rm(list=ls(all=t))
filename <- "endline" # !!!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
# !!!No Direct PII
# !!!No Direct PII-team
!!!Include relevant variables, but check their population size first to confirm they are <100,000
mydata$endline_child_municipality <- as.numeric(mydata$endline_child_municipality)
locvars <- c("endline_villagename",
"endline_settlement",
"endline_municipality",
"endline_wardno",
"endline_child_municipality",
"endline_child_wardno",
"endline_child_villagename",
"endline_child_settlement")
mydata <- encode_location (variables= locvars, missing=999999)
## [1] "Frequency table before encoding"
## endline_villagename. Villagename
## 'Ranighat .parariya /kharsal
## 7 7 4
## /Rajesh gupta aadarsa tol Aadarsa tol
## 6 3 5
## aadarsamani tol Aadarsh tole Aadarsha tol
## 6 6 28
## Aakala Aalau aarba
## 5 33 17
## Aarba Adalat road adarshnagar
## 11 3 5
## Adarshnagar Adarsnagar akala
## 8 4 11
## Alakha Road Apauni arba
## 8 21 24
## Arba arbaa Armalakot
## 24 3 23
## arva Ashok batika Ashokbatica
## 35 6 10
## Ashokbatika Ashokvatika Atharaha
## 40 2 42
## athraha Athraha badhahare tol
## 8 46 6
## bagaicha Bahuwari Baisno devi tole
## 4 20 4
## Bajrang tole Banahari Bangau
## 41 29 53
## Barabighaha Barwa Basant inarwa
## 4 15 28
## Basantpur tole Basantpure Basudavpur
## 15 7 6
## Basudevpur Basydevpur Bausevpur
## 157 5 2
## Bawanipur Bawaniyapur Bayo khola
## 7 6 17
## Bayokhola Belganar Belganari
## 5 2 26
## Beltakura Bhagawati tole Bhagwatitol
## 3 8 32
## bhakti path bhalam Bhalam
## 9 39 57
## Bhaluwahi tole bhandari dhar bhansartole
## 4 4 11
## Bhansartole bhastal bhati path
## 24 13 3
## Bhawanipur Bhawaniyapur Bhediyahi
## 129 194 20
## Bijayanagar Bijeynagar Bindabasini
## 4 81 49
## Bindawasini birendra gufa Birgunj
## 75 18 39
## Birta Brahmpur Brita Nursing Campus Area
## 114 93 3
## Budagaun tole Bundabasini Capkaiya
## 11 4 7
## Center parseni tol Centre Parseni tole Chailaheli
## 4 4 4
## Chailai chanautae Chanora parariya
## 11 13 16
## chanutae chanute chapkaiya
## 16 6 4
## Chapkaiya Chapkaiys Chapkaya
## 297 6 6
## Chapksiya Chappa dada Chhapkaiya
## 3 4 184
## Chitraguptnagar chour chowk
## 32 13 4
## Cigarette factory Dabar tole Dadathok
## 4 3 4
## Dadathok tol Dadrini Danda sukaura
## 3 11 8
## Dasarath nager Dasharthnager Daxin nawalpur tole
## 4 4 3
## Deurali piple Devi chook Dhadagari
## 22 4 25
## Dhaddagari Dhalepipal dhaurali
## 5 6 4
## Dhurmi Dihi gau Dinesh sah
## 30 6 5
## Dripot dripot sirsiya Dryport tol
## 8 7 5
## Fulbari Furthi chook Furthichook
## 106 54 11
## Furtichook Gahatera Gahawa
## 4 14 51
## garjati Garmikhola gauri khor
## 16 5 3
## Gaurigau Gaushala road Geetanagae
## 20 4 3
## Geetanagar Geetanagr ghadgai
## 331 4 12
## ghadhai Ghadi ghanduke chouk
## 7 12 3
## Ghantaghar Ghariwara Ghariwarha
## 6 17 44
## Gharmikhola ghatgai Ghoraneti
## 4 7 5
## Ghoraneti tol Ghorneti Ghurmi
## 13 5 14
## Ghusari Ghusari tole Ghushauri tole
## 6 5 5
## Gitpur tole Gogimani Golauri
## 5 4 6
## Golouri Gopal chook Gshawa
## 6 11 5
## Gyanjyoti Halawar haldharko chautara
## 4 10 5
## Halwar Hamagara hanuman nagar
## 4 6 9
## hanumanagar hanumannagae hanumannagar
## 23 4 4
## hanunam nagar Haripaura Haripauri
## 3 10 5
## Harpatganj Harpatgunj Hasnapur
## 87 43 8
## Hatiya Hatti lote Hemanagar
## 37 5 7
## Hemangar hemja Hemja
## 4 110 109
## Himalay tole Himalayan tole Himaltole
## 45 6 6
## Himalya tole Indarpue Indarpur
## 3 5 195
## jagriti tole Jagriti tole Jail road
## 6 6 5
## Jail tol Jailroad Jaispur
## 9 23 217
## JanaJagriti tol Jaspur Jaumare
## 5 13 8
## Jimire dil Jispur Jumleti tol
## 5 6 7
## Kachila Kachili Kahu
## 8 28 51
## kahun Kahun Kalakhola
## 48 4 10
## Kalakhola pulchok Kalimati Kalyakhola vewdar
## 3 4 7
## Kalyanpur Kanchanpur road karai chautari
## 3 3 6
## Kataha kaun Kawari
## 30 68 13
## Kesarbagha khadha Khadre
## 4 6 8
## Khadrye khaluwatole Khaluwatole
## 13 6 9
## khaluwatole sirsiya Khaluwatole sirsiya Khaluwatole Sirsiya
## 6 5 5
## khalwa sirisiya khalwa tol Khalwa tol
## 6 5 5
## Khardye Kharkhola Kharsal
## 3 3 126
## Kharsal purnipokhari Kharsal tole Khas karandoo
## 8 10 6
## Khas karkandoo khastar khaster
## 160 14 9
## Khumkhane Kimdi Kirana line
## 21 5 27
## Kirishna nagar Koeritole udaypur ghurmi kristi
## 5 7 72
## Kristi Kuhari Kulayen marga
## 89 3 6
## Kumal gau Kumar tole Kumhal tol
## 2 4 20
## Kumhaltole Kuwari Kwangi
## 6 7 23
## Kwongi Kwonig Lachhamamu
## 5 6 7
## Lachhamanu Lachhumanu Lalmateya
## 43 8 10
## Lamachaur lamachaure Lamaswara
## 12 11 4
## Lamidada Lamidamar Lilja tole
## 3 3 5
## Madhumaya thapa Mahabir sthan Mahabirsthaan
## 5 5 19
## Mahabirsthan Mainroad Maisthaan
## 6 9 15
## Maisthan Manaidada Manakamana tol
## 121 3 3
## Mandannagar Mangalpur Manihari
## 4 54 62
## Manikapir Manikapur Matera tole
## 4 150 4
## Mathaelno halwar tol Mathalno halwar Maujetole
## 3 3 17
## methlang Methlang Mohanpur
## 6 5 6
## Mohonpur Moteratole Motipur
## 4 4 5
## moujetole Mulibhagaicha Murli
## 5 5 112
## Murli Bagaicha Murlibagaica Murlibagaicha
## 7 5 29
## Murlibhagaicha Musilamtol Muslimtol
## 2 15 20
## Nabin chook Nagarpalika road Nagawa
## 7 36 20
## Naguwa Nagwa Namuna tole
## 142 70 5
## Naule tol Nauli tol Naulpur
## 4 5 23
## Naya gaun Naya tole Naya tole mruli
## 6 22 6
## Nayabasti shreepur Nayagaun nirmal pokhari
## 5 21 45
## Nirmal pokhari nirmalpokhari Nirmalpokhari
## 42 12 48
## padale padam pokhari padampokhari
## 3 27 7
## Paddha padhali pain tanki
## 9 28 4
## Pani tanki Panitanki Parariya
## 4 5 12
## Parasanagar Parasnagar Paraspur
## 4 12 210
## Parks,nagar,Sano,pipra Parsauni Parwanipur
## 5 58 196
## Paschim rampur Patahani pateheni
## 43 22 5
## Pathani patihani Patihani
## 5 73 206
## patihani town patiheni patlahara
## 24 7 14
## Piara Pipara Piple
## 19 48 3
## Pipra Pipra,awash,ariya Piprahwa
## 4 5 222
## pokhara Pokharel tole Pokheral tole
## 5 4 15
## Pokherel tole Pokhrel tole Pragatinagar
## 3 7 3
## Prasauni Puaina Puaraina
## 55 6 7
## Pulchowk pullar Pumbdi
## 6 8 5
## Pumdhi Pumdi Pumdi bhumdi
## 5 30 10
## Pumdi vumdi Pumdibhumdi Pumdikot
## 39 12 8
## Pundgi pundi vundi pundivundi
## 6 19 4
## Puraina Puraini Purba rampur
## 264 238 13
## Puripokhari Purnipikhari Purnipokari
## 4 5 5
## Purnipokhari Raam tole Raampur
## 9 12 44
## Radhemai Rahamatpur Raikhalyan
## 93 15 10
## Raikhelyan Rajbiraj Rajbiraj Kharsal
## 5 335 5
## Rajdevi Rajdevi road Rajdevi tol
## 4 11 5
## Rajdevi tole Rajhena Rajiraj
## 88 3 5
## Ram tole Ram,gaduwa Ramgaduwa
## 4 7 226
## Ramgadwa rampur Rampur
## 41 4 17
## Rangasala tole Ranighat Ranighat tol
## 3 188 14
## Ranighat tole Ranighat,gashuwara road Resamkoti
## 8 6 15
## Resham kothi Resham Kothi Reshamkhoti
## 66 14 27
