DOL-ILAB SDC - baseline
Pablo Diego-Rosell, PhD
2021-04-03
- Setup filenames
- Setup data, functions and create dictionary for dataset review
- Direct PII: variables to be removed
- Direct PII-team: Encode interviewer names, which may be useful for analysis of interviewer effects
- Small locations: Encode locations with pop <100,000 using random large numbers
- Indirect PII - Ordinal: Global recode or Top/bottom coding for extreme values
- Matching and crosstabulations: Run automated PII check
- Open-ends: review responses for any sensitive information, redact as necessary
- GPS data: Displace
- Save processed data in Stata and SPSS format
rm(list=ls(all=t))Setup filenames
filename <- "baseline" # !!!Update filename
functions_vers <- "functions_1.6.R" # !!!Update helper functions fileSetup data, functions and create dictionary for dataset review
source (functions_vers)
#mydata <- mydata [1:10,] # remove '#' from #mydata if you want to conduct a fast check on 10 rows. 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
#!!!Save flagged dictionary in .xlsx format, add "DatasetReview" to name and continue processing data with subset of flagged variablesDirect PII: variables to be removed
# !!!No Direct PIIDirect PII-team: Encode interviewer names, which may be useful for analysis of interviewer effects
# !!!No Direct PII-teamSmall locations: Encode locations with pop <100,000 using random large numbers
!!!Include relevant variables, but check their population size first to confirm they are <100,000
locvars <- c("baseline_villagename",
"baseline_settlement",
"baseline_municipality",
"baseline_wardno",
"baseline_child_municipality",
"baseline_child_wardno",
"baseline_child_villagename",
"baseline_child_settlement")
mydata <- encode_location (variables= locvars, missing=999999)## [1] "Frequency table before encoding"
