext",

"ResultAnalyticalMethod.MethodName",

"ResultAnalyticalMethod.MethodDescriptionText",

"LaboratoryName",

"ResultLaboratoryCommentText",

"DetectionQuantitationLimitTypeName",

"ProviderName",

"MonitoringLocationDescriptionText",

"HUCEightDigitCode",

"HorizontalCoordinateReferenceSystemDatumName",

"VerticalCoordinateReferenceSystemDatumName",

"AquiferName",

"LocalAqfrName",

"FormationTypeText",

"AquiferTypeName",

"ProjectDescriptionText",

"SamplingDesignTypeCode",

"QAPPApprovalAgencyName",

"TADA.ActivityMediaName",

"TADA.CharacteristicName",

"CharacteristicNameUserSupplied",

"TADA.MeasureQualifierCode.Def",

"TADA.MethodSpeciationName",

"TADA.ResultSampleFractionText",

"TADA.ComparableDataIdentifier",

"TADA.MonitoringLocationTypeName",

"AssemblageSampledName",

"BiologicalIntentName"

)

}

if (display == "all") {

cols <- names(df)

}

df <- df |> dplyr::select(dplyr::contains(cols))

# CREATE LIST OF FIELDS

# Find count of unique values in each column

col.names <- data.frame(Count = apply(df, 2, function(x) length(unique(x))))

# Create "Fields" column from row names

col.names$Fields <- row.names(col.names)

# Remove row names

row.names(col.names) <- NULL

# Reorder columns

col.names <- col.names[, c(2, 1)]

# Reorder Count column in col.names from largest to smallest number

col.names <- col.names |> dplyr::arrange(desc(Count))

return(col.names)

}

#' Field Values Summary Table

#' Function creates a dataframe containing the relative proportions of values in a given field in a TADA dataset.

#' @param .data TADA dataframe

#' @param field The field (column) the user would like to see represented in a pie chart.

#' @return A summary dataframe.

#' @export

#' @examples

#' # Load example dataset:

#' utils::data(Data_Nutrients_UT)

#' # Create a list of parameters in the dataset and the number of records of

#' # each parameter:

#' TADA_FieldValuesTable(Data_Nutrients_UT, field = "TADA.CharacteristicName")

TADA_FieldValuesTable <- function(

.data,

field = "null",

characteristicName = "null"

) {

# check .data is data.frame

TADA_CheckType(.data, "data.frame", "Input object")

# Check if the input data frame is empty

if (nrow(.data) == 0) {

message("The entered data frame is empty. The function will not run.")

return(NULL) # Exit the function early

}

if (!field %in% names(.data)) {

stop(

"Field input does not exist in dataset. Please populate the 'field' argument with a valid field name. Enter ?TADA_FieldValuesTable in console for more information."

)

}

# change NAs to "NA" (character string)

.data[[field]][is.na(.data[[field]])] <- "NA"

# filter to characteristic if provided

if (!characteristicName %in% c("null")) {

.data <- .data |>

dplyr::filter(TADA.CharacteristicName %in% characteristicName)

if (dim(.data)[1] < 1) {

stop(

"Characteristic name(s) provided are not contained within the input dataset. Note that TADA converts characteristic names to ALL CAPS for easier harmonization."

)

}

dat <- as.data.frame(table(.data[, field]))

names(dat) <- c("Value", "Count")

dat <- dat |> dplyr::arrange(desc(Count))

return(dat)

}

#' TADA_MediaFilter

#' Process a TADA dataframe to flag or filter data by media type.

#' Behavior overview:

#' - If `clean = FALSE`, classify each row and add `TADA.Media.Flag`. The function

#' prints a single message with counts by media and returns the original data with the flag.

#' - If `clean = TRUE`, classify and then remove rows whose media types are selected

#' via function toggles. The function prints a single message with counts by media

#' before filtering, and then a single summary message:

#' - When no toggles are selected (all FALSE): informs the user and returns the original

#' data without `TADA.Media.Flag`.

#' - When some toggles are selected but no rows match (e.g., data already contain only SURFACE WATER):

#' informs the user that nothing was removed and returns the data unchanged (without flag columns).