## Reshamkothi ReshamKothi Reshamkoti
## 6 6 54
## ReshamKoti Reshsm kothi Ryale patle
## 4 7 26
## Ryale Patle Sabaithuwa Sabaituwa
## 5 6 17
## Sabauthuwa Sabitawa sahar dhar
## 9 13 5
## sahardhar Sai krishna tola Sai krishna tole
## 5 5 4
## Saibutwa Saikrishana tole Sajha tol
## 13 3 12
## Sajha tole santipur Saptaha ko dil
## 3 29 5
## sarangkot Sarangkot Sarankot
## 33 22 10
## Saranpur Saraswati tole Sarboday tole
## 63 8 9
## Sarbodey tole sardhar sardhgar
## 4 24 7
## Sarswati tole sauda chautra Shanti tole
## 24 5 12
## Shekh Shiavanagar Shimra gau tol
## 4 4 7
## Shimra gaun Shiromanar Shiromaninagar
## 5 6 12
## shirsiya shiva nagar Shiva sakti tole
## 4 22 4
## Shiva shakti Shivaghat shivanagar
## 2 9 48
## Shivanagar Shivdakti tole Shivnagar
## 161 4 20
## Shreepur Shreepur,ranighat Simlegaira
## 104 11 4
## Simlegaire Sirbani Siromaninagar
## 7 7 3
## Sirshiya Sirshyia Sirsiya
## 43 7 21
## Sisobari Sitalapur Sitalpur
## 27 3 9
## Sivanagar Sreepur srisiya vansar tol
## 58 71 8
## Srswati tole Suagauli birta Sugali
## 4 7 8
## Sugauli SUGAULI Sugauli birta
## 62 9 74
## Sukarua Sukaura Sundar basti tole
## 5 10 7
## Sunderbasti Surya nagar Swarn
## 16 9 4
## Swarn tole swikhet swikot
## 64 12 4
## Talno halawar Talno halwar tol Tarigain
## 5 7 10
## Tarigau Tarigau Basbot Tarigau sano rajeura
## 104 5 4
## Tarigau tharu gaun Tarigaun Tatrigachi
## 5 32 6
## Tejara tole Tejarath tole Tejaratole
## 11 9 33
## Tejrath tole Tetari gachhi Tetri gachi
## 7 15 12
## Tetri tole Tetrigachi Thapa tol
## 7 11 15
## Tharu gau tol Tuhure pasal Uadayapur
## 12 3 17
## Udaepur ghurmi Udahapur Uday pur ghurmi
## 8 10 5
## Udayapur Udayapur ghurmi Udaypue
## 221 8 6
## Udaypur Udaypur bhurmi Udaypur ghurmi
## 52 13 47
## Udaypur Gurmi Udaypur,ghurmi Urahari
## 11 4 28
## Utarsukaura Uttar nawalpur tole Uttar sukaura
## 3 6 10
## valam Valam Valuwahi tole
## 29 35 6
## Vansar sirisiya vedi vhimsen nagar
## 3 4 5
## vimshen nagar vishennagar Vishwa
## 4 7 10
## Viswa yamdi Yamdi
## 4 4 4
## yamdi tol
## 3
## [1] "Frequency table after encoding"
## endline_villagename. Villagename
## 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323
## 5 3 142 8 13 8 7 5 4 68 5 6 35 32 5 57 161 18 4 6 104 5
## 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345
## 11 3 5 5 7 15 26 12 6 7 4 6 9 8 4 24 5 3 8 6 4 3
## 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367
## 7 6 22 4 40 4 8 5 7 17 157 5 3 6 62 9 4 11 129 9 5 4
## 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389
## 9 11 7 3 7 4 6 4 11 6 54 6 5 28 6 331 29 23 21 93 9 24
## 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411
## 7 75 7 24 6 12 5 27 7 9 11 13 3 3 7 10 5 2 5 9 5 5
## 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433
## 5 5 12 217 5 6 20 3 71 222 74 5 8 51 22 13 13 11 8 8 23 4
## 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455
## 9 112 5 4 32 11 33 4 21 4 64 12 5 19 5 42 21 2 44 3 4 9
## 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477
## 109 11 7 3 4 126 89 4 8 3 41 9 11 5 12 264 10 37 4 3 54 11
## 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499
## 13 15 5 150 3 11 4 5 4 81 4 33 7 3 8 26 5 5 3 43 7 46
## 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521
## 4 5 33 6 48 43 4 4 20 5 7 3 8 4 5 28 4 6 14 62 16 3
## 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543
## 35 196 121 36 4 5 88 4 12 41 17 4 4 48 4 6 3 6 5 5 11 4
## 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565
## 4 5 4 7 7 4 39 8 20 8 15 195 8 6 4 28 6 6 19 10 8 39
## 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587
## 15 10 5 6 12 7 6 23 6 72 3 5 6 3 10 15 4 43 4 22 15 70
## 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609
## 10 15 6 6 20 14 93 22 6 25 7 13 10 6 3 106 15 12 4 8 63 221
## 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631
## 2 17 45 6 10 7 17 45 24 7 29 4 3 12 28 52 5 22 32 5 5 3
## 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653
## 4 5 4 3 14 5 4 4 9 210 7 6 53 7 4 5 9 10 5 17 5 27
## 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675
## 6 3 5 3 7 55 54 7 4 5 4 6 7 24 27 3 6 3 2 13 8 4
## 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697
## 6 6 4 3 43 42 5 14 297 16 4 7 184 44 10 2 104 9 5 47 23 49
## 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719
## 226 9 4 16 4 194 6 238 4 4 73 11 114 3 29 23 12 4 5 5 206 12
## 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741
## 13 4 27 7 4 8 12 2 4 4 7 6 10 10 5 14 51 13 9 5 335 48
## 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763
## 15 3 110 6 5 30 3 58 8 3 12 30 5 160 5 7 24 188 7 6 39 10
## 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785
## 3 3 5 6 4 12 8 5 4 3 6 5 19 7 30 14 5 6 48 16 4 3
## 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807
## 10 20 4 4 4 13 4 87 3 29 4 6 4 5 5 5 6 17 58 3 66 5
## 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824
## 21 6 3 17 7 5 13 6 4 20 4 6 28 6 20 5 11
## [1] "Frequency table before encoding"
## endline_settlement.
## aadarsa pokhrel tol aadarsa tol Aadarsamani
## 6 3 3
## aadarsamani tol Aadarsapokhrel Aadarsh tole
## 6 4 6
## aadhars tol Aalam Aalau
## 4 4 33
## Aanapurna Tol Adalat road Adarsa tol
## 4 3 5
## Adarsha mani tol adarshnagar Adarshnagar
## 14 5 8
## Adarsnagar Adhikari gaun Agree gaurishankar
## 4 4 6
## Ahmad pur Ahmad tole akala
## 4 6 5
## akla Aklekhet Alakha Road
## 6 4 8
## Alanagar Amar Amarbasti
## 20 4 7
## Ammarbasti Anapurna Tol annapurna tol
## 22 4 8
## Apauni arbhote Armalakot
## 58 5 23
## Ashok batika Ashokbatika Ashokvatika
## 6 50 2
## Atharaha athraha Athraha
## 42 8 46
## Babu gaun Babugaun Badahare
## 61 22 6
## Badalthar Badare badhahare
## 3 5 6
## Badhare bagaicha Bagaicha
## 4 4 5
## Bagale lamaswara Bagbani tol Baghakholi tol
## 4 3 4
## Bagyasworitol Bahaun tol Bahaya khola
## 4 4 3
## baheri bahu khola Bahu khola
## 6 5 3
## Bahueari Bahuhori Bahuwari
## 4 19 16
## Bahyekhola Bajrang Bajrang tole
## 3 3 13
## Bajrangi tole Bale gaun Balegaun
## 43 45 88
## Banahari Band tole Bandh tole
## 13 3 5
## Bangau Banjare gaau bankatta
## 12 43 9
## Banpale barabuta baralthar
## 5 5 7
## Barampuri Barauji Barewa
## 16 21 28
## Barwa Basan purawa Basantpur
## 4 8 15
## Basantpure Basbot Baseri
## 7 12 8
## baseri tol baspani bastal
## 4 4 13
## Bayo khola Bayokhola Besigidari
## 17 5 4
## Bhadagau bhadgau bhadhahara
## 4 2 5
## bhadhara Bhagaerithana Bhagari than
## 4 3 5
## Bhagawan Tola Bhagawanpur Bhagawati tole
## 12 4 8
## Bhagwatitol bhakti path Bhaktipath
## 32 9 6
## bhalam Bhaluwahi nadhi Bhandari
## 17 4 3
## bhandari dhar Bhandaridahar Bhanichok
## 4 6 5
## bhanjyang tilahar tol Bhansartol bhansartole
## 9 6 6
## Bhansartole bhanshartole Bhanuchowk
## 24 5 9
## Bhata tole bhati path Bhawani tol
## 5 3 12
## Bhawanipur Bhayakhola Bhayapur
## 43 5 15
## Bhediyahi BhgawotiTol Bhiri road
## 20 6 8
## Bhitri road Bhola chowk Bhujai gaun
## 26 5 3
## Bhujaigaun Bhuji gaun Bhujigaun
## 28 3 31
## Bhuwanpur Bhuyarmandir tol Bidhyapith school paxadi
## 4 3 5
## Bijaya tole Bijeynagar Bijeynagar bazar
## 6 10 3
## Bijeynagar dairy Bijeynagar,way to haraiya Bindawasini
## 8 7 86
## Bindraban birendra gufa Birta
## 7 18 195
## BIRTA Bishanu Bishnupur
## 4 3 18
## bodhare botetol Budagau
## 5 21 11
## Buketi Chahari chook Chailahe
## 4 6 4
## Chailai Chamar tol and dhobi tol Chamartol and dhobitol
## 4 11 4
## chanatae chanauta chanautae
## 4 7 9
## chanaute Chanora parariya chanutae
## 7 16 3
## chanute Chapa Chapkaiya bazar
## 6 5 4
## Chapkaiya kawadi tol Chapkaiya tol Chathghat
## 7 4 11
## chauntae Chauthari tol Chhapkaiya
## 6 4 32
## Chilaune kharka chilaunekharka Chimeki tol
## 3 4 4
## Chiranjeevi chok Chiranjivi chowk Chitraguptnagar
## 3 15 42
## Chock bajar Chokbazar Choudhaghare
## 4 5 13
## chour chowk Cigarette factory
## 13 4 8
## Cigarette factory area Cold store Copan gunj
## 4 31 6
## Dabar Dadathok tol Dadathok tole
## 3 4 3
## Dadre Dadrini tol Dahara
## 12 11 3
## Dakshin tole Dakshina tole Damodar
## 13 7 4
## Dandagaun tol Dandathok Dangisaran tol
## 3 3 3
## Darahi tol daredeurali Darsa nager
## 3 5 6
## Dasarath nager Deurali Devi chook
## 8 4 5
## Devichowk Devnagar Devthan
## 6 34 14
## Devthan tlo Devthan tol Dhadagari
## 11 5 6
## Dhaddagari dhakalthar Dhale Pipal
## 5 8 3
## Dhalepipal dhanauji Dharahi tol
## 6 5 5
## Dharai tol dhaurali Dhore gaun
## 5 8 69
## Dihi Dihi gau Dihi tol
## 6 8 6
## Dr koloni Driport Driport Tol
## 6 9 4
## Dripot Duhar tole Dumari
## 8 6 31
## Dumri Duwar Duwar tol
## 69 5 5
## Duwar tole fedi fokshing deurali
## 10 4 3
## Fulbari Fulbari tol Furthi chook
## 14 5 42
## Furthi chook bazer Furthi cook Futaha
## 6 6 10
## Gadash tol Gahatera Gail road
## 6 14 5
## Gairiswara Ganaganagar Ganesh chok
## 2 5 13
## Ganesh marg,trichowk Ganesh tole Ganeshgunj
## 11 5 17
## Ganganaga Ganganagar Gangapur
## 5 65 41
## Gangarampura Gangarampurwa Gapalgunj
## 10 10 9
## garbetandi garjati Gaucharan
## 4 16 4
## gauri khor Gauri shanker tol Gaurigau
## 3 2 5
## Gayarjati Gayatri tole Geeta mandir
## 4 10 8
## Geetanagar Geetanagar bazar ghadgai
## 14 6 19
## ghandruke chouk Ghariwara Ghariwarha
## 3 17 44
## ghatgai ghimire chok tol Ghimire chowk
## 7 7 4
## Ghimire tol Ghoraneti Ghorneti
## 4 18 5
## Ghumtichook Ghurali tol Ghurmi
## 5 5 66
## Ghurmi udayapur Ghusakpur Ghusari
## 4 10 5
## Ghusauri Ghushari Ghusukpur
## 5 6 33
## Gitpur Gopal chook Gopalgunj
## 5 5 20
## Gorkhali Gorkhali tole Gouri purwa
## 42 57 8
## Gouriipurwa Gouripurwa Govind chok
## 5 12 7
## Gumba chour Gurmi Gurung gau
## 4 7 17
## Gurung tol Gurungchowk Gyanjyoti
## 5 6 4
## Gyarjati gyarjyoti Gyatrinagar
## 6 12 4
## Hal pachadi Halawar haldharko chautara
## 18 8 5
## Halwar hanuman nagar Hanuman nagar road
## 21 12 12
## Hanuman nagar tole hanumanaga hanumanagar
## 6 4 6
## hanumannagar Haripaura Haripauri
## 24 8 15
## Harpatganj Harpatgunj Harpatjanj
## 32 43 4
## Hatiya Hemanagar Himal
## 42 7 7
## Himalay tole Himalaya tole Himalayan tole
## 39 8 6
## Himalsy tole Himaltole Himalya tole
## 11 6 3
## Hulakimarg Inaruwa Inaruwamaniyari
## 11 37 46
## Inarwa Indargaau Indrapuri
## 28 44 18
## Indrapuri chok Jagaran jagarit tol
## 15 4 2
## Jagriri jagriti Jagriti
## 5 12 19
## jagriti tol Jagriti tol Jagriti Tol
## 10 10 5
## Jagritinagar Jail tol Jailroad
## 9 9 23
## Jaimare Jaispur Jamnaha
## 4 223 77
## Janajagaran tol Janajagran tol Janajagrati
## 13 5 3
## Janakeswori janjagriti tol Jaspur
## 3 4 9
## Jayanagar Jayananesh Jelrod
## 42 4 4
## Jhakaruwa Jhakrawathuti tol Jhanjhane
## 6 5 17
## Jhumaryathuti tol Jhumaryatol Jimire dil
## 4 5 5
## Jodhapurwa joti chock Kabadi tol
## 29 7 9
## Kabhre tole, Indrachowk Kabhreghat Kachili
## 6 11 7
## Kachili tol Kalakhola Kalayanpur bazer
## 5 13 3
## kalika tol Kalikhola Kalimati
## 4 5 8
## Kaltu pokhari Kalya khola vewdar Kalyankari tol
## 4 7 4
## kamere pani kamere pani tol Kanchanpur road
## 9 5 6
## Kanchi chok Kanthipur Kantipur
## 15 22 5
## Kantipur tol Kapadevi tol karai chautari
## 4 4 6
## Karkandoo Karki chok karki gaun
## 28 7 4
## Karkichok Karkichowk karkigau
## 8 3 4
## Karmohna Kasarbag kasari
## 20 6 4
## kaseri Kaseri Kaseri dumre
## 4 3 3
## kastan kaster Katahasami tol
## 5 3 2
## Katilya Katulya kaun deurali
## 15 4 4
## kaun tol Kawari Kehuniya
## 12 13 25
## Kepa chock Kesarbagha kesari
## 4 4 3
## Kesharbag khadha thare Khadkathar
## 26 6 7
## Khadrye Khalla Puraini Khalla Puraini
## 14 8 84
## khalutole khaluwatole Khaluwatole
## 6 7 10
## khaluwatole sirsiya Khaluwatole sirsiya khalwa sirisiya
## 6 14 6
## Khalwa Tol khalwa tol sirisiya Khalwatole
## 5 5 11
## Kharkhola tol Kharsal Kharsal methil tole
## 3 64 4
## Kharsal tole Khas karkandoo khastar
## 67 4 6
## khaster Khatikanpurwa Khatri gau
## 9 4 3
## Khatri tol Khatsal tole Khayarghari
## 3 2 17
## Khayarghari chowki Khittari khlwatole
## 12 3 8
## Khlwatole Khumkhane Kigrinpurwa
## 6 21 34
## Kirana line Kodi Koeritole
## 21 10 8
## Koeritole udaypur ghurmi kohadi Kohadi
## 7 11 40
## Koiri tole Koiripatti Krishnamandir tole
## 52 20 10
## Kuhari Kukunswara Kulain
## 3 4 4
## kulain marga Kulain marga Kulainmarga
## 3 4 3
## Kulani tole kulayan kulayan marg
## 3 4 9
## kulayan marga kulayan tol Kulayen marga
## 8 8 6
## Kulayen tol Kumal Kumar tole
## 3 2 4
## Kumeya Kumhal tol Kumhal tole
## 5 20 6
## Kumhaltole kumiya Kusanchour
## 6 6 12
## Kusinchour Kuwari Laath gaali
## 12 7 32
## Laath gali Laathgaali Lachhamanu
## 5 9 58
## Lagdahawa Lagdhawa Lalapurwa
## 11 58 57
## laliguras tol Laliguras tol Laligurash
## 15 4 4
## Lalmatya tol lama khet Lamachowk
## 3 4 13
## Lamakhet Lamichane thar Lamidada tol
## 9 4 3
## Lamidamar Laptanchwok Lilja tole
## 3 5 10
## Lodhai Lodhai gau Lodhaigau
## 5 24 9
## Lodhayi goun Lonionpurawa Lonionpurwa
## 5 5 5
## Loniyan purawa Loniyanpurwa Loniyonpurwa
## 9 23 5
## Lukunsawara Luxmannager Maanpur
## 8 4 10
## machapuchre tol Machhapucher tol Machhapuchre tol
## 5 6 3
## Madjid tol Magartole Mahajid Tol
## 13 5 7
## Mahapurwa Mai mandir tol Main road
## 39 19 12
## Mainroad Maisthaan Maisthan
## 19 7 77
## Majdada Malpot tole Manaidada
## 5 12 3
## Manakamana tol Mandannagar Mangalpur
## 8 4 6
## Mangalpur bazer Mangalpur bazer vitra Mangalpur vitra
## 22 5 3
## Manihari Manihari tol Manikapur
## 49 4 12
## Maniyadanda tol Mannipur mansara tol
## 3 10 3
## Masjid tol Masjid tole Maszid tole
## 22 25 8
## Matera Mathighar maujetole
## 4 8 5
## Maujetole maula tol Maula tol
## 22 4 6
## methlang Milan tol Milantol
## 9 6 7
## Milijulichowk Minabazar