## baseline_villagename. Name of the village/ community
## 'Ranighat .parariya /kharsal /Rajesh gupta
## 7 7 4 5
## aadarsa tol Aadarsa tol aadarsamani tol Aadarsh tole
## 3 5 9 5
## Aadarsha tol aadarshnagar Aadrash tole Aakala
## 34 5 6 5
## Aalau aarba Aarba Adalat road
## 61 13 10 16
## adarshnagar Adarshnagar Adarsnagar akala
## 5 8 4 14
## Alakha Road Apauni arba Arba
## 8 35 22 30
## arbaa Armalakot arva Ashok batika
## 2 27 40 6
## Ashokbatica Ashokbatika Ashokvatika Atharaha
## 10 37 2 41
## athraha Athraha badhahare tol bagaicha
## 8 45 5 4
## Bahuwari Baisno devi tole bajo khet Bajrang tole
## 18 3 3 41
## Balgenari Banahari Bangau bankatta
## 4 39 49 5
## Barabighaha Barwa Basant inarwa Basantpur tole
## 4 32 22 15
## Basantpure Baskot Basudavpur Basudevpur
## 5 4 5 184
## Basydevpur Bausevpur Bawanipur Bawaniyapur
## 5 2 7 6
## Bayo khola Bayokhola bc Belanye
## 17 5 7 2
## Belganar Belganari Beltakura Bhagawati tole
## 2 29 9 8
## Bhagwatitol bhakti path bhalam Bhalam
## 32 8 51 72
## Bhaluwahi tole bhandari dhar bhansartole Bhansartole
## 4 14 11 24
## bhastal bhati path bhatipath Bhawanipir
## 13 3 5 6
## Bhawanipur Bhawaniyapur Bhediyahi Bhiswa
## 146 212 25 4
## Bijayanagar Bijeynagar Bindabasini Bindawasini
## 4 101 59 63
## birendra gufa Birgunj Birta Birta bazà r
## 17 46 129 7
## Birta nursing campus Brahmpur Brita Nursing Campus Area Budagaun tole
## 5 121 3 21
## Bundabasini Capkaiya Center parseni tol Centre Parseni tole
## 4 7 4 4
## Chailaheli Chailai chanautae Chanora parariya
## 4 18 13 26
## chanutae chanute chapkaiya Chapkaiya
## 16 5 4 385
## Chapkaiys Chapkaya Chapksiya Chappa dada
## 3 5 3 4
## Chhapkaiya Chhapwa dada Chitraguptnagar chour
## 196 1 30 11
## chowk Chowk bhola Cigarette factory Dabar tole
## 6 5 4 3
## Dadathok Dadathok tol Dadrini Danda sukaura
## 9 2 11 8
## Dasarath nager Dasharthnager Daxin nawalpur tole Deurali piple
## 7 4 3 23
## Devi chook Dhadagari Dhaddagari Dhalepipal
## 5 36 7 6
## dhanauji dhaurali Dhurmi Dihi gau
## 6 4 30 6
## Dinesh sah Dripot dripot sirsiya Dryport tol
## 5 9 7 10
## Fulbari Furathi chook Furthi chook Furthichook
## 140 4 72 10
## Furtichook Gahatera Gahawa garjathi
## 4 14 71 4
## garjati Garmikhola gauri khor Gaurigau
## 15 5 3 20
## Gaushala road Gaushwara Geetanagae Geetanagar
## 4 7 3 405
## Geetanagr Geetnagar ghadgai ghadhai
## 4 4 16 11
## Ghadi ghanduke chouk Ghantaghar Gharimukhla
## 12 3 6 3
## Ghariwara Ghariwarha Gharmikhola ghatgai
## 16 53 10 7
## Ghoraneti Ghoraneti tol Ghorneti Ghurmi
## 5 17 9 14
## Ghusari Ghusari tole Ghushauri tole gimirae
## 5 5 5 6
## Gitpur tole Gogimani Golauri Golouri
## 5 4 13 9
## Gopal chook Gshawa Gurudev ram Gyanjyoti
## 28 5 4 5
## Halawar haldharko chautara Halwar Hamagara
## 9 5 3 6
## hanuman nagar hanumanagar hanumannagae hanumannagar
## 11 23 4 10
## hanunam nagar Haripaura Haripauri Harpatgaj
## 3 10 5 5
## Harpatganj Harpatgunj Hasnapur Hatiya
## 74 51 24 79
## Hatti lote Hemanagar Hemangar hemja
## 5 13 4 112
## Hemja Himalay Himalay tole Himalayan tole
## 151 3 76 6
## Himaltole Himalya tole Indarpue Indarpur
## 6 3 5 198
## jagriti tole Jagriti tole Jail road Jail tol
## 5 5 5 9
## Jailroad Jaispur JanaJagriti tol Jaspur
## 20 257 5 25
## Jaumare Jimire dil Jispur Jogindra Raut kurmi
## 8 5 6 3
## Jumleti Jumleti tol Kachila Kachili
## 10 7 40 33
## Kahu kahun Kahun Kalakhola
## 53 39 2 9
## Kalakhola pulchok Kalimati Kaltu pokhari Kalyakhola vewdar
## 3 4 3 6
## Kalyanpur Kanchan pur road Kanchanpur road Kanchanpurroad
## 3 3 7 4
## Kapadadevi Kaprae ghat karai chautari Kataha
## 10 4 6 33
## kaun Kawari Kesarbagha khadha
## 79 13 4 6
## Khadre Khadrye khaluwatole Khaluwatole
## 8 26 10 9
## khaluwatole sirsiya Khaluwatole sirsiya Khaluwatole Sirsiya khalwa sirisiya
## 9 5 5 6
## khalwa tol Khalwa tol Khardye Kharkhola
## 5 5 4 3
## Kharsal Kharsal purnipokhari Kharsal tole Khas karandoo
## 179 8 10 6
## Khas karkandoo khastar khaster Khumkhane
## 182 12 18 21
## Kimdi Kirana line Kirishna nagar Koeritole udaypur ghurmi
## 5 39 5 7
## kristi Kristi Kuhari Kulayen marga
## 70 95 3 6
## Kumal gau Kumar tole Kumartole Kumhal tol
## 2 4 10 41
## Kumhaltole Kuwari Kwangi Kwonagi
## 6 7 23 2
## Kwongi Kwonig Lachhamamu Lachhamanu
## 5 10 7 43
## Lachhamsnu Lachhumanu Lalmateya Lalmatya
## 5 6 10 5
## Lamachaur lamachaure Lamaswara Lamidada
## 11 12 4 3
## Lamidamar Lilja tole Madhumaya thapa Mahabir sthan
## 3 5 5 5
## Mahabirsthaan Mahabirsthan Main road Mainroad
## 24 6 8 9
## Maisthaan Maisthan Manaidada Manakamana tol
## 28 184 3 3
## Mandannagar Mandantole Mangalpur Manihari
## 7 3 75 79
## Manikapir Manikapur Matera tole Mathaelno halwar tol
## 4 213 4 3
## Mathalno halwar Maujetole maujetole sirsiya maula tol bikas
## 3 20 5 3
## methlang Methlang Minabajar Mohanpur
## 6 5 5 5
## Mohon pur tol Mohonpur Moteratole Motipur
## 4 4 4 10
## moujetole Mulibhagaicha Mulkot tol Murli
## 5 5 6 101
## Murli Bagaicha Murlibagaica Murlibagaicha Murlibhagaicha
## 5 5 29 2
## Musilamtol Muslimtol Nabin chook Nagarpalika road
## 19 45 11 55
## Nagawa Naguwa Nagwa Namuna tole
## 20 154 71 5
## Naule tol Nauli tol Naulpur Naya gaun
## 4 5 34 6
## Naya tole Naya tole mruli Nayabasti shreepur Nayagaun
## 22 6 5 21
## Nayatole murli nirmal pkhari nirmal pokhari Nirmal pokhari
## 15 3 57 44
## nirmalpokhari Nirmalpokhari padale padam pokhari
## 9 51 3 28
## padampokhari Paddha padhali Padham pokhare
## 10 23 26 8
## pain tanki Pani tanki Panitanki Parariya
## 4 4 5 21
## Parasanagar Parasnagar Paraspur Park as,nagar
## 4 12 224 4
## Parkash,nagar,sano basti Parks,nagar,Sano,pipra Parsauni Parseni bajar