#' - When rows are removed: reports how many rows were removed and returns cleaned data

#' (without `TADA.Media.Flag`).

#' Additionally, a warning is issued if all media toggles are `TRUE` (which would remove

#' all media types), and if the filter removes all rows.

#' Inputs used for classification:

#' - `MonitoringLocationTypeName`, `ActivityMediaSubdivisionName` and `ActivityMediaName`

#' - Groundwater-related fields: `AquiferName`, `AquiferTypeName`, `LocalAqfrName`,

#' `ConstructionDateText`, `WellDepthMeasure.MeasureValue`, `WellDepthMeasure.MeasureUnitCode`,

#' `WellHoleDepthMeasure.MeasureValue`, `WellHoleDepthMeasure.MeasureUnitCode`

#' Classification details:

#' - ActivityMediaName of "Soil", "Sediment" (and common variants like "Soil or Sediment")

#' map to `SEDIMENT` even if groundwater fields are present.

#' - `ActivityMediaSubdivisionName` is reviewed for identifying "Surface Water",

#' "Groundwater", and "Sediment".

#' - If the subdivision is blank and `ActivityMediaName` is "Water" with no groundwater

#' fields present, the row is classified as `SURFACE WATER`.

#' - Groundwater fields are considered present if they are non-NA and non-blank (for

#' character/factor fields) or non-NA (for numeric fields).

#' - If a monitoring location type reference is available via `TADA_GetMonLocTypeRef()`,

#' its `TADA.Media.Flag` takes precedence when present.

#' - Media flags are normalized to the core set: `SURFACE WATER`, `GROUNDWATER`,

#' `SEDIMENT`, `OTHER`. Values such as `HABITAT`, `AIR`, `BIOLOGICAL`, empty strings,

#' or non-core values are coerced to `OTHER`.

#' Requirements and defaults:

#' - Required columns: `MonitoringLocationTypeName`, `ActivityMediaSubdivisionName`, and `AquiferName`.

#' If any required columns are missing (rare), the function stops with an error of the form

#' "Missing required columns: <col1>, <col2>, ...".

#' - Optional columns are created if missing (with appropriate types) and filled with `NA`:

#' `ActivityMediaName`, `AquiferTypeName`, `LocalAqfrName`, `ConstructionDateText`,

#' `WellDepthMeasure.MeasureValue` (numeric), `WellDepthMeasure.MeasureUnitCode`,

#' `WellHoleDepthMeasure.MeasureValue` (numeric), `WellHoleDepthMeasure.MeasureUnitCode`.

#' - If the input data frame has 0 rows, the function emits a message and returns `NULL`.

#' @param .data A data frame representing a TADA profile object.

#' @param clean Logical. If `TRUE`, remove rows according to the media toggles and return

#' data without flag columns. If `FALSE`, only flag media and return all rows with

#' `TADA.Media.Flag`. Default `FALSE`.

#' @param surface_water Logical (used only when `clean = TRUE`). If `TRUE`, remove `SURFACE WATER` results. Default `FALSE`.

#' @param ground_water Logical (used only when `clean = TRUE`). If `TRUE`, remove `GROUNDWATER` results. Default `FALSE`.

#' @param sediment Logical (used only when `clean = TRUE`). If `TRUE`, remove `SEDIMENT` results. Default `FALSE`.

#' @param other Logical (used only when `clean = TRUE`). If `TRUE`, remove `OTHER` results. Default `FALSE`.

#' @return A data frame.

#' - If `clean = FALSE`, returns all rows with the column `TADA.Media.Flag` added.

#' - If `clean = TRUE`, returns rows with the selected media removed and no flag columns added.

#' - If the input has 0 rows, returns `NULL`.

#' @section Messages and warnings:

#' - Always prints counts by media before filtering.

#' - When `clean = TRUE`, prints a single summary message indicating whether rows were removed,

#' whether no rows matched the selected media, or whether no toggles were selected.

#' - Warns if all media toggles are `TRUE`, and if all rows were removed by the filter.

#' @seealso [TADA_GetMonLocTypeRef()], [TADA_OrderCols()]

#' @export

#' @examples

#' utils::data(Data_R5_TADAPackageDemo)

#' # Example 1: Do not clean; classify media and add TADA.Media.Flag

#' Data_Flag <- TADA_MediaFilter(

#' Data_R5_TADAPackageDemo,

#' clean = FALSE

#' )

#' unique(Data_Flag$TADA.Media.Flag)

#' # Example 2: Remove groundwater and sediment; no flag column in output

#' Data_Clean1 <- TADA_MediaFilter(

#' Data_R5_TADAPackageDemo,

#' clean = TRUE,

#' ground_water = TRUE,

#' sediment = TRUE

#' )

#' "TADA.Media.Flag" %in% names(Data_Clean1) # FALSE

#' # Example 3: Keep only surface water by removing groundwater, sediment, and other

#' Data_Clean2 <- TADA_MediaFilter(

#' Data_R5_TADAPackageDemo,

#' clean = TRUE,

#' ground_water = TRUE,

#' sediment = TRUE,

#' other = TRUE

#' )

#' # Example 4: Remove surface water only

#' Data_Clean3 <- TADA_MediaFilter(

#' Data_R5_TADAPackageDemo,

#' clean = TRUE,

#' surface_water = TRUE

#' )

TADA_MediaFilter <- function(

.data,

clean = FALSE,

surface_water = FALSE,

ground_water = FALSE,

sediment = FALSE,

other = FALSE

) {

# Validate input

if (!is.data.frame(.data)) {

stop("Input object must be a data frame.")

}

if (nrow(.data) == 0) {

message("The entered data frame is empty. The function will not run.")

return(NULL)

}

# Require only MonitoringLocationTypeName; other fields are optional and will be created if missing

required_columns <- c(

"MonitoringLocationTypeName",

"AquiferName",

"ActivityMediaSubdivisionName"

)

missing_required <- setdiff(required_columns, names(.data))

if (length(missing_required) > 0) {

stop(paste(

"Missing required columns:",

paste(missing_required, collapse = ", ")

))

}

# Ensure optional columns exist to avoid downstream errors

# Split aquifer metadata (do NOT imply groundwater) from true well/GW fields

aquifer_meta_cols <- c("AquiferName", "AquiferTypeName", "LocalAqfrName")

gw_field_cols <- c(

"ConstructionDateText",

"WellDepthMeasure.MeasureValue",

"WellDepthMeasure.MeasureUnitCode",

"WellHoleDepthMeasure.MeasureValue",

"WellHoleDepthMeasure.MeasureUnitCode"

)

optional_cols <- c(

"ActivityMediaSubdivisionName",

"ActivityMediaName",

aquifer_meta_cols,

gw_field_cols

)

for (col in setdiff(optional_cols, names(.data))) {

.data[[col]] <- NA_character_

}

# Read the monitoring location reference table

monitoring_location_types <- TADA_GetMonLocTypeRef()

# Initialize has_ref and standardize names for robust detection

has_ref <- FALSE

std_names <- tolower(gsub("[^a-z.]", "", names(monitoring_location_types)))

# Exact matches: "Name" and "TADA.Media.Flag" (after normalization)

idx_name <- match("name", std_names)

idx_flag <- match("tada.media.flag", std_names)

if (!is.na(idx_name)) {

names(monitoring_location_types)[idx_name] <- "Name"

monitoring_location_types <- monitoring_location_types |>

dplyr::mutate(Name = toupper(.data$Name))

has_ref <- TRUE

} else {

has_ref <- FALSE

}

if (!is.na(idx_flag)) {

names(monitoring_location_types)[idx_flag] <- "Ref.TADA.Media.Flag"

} else if (has_ref) {

monitoring_location_types$Ref.TADA.Media.Flag <- NA_character_

}

# Uppercase ML type name in .data for reliable joining

.data <- .data |>

dplyr::mutate(

MonitoringLocationTypeName = toupper(.data$MonitoringLocationTypeName)

)

# Build a groundwater indicator from true well/GW fields only (exclude aquifer metadata)

present_gw_field_cols <- intersect(gw_field_cols, names(.data))

if (length(present_gw_field_cols) > 0) {

gw_logicals <- lapply(present_gw_field_cols, function(col) {

x <- .data[[col]]

if (is.factor(x)) {

x <- as.character(x)

}

if (is.character(x)) {

!is.na(x) & nzchar(trimws(x))

} else {

!is.na(x)

}

})

gw_has_fields <- Reduce(`|`, gw_logicals, init = rep(FALSE, nrow(.data)))

} else {

gw_has_fields <- rep(FALSE, nrow(.data))

}

.data$gw_has_fields <- gw_has_fields

# Precompute normalized media/subdivision for robust comparisons

ams <- tolower(trimws(as.character(.data$ActivityMediaSubdivisionName)))

am <- tolower(trimws(as.character(.data$ActivityMediaName)))

ams_blank <- is.na(ams) | !nzchar(ams)

am_blank <- is.na(am) | !nzchar(am)

# Classify media from data columns

.data <- .data |>

dplyr::mutate(

TADA.Media.Flag = dplyr::case_when(

# 1) Sediment by media name ...

!am_blank &

am %in%

"sediment",

"soil",

"soil or sediment",

"soil/sediment",

"soil-sediment"

) ~ "SEDIMENT",

# 2) Sediment by subdivision

ams == "sediment" ~ "SEDIMENT",

# 3) Groundwater by subdivision, well/GW fields, or WELL location type (override any SW subdivision)

ams == "groundwater" |

gw_has_fields |

grepl("\\bWELL\\b", .data$MonitoringLocationTypeName) ~ "GROUNDWATER",

# 4) Explicit surface water subdivision

ams == "surface water" ~ "SURFACE WATER",

# 5) Water heuristic (no GW fields) -> SURFACE WATER

ams_blank &

!am_blank &

am == "water" &

!gw_has_fields ~ "SURFACE WATER",

# 6) Keep other ActivityMediaName as-is

!am_blank & am != "water" ~ toupper(trimws(as.character(

.data$ActivityMediaName

))),

TRUE ~ NA_character_

)

# Join with reference (if available) and coalesce media flag

if (isTRUE(has_ref)) {

.data <- .data |>

dplyr::left_join(

monitoring_location_types,

by = c("MonitoringLocationTypeName" = "Name")

) |>

dplyr::mutate(

# Prefer computed classification; use reference as fallback

TADA.Media.Flag = dplyr::coalesce(

.data$TADA.Media.Flag,

.data$Ref.TADA.Media.Flag,

"OTHER"

)

) |>

dplyr::select(-dplyr::any_of(c("Ref.TADA.Media.Flag")))

} else {

.data <- .data |>

dplyr::mutate(

TADA.Media.Flag = dplyr::coalesce(.data$TADA.Media.Flag, "OTHER")

)

}

# Normalize TADA.Media.Flag to core values

core_flags <- c("SURFACE WATER", "GROUNDWATER", "SEDIMENT", "OTHER")

.data <- .data |>

dplyr::mutate(

TADA.Media.Flag = toupper(trimws(.data$TADA.Media.Flag)),

TADA.Media.Flag = dplyr::case_when(

is.na(.data$TADA.Media.Flag) ~ "OTHER",

.data$TADA.Media.Flag == "" ~ "OTHER",

.data$TADA.Media.Flag %in% c("HABITAT", "AIR", "BIOLOGICAL") ~ "OTHER",

!(.data$TADA.Media.Flag %in% core_flags) ~ "OTHER",

TRUE ~ .data$TADA.Media.Flag

)

# Aquifer-only classification: aquifer metadata present, no well/GW fields, and not a well location

aquifer_meta_present <- Reduce(

`|`,

lapply(intersect(aquifer_meta_cols, names(.data)), function(col) {

x <- .data[[col]]

if (is.factor(x)) {

x <- as.character(x)

}

if (is.character(x)) !is.na(x) & nzchar(trimws(x)) else !is.na(x)

}),

init = rep(FALSE, nrow(.data))