Mohanpur
## 11 4 75
## Mohonpur tol Motera Moti tol
## 4 4 2
## Motitol Moujetole murli
## 3 6 6
## Murli Murlibagaicha MurliBagaicha
## 122 35 7
## Murlibhagaicha Murlubagaicha Musilamtol
## 2 4 9
## Muslimtol Musulamtol Nabin chook
## 20 6 7
## Nabin chook vitra nachnechaur Nachnechaur
## 3 24 4
## Nachnichour Nadai gaun Nadaigaun
## 5 14 19
## Naditole Nagarpalika road Nagawa
## 20 36 20
## Naguwa Nagwa naharchowk
## 69 81 5
## Naharpurwa Namuna Namuna tol
## 28 5 8
## Namunatol Narbadha tol Natanpurwa
## 10 3 43
## Nawalpur Naya Basti Nayabasti
## 9 8 18
## Nayagaun Nayak tol Nayatole murli
## 19 9 6
## Neta chowk Neuli tol nirmal pokhari
## 4 13 24
## Nirmal pokhari Nirmal pokhri Nirmalpokhari
## 9 5 10
## Nursery chowk Paan mandi Paangaali
## 7 4 6
## Pabitra tol Pachhim sukaura padale
## 16 5 3
## padam pokhari padampokhari Padampokhari
## 9 19 25
## Paddha Pade ghumti padhali
## 5 6 28
## Pain tanki Pakaudi Pande ghumti
## 4 24 6
## Pani tanki Panitanki Panitanki,chamartol
## 4 31 7
## Parajuli chok Parariya Parasapur
## 10 19 6
## Parasnagar Paraspur Pargati tol
## 7 80 4
## Parsanpurawa Parsanpurwa Parsauni
## 10 6 58
## Parseni Parwanipur Pasupati
## 22 37 10
## Patel,nagar Patelnagar Patelnager
## 7 8 16
## Patelnegar patihani patihani bazar
## 6 7 32
## patihani town Patihani town patiheni bazar
## 24 14 7
## patlahara Patle Phoolwari Tol
## 14 13 12
## Pipaldali Pipara Piple
## 10 17 3
## Pipra Pokharel tole Pokheral tole
## 9 4 4
## Pokheral tole Pokherel tole Pokhrel tole
## 21 3 4
## Pokhrel Tole Pothedarpurwa Pragatinagar
## 3 29 3
## Pragatitol Pragtinagar Prasauni
## 4 22 55
## Professor colony Profhesar koloni Pulchowk
## 3 8 6
## pullar Pullar Pumdi kot
## 8 5 8
## Pumdikot Punari pokahri punti dada
## 28 4 3
## Puraina Puraini Purba rampur
## 126 37 23
## Purnipokari Purnipokhari Purnipokhari tole
## 5 11 15
## Raahamatpur Raam tole Raampur
## 5 12 37
## Raamur Radakrishna Radha krishna Tol
## 7 6 15
## radhakrishna tol Radhakrishna tol Radhakrisna
## 17 24 3
## radhakrisna tol Radhakrisna tole Radhapur
## 13 7 86
## Radhemai Rahamat tol Rahamatpur
## 93 3 10
## Rahsmad tol Raikhelyan Rajdevi
## 3 5 23
## Rajdevi tole Rajdevi road Rajdevi tole
## 5 7 52
## Rajhana tol Rajhanatol Rajhena tharugau
## 4 11 3
## Rajheni tol Ram tol Ram tole
## 5 4 8
## ramchok Rameshorpurwa Ramgaduwa
## 4 61 220
## Ramgadwa Ramghadwa rammandir
## 41 5 6
## Ramtole Ramwapur Rangaduwa
## 19 83 8
## Rangasala tole Ranighat Ranighat tole
## 3 226 5
## Resamkoti Resham khoti Resham kothi
## 5 10 57
## Resham Kothi Reshamkhoti Reshamkothi
## 24 15 30
## Reshamkoti Road tol Ryale
## 58 7 6
## Ryalechaur tol Sabaithuwa Sabaituw
## 4 15 7
## Sabaituwa Sabitawa Sagaramatha tol
## 23 13 6
## Sai krishna Sai krishna tole Saikrishana
## 4 7 3
## Saja sajha sidhartha sajha sidhartha tol
## 4 5 6
## sajha tol Sajha tol Sajha tole
## 13 12 3
## sangam tol Sangam tol Sangamtol
## 5 5 4
## Sano ganeshganj Sano ganeshjung Sano gaun
## 5 5 5
## Sano pipra Sano Rajauara Sanogarhi
## 5 4 5
## santaneswor tol Santinagar santipur
## 8 4 33
## Saptaha ko dil Saranpur Saraswati tole
## 5 4 3
## Sarbodai tole Sarboday tole Sarbodeytole
## 6 3 4
## sardhar Sarswati tole saudaha
## 36 19 10
## Saudahachautara saudha chautara Sauraha
## 5 5 40
## School road School road/ malpot road Seara
## 8 7 6
## Shanti Shanti tol Shanti tole
## 15 19 12
## Shantichowk Shantitol shardhar
## 20 19 5
## Shepas patel Shimra gaun Shimragau
## 3 5 7
## Shiromani Shiromaninagar Shiva chock
## 7 17 6
## Shiva choka Shiva Shakti tol Shiva sundar Tol
## 4 2 5
## Shivaghat aagadi Shivanagar Shivasakti
## 5 65 4
## Shivashakti tol shivasundar tol Shivnagar
## 4 4 17
## Shivthan Shreepur sibalaya
## 43 110 12
## Sibalaya Sibasundar tol simalchair
## 11 10 4
## simalchaur Simalchaur simalchaur tol
## 16 14 7
## Simalchhaur Simlegaire Sirbani
## 6 7 7
## Sirha road Sirishiya Sirisiya khalwa
## 4 5 5
## Siromaninagar Sirsiya sisneri
## 3 10 5
## sisneri tol Sisobari Sitalpur
## 5 27 9
## Sitalpur.tol Sreepur Srijana nagar
## 3 39 12
## Srswati tole Sugauli Sugauli birta
## 10 10 97
## suikhet Suikhet Suiya
## 6 26 84
## Sukaura tol Sukhrampurwa Sukreseori tol
## 7 9 4
## Sukresori Sukumbasi tol Sunaulo tol
## 4 4 6
## Sunaulo tol Sundada sundada khet
## 6 14 12
## Sundar chok sundar santi chock Sundarbasti
## 4 4 7
## Sundarchok Sunder basti Sunderbasti
## 4 4 65
## Surgigaun Surya nagar Suryanagar
## 6 5 9
## Suuya Swara swaraha
## 4 10 9
## swaraha dandathok Swarn tole Swarna tole
## 4 68 4
## swekhet swikhet swikot
## 15 20 10
## Syalghari Taajpur talla chaur
## 19 70 10
## tallo yamdi Tanga tole Tangpasri
## 4 8 79
## Tarigai tharugaun Tarigain Tarigau tharugau
## 4 5 6
## Tejara tole Tejarath Tejaratole
## 11 9 33
## Tejrath Telipatti Telipatti mas
## 7 15 7
## Telipatti mass Teliyanpur Tetari gachhi
## 5 26 15
## Tetri chok Tetri gachi Tetri tole
## 4 6 11
## Tetrigachi Tetrihi tole thanapati tol
## 9 6 3
## Thangaxi thapa Thapa gau
## 5 3 3
## Thapa tol Thaple tilahar Tharugau
## 22 6 13
## Tharugaun Thukaila Thula chawor tol
## 11 14 2
## thulachaur Thulachaur Thulachhaur
## 3 3 10
## Thulibesi marga Thulo ghadi Thulo pipara
## 4 12 31
## Thulobesi marga Thutitol Tilahar
## 7 6 9
## Timalchour Timalichour Treebeni tol
## 5 10 4
## Treebenitol Tribeni tol Tuhure pasal
## 3 8 3
## Tumke Udayapur Udaypur
## 8 6 8
## Udaypur bhrumi Udaypur bhurmi Udaypur ghurmi
## 3 5 23
## Udaypur gurmi Udaypur Gurmi Udaypurbhurmi
## 4 7 5
## Ujalnagar Ujjalnagar Ujjewalnagar
## 4 7 4
## Ujjwaknagar Ujjwalchok Ujjwalnagar
## 4 5 87
## UjjwalNagar Upauni Urahari
## 3 14 4
## Urahari gothua danda Urahari rajmarg tol Utarsukaura
## 2 12 3
## Uttar sukaura Uttarsukaura Vagawanpur
## 10 5 5
## Valuwahi Valuwahi kath Valuwahi tole
## 5 4 6
## Vangushara Vansar sirisiya vansar tol
## 21 3 8
## Vatha tole Vawanpur Vhagabati
## 12 11 3
## Vhagawanpur Vidhyapith Vidhyapith school
## 7 13 3
## vimsennagar vimshen nagar vimshen nager
## 6 14 4
## vimshennagar Vishwa Viswa
## 24 10 4
## word office Yaklya sal yamdi
## 4 5 10
## Yamdi yamdi tol Yamdi Tol
## 9 7 4
## Yamdi tol bikash Yatimkha tol Yatimkhana tol
## 4 9 16
## Ystimkhana tol
## 6
## [1] "Frequency table after encoding"
## endline_settlement.
## 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130
## 4 39 3 5 15 43 12 4 8 3 6 65 93 6 7 5 42 4
## 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148
## 5 5 3 6 10 3 17 6 24 3 4 6 6 3 3 11 7 13
## 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166
## 12 26 4 10 4 7 14 4 9 7 3 4 13 8 7 27 33 4
## 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184
## 7 5 42 4 13 8 10 8 9 12 8 13 223 4 8 14 4 4
## 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202
## 6 6 7 4 24 5 4 9 3 10 4 6 4 4 4 31 4 12
## 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220
## 6 4 3 4 3 4 9 88 7 68 19 3 5 5 6 4 8 5
## 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238
## 6 3 6 5 4 5 4 15 5 4 5 7 20 21 3 10 5 5
## 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256
## 12 55 12 7 5 3 5 52 4 13 12 5 84 5 8 5 8 75
## 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274
## 12 2 6 37 3 2 4 23 10 5 32 26 45 20 6 4 5 11
## 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292
## 22 6 10 6 7 5 5 16 64 16 9 69 4 3 7 3 19 8
## 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310
## 61 3 4 19 3 6 7 4 8 4 11 13 8 3 9 4 5 5
## 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328
## 7 12 8 19 9 5 6 41 7 5 7 4 4 5 6 8 4 9
## 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346
## 18 19 5 87 9 5 8 24 4 3 14 4 4 10 5 5 20 5
## 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364
## 8 28 3 4 3 61 6 42 8 6 10 6 5 5 4 69 6 23
## 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382
## 3 22 4 3 80 6 23 9 4 22 9 2 4 6 4 8 5 8
## 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400
## 8 10 22 3 6 3 3 25 5 9 31 4 5 6 5 50 4 18
## 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418
## 4 10 17 9 21 36 5 19 4 5 52 3 2 13 6 37 3 16
## 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436
## 3 34 3 9 12 3 5 7 4 12 7 3 11 14 6 11 12 26
## 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454
## 5 4 19 15 11 3 10 5 13 15 6 15 3 11 58 7 7 24
## 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472
## 11 22 5 67 9 32 5 5 5 24 11 5 4 6 5 5 15 28
## 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490
## 5 4 23 86 12 9 15 8 6 83 4 5 8 5 3 4 6 8
## 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508
## 4 5 6 8 5 33 7 4 5 4 8 4 10 5 5 4 220 6
## 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526
## 5 8 24 17 4 4 10 43 12 9 7 15 37 5 4 12 5 5
## 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544
## 3 8 226 9 2 4 4 4 4 4 33 7 2 7 122 6 9 12
## 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562
## 7 3 21 6 6 5 20 3 3 6 29 9 66 4 7 4 12 3
## 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580
## 5 12 4 8 43 18 4 7 4 5 4 15 13 7 43 3 2 17
## 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598
## 4 4 5 16 4 4 58 10 79 3 6 4 3 11 25 3 5 8
## 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616
## 46 35 6 4 36 7 2 6 3 28 6 10 7 7 6 4 5 4
## 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634
## 6 10 3 6 17 77 4 19 5 3 7 3 5 5 4 13 6 5
## 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652
## 19 13 3 7 16 9 20 10 7 8 22 3 3 3 3 97 15 6
## 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670
## 5 6 7 5 4 10 110 8 3 7 4 126 6 4 3 21 11 5
## 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688
## 6 15 24 11 6 28 4 3 8 7 5 28 5 6 4 10 23 4
## 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706
## 4 7 6 23 18 19 5 21 3 5 20 9 22 3 10 7 44 8
## 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724
## 3 13 84 4 14 5 11 3 57 9 14 4 10 5 4 8 4 42
## 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742
## 4 8 58 6 16 7 9 6 14 4 4 81 4 17 6 6 4 42
## 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760
## 6 11 19 10 7 5 3 13 4 3 4 65 5 69 4 8 4 4
## 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778
## 12 4 16 4 17 3 5 4 13 6 3 14 4 12 6 6 9 7
## 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796
## 6 6 5 5 13 6 29 8 11 12 37 5 9 5 5 6 5 5
## 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814
## 6 18 6 6 6 5 3 4 12 3 10 5 16 5 13 5 3 3
## 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832
## 8 5 8 4 5 15 19 10 21 2 6 5 15 12 10 6 18 15
## 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850
## 24 14 6 14 4 3 8 22 17 4 6 7 20 3 11 3 6 57
## 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868
## 9 4 4 4 5 17 3 5 4 22 3 17 9 19 77 9 4 5
## 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886
## 5 4 11 6 17 28 195 3 3 12 4 7 6 9 5 3 34 11
## 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904
## 58 43 5 10 5 43 10 8 10 9 6 4 3 7 4 6 3 5
## 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922
## 7 4 3 9 6 46 4 4 24 31 11 5 4 6 39 6 3 31
## 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940
## 10 14 7 5 57 7 13 4 20 4 4 8 4 6 7 6 5 6
## 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958
## 4 6 14 3 5 5 7 4 2 33 4 5 8 14 4 4 8 12
## 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976
## 3 30 4 4 4 26 39 3 2 12 20 6 20 32 9 14 65 23
## 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994
## 7 10 28 19 7 4 6 20 11 10 10 4 7 5 49 3 25 13
## 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012
## 41 3 21 4 4 5 4 6 15 7 15 40 4 6 40 7 24 32
## 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030
## 2 10 5 3 14 4 32 5 8 42 13 10 4 8 2 12 31 70
## 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043
## 4 3 10 3 3 4 5 58 4 44 3 86 9
## [1] "Frequency table before encoding"
## endline_municipality.
## 1 2 3 4 5 6
## 1831 4555 2331 1760 1202 1017
## [1] "Frequency table after encoding"
## endline_municipality.
## 747 748 749 750 751 752
## 2331 1202 1760 4555 1017 1831
## [1] "Frequency table before encoding"
## endline_wardno.
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
## 260 363 336 163 166 220 268 197 221 232 57 243 321 329 326 530 404 623 828 680 632 597
## 23 24 25 26 27 28 29 30
## 614 668 582 614 807 692 512 211
## [1] "Frequency table after encoding"
## endline_wardno.
## 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132
## 163 828 260 623 668 336 197 166 632 582 404 321 530 329 363 614 211 326 232 614 680 220
## 133 134 135 136 137 138 139 140
## 597 807 692 221 57 268 243 512
## [1] "Frequency table before encoding"
## endline_child_municipality.
## 1 2 3 4 5 <NA>
## 16 1 10 141 14 12514
## [1] "Frequency table after encoding"
## endline_child_municipality.
## 497 498 499 500 501 <NA>
## 1 14 10 16 141 12514
## [1] "Frequency table before encoding"
## endline_child_wardno.
## 3 4 5 9 10 18 19 20 21 22 23 24 25 26 27
## 2 3 4 2 3 8 9 22 13 3 3 22 31 14 1
## 28 29 <NA>
## 23 19 12514
## [1] "Frequency table after encoding"
## endline_child_wardno.
## 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848
## 1 22 13 3 3 8 2 23 14 3 9 2 22 3 19
## 849 850 <NA>
## 4 31 12514
## [1] "Frequency table before encoding"
## endline_child_villagename. Villagename
## Aadarsha tol Athraha Bhalam bhandari dhar
## 12514 5 1 6 1
## bhati path birendra gufa chanutae chowk Deurali piple
## 1 2 2 1 5
## Dihi gau Garmikhola gauri khor Geetanagar Gharmikhola
## 1 1 1 4 2
## Gogimani hemja Hemja Jagriti tole Kahu
## 1 5 7 1 5
## kahun Kanchanpur road kaun khadha khaster
## 6 1 2 2 1
## Kirana line Kirishna nagar kristi Kristi Lamachaur
## 2 3 9 13 1
## Lamaswara Manakamana tol nirmal pokhari padhali Patahani
## 1 1 1 2 3
## patihani Patihani Piprahwa Pokherel tole Pumbdi
## 1 2 2 1 1
## Pumdi Pumdi bhumdi Pumdi vumdi Pumdikot pundi vundi
## 5 3 4 3 5
## pundivundi Rajbiraj Rajdevi tole Ryale patle sarangkot
## 2 5 2 5 2
## Sarangkot sardhar sauda chautra shiva nagar shivanagar
## 5 2 1 3 1
## Simlegaira Simlegaire Sivanagar swikhet Udayapur
## 2 1 2 2 8
## valam Valam vedi
## 1 10 1
## [1] "Frequency table after encoding"
## endline_child_villagename. Villagename
## 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915
## 1 5 2 1 1 1 8 1 3 2 2 6 2 5 1
## 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930
## 3 1 2 6 3 1 1 1 1 2 4 2 2 1 1
## 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945
## 2 9 1 5 1 10 2 1 4 2 1 2 1 7 1
## 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960
## 2 5 5 12514 13 2 5 3 1 5 2 1 1 1 5
## 961 962 963
## 5 3 2
## [1] "Frequency table before encoding"
## endline_child_settlement.
## Adarsha mani tol Adhikari gaun Athraha
## 12514 3 2 1
## Bagale lamaswara Bahaya khola Baseri baseri tol
## 1 1 1 1
## Besigidari Bhadagau Bhaktipath bhandari dhar
## 2 2 1 1
## bhanjyang tilahar tol bhati path birendra gufa chanaute
## 1 1 2 1
## chanutae chowk Dadre Dihi
## 1 1 3 1
## Dihi gau Dihi tol Dumari Dumri
## 2 1 2 1
## fedi Ganganagar Gangapur Gaucharan
## 1 1 4 1
## gauri khor Gorkhali tole Gouri purwa Gouripurwa
## 1 2 1 1
## Gurung gau Himalay tole Indrapuri chok jagarit tol
## 2 3 2 1
## Jagriri jagriti Jagriti jagriti tol
## 1 1 3 4
## Jagriti tol Jagriti Tol Jaimare Jayanagar
## 2 2 1 2
## Kalikhola Kalimati Kanchanpur road karki gaun
## 1 2 1 2
## karkigau kaster khadha thare Khadkathar
## 2 1 2 3
## Khittari Kirana line Kulani tole Kulayen tol
## 1 1 1 1
## Kusanchour Kusinchour Lalapurwa laliguras tol
## 1 3 1 4
## lama khet Lamichane thar Lodhai gau Manakamana tol
## 1 2 1 1
## Mathighar maula tol Maula tol nachnechaur
## 1 1 1 1
## Padampokhari padhali patihani bazar Patle
## 1 2 1 2
## Pipaldali Pokheral tole Pokherel tole Pumdikot
## 2 1 1 5
## punti dada Radha krishna Tol radhakrishna tol Radhakrishna tol
## 1 5 1 2
## Rajdevi tole Ryalechaur tol santaneswor tol santipur
## 2 1 2 3
## sardhar saudaha saudha chautara Shiva sundar Tol
## 2 2 1 1
## simalchaur Simalchaur simalchaur tol Simalchhaur
## 1 3 2 2
## Simlegaire Suikhet Sunder basti Sunderbasti
## 1 3 1 2
## swaraha swaraha dandathok swekhet swikhet
## 2 2 2 4
## Syalghari Tangpasri Thulachaur Thulibesi marga
## 3 2 1 2
## Timalchour Timalichour Ujjwalnagar vimshennagar
## 2 2 1 1
## [1] "Frequency table after encoding"
## endline_child_settlement.
## 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008
## 4 1 1 1 1 2 1 1 2 1 1 1 2 2 1
## 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023
## 1 1 3 2 1 2 1 1 2 2 3 2 2 1 3
## 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038
## 1 1 2 1 1 2 3 2 1 2 1 1 1 2 1
## 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053
## 1 1 1 1 1 3 1 2 1 1 3 1 2 12514 1
## 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068
## 4 2 2 3 1 3 1 2 3 2 2 4 2 1 1
## 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083
## 2 2 4 1 2 1 3 1 1 5 2 1 1 2 2
## 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098
## 2 1 1 2 1 1 2 2 5 2 1 1 1 1 2
## 1099 1100 1101
## 1 1 1
# Focus on variables with a "Lowest Freq" of 10 or less.
mydata <- top_recode ("endline_s3q3", break_point=80, missing=999999) # Topcode cases age 80 or older
## [1] "Frequency table before encoding"
## endline_s3q3. Age in completed years at the time of survey:
## 0 1 2 3 4 5 6 7 8
## 49 50 99 122 242 173 269 305 373
## 9 10 11 12 13 14 15 16 17
## 299 374 326 361 365 386 408 325 253
## 18 19 20 21 22 23 24 25 26
## 301 159 157 91 105 95 59 132 128
## 27 28 29 30 31 32 33 34 35
## 132 123 103 242 148 197 169 128 355
## 36 37 38 39 40 41 42 43 44
## 273 141 221 114 332 167 140 121 62
## 45 46 47 48 49 50 51 52 53
## 186 101 51 98 45 126 70 59 46
## 54 55 56 57 58 59 60 61 62
## 29 101 67 34 47 25 99 73 44
## 63 64 65 66 67 68 69 70 71
## 47 25 97 51 29 36 30 68 32
## 72 73 74 75 76 77 78 79 80
## 31 27 14 51 21 12 8 6 14
## 81 82 83 84 85 86 87 88 89
## 9 8 9 7 2 6 3 3 2
## 90 91 92 93 96 98 100 984804437 <NA>
## 2 2 1 1 2 2 3 1 1559
## [1] "Frequency table after encoding"
## endline_s3q3. Age in completed years at the time of survey:
## 0 1 2 3 4 5 6 7
## 49 50 99 122 242 173 269 305
## 8 9 10 11 12 13 14 15
## 373 299 374 326 361 365 386 408
## 16 17 18 19 20 21 22 23
## 325 253 301 159 157 91 105 95
## 24 25 26 27 28 29 30 31
## 59 132 128 132 123 103 242 148
## 32 33 34 35 36 37 38 39
## 197 169 128 355 273 141 221 114
## 40 41 42 43 44 45 46 47
## 332 167 140 121 62 186 101 51
## 48 49 50 51 52 53 54 55
## 98 45 126 70 59 46 29 101
## 56 57 58 59 60 61 62 63
## 67 34 47 25 99 73 44 47
## 64 65 66 67 68 69 70 71
## 25 97 51 29 36 30 68 32
## 72 73 74 75 76 77 78 79
## 31 27 14 51 21 12 8 6
## 80 or more <NA>
## 77 1559
mydata <- top_recode ("endline_s4q7", break_point=60, missing=999999) # Topcode cases with 60 or farther
## [1] "Frequency table before encoding"
## endline_s4q7. How far is the school from home?
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
## 184 1295 657 321 120 118 44 15 13 2 17 5 5 4 1 8 3 1
## 18 20 30 45 215 <NA>
## 1 1 2 2 1 9876
## [1] "Frequency table after encoding"
## endline_s4q7. How far is the school from home?
## 0 1 2 3 4 5 6 7
## 184 1295 657 321 120 118 44 15
## 8 9 10 11 12 13 14 15
## 13 2 17 5 5 4 1 8
## 16 17 18 20 30 45 60 or more <NA>
## 3 1 1 1 2 2 1 9876
mydata <- top_recode ("endline_child_nhhmmbrs", break_point=10, missing=999999) # Topcode cases with 10 or more members
## [1] "Frequency table before encoding"
## endline_child_nhhmmbrs.
## 1 2 3 4 5 6 7 8 10 <NA>
## 2 15 34 57 33 23 8 7 3 12514
## [1] "Frequency table after encoding"
## endline_child_nhhmmbrs. 10
## 1 2 3 4 5 6 7 8
## 2 15 34 57 33 23 8 7
## 10 or more <NA>
## 3 12514
mydata <- top_recode ("endline_s17q7", break_point=60, missing=999999) # Topcode cases with 60 or farther
## [1] "Frequency table before encoding"
## endline_s17q7. How far is the school from home?
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
## 166 1198 635 293 120 117 37 14 9 2 22 4 7 4 2
## 15 16 18 20 24 25 30 40 60 130 <NA>
## 2 3 1 1 1 1 1 1 1 2 10052
## [1] "Frequency table after encoding"
## endline_s17q7. How far is the school from home?
## 0 1 2 3 4 5 6 7
## 166 1198 635 293 120 117 37 14
## 8 9 10 11 12 13 14 15
## 9 2 22 4 7 4 2 2
## 16 18 20 24 25 30 40 60 or more
## 3 1 1 1 1 1 1 3
## <NA>
## 10052
mydata <- top_recode ("endline_s4q8", break_point=60, missing=999999) # Topcode cases with 60 or longer
## [1] "Frequency table before encoding"
## endline_s4q8. How long does it take to get to this school?
## 0 1 2 3 4 5 6 7 10 12 14 15 16 18 20 22 25 28
## 1 43 41 10 1 617 3 12 971 7 2 864 3 14 536 2 212 2
## 30 35 38 40 45 50 60 70 75 90 105 120 180 <NA>
## 536 25 5 25 42 12 66 1 1 12 1 8 1 8620
## [1] "Frequency table after encoding"
## endline_s4q8. How long does it take to get to this school?
## 0 1 2 3 4 5 6 7
## 1 43 41 10 1 617 3 12
## 10 12 14 15 16 18 20 22
## 971 7 2 864 3 14 536 2
## 25 28 30 35 38 40 45 50
## 212 2 536 25 5 25 42 12
## 60 or more <NA>
## 90 8620
# !!!Include relevant variables in list below
indirect_PII <- c("endline_s5q6c",
"endline_s19q4c",
"endline_s5q6_2c",
"endline_s19q4bc",
"endline_nhhmmbrs",
"endline_s8q0",
"endline_s10q6b",
"endline_s11q2",
"endline_s11q3",
"endline_s11q4",
"endline_s11q5",
"endline_s11q6",
"endline_s11q7",
"endline_s11q8",
"endline_s11q9",
"endline_s3q2",
"endline_s3q2a",
"endline_s3q3",
"endline_s3q4",
"endline_s3q5_1",
"endline_onlychild",
"endline_s3q6",
"endline_s3q7",
"endline_s3q8",
"endline_s3q9a",
"endline_s3q9b",
"endline_s3q9c",
"endline_s3q9d",
"endline_s3q9e",
"endline_s3q10",
"endline_s4q1",
"endline_s4q2",
"endline_s4q3",
"endline_s4q3_1",
"endline_s4q4",
"endline_s4q7",
"endline_s4q8",
"endline_s4q9",
"endline_s5q1",
"endline_s5q2a",
"endline_s5q2b",
"endline_s5q2c",
"endline_s5q2d",
"endline_s5q2e",
"endline_s5q2f",
"endline_s5q2g",
"endline_s5q2h",
"endline_s5q3",
"endline_s5q4a",
"endline_s5q4b",
"endline_s5q4c",
"endline_s5q4d",
"endline_s5q4e",
"endline_s5q4f",
"endline_s5q4g",
"endline_s5q4h",
"endline_s5q4i",
"endline_s5q5",
"endline_s5q6a",
"endline_s5q7",
"endline_s5q8",
"endline_s5q9",
"endline_s5q11",
"endline_s5q12",
"endline_s5q15",
"endline_s5q16",
"endline_s5q18",
"endline_s6q2",
"endline_s6q7",
"endline_s6q8",
"endline_s16q3",
"endline_s17q1",
"endline_s17q2",
"endline_s17q3",
"endline_s17q4",
"endline_s17q7",
"endline_s17q8",
"endline_s18q2a",
"endline_s18q2b",
"endline_s18q2c",
"endline_s18q2d",
"endline_s18q2e",
"endline_s18q2f",
"endline_s18q2g",
"endline_s18q2h",
"endline_s18q4",
"endline_s19q1",
"endline_s19q2a",
"endline_s19q2b",
"endline_s19q2c",
"endline_s19q2d",
"endline_s19q2e",
"endline_s19q2f",
"endline_s19q2g",
"endline_s19q2h",
"endline_s19q2i",
"endline_s19q3",
"endline_s19q4a",
"endline_s19q5",
"endline_s19q6",
"endline_s19q7",
"endline_s19q12",
"endline_s19q13",
"endline_s19q14",
"endline_s20q2",
"endline_s20q4",
"endline_s20q5",
"endline_s20q7",
"endline_s20q8",
"endline_child_nhhmmbrs",
"endline_numhhmbrs",
"endline_female",
"endline_sexresp",
"endline_ppi1",
"endline_ppi2",
"endline_ppi3",
"endline_ppi4",
"endline_ppi5",
"endline_ppi6",
"endline_ppi7",
"endline_ppi8",
"endline_ppi9",
"endline_ppi10",
"endline_CL_P",
"endline_CL_C",
"endline_CLC5_11",
"endline_CLC12_13",
"endline_CLC14_15",
"endline_CLP5_11",
"endline_CLP12_13",
"endline_CLP14_15")
capture_tables (indirect_PII)
# Recode those with very specific values
# Removed, as verbatim responses are partially or entirely in Nepali.
dropvars <- c("endline_occup0", "endline_occup1", "endline_ind0", "endline_ind1")
mydata <- mydata[!names(mydata) %in% dropvars]
# Top code household composition variables with large and unusual numbers
mydata <- top_recode ("endline_nhhmmbrs", break_point=10, missing=c(999999)) # Topcode cases with 10 or more members
## [1] "Frequency table before encoding"
## endline_nhhmmbrs.
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## 8 204 1212 2444 2810 2214 1174 942 440 354 222 258 120 29 103 49 35 18
## 19 26
## 40 20
## [1] "Frequency table after encoding"
## endline_nhhmmbrs. 10
## 1 2 3 4 5 6 7 8
## 8 204 1212 2444 2810 2214 1174 942
## 9 10 or more
## 440 1248
mydata <- top_recode ("endline_numhhmbrs", break_point=10, missing=c(999999)) # Topcode cases with 10 or more members
## [1] "Frequency table before encoding"
## endline_numhhmbrs.
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## 1 84 912 2192 2645 2346 1379 864 630 510 264 240 221 98 75 64 34 36
## 19 20 21
## 19 40 42
## [1] "Frequency table after encoding"
## endline_numhhmbrs. 10
## 1 2 3 4 5 6 7 8
## 1 84 912 2192 2645 2346 1379 864
## 9 10 or more
## 630 1643
# Based on dictionary inspection, select variables for creating sdcMicro object
# See: https://sdcpractice.readthedocs.io/en/latest/anon_methods.html
# All variable names should correspond to the names in the data file
# selected categorical key variables: gender, occupation/education and age
mydata$sex <- mydata$endline_s3q2
mydata$sex [is.na(mydata$sex)] <- mydata$endline_s3q2a[is.na(mydata$sex)]
selectedKeyVars = c('sex', 'endline_s3q8', 'endline_s3q3') ##!!! Replace with candidate categorical demo vars
# weight variable
# !!! No weight
# selectedWeightVar = c('projwt') ##!!! Replace with weight var
# household id variable (cluster)
selectedHouseholdID = c('hhid')
# creating the sdcMicro object with the assigned variables
sdcInitial <- createSdcObj(dat = mydata,
keyVars = selectedKeyVars,
hhId = selectedHouseholdID)
sdcInitial
## The input dataset consists of 12696 rows and 353 variables.
## --> Categorical key variables: sex, endline_s3q8, endline_s3q3
## --> Cluster/Household-Id variable: hhid
## ----------------------------------------------------------------------
## Information on categorical key variables:
##
## Reported is the number, mean size and size of the smallest category >0 for recoded variables.
## In parenthesis, the same statistics are shown for the unmodified data.
## Note: NA (missings) are counted as seperate categories!
## Key Variable Number of categories Mean size Size of smallest (>0)
## sex 3 (3) 5567.000 (5567.000) 5414 (5414)
## endline_s3q8 10 (10) 1174.444 (1174.444) 11 (11)
## endline_s3q3 82 (82) 137.494 (137.494) 6 (6)
## ----------------------------------------------------------------------
## Infos on 2/3-Anonymity:
##
## Number of observations violating
## - 2-anonymity: 152 (1.197%)
## - 3-anonymity: 313 (2.465%)
## - 5-anonymity: 679 (5.348%)
##
## ----------------------------------------------------------------------
Show values of key variable of records that violate k-anonymity
notAnon <- sdcInitial@risk$individual[,2] < 2 # for 2-anonymity
as.data.frame(mydata[notAnon,selectedKeyVars])
## sex endline_s3q8 endline_s3q3
## 1 0 98 70
## 2 1 98 39
## 3 0 98 75
## 4 1 4 47
## 5 1 99 60
## 6 0 4 60
## 7 0 4 56
## 8 0 3 64
## 9 1 6 75
## 10 0 5 73
## 11 1 5 63
## 12 0 2 69
## 13 1 5 52
## 14 1 6 68
## 15 0 4 58
## 16 0 6 18
## 17 0 6 36
## 18 1 5 31
## 19 1 4 48
## 20 0 99 10
## 21 1 6 22
## 22 0 6 59
## 23 0 6 64
## 24 1 98 34
## 25 0 2 5
## 26 0 4 78
## 27 1 6 73
## 28 0 98 6
## 29 0 98 73
## 30 0 6 46
## 31 0 6 22
## 32 1 6 19
## 33 0 4 57
## 34 0 5 56
## 35 0 2 7
## 36 1 6 41
## 37 1 5 44
## 38 1 6 13
## 39 1 2 59
## 40 1 5 38
## 41 0 6 8
## 42 1 3 59
## 43 1 6 52
## 44 0 3 59
## 45 1 0 79
## 46 1 98 68
## 47 0 6 43
## 48 0 2 79
## 49 1 99 13
## 50 1 6 36
## 51 0 98 36
## 52 0 4 68
## 53 1 2 56
## 54 1 2 51
## 55 0 6 48
## 56 0 98 58
## 57 1 6 72
## 58 0 6 75
## 59 0 5 53
## 60 1 3 56
## 61 1 98 61
## 62 1 4 64
## 63 1 3 65
## 64 1 6 46
## 65 0 6 32
## 66 1 2 52
## 67 1 2 69
## 68 0 4 55
## 69 1 2 70
## 70 0 3 11
## 71 0 6 29
## 72 1 3 63
## 73 1 6 67
## 74 1 6 59
## 75 0 4 12
## 76 0 5 58
## 77 1 98 8
## 78 1 6 37
## 79 1 3 71
## 80 1 98 32
## 81 1 6 24
## 82 1 2 8
## 83 0 6 35
## 84 1 6 60
## 85 1 3 10
## 86 0 6 70
## 87 1 98 75
## 88 0 5 55
## 89 0 3 60
## 90 1 2 57
## 91 0 6 31
## 92 0 4 13
## 93 0 0 9
## 94 1 6 23
## 95 1 2 53
## 96 0 3 57
## 97 1 98 35
## 98 0 6 9
## 99 0 98 40
## 100 1 99 36
## 101 0 6 19
## 102 1 1 79
## 103 0 6 39
## 104 0 98 65
## 105 1 6 38
## 106 0 5 65
## 107 0 98 80
## 108 1 98 78
## 109 0 2 61
## 110 1 1 76
## 111 0 98 60
## 112 0 6 73
## 113 1 4 49
## 114 0 5 62
## 115 1 6 63
## 116 0 2 59
## 117 1 6 32
## 118 0 98 26
## 119 1 6 29
## 120 0 4 51
## 121 1 98 11
## 122 1 6 35
## 123 1 2 65
## 124 1 98 50
## 125 1 1 72
## 126 1 3 73
## 127 1 6 28
## 128 0 5 60
## 129 1 5 59
## 130 0 6 66
## 131 0 3 73
## 132 1 5 46
## 133 0 1 24
## 134 0 98 55
## 135 1 98 72
## 136 0 6 40
## 137 1 6 26
## 138 0 0 12
## 139 0 3 12
## 140 1 1 78
## 141 1 5 42
## 142 0 2 57
## 143 1 6 42
## 144 0 3 10
## 145 0 2 54
## 146 0 98 69
## 147 1 6 31
## 148 1 99 14
## 149 0 99 16
## 150 1 98 6
## 151 1 6 12
## 152 0 0 79
sdcFinal <- localSuppression(sdcInitial)
# Recombining anonymized variables (exclude children, as critical for analysis)
extractManipData(sdcFinal)[notAnon,selectedKeyVars] # manipulated variables HH
## Warning in if (cc != class(v_p)) {: the condition has length > 1 and only the first element
## will be used
## Warning in if (cc != class(v_p)) {: the condition has length > 1 and only the first element
## will be used
## Warning in if (cc != class(v_p)) {: the condition has length > 1 and only the first element
## will be used
## sex endline_s3q8 endline_s3q3
## 43 0 98 NA
## 47 1 98 NA
## 187 0 98 NA
## 220 1 4 NA
## 221 1 99 NA
## 514 0 4 NA
## 562 0 4 NA
## 946 0 3 NA
## 1004 1 6 NA
## 1008 0 5 NA
## 1009 1 5 NA
## 1108 0 2 NA
## 1201 1 5 NA
## 1254 1 6 NA
## 1274 0 4 NA
## 1380 0 6 NA
## 1381 0 6 NA
## 1392 1 5 NA
## 1544 1 4 NA
## 1562 0 99 NA
## 1594 1 6 NA
## 1694 0 6 NA
## 1800 0 6 NA
## 1809 1 98 NA
## 1830 0 2 NA
## 2005 0 4 NA
## 2125 1 6 NA
## 2167 0 98 NA
## 2383 0 98 NA
## 2594 0 6 NA
## 2701 0 6 NA
## 2703 1 6 NA
## 2959 0 4 NA
## 3221 0 5 NA
## 3322 0 2 NA
## 3362 1 6 NA
## 3493 1 5 NA
## 3528 1 6 NA
## 3756 1 2 NA
## 4039 1 5 NA
## 4078 0 6 NA
## 4129 1 3 NA
## 4555 1 6 NA
## 4587 0 3 NA
## 4613 1 0 NA
## 4745 1 98 NA
## 4984 0 6 NA
## 5062 0 2 NA
## 5108 1 99 NA
## 5114 1 6 NA
## 5128 0 98 NA
## 5152 0 4 NA
## 5203 1 2 NA
## 5246 1 2 NA
## 5250 0 6 NA
## 5318 0 98 NA
## 5377 1 6 NA
## 5380 0 6 NA
## 5421 0 5 NA
## 5462 1 3 NA
## 5743 1 98 NA
## 5777 1 4 NA
## 5809 1 3 NA
## 6003 1 6 NA
## 6018 0 6 NA
## 6071 1 2 NA
## 6106 1 2 NA
## 6244 0 4 NA
## 6247 1 2 NA
## 6328 0 3 NA
## 6488 0 6 NA
## 6520 1 3 NA
## 6547 1 6 NA
## 6623 1 6 NA
## 6669 0 4 NA
## 6738 0 5 NA
## 6889 1 98 NA
## 6917 1 6 NA
## 7199 1 3 NA
## 7262 1 98 NA
## 7306 1 6 NA
## 7445 1 2 NA
## 7465 0 6 NA
## 7496 1 6 NA
## 7809 1 3 NA
## 7838 0 6 NA
## 7930 1 98 NA
## 7980 0 5 NA
## 7997 0 3 NA
## 8003 1 2 NA
## 8136 0 6 NA
## 8142 0 4 NA
## 8176 0 0 NA
## 8231 1 6 NA
## 8393 1 2 NA
## 8458 0 3 NA
## 8484 1 98 NA
## 8527 0 6 NA
## 8531 0 98 NA
## 8534 1 99 NA
## 8653 0 6 NA
## 8658 1 1 NA
## 8693 0 6 NA
## 8735 0 98 NA
## 8742 1 6 NA
## 8772 0 5 NA
## 9425 0 98 NA
## 9430 1 98 NA
## 9442 0 2 NA
## 9549 1 1 NA
## 9651 0 98 NA
## 9694 0 6 NA
## 9798 1 4 NA
## 9845 0 5 NA
## 9877 1 6 NA
## 10126 0 2 NA
## 10183 1 6 NA
## 10203 0 98 NA
## 10264 1 6 NA
## 10307 0 4 NA
## 10329 1 98 NA
## 10365 1 6 NA
## 10425 1 2 NA
## 10507 1 98 NA
## 10523 1 1 NA
## 10548 1 3 NA
## 10559 1 6 NA
## 10657 0 5 NA
## 10683 1 5 NA
## 10757 0 6 NA
## 10803 0 3 NA
## 10805 1 5 NA
## 10937 0 1 NA
## 11000 0 98 NA
## 11057 1 98 NA
## 11073 0 6 NA
## 11116 1 6 NA
## 11189 0 0 NA
## 11221 0 3 NA
## 11583 1 1 NA
## 11604 1 5 NA
## 11607 0 2 NA
## 11610 1 6 NA
## 12035 0 3 NA
## 12276 0 2 NA
## 12328 0 98 NA
## 12393 1 6 NA
## 12410 1 99 NA
## 12411 0 99 NA
## 12579 1 98 NA
## 12580 1 6 NA
## 12692 0 0 NA
mydata [notAnon & mydata$endline_s3q3 >17,"endline_s3q3"] <- NA
#Check that 2-anonimity is now maintained
createSdcObj(dat = mydata, keyVars = selectedKeyVars, hhId = selectedHouseholdID)
## The input dataset consists of 12696 rows and 353 variables.
## --> Categorical key variables: sex, endline_s3q8, endline_s3q3
## --> Cluster/Household-Id variable: hhid
## ----------------------------------------------------------------------
## Information on categorical key variables:
##
## Reported is the number, mean size and size of the smallest category >0 for recoded variables.
## In parenthesis, the same statistics are shown for the unmodified data.
## Note: NA (missings) are counted as seperate categories!
## Key Variable Number of categories Mean size Size of smallest (>0)
## sex 3 (3) 5567.000 (5567.000) 5414 (5414)
## endline_s3q8 10 (10) 1174.444 (1174.444) 11 (11)
## endline_s3q3 82 (82) 135.901 (135.901) 2 (2)
## ----------------------------------------------------------------------
## Infos on 2/3-Anonymity:
##
## Number of observations violating
## - 2-anonymity: 2 (0.016%)
## - 3-anonymity: 4 (0.032%)
## - 5-anonymity: 63 (0.496%)
##
## ----------------------------------------------------------------------
# 2 cases remain that do not meet 2-anonymity.
# Since these cases involve children, and providing 2-anonimity would require supressing their age, on further steps are implemented to maintain the integrity of the data.
mydata <- mydata[!names(mydata) %in% "sex"]
# !!! Identify open-end variables here:
open_ends <- c("occup0",
"occup1",
"ind0",
"ind1",
"endline_s9q5other",
"endline_s9q6other",
"endline_s10q5other",
"endline_s10q10other",
"endline_s11q3other",
"endline_s11q6other",
"endline_s13q1other",
"endline_s3q1other",
"endline_s3q5other",
"endline_s4q6other",
"endline_s4q9other",
"endline_s4q11other",
"endline_s5q6",
"endline_s5q6_2",
"endline_s17q6other",
"endline_s17q9other",
"endline_s18q3other",
"endline_s19q4",
"endline_s19q4b")
report_open (list_open_ends = open_ends)
# Review "verbatims.csv". Identify variables to be deleted or redacted and their row number
mydata <- mydata[!names(mydata) %in% open_ends] # SDC risk could not be ascertained as all verbatims are partially or completely in Nepali.
# !!! No GPS data.
Adds "_PU" (Public Use) to the end of the name
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)