tole
## 7 5 78 4
## Parwanipur ParwaniPur Paschim rampur Patahani
## 256 4 51 26
## Patariya pateheni Pathani patihani
## 8 5 5 79
## Patihani patihani town patiheni patlahara
## 284 22 7 24
## Piara Pipara Pipara,aawas,ariya Piple
## 19 69 4 3
## Pipra Pipra,awash,ariya Piprahwa Pirgau
## 4 5 234 4
## pokhara Pokharel tole Pokheral tole Pokherel tole
## 4 4 23 3
## Pokhral tole Pokhrel tole Pragatinagar Prasauni
## 5 7 3 99
## Puaina Puaraina Pulchowk pullar
## 5 6 6 6
## Pumbdi Pumdhi Pumdi Pumdi bhumdi
## 5 5 30 9
## Pumdi vumdi Pumdibhumdi Pumdikot Pundgi
## 38 11 6 6
## pundi vundi pundivundi Puraina Puraini
## 20 4 249 236
## Purba rampur Puripokhari Purnipikhari Purnipokari
## 11 4 5 4
## Purnipokhari Raam tole Raampur Radha devi gurung
## 9 15 53 3
## Radhemai Rahamatpur Raikhalyan Raikhelyan
## 100 28 10 5
## Rajbiraj Rajbiraj Kharsal Rajdevi Rajdevi road
## 426 5 4 11
## Rajdevi tol Rajdevi tole Rajhena Rajiraj
## 5 92 3 10
## Rakbiraj Ram tole Ram,gaduwa Ramgaduwa
## 4 4 5 210
## Ramgadwa rampur Rampur Rangasala tole
## 40 4 21 3
## Ranighat Ranighat tol Ranighat tole Ranighat,gashuwara road
## 249 12 8 6
## Resamkoti Resham kothi Resham Kothi Reshamkhoti
## 14 77 14 25
## Reshamkothi ReshamKothi Reshamkoti ReshamKoti
## 6 5 63 4
## Reshsm kothi Ryale patle Ryale Patle Sabaithuwa
## 7 24 5 22
## Sabaituwa Sabathuwa Sabauthuwa Sabitawa
## 19 8 9 11
## sahar dhar sahardhar Sai krishna tola Sai krishna tole
## 5 11 5 4
## Saibutwa Saikrishana tole Sajha tol Sajha tole
## 12 3 12 3
## santipur Saorn tole Saptaha ko dil sarangkot
## 38 5 5 54
## Sarangkot Sarankot Saranpur Saraswati tole
## 26 10 73 14
## Sarboday tole Sarbodey tole Sardar tole sardhar
## 9 4 9 24
## sardhgar Sarswati tole sauda chautra Shanti gyan bikash
## 7 29 5 4
## Shanti gyan bikash tol Shanti tole Shekh Shiavanagar
## 2 11 4 4
## Shimra gau tol Shimra gaun Shiromanar Shiromaninagar
## 6 5 5 24
## shirsiya shiva nagar Shiva sakti tole Shiva shakti
## 4 25 4 2
## Shivaghat shivanagar Shivanagar Shivdakti tole
## 9 53 195 4
## Shivnagar Shreepur Shreepur,ranighat Simlegaira
## 23 110 10 4
## Simlegaire Sirbani Sirha rod Siromaninagar
## 6 7 4 3
## Sirshiya Sirshyia Sirsiya Sisobari
## 81 7 29 33
## Sitalapur Sitalpur Siva shakti tole Sivanagar
## 3 9 4 81
## Srawati tole Sreepur srisiya vansar tol Srswati tole
## 7 66 8 19
## Suagauli birta Subash shah Sugali Sugali birta
## 7 6 8 8
## Sugauli SUGAULI Sugauli birta Sukarua
## 66 9 73 5
## Sukaura Sundar basti tole Sunderbasti Surya nagar
## 10 7 21 8
## Suryodaynagar Swarn Swarn tole swikhet
## 3 4 71 12
## swikot Talno halawar Talno halwar tol Tarigain
## 3 5 11 10
## Tarigau Tarigau Basbot Tarigau sano rajeura Tarigau tharu gaun
## 147 4 4 5
## Tarigaun Tatrigachi Tejara tole Tejarath tole
## 39 5 9 9
## Tejaratole Tejrath tole Tetari gachhi Tetri gachi
## 32 7 19 12
## Tetri tole Tetrigachi Thangachi Thapa tol
## 7 11 4 18
## Tharu gau tol themja Thulo,pipra Tuhure pasal
## 12 4 4 5
## Uadayapur Udaepur ghurmi Udahapur Uday pur ghurmi
## 17 8 10 5
## Udayapur Udayapur ghurmi Udaypue Udaypur
## 223 8 6 61
## Udaypur bhurmi Udaypur ghurmi Udaypur Gurmi Udaypur,ghurmi
## 13 44 11 4
## Urahari Utarsukaura Uttar nawalpur tole Uttar sukaura
## 51 3 6 9
## valam Valam Valuwahi tole Vansar sirisiya
## 25 40 5 3
## Vanshar sirisiya vedi vhimsen nagar vimshen nagar
## 8 4 5 4
## vimshennagar vishennagar Vishwa Viswa
## 4 7 22 3
## West Rampur yamdi Yamdi yamdi tol
## 4 4 12 3
## yandi tol
## 3
## [1] "Frequency table after encoding"
## baseline_villagename. Name of the village/ community
## 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495
## 3 78 5 10 79 5 19 8 73 5 26 4 9 17 4 10 5 154 5 71 10 19 6
## 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518
## 4 5 3 32 44 5 16 3 10 7 66 9 30 8 3 3 7 3 5 198 4 4 6
## 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541
## 11 12 10 4 15 2 6 24 23 4 4 40 4 7 4 3 14 4 2 5 4 8 4
## 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564
## 4 6 5 6 4 146 6 5 4 3 20 5 4 5 24 5 28 10 5 5 10 5 9
## 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587
## 37 5 5 4 4 53 3 4 6 5 11 5 3 7 6 15 3 5 15 3 7 6 74
## 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610
## 3 24 8 9 4 7 4 11 3 3 26 5 18 3 12 36 5 4 4 25 39 6 29
## 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633
## 71 20 4 3 3 9 179 21 53 6 6 99 3 4 6 3 24 4 8 11 5 76 8
## 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656
## 5 30 4 24 66 33 5 5 7 40 7 10 28 8 5 10 4 70 4 25 7 4 4
## 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679
## 6 249 5 4 4 2 6 3 5 57 7 3 4 5 4 2 13 3 8 14 5 4 4
## 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702
## 195 4 6 23 28 223 256 7 151 5 5 23 5 4 14 236 4 6 5 405 4 6 20
## 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725
## 6 3 9 5 10 59 3 4 10 110 5 13 3 2 8 8 4 426 5 35 3 22 5
## 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748
## 2 61 5 4 7 7 5 257 6 12 51 7 4 5 6 9 24 3 8 5 21 4 3
## 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771
## 3 5 5 2 18 26 26 13 23 12 249 196 5 11 4 7 6 7 32 13 23 2 101
## 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794
## 41 4 184 20 4 5 10 5 4 10 5 3 79 5 13 5 7 184 5 5 8 7 23
## 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817
## 17 7 11 9 6 213 9 51 9 73 5 5 9 16 12 43 32 385 72 10 5 45 9
## 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840
## 54 6 21 5 4 5 3 39 7 3 61 26 3 6 5 100 5 75 4 7 6 22 3
## 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863
## 63 7 5 10 6 22 6 3 12 234 8 5 71 41 21 10 16 9 44 4 11 8 9
## 