rm(list=ls(all=t))
filename <- "SAP2016 Secundaria RAW1 NOPII" # !!!Update filename
functions_vers <- "functions_1.7.R" # !!!Update helper functions file
source (functions_vers)
Visually inspect variables in "dictionary.csv" and flag for risk, using the following flags:
# Direct PII: Respondent Names, Addresses, Identification Numbers, Phone Numbers
# Direct PII-team: Interviewer Names, other field team names
# Indirect PII-ordinal: Date of birth, Age, income, education, household composition.
# Indirect PII-categorical: Gender, education, ethnicity, nationality,
# occupation, employer, head of household, marital status
# GPS: Longitude, Latitude
# Small Location: Location (<100,000)
# Large Location (>100,000)
# Weight: weightVar
# Household ID: hhId,
# Open-ends: Review responses for any sensitive information, redact as necessary
# !!!Include any Direct PII variables
dropvars <- c("A_NOM",
"C_NOM",
"A_APEPAT",
"C_APEPAT",
"A_APEMAT",
"C_APEMAT",
"J_DNI",
"L_DNI")
mydata <- mydata[!names(mydata) %in% dropvars]
# !!! Remove as it contains identifying information
dropvars <- c("DIGITA")
mydata <- mydata[!names(mydata) %in% dropvars]
# !!!Replace vector in "variables" field below with relevant variable names
mydata <- encode_direct_PII_team (variables=c("ENCUES"))
## [1] "Frequency table before encoding"
## ENCUES. Codigo del Encuestador
## No indica 1 2 3 4 5 6 7
## 1989 537 429 21 517 896 701 3
## 9 10 11 12 13 14 15 16
## 1086 947 439 2 1410 1175 373 6
## 17 19 20 21 22 24 25 26
## 872 1 267 10 628 978 341 1
## 33 112 202 234 300 666
## 1 1 1 1 1 1
## [1] "Frequency table after encoding"
## ENCUES. Codigo del Encuestador
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
## 1989 537 429 21 517 896 701 3 1086 947 439 2 1410 1175 373 6 872
## 18 19 20 21 22 23 24 25 26 27 28 29 30
## 1 267 10 628 978 341 1 1 1 1 1 1 1
# !!! Remove as it contains identifying information
dropvars <- c("NOMESC")
mydata <- mydata[!names(mydata) %in% dropvars]
# !!!Include relevant variables, but check their population size first to confirm they are <100,000
locvars <- c("CODLOC", "CODMOD")
mydata <- encode_location (variables= locvars, missing=999999)
## [1] "Frequency table before encoding"
## CODLOC. Codigo de Local
## No Indica 140048 142491 148313 291278 292490 292579 292678
## 81 24 8 4 8 17 7 11
## 298053 300739 301376 301668 301687 301692 301809 306675
## 1 8 5 14 2 4 1 13
## 307458 307477 311120 314519 315062 318348 319319 320426
## 9 3 2 6 3 5 8 21
## 320450 324429 324542 324561 324617 324655 324721 324759
## 2 5 2 11 9 3 4 1
## 324976 324981 325235 325264 325400 325508 332943 332962
## 2 1 6 4 9 10 1 8
## 333103 333377 333396 333400 333443 338681 338695 338817
## 1 39 46 10 1 5 16 9
## 338841 343814 343828 344111 346681 365231 687383 687887
## 6 6 12 6 14 7 3 1
## <NA>
## 13110
## [1] "Frequency table after encoding"
## CODLOC. Codigo de Local
## 467 468 469 470 471 472 473 474 475 476 477 478 479 480
## 21 6 8 3 11 3 2 10 1 4 17 5 10 1
## 481 482 483 484 485 486 487 488 489 490 491 492 493 494
## 6 11 9 39 1 1 3 9 2 2 9 4 5 8
## 495 496 497 498 499 500 501 502 503 504 505 506 507 508
## 16 2 14 6 6 14 1 8 3 13 81 5 6 5
## 509 510 511 512 513 514 515 516 517 518 519 520 521 522
## 2 8 7 4 1 12 24 9 8 1 7 1 4 46
## <NA>
## 13110
## [1] "Frequency table