)

non_well_loc <- !grepl(

"\\bWELL\\b",

.data$MonitoringLocationTypeName,

ignore.case = TRUE

)

idx_aquifer_only <- aquifer_meta_present & !.data$gw_has_fields & non_well_loc

.data <- .data |>

dplyr::mutate(

TADA.Media.Flag = dplyr::case_when(

idx_aquifer_only &

(is.na(.data$TADA.Media.Flag) |

.data$TADA.Media.Flag == "" |

.data$TADA.Media.Flag == "SURFACE WATER") ~ "OTHER",

TRUE ~ .data$TADA.Media.Flag

)

# Build removal set based on arguments (used only when clean = TRUE)

remove_media <- c(

if (isTRUE(surface_water)) "SURFACE WATER",

if (isTRUE(ground_water)) "GROUNDWATER",

if (isTRUE(sediment)) "SEDIMENT",

if (isTRUE(other)) "OTHER"

)

if (clean) {

# Pre-flight guard: warn when all media toggles are TRUE

all_selected <- isTRUE(surface_water) &&

isTRUE(ground_water) &&

isTRUE(sediment) &&

isTRUE(other)

if (all_selected) {

warning("All media types are selected for removal", call. = FALSE)

}

# Helpers for robust counts (always return a 4-length named vector)

count_by_core <- function(v) {

f <- factor(v, levels = core_flags)

stats::setNames(

tabulate(as.integer(f), nbins = length(core_flags)),

core_flags

)

}

format_counts <- function(cnts) {

paste(

sprintf("%s: %s", names(cnts), format(cnts, big.mark = ",")),

collapse = " | "

)

}

# Counts BEFORE filtering

flags_before <- .data$TADA.Media.Flag

counts_pre <- count_by_core(flags_before)

message(sprintf(

"TADA_MediaFilter: Counts by media (before filter) - %s",

format_counts(counts_pre)

))

# Determine rows to remove and perform removal

removed_idx <- flags_before %in% remove_media

n_remove <- sum(removed_idx)

.data <- .data[!removed_idx, , drop = FALSE]

# Warn if all rows were removed, except when all toggles were TRUE

if (nrow(.data) == 0 && !all_selected) {

warning(

"TADA_MediaFilter: All rows were removed by the media filter.",

call. = FALSE

)

}

# Drop flag/helper columns and order

.data <- .data |>

dplyr::select(-dplyr::any_of(c("TADA.Media.Flag", "gw_has_fields"))) |>

TADA_OrderCols()

# Final single summary message

if (length(remove_media) == 0) {

message(

"TADA_MediaFilter: No media types selected for removal (all toggles are FALSE). Returning original data without TADA.Media.Flag."

)

} else if (n_remove == 0) {

message(sprintf(

"TADA_MediaFilter: No rows matched the selected media types for removal (%s). Returning data unchanged (without flag columns).",

paste(remove_media, collapse = ", ")

))

} else {

message(sprintf(

"TADA_MediaFilter: Removed %s rows matching media types: %s. Returning cleaned data without flag columns.",

format(n_remove, big.mark = ","),

paste(remove_media, collapse = ", ")

))

}

} else {

# Counts when not cleaning

count_by_core <- function(v) {

f <- factor(v, levels = core_flags)

stats::setNames(

tabulate(as.integer(f), nbins = length(core_flags)),

core_flags

)

}

format_counts <- function(cnts) {

paste(

sprintf("%s: %s", names(cnts), format(cnts, big.mark = ",")),

collapse = " | "

)

}

counts <- count_by_core(.data$TADA.Media.Flag)

message(sprintf(

"TADA_MediaFilter: Counts by media - %s",

format_counts(counts)

))

# Do not clean; keep flag and drop helper

.data <- .data |>

dplyr::select(-dplyr::any_of("gw_has_fields")) |>

TADA_OrderCols()

message(

"TADA_MediaFilter: Returning all results with TADA.Media.Flag; media toggles ignored because clean = FALSE."

)

}

return(.data)

}

FilesExpand file tree

autoFilter.R

Latest commit

History

autoFilter.R

File metadata and controls