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886
## 77 34 79 11 12 4 7 21 29 212 21 5 5 8 10 9 5 10 5 7 4 7 95
## 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909
## 92 140 4 3 5 15 2 39 8 5 4 14 4 25 11 7 19 12 5 4 5 46 9
## 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932
## 10 4 8 4 5 6 9 5 14 5 3 6 5 22 3 14 2 27 5 9 4 5 9
## 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955
## 4 49 53 63 25 10 4 5 6 11 182 9 4 4 51 53 5 7 30 4 11 9 3
## 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978
## 5 40 4 69 6 55 33 5 3 34 72 101 20 5 39 11 24 3 9 5 10 6 29
## 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001
## 147 11 18 4 7 25 6 4 38 19 2 29 41 79 17 18 3 6 129 19 210 51 81
## 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024
## 3 16 2 3 6 8 6 17 8 45 4 14 11 11 22 4 7 4 28 22 13 40 3
## 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047
## 1 4 3 4 5 33 3 12 224 8 81 5 3 4 13 4 10 284 4 12 7 12 121
## 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069
## 5 10 112 6 3 5 9 5 38 5 5 4 11 4 9 3 4 6 51 30 4 8
## [1] "Frequency table before encoding"
## baseline_settlement. Name of the Settlements
## aadarsa mani aadarsa pokhrel tol aadarsa tol Aadarsamani
## 3 6 6 3
## aadarsamani tol Aadarsapokhrel Aadarsh tole aadarshnagae
## 6 4 11 5
## aadhars tol Aalam Aalau Aanapurna Tol
## 4 4 61 9
## Adalat road Adarsa tol Adarsha mani tol adarshnagar
## 16 5 14 5
## Adarshnagar Adarsnagar Adhikari gaun Agree gaurishankar
## 8 4 4 5
## Ahmad pur Ahmad tole akala akla
## 6 6 9 5
## Aklekhet Alakha Road Alanagar Amar
## 4 8 24 4
## Amarapuri Amarbasti Ammarbasti Anapurna Tol
## 2 11 26 4
## annapurna tol Apauni arbhote Armalakot
## 8 66 4 27
## Ashok batika Ashokbatika Ashokvatika Atharaha
## 6 47 2 41
## athraha Athraha Babaitol Babu gaun
## 8 45 4 62
## Babugaun Badahare Badalthar Badare
## 36 6 3 5
## badhahare Badhare bagaicha Bagaicha
## 5 4 4 5
## Bagale lamaswara Bagbani tol Baghakholi tol Bagyasworitol
## 4 4 7 4
## Bahaun tol Bahaya khola baheri bahu khola
## 3 2 5 5
## Bahu khola Bahueari Bahuhori Bahuwari
## 3 4 19 14
## Bahyekhola bajhare Bajrang Bajrang tole
## 3 5 3 13
## Bajrangi tole Bale gaun Balegaun Banahari
## 57 44 93 9
## Band tole Bandh tole Bangau Banjare gaau
## 3 5 10 45
## bankatta Banpale barabuta baralthar
## 14 5 5 4
## Barampuri Barauji Barewa Barwa
## 24 30 28 21
## Basan purawa Basantpur Basantpure Basbot
## 8 15 5 16
## Baseri baseri tol Baskot baspani
## 8 2 4 2
## bastal Bawan tol Baya khola Bayo khola
## 13 6 4 17
## Bayokhola bc Belanye.tol besi gidhri khola
## 5 7 2 4
## Besigidari Bhadagau bhadgau bhadhahara
## 4 4 2 5
## bhadhara Bhagaerithana Bhagari than Bhagawan Tola
## 4 3 4 12
## Bhagawanpur Bhagawati tole Bhagwatitol bhajo khet
## 10 8 32 3
## bhakti path Bhaktipath bhalam Bhaluwae
## 8 6 29 8
## Bhaluwahi nadhi Bhandari bhandari dhar Bhandaridahar
## 4 3 14 5
## Bhandarigaun Bhanichok bhanjyang tilahar tol Bhansartol
## 4 5 5 6
## bhansartole Bhansartole bhanshartole Bhanuchowk
## 6 24 5 9
## Bhata tole bhati path bhatipath Bhawani tol
## 5 3 5 12
## Bhawanipur Bhayakhola Bhayapur Bhediyahi
## 41 5 15 25
## BhgawotiTol Bhiri road Bhiswa Bhitri road
## 6 12 10 42
## Bhola chowk Bhujai gaun Bhujaigaun Bhuji gaun
## 5 3 31 7
## Bhujigaun Bhuwanpur Bhuyarmandir tol Bidhyapith school paxadi
## 31 4 3 5
## Bijaya tole Bijeynagar Bijeynagar bazar Bijeynagar dairy
## 6 14 3 8
## Bijeynagar,way to haraiya Bindawasini Bindraban birendra gufa
## 4 74 7 17
## Birta BIRTA Birtakhet Bishanu
## 226 4 6 3
## Bishnupur bodhare Bokati botetol
## 25 4 4 24
## Bramtol Britakhet Budagau Budagaun
## 4 3 12 9
## Buketi Campus gali Chahari chook Chailahe
## 4 3 6 4
## Chailai Chamar tol and dhobi tol Chamartol and dhobitol chanatae
## 8 11 4 4
## chanauta chanautae chanaute Chanora parariya
## 8 9 7 26
## chanutae chanute Chapa Chapkaiya bazar
## 3 5 5 11
## Chapkaiya kawadi tol Chapkaiya tol Chapkaiya tole Chapkaiya tôle
## 7 4 5 3
## Chathghat chauntae Chauthari tol Chhapkaiya
## 14 6 4 33
## Chilaune kharka chilaunekharka Chimeki tol Chiranjeevi chok
## 3 3 4 3
## Chiranjivi chowk Chitagupt nagar Chitraguptnagar Chock bajar
## 15 5 40 4
## Chokbazar Choudhaghare chour chowk
## 5 13 11 6
## Cigarette factory Cigarette factory area Cold store Copan gunj
## 8 4 42 5
## Dabar Dadathok Dadathok tol Dadathok tole
## 3 3 6 2
## Daddhagari Dadre Dadrini tol Dahara
## 2 12 11 3
## Dakshin tole Dakshina tole Damodar Dandagaun tol
## 33 7 4 2
## Dandathok Dangisaran tol Darahi tol Darai tole
## 3 3 3 4
## daredeurali Darsa nager Dasarath nager Dasarathnager
## 3 5 7 3
## Dasarth nager Deurali Devi chook Devichook
## 4 4 10 3
## Devichowk Devnagar Devthan Devthan tlo
## 6 49 13 10
## Devthan tol Dhadagari Dhaddagari dhakalthar
## 5 10 5 11
## Dhale Pipal Dhalepipal dhanauji Dharahi tol
## 3 6 11 5
## Dharahi tol (purbi) Dharai tol dhaurali Dhore gaun
## 5 5 8 67
## Dihi Dihi gau Dihi tol Dr koloni
## 6 8 5 6
## Driport Driport Tol Dripot Dryport tol
## 10 4 9 5
## Duhar tole Dumari Dumri Duwar
## 6 31 93 5
## Duwar tol Duwar tole fedi fokshing deurali
## 5 9 4 3
## foksing deurali Fulbari Fulbari tol Furhi chook
## 1 36 5 3
## Furthi chook Furthi chook bazer Furthi cook Futaha
## 57 5 6 10
## Gadash tol Gahatera Gail road Gairiswara
## 6 14 5 2
## Ganaganagar Ganesh chok Ganesh marg,trichowk Ganesh tole
## 4 13 9 5
## Ganeshgunj Ganganaga Ganganagar Gangapur
## 18 5 111 41
## Gangarampura Gangarampurwa Gapalgunj garbetandi
## 8 10 6 3
## garjati Gaucharan Gauri gaun gauri khor
## 19 4 3 3
## Gauri shanker tol Gaurigau Gaushwara Gayarjati
## 2 10 7 4
## Gayatri tole Geeta mandir Geetanagar