before encoding"
## CODMOD. C<f3>digo Modular
## No Indica 209387 209510 209536 209908 209916 209965 209973
## 81 5 40 75 45 18 80 22
## 233130 236117 236224 236364 245688 302893 302968 318931
## 1 54 12 12 91 14 37 15
## 318949 325563 325589 325605 325647 325654 325670 325712
## 3 123 40 27 87 1 77 4
## 328047 328153 328872 329573 334680 334714 334722 334771
## 21 11 8 73 7 5 26 7
## 336560 336594 337436 337766 340281 340299 340323 340331
## 58 106 89 21 6 4 11 8
## 340356 340364 340380 340398 340414 340463 432773 434076
## 12 6 123 10 58 10 12 10
## 434175 434746 436709 437210 437228 437236 437277 437285
## 1 2 8 32 137 140 108 37
## 437335 437343 437368 437509 437707 437715 437723 449827
## 26 126 2 10 33 111 42 3
## 466342 466722 469700 481820 488619 488635 493544 495150
## 4 6 70 1 59 38 118 2
## 495812 496166 497081 499699 500124 500611 501676 501809
## 14 58 2 93 14 7 81 4
## 501908 502104 502484 502633 522318 523423 523621 525857
## 47 3 92 62 14 47 37 1
## 527572 536128 536326 546002 555862 556340 556472 556571
## 6 27 40 56 45 82 28 76
## 566141 566158 566430 566463 566471 567750 573352 578286
## 87 3 64 57 73 42 37 77
## 578294 578336 578351 578393 578435 578492 578542 579151
## 74 64 55 3 47 75 83 101
## 581710 581736 581876 581892 581900 582122 582148 582163
## 11 111 88 65 53 5 45 68
## 582189 582833 582866 582890 582981 583013 583203 583476
## 1 79 4 57 49 134 3 7
## 583567 590133 596007 598581 601492 605469 605501 607143
## 46 3 1 57 30 145 5 1
## 607432 607697 632356 639922 642892 643221 643783 643841
## 3 100 6 5 4 2 4 3
## 644880 647065 650036 659896 662940 662957 663005 663096
## 51 123 32 85 5 12 15 88
## 663120 663138 663559 664706 665265 682229 682260 690008
## 5 35 92 2 107 115 30 73
## 690024 691931 692434 692707 693499 694588 694604 703215
## 1 94 114 4 94 59 8 90
## 703223 703231 703249 703744 703751 705053 705772 720235
## 106 87 99 61 109 77 10 25
## 725770 725861 728337 728717 730515 732321 732347 743831
## 46 22 4 166 6 16 85 66
## 744573 751230 762773 762856 762864 763169 764928 764936
## 2 19 77 69 107 1 1 45
## 765297 765305 765370 773788 774455 776229 777680 777995
## 52 17 2 68 7 4 80 46
## 778027 778076 779041 781369 782045 782664 826479 832279
## 1 3 81 134 1 101 6 21
## 832311 869032 869198 869222 869230 872515 874206 884510
## 11 3 42 2 57 122 8 11
## 884528 884536 884544 884551 884585 900670 900761 900910
## 48 5 30 91 90 1 65 98
## 901033 901066 901124 915256 927814 1008440 1008960 1009802
## 82 84 2 75 110 60 29 8
## 1009844 1010149 1034016 1041631 1045434 1045632 1053628 1053693
## 27 30 6 149 11 6 53 137
## 1054196 1054352 1054394 1054436 1062942 1063106 1070036 1072727
## 118 72 15 118 11 40 50 60
## 1073212 1074301 1083674 1083716 1083815 1084508 1147933 1148014
## 50 18 14 28 59 33 4 31
## 1152941 1152982 1153022 1153261 1194380 1194810 1195189 1195577
## 119 62 2 3 36 4 29 1
## 1195874 1210137 1222595 1240357 1240720 1247832 1248509 1254192
## 68 73 1 2 66 7 77 1
## 1258334 1258649 1262211 1279124 1309392 1322593 1336072 1346576
## 5 1 11 50 110 3 8 40
## 1369503 1370378 1381987 1390137 1390798 1393453 1398148 1423615
## 60 1 7 1 54 138 35 23
## 1432772 1473644 1474964 1475011 1475201 1475284 1475755 1484443
## 3 57 100 92 69 46 1 48
## 1495365 1495407 1496355 1497056 1500354 1501451 1505494 1507532
## 33 56 2 93 5 94 138 62
## 1512789 1527316 1558725 1641521 1661271
## 54 3 125 17 46
## [1] "Frequency table after encoding"
## CODMOD. C<f3>digo Modular
## 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189
## 42 77 11 40 61 33 8 26 52 2 27 8 33 85 15 75 1 94 70 77 56
## 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210
## 65 106 125 6 1 10 11 4 14 29 33 18 1 1 54 5 4 106 18 35 118
## 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231
## 3 48 123 1 58 7 12 5 45 37 10 110 110 12 30 111 107 3 60 100 5
## 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252
## 138 91 77 1 2 87 145 7 92 10 21 14 11 8 8 72 62 60 90 73 73
## 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273
## 68 7 2 166 4 119 17 5 3 93 138 3 12 7 90 1 75 81 6 1 73
## 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294
## 56 45 15 8 46 3 11 2 1 109 5 50 31 69 91 1 8 12 92 57 76
## 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315
## 46 77 28 40 3 45 80 2 3 87 2 64 47 93 50 6 6 14 84 4 149
## 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336
## 10 2 2 30 123 11 27 27 89 8 87 140 3 36 7 40 92 57 1 137 4
## 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357
## 1 101 49 3 32 1 35 46 3 59 57 118 45 7 6 4 94 81 122 1 1
## 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378
## 99 62 29 74 54 60 98 4 58 59 22 22 28 137 88 68 25 11 26 5 1
## 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399
## 77 82 114 2 58 66 57 80 2 1 12 1 3 14 3 3 23 111 10 59 5
## 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420
## 6 51 1 37 15 134 38 46 69 53 1 83 54 3 107 126 118 55 64 1 7
## 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441
## 62 30 66 14 21 4 2 100 53 47 4 4 21 79 82 6 88 115 40 46 40
## 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462
## 134 101 11 17 94 19 6 4 37 85 42 123 11 37 3 30 16 4 48 2 2
## 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477
## 42 68 57 6 108 50 5 65 81 75 32 47 73 5 5
# Focus on variables with a "Lowest Freq" in dictionary of 30 or less.
dropvars <- c("D_DD",
"D_MM",
"F_DD",
"F_MM")
mydata <- mydata[!names(mydata) %in% dropvars]
# !!!Include relevant variables in list below (Indirect PII - Categorical, and Ordinal if not processed yet)
indirect_PII <- c("NIVESC",
"G_SEXO",
"I_SEXO",
"Q35A",
"Q40",
"Q42_1",
"Q42_2",
"Q42_3",
"Q42_4",
"Q42_5")
capture_tables (indirect_PII)
# Recode those with very specific values.
# !!! No very specific values
# !!!Insufficient demographic data
# !!! Identify open-end variables here:
open_ends <- c("Q11_B",
"Q12_B",
"Q17_B",
"Q18_B",
"Q23B",
"Q23D")
report_open (list_open_ends = open_ends)
# Review "verbatims.csv". Identify variables to be deleted or redacted and their row number
# !!! Remove, as they contain a lot of sensitive information and they are in Spanish.
mydata <- mydata[!names(mydata) %in% "Q11_B"]
mydata <- mydata[!names(mydata) %in% "Q12_B"]
mydata <- mydata[!names(mydata) %in% "Q17_B"]
mydata <- mydata[!names(mydata) %in% "Q18_B"]
mydata <- mydata[!names(mydata) %in% "Q23B"]
mydata <- mydata[!names(mydata) %in% "Q23D"]
# !!! No GPS data
haven::write_dta(mydata, paste0(filename, "_PU.dta"))
haven::write_sav(mydata, paste0(filename, "_PU.sav"))
# Add report title dynamically
title_var <- paste0("DOL-ILAB SDC - ", filename)