Geetanagar bazar
## 10 21 14 11
## ghadgai Ghalegaun ghandruke chouk Ghariwara
## 30 7 3 16
## Ghariwarha ghatgai ghimire chok tol Ghimire chowk
## 53 7 6 4
## Ghimire tol Ghoradabra tol Ghoraneti Ghorneti
## 2 7 22 9
## Ghumtichook Ghurali tol Ghurmi Ghurmi udayapur
## 5 4 76 4
## Ghusakpur Ghusari Ghusauri Ghushari
## 10 5 5 5
## Ghusukpur gimire Gitpur Gopal chook
## 33 6 5 23
## Gopalgunj Gorkhali Gorkhali tole Goswara road
## 25 62 69 6
## Gouri purwa Gouriipurwa Gouripurwa Govind chok
## 8 5 12 7
## Gumba chour Gumbachour Gurmi Gurung gau
## 3 3 7 18
## Gurung tol Gurungchowk Gyaneshwor chowk Gyanjyoti
## 5 6 7 5
## Gyarjati gyarjyoti Gyatrinagar Hal pachadi
## 6 16 4 16
## Halawar haldharko chautara Halwar hanuman nagar
## 8 5 24 14
## Hanuman nagar road Hanuman nagar tole hanumanaga hanumanagar
## 12 5 5 6
## hanumannagar Haraiya Haraiya way Haripaura
## 28 4 3 15
## Haripauri Harpatganj Harpatgunj Harpatjanj
## 15 33 51 3
## Hasnapur Hatiya Hema Nagar tol Hemanagar
## 7 84 6 7
## Hemja Himal Himalay Himalay tole
## 3 11 6 66
## Himalaya Himalaya tole Himalayan tole Himalsy tole
## 8 8 6 10
## Himaltole Himaly tole Himalya tole Hulaki tol
## 6 2 3 5
## Hulakimarg Inaruwa Inaruwamaniyari Inarwa
## 11 29 67 22
## Indargaau Indrapuri Indrapuri chok Jagaran
## 44 18 15 4
## jagarit tol Jagriri jagriti Jagriti
## 2 5 12 16
## jagriti tol Jagriti tol Jagriti Tol Jagritinagar
## 9 10 5 21
## Jail tol Jailroad Jaimare Jaispur
## 9 20 4 263
## Jamnaha Janajagaran tol Janajagran tol Janajagrati
## 80 12 5 4
## janajagriti tol Janakeswori janjagriti tol Jaspur
## 2 9 8 14
## Jayanagar Jayananesh Jaynagar Jelrod
## 49 4 3 4
## Jhakaruwa Jhakrawathuti tol Jhanjhane Jhumaryathuti tol
## 6 5 17 4
## Jhumaryatol Jimire dil Jodhapurwa joti chock
## 5 5 28 9
## Jumleti Kabadi tol Kabhre tole, Indrachowk Kabhreghat
## 10 6 5 11
## Kachili Kachili tol Kalakhola Kalayanpur bazer
## 12 5 12 3
## kalika tol Kalikhola Kalimati Kaltu line
## 4 5 8 3
## Kaltu pokhari Kalwatole Kalya khola vewdar Kalyankari tol
## 3 6 6 4
## kamere pani kamere pani tol Kanchanpur road Kanchanpurtole
## 8 4 13 4
## Kanchi chok Kanthipur Kantipur Kantipur tol
## 14 40 7 4
## Kapadadevi Kapadevi tol Kapre chat chook karai chautari
## 5 4 4 6
## Karkandoo Karki chok karki gaun Karkichok
## 33 7 1 8
## Karkichowk karkigau karkigsum Karmohna
## 3 3 1 18
## Kasarbag kasari kaseri Kaseri
## 6 3 2 7
## Kaseri dumre kaseri tol kastan kaster
## 3 2 4 2
## Kataha sami tol Katahasami tol Katilya Katulya
## 3 2 18 4
## Kaulash chok kaun deurali kaun tol kauntol
## 7 3 12 5
## Kawari Kehuniya Kepa chock Kesarbagha
## 13 35 4 4
## kesari Kesharbag khadha thare Khadkathar
## 2 38 6 7
## Khadrya Khadrye Khalla Puraini Khalla Puraini
## 2 18 7 69
## khalutole khaluwatole Khaluwatole khaluwatole sirsiya
## 6 7 10 9
## Khaluwatole sirsiya khaluwatolw khalwa sirisiya Khalwa Tol
## 14 4 6 5
## khalwa tol sirisiya khalwa tole Khalwatole Kharkhola tol
## 5 7 30 3
## Kharsal Kharsal dev tole Kharsal methil tole Kharsal tole
## 84 5 4 84
## Khas karkandoo khastar khaster Khatikanpurwa
## 4 6 18 4
## Khatri gau khatri tol Khatri tol Khatsal tole
## 3 3 3 2
## Khayarghari Khayarghari chowki Khittari khlwatole
## 22 12 2 8
## Khlwatole Khora Khumkhane Kigrinpurwa
## 6 4 21 33
## Kirana line Kodi Koeritole Koeritole udaypur ghurmi
## 24 9 8 7
## kohadi Kohadi Koiri tole Koiripatti
## 9 39 54 18
## Krishnamandir tol Krishnamandir tole Kuhari Kukunswara
## 2 8 3 4
## Kulain kulain marga Kulain marga Kulainmarga
## 4 3 4 2
## Kulani tole kulayan kulayan marg kulayan marga
## 3 3 7 5
## kulayan tol Kulayen marga Kulayen tol Kumal
## 7 6 3 2
## Kumar tole Kumartole Kumeya Kumhal tol
## 4 10 5 41
## Kumhal tole Kumhaltole kumiya Kusanchour
## 10 9 6 12
## Kusinchour Kuwari Laath gaali Laath gali
## 12 7 32 5
## Laathgaali Lachhamanu Lagdahawa Lagdhawa
## 8 61 11 68
## Lalapurwa laliguras tol Laliguras tol Laligurash
## 57 14 5 3
## Lalmatya tol lama khet Lamachowk Lamakhet
## 3 5 13 8
## Lamichane thar Lamichane tole Lamidada tol Lamidamar
## 4 3 3 3
## lampata Laptanchwok Laxman tol Lilja tole
## 2 5 4 10
## Lodhai Lodhai gau Lodhaigau Lodhayi goun
## 5 23 9 5
## Lonionpurawa Lonionpurwa Loniyan purawa Loniyanpurwa
## 5 5 12 25
## Loniyonpurwa Lukunsawara Luxman tol Luxmannager
## 5 8 7 4
## Maanpur machapuchare machapuchre tol Machapuchre Tol
## 16 3 6 3
## Machhapucher tol Machhapuchre tol Madjid tol Magar tole
## 6 6 13 2
## Magartole Mahajid Tol Mahapurwa Maheswor tol
## 12 6 39 4
## Mai mandir tol Main road Mainroad Maisthaan
## 19 20 27 7
## Maisthan Majdada Majida tol Malpot tole
## 106 5 4 11
## Manahari Manaidada Manakamana chook Manakamana chowk
## 3 3 6 4
## Manakamana tol Mandaltole Mandannagar Mangalpur
## 8 3 7 6
## Mangalpur bazer Mangalpur bazer vitra Mangalpur vitra Manihari
## 19 4 2 66
## Manihari tol Manikapur manisibalaya tol Maniyadanda tol
## 4 30 5 2
## Mannipur mansara tol Masjid tol Masjid tole
## 10 1 34 23
## Maszid tole Matera Mathighar maujetole
## 6 4 7 10
## Maujetole Maujetole sirsirya Maula maula tol
## 22 8 4 9
## Maula tol Melijuli tol methlang Milan tol
## 6 6 11 32
## Milantol Milijulichok Milijulichowk Minabajar
## 22 8 11 5
## Minabazar Mohanpur Mohanpur tol Mohonpur
## 7 74 5 4
## Mohonpur tol Motera Moti tol Motitol
## 4 4 2 8
## Moujetole Mulkot murli Murli
## 6 6 6 111
## Murlibagaicha MurliBagaicha Murlibhagaicha Murlubagaicha
## 35 5 2 4
## Musilamtol Muslimtol Musulamtol Nabajoti tol
## 13 45 6 7
## Nabin chook Nabin chook vitra Nachne chaur nachnechaur
## 14 3 4 20
## Nachnechaur Nachnichour Nadai gaun Nadaigaun
## 3 4 13 18
## Naditole Nagarpalika road Nagawa Naguwa
## 19 55 20 69
## Nagwa naharchowk Naharpurwa Namuna
## 82 3 26 5
## Namuna tol Namunatol Narbadha tol Natanpurwa
## 18 17 3 57
## Naulpur tol Nawalpur Naya Basti Nayabasti
## 6 9 8 24
## Nayagaun Nayak tol Nayatole murli Neta chowk
## 19 9 21 4
## Neuli Neuli tol nirmal pokhari Nirmal pokhari
## 6 13 31 6
## Nirmal pokhri Nirmalpokhari Nursery chowk Paan mandi
## 5 10 7 4
## Paangaali Pabitra tol Pachhim sukaura padale
## 6 16 5 3
## padam pokhari Padam pokhari padampokhari Padampokhari
## 11 4 21 43
## Paddha Pade ghumti padeli padhali
## 10 6 6 26
## Padham pokhare Pain tanki Pakaudi Pande ghumti
## 8 4 20 6
## Pani tanki Panitanki Panitanki,chamartol Parajuli chok
## 4 38 7 10
## Parariya Parasapur Parasnagar Paraspur
## 36 4 7 102
## Parbatinagar Pargati tol Park as,nagar sanopipra Parsanpurawa
## 24 4 4 10
## Parsanpurwa Parsauni Parseni Parwanipur
## 6 78 24 77
## ParwaniPur Pasupati Patel,nagar Patelnagar
## 5 10 7 8
## Patelnager Patelnegar patihani patihani bazar
## 12 4 13 30
## patihani town Patihani town patiheni bazar patlahara
## 22 14 7 21
## Patle Phokshing Phoolwari Tol Pipaldali
## 11 5 12 14
## Pipara Piple Pipra Pirgau
## 28 3 9 1
## Pokharel tole Pokhari tol Pokheral tole Pokheral tole
## 4 4 4 34
## Pokherel tole Pokhral tole Pokhreal tole Pokhrel tole
## 3 1 4 8
## Pokhrel Tole Pothedarpurwa Pragatinagar Pragatitol
## 3 32 13 4
## Pragtinagar Prasauni Professor colony Profhesar koloni
## 20 92 3 6
## Prssauni Pulchowk pullar Pullar
## 7 6 6 5
## Pumdi kot Pumdikot Punari pokahri punti dada
## 8 24 4 2
## Puraina Puraini Purba rampur Purnipokari
## 118 52 25 5
## Purnipokhari Purnipokhari tole Raahamatpur Raam tole
## 11 14 5 15
## Raampur Raamur Radakrishna Radakrisna
## 46 7 6 5
## Radha krishna Radha krishna Tol Radhakrishna radhakrishna tol
## 5 13 5 10
## Radhakrishna tol Radhakrishna tole Radhakrisna radhakrisna tol
## 28 7 3 9
## Radhakrisna tole Radhapur Radhemai Rahamat tol
## 7 102 96 4
## Rahamatpur Rahsmad tol Raikhelyan Rajaura sano gaun
## 23 3 5 3
## Rajdevi Rajdevi tole Rajdevi road Rajdevi tole
## 26 5 7 52
## Rajhana tol Rajhanatol Rajhena tharugau Rajheni tol
## 4 14 3 5
## Ram Ram tol Ram tole ramchok
## 4 4 8 3
## Ramchowk Rameshorpurwa Ramgadawa Ramgaduwa
## 5 81 7 196
## Ramgadwa Ramghadwa rammandir Ramtole
## 40 5 5 33
## Ramwapur Rangaduwa Rangasala tole Ranighat
## 80 7 3 280
## Ranighat tole Resamkoti Resham khoti Resham kothi
## 5 5 9 55
## Resham Kothi Reshamkhoti Reshamkothi Reshamkoti
## 24 15 27 67
## Reshan kothi Risinge tole Road tol Ryale
## 13 3 7 6
## Ryalechaur tol Sabaithuwa Sabaituw Sabaituwa
## 4 39 6 25
## Sabitawa Sagaramatha tol sahardhar Sai krishna
## 11 6 6 4
## Sai krishna tole Saikrishana Saja Sajha
## 6 3 4 3
## sajha sidhartha sajha sidhartha tol sajha tol Sajha tol
## 5 5 18 12
## Sajha tole sangam tol Sangam tol Sangamtol
## 3 4 5 4
## Sano ganeshganj Sano ganeshgunj Sano ganeshjung Sano gaun
## 5 4 3 5
## Sano pipra Sano Rajauara Sano,pipra Sanoganesh gunj
## 5 4 7 2
## Sanogarhi Sanogau santaneswor tol Santi chook
## 5 5 7 4
## Santi tole Santinagar santipur Saptaha ko dil
## 3 4 42 5
## Saranpur Saraswati tole Sarbodai tole Sarboday tole
## 10 9 6 3
## Sarbodeytole Sardar tole sardhar Sarswati tole
## 4 9 36 21
## Satyanarayan tole saudaha saudaha chautara Saudahachautara
## 4 13 3 5
## saudha chautara Sauraha Saworn tole School road
## 5 43 5 7
## School road/ malpot road Seara Shanti Shanti tol
## 6 6 20 24
## Shanti tole Shantichowk Shantitol shardhar
## 11 23 24 5
## Shepas patel Shibnagar Shimra gaun Shimragau
## 3 4 5 6
## Shinagar Shiromani Shiromaninagar Shiromaninager
## 3 5 25 4
## Shiva chock Shiva choka Shiva Shakti tol Shiva sundar
## 6 4 2 4
## Shiva sundar Tol Shivaghat aagadi Shivanagar Shivasakti
## 5 5 85 4
## shivasakti marga Shivashakti tol shivasundar tol Shivnagar
## 3 10 4 17
## Shivthan Shreepur sibalaya Sibalaya
## 52 119 15 11
## Sibasundar tol Sibsundar simalchair simalchaur
## 10 6 4 15
## Simalchaur simalchaur tol Simalchhaur Simlegaire
## 13 7 6 6
## simnasara Siraha road sirbani Sirbani
## 2 4 3 7
## Sirha road Sirha rod Sirishiya Sirisiya khalwa
## 4 4 5 5
## Siromaninagar Sirsiya sisneri sisneri tol
## 3 18 2 4
## Sisobari Siswadi Sitalpur Sitalpur.tol
## 38 3 29 3
## Srawati tole Sreepur Srijana nagar Srswati tole
## 7 35 12 3
## Srswati tole Srswatitole Sugauli Sugauli birta
## 10 12 10 104
## suikhet Suikhet Suiya Sukaura tol
## 10 26 85 7
## Sukhet Sukhrampurwa Sukidaha Sukreseori tol
## 4 17 3 4
## Sukresori Sukumbasi tol Sukyatol Sunaulo tol
## 4 5 4 6
## Sunaulo tol Sundada sundada khet Sundada khet
## 6 14 12 2
## Sundar chok sundar santi chock Sundarbasti Sundarchok
## 4 4 7 4
## Sunder basti Sunderbasti Surgigaun surkhet
## 4 72 6 3
## Surya nagar Suryanagar Suryodaynagar Suuya
## 5 8 3 4
## suwara Swahara Swara swara tol
## 3 1 10 2
## swaraha swaraha dandathok Swarn tole Swarna tole
## 6 3 75 4
## swekhet swethet swikhet swikot
## 14 4 19 6
## Syalghari Taajpur Tadi bisauna talla chaur
## 21 93 4 9
## tallo yamdi Tanga tole Tangparsi Tangpasri
## 4 7 5 82
## Tarigai tharugaun Tarigain Tarigau tharugau Tejara tole
## 4 5 6 9
## [ reached getOption("max.print") -- omitted 101 entries ]
## [1] "Frequency table after encoding"
## baseline_settlement. Name of the Settlements
## 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945
## 93 15 20 7 5 3 3 4 12 6 3 35 2 13 4 14 3 38 7 4 111 5 8
## 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968
## 6 5 4 9 13 1 5 6 8 4 5 13 12 41 2 12 4 4 3 67 3 5 9
## 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991
## 5 6 6 3 6 17 11 8 3 14 3 7 1 5 3 30 11 5 10 4 5 3 18
## 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014
## 2 3 25 4 4 9 93 5 7 5 4 14 6 26 10 4 5 3 6 7 5 2 7
## 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037
## 6 24 10 6 16 2 6 7 9 3 5 3 4 9 14 16 12 8 11 3 4 11 12
## 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060
## 7 24 5 11 3 6 3 13 34 4 84 2 3 5 32 8 8 4 31 7 8 5 30
## 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083
## 4 6 12 3 14 4 13 4 9 3 4 8 3 6 9 20 23 5 15 5 14 6 9
## 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106
## 4 20 10 11 5 5 2 24 30 6 9 24 8 11 8 6 4 4 6 4 34 1 19
## 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129
## 4 12 8 12 5 11 7 8 12 77 2 13 3 42 13 7 5 21 10 7 6 7 49
## 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152
## 2 8 61 4 7 19 5 14 12 26 11 52 4 14 24 4 10 4 14 7 4 17 24
## 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175
## 3 15 4 5 6 7 7 3 4 4 2 3 7 10 1 1 2 4 5 10 4 44 3
## 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198
## 4 4 18 3 5 20 55 6 7 24 10 5 5 8 21 4 11 7 17 5 6 5 24
## 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221
## 4 10 11 4 5 9 82 4 6 10 6 3 3 4 43 6 3 3 18 3 7 10 12
## 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244
## 6 5 14 10 4 7 14 39 3 22 10 6 2 4 4 6 7 4 24 5 3 3 52
## 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267
## 4 3 53 5 18 11 67 4 14 5 5 7 12 5 5 41 4 5 10 4 3 106 6
## 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290
## 8 4 15 66 5 41 3 5 16 96 4 7 4 12 12 28 2 15 9 17 4 6 57
## 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313
## 8 4 5 4 3 1 9 12 4 4 4 19 4 25 4 12 4 3 5 1 3 4 3
## 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336
## 8 16 5 5 5 38 4 6 69 2 5 5 81 3 8 5 3 12 2 21 3 3 7
## 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359
## 4 5 3 3 5 3 15 20 3 5 10 10 3 5 49 16 75 8 47 2 4 8 3
## 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382
## 4 4 4 226 6 5 7 6 5 7 3 5 7 24 66 5 5 5 18 8 8 4 5
## 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405
## 4 23 6 24 4 5 6 30 4 8 2 11 4 17 33 4 8 8 5 4 92 5 9
## 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428
## 4 7 5 4 11 5 14 7 3 4 2 9 3 41 3 3 5 36 5 27 32 5 5
## 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451
## 7 2 6 5 39 3 6 16 4 4 3 6 23 15 7 7 13 13 3 12 44 4 6
## 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474
## 4 4 12 23 8 4 5 4 196 25 2 25 8 6 4 4 18 3 6 69 4 4 5
## 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497
## 6 6 9 10 5 7 4 11 4 2 3 6 33 28 8 7 3 5 4 15 2 3 3
## 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520
## 4 3 4 4 4 3 28 27 111 6 5 7 4 3 4 69 3 7 4 74 3 3 3
## 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543
## 4 36 38 6 13 3 35 14 7 5 10 8 6 8 6 5 10 17 2 3 72 5 12
## 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566
## 12 14 16 4 8 80 4 4 4 7 21 4 3 5 10 6 13 9 29 2 5 3 38
## 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589
## 6 4 21 9 93 6 3 8 5 3 2 13 6 11 2 4 4 4 10 11 5 3 2
## 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612
## 7 4 39 11 36 6 4 4 4 18 7 10 7 5 119 102 4 12 4 9 7 4 4
## 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635
## 13 42 11 18 4 7 5 6 10 5 4 10 51 3 4 5 13 13 8 2 4 102 19
## 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658
## 3 11 55 3 9 14 4 31 5 6 26 61 4 5 67 4 3 6 5 22 4 29 8
## 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681
## 4 5 21 5 8 5 12 4 12 5 16 24 6 14 5 8 5 4 4 3 3 46 4
## 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704
## 3 4 3 4 4 68 6 3 6 7 4 5 3 6 6 5 5 30 2 9 4 5 2
## 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727
## 5 6 4 78 7 33 9 10 6 5 2 24 6 9 2 22 84 21 6 4 104 5 5
## 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750
## 4 40 5 6 3 2 22 8 32 8 6 5 14 23 7 3 6 32 6 5 20 14 3
## 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773
## 5 6 20 6 30 10 2 6 3 5 45 4 6 5 5 3 7 3 9 9 6 8 6
## 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796
## 4 3 15 3 19 4 6 9 12 3 57 5 9 21 5 5 4 3 18 4 45 6 3
## 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819
## 9 5 2 4 11 40 8 6 6 33 22 66 7 82 15 7 2 17 14 10 7 3 7
## 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842
## 19 14 10 14 3 4 13 6 4 4 36 4 4 7 5 2 3 4 7 3 85 11 4
## 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865
## 19 8 6 118 13 28 3 3 12 2 15 26 7 33 8 20 5 3 54 35 3 5 4
## 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888
## 76 11 3 80 5 3 3 4 5 26 11 6 4 5 28 8 5 6 8 115 4 4 6
## 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911
## 6 4 5 4 5 18 74 9 12 6 2 3 10 32 3 85 4 5 6 10 40 13 45
## 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922
## 25 5 27 5 9 4 7 84 4 12 2
## [ reached getOption("max.print") -- omitted 101 entries ]
## [1] "Frequency table before encoding"
## baseline_municipality. select the municipality where this household is located
## Bharatpur Municipality Birgunj Municipality Nepalgunj Municipality Pokhara Municipality Rajbiraj Municipality
## 2315 5370 2520 1971 1636
## Tulsipur Municipality
## 1314
## [1] "Frequency table after encoding"
## baseline_municipality. select the municipality where this household is located
## 747 748 749 750 751 752
## 5370 1971 2315 1314 2520 1636
## [1] "Frequency table before encoding"
## baseline_wardno. Ward No.
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
## 429 421 410 222 226 301 302 269 258 292 89 355 376 339 376 607 537 750 970 814 727 645 671 794 653 834 831 846 565
## 30
## 217
## [1] "Frequency table after encoding"
## baseline_wardno. Ward No.
## 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
## 226 258 970 794 410 222 565 814 607 750 429 834 376 537 89 217 376 269 846 292 671 339 645 355 302 727 653 831 421
## 140
## 301
## [1] "Frequency table before encoding"
## baseline_child_municipality. select the municipality where this household is located
## Bharatpur Municipality Birgunj Municipality Nepalgunj Municipality Pokhara Municipality Rajbiraj Municipality
## 61 54 170 210 27
## Tulsipur Municipality <NA>
## 125 14479
## [1] "Frequency table after encoding"
## baseline_child_municipality. select the municipality where this household is located
## 497 498 499 500 501 502 <NA>
## 27 61 210 170 125 54 14479
## [1] "Frequency table before encoding"
## baseline_child_wardno. Ward No.
## 1 2 3 4 5 6 7 8 9 10 12 13 14 15 16 17 18 19 20
## 17 15 14 12 3 5 3 4 1 2 38 29 16 28 11 3 41 7 26
## 21 22 23 24 25 26 27 28 29 <NA>
## 8 39 75 2 27 59 49 61 52 14479
## [1] "Frequency table after encoding"
## baseline_child_wardno. Ward No.
## 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977
## 41 16 3 75 26 39 27 15 29 59 61 3 11 12 7 5 1 17 3
## 978 979 980 981 982 983 984 985 986 <NA>
## 2 52 38 8 49 4 2 14 28 14479
## [1] "Frequency table before encoding"
## baseline_child_villagename. Name of the village/ community
## aadarsamani tol bikash Aadarsha tol Aarba Adarsha tol
## 14479 2 4 2 1
## Alakha Road Armalakot Asarsha tol badhahare tol baja khet
## 2 11 2 1 2
## Bajrang tole Banahari Bangau Barabigha Basantpur
## 1 5 1 1 3
## Basantpur tole Basbot Basudevpur bc bhalam
## 2 1 22 3 16
## Bhawaniyapur birendra gufa Birgunj Birta Birtakhet
## 4 5 1 10 1
## Brahmpur Britakhet Chailai Chanora parariya Chapkaiya
## 1 1 7 3 16
## Chapksiya Chhapkaiya Daxin nawalpur Deurali piple dhaurali
## 1 14 2 1 3
## Dihi gau fedi Fulbari Furthi chook Ganeshman chowk
## 1 1 1 5 1
## Garmikhola Gaurigau Geetanagar ghadgai Gopal chook
## 1 2 10 4 1
## Gyanjyoti Haripaura Haripauri Hasnapur Hatiya
## 1 1 4 1 7
## Hemanagar Hemanahar hemja Hemja Indarpur
## 1 1 27 32 34
## Jail tol Jailroad Jaimare Jumleti Kachila
## 1 2 4 4 2
## Kachili Kahu Kalakhola Kalakhola pulchok khadha
## 5 1 1 2 1
## khastar khaster Kirana line Kumalgaub tole Kumhal tol
## 1 5 1 1 1
## Kwonag Kwonagi Lamidada Manakamana tol Mangalpur
## 3 1 2 2 4
## Manikapur Matera tole maula tol Niramalpokhari nirmal pokhari
## 8 2 1 2 17
## Nirmal pokhari nirmalokhari nirmalpokhari Nirmalpokhari Paddha
## 8 1 6 7 2
## padhali Paraspur Patahani patihani Patihani
## 6 34 3 11 6
## patihani bazar patlahara Pulchowk Puraina Puraini
## 2 3 3 23 21
## Purba rampur Raam tole Raikhalyan Rajbiraj Rajdevi tole
## 1 5 5 2 1
## Rajhena Rangashala Resham Kothi Ryale patle santipur
## 4 1 1 5 3
## sarangkot Sarangkot Sarankot Saranpur Sarwati tole
## 4 5 3 2 1
## saudha chautara Semragau tol Shimra gaun tole Shivanagar Shreepur
## 2 2 2 2 3
## Sirha rod suikhet Swarn tole swikot talla chaure
## 1 2 2 1 1
## Tarigai Tarigau Tarigaun Thuti Tyale patle
## 1 44 6 2 2
## Udayapur Urahari Uttar nawalpur vimshen nagar yamdi
## 24 1 4 2 2
## [1] "Frequency table after encoding"
## baseline_child_villagename. Name of the village/ community
## 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570
## 3 2 4 2 1 1 4 1 3 4 1 3 2 2 4 11 23 1 1
## 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589
## 1 4 2 5 16 2 3 5 5 3 8 1 2 7 1 1 2 1 3
## 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608
## 1 1 1 1 3 4 2 5 2 1 2 22 16 34 1 5 11 2 2
## 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627
## 21 1 1 5 1 2 6 2 4 2 6 2 7 1 2 3 5 1 2
## 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646
## 1 44 5 1 1 34 2 1 2 24 1 6 3 2 4 8 1 1 27
## 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665
## 1 2 1 4 1 2 1 14 2 1 7 17 1 2 1 1 5 1 1
## 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681
## 32 2 2 2 3 10 4 10 1 14479 1 3 6 1 1 1
## [1] "Frequency table before encoding"
## baseline_child_settlement. Name of the Settlements
## Aadarsamanitol aadarsapokheral tol Aadarsapokhrel
## 14479 1 3 2
## aadarsha mani tol Adarsha mani tol Adarsha tol Alakha Road
## 2 3 2 2
## Ammarbasti Armalakot As Babugaun
## 2 11 1 1
## Badahare badhahare tol Baghakholi tol Bagyaswori
## 1 1 1 2
## bahalam bajakhet Bajrang Balegaun
## 2 2 1 3
## Bangau Banjare gaau Barauji Basantpur
## 1 1 5 5
## Basbot Basbot tol Baskot baspani
## 1 1 2 1
## bastal bc bgalam bhadhara
## 3 3 3 1
## bhalam Bhandari birendra birendra gufa
## 7 2 3 2
## Birta botetol Brita nursing campus area Buspark
## 9 6 2 1
## Campus gali Chailai Chanora parariya Chapa
## 1 4 3 3
## Chhapkaiya chilaunekharka chilaunekharke Chiranjeevi chowk
## 1 1 1 2
## Chokbazar Cold store Dadre Damodar
## 2 4 4 1
## Devi chook Dharahi tol dhaurali Dhore gaun
## 2 1 3 4
## Dihi Dumri fedi sahara foksing deurali
## 1 1 1 2
## Fulbari Furthi chook gaida chouk Gangapur
## 1 5 3 13
## Gaurigau Geetanagar ghadgai gharbetadi
## 5 1 1 2
## Gharmikhola Gopal chook Gourigaun Gurung gau
## 1 1 1 1
## Gyanjoti Gyarjati Haripaura Haripauri
## 1 3 10 4
## Hatiya Hemanagar Himal tol Hulakimarg
## 7 1 1 3
## Hulakimarga Indargaau Jagritinagar Jail tol
## 1 8 2 1
## Jailroad Jaimare Jankeswari tol Jhajrawathuti tol
## 2 3 1 3
## Jhakruwathuti tol Jodhapurwa Jumleti jyoti chock
## 2 5 3 1
## Kachili Kalakhola Kalikhola Kanthipur
## 2 3 1 11
## Kesarbag Kesharbag khadha Khadka
## 3 1 1 2
## Khalla Puraini khastar khaster Kigrinpurwa
## 7 1 5 6
## Koiripatti Kulain Kumalgau Kumhal tol
## 3 2 1 1
## Lalapur Lalapurwa Lalaypurwa lama khet
## 1 5 1 3
## Lamakhet Lamidada tol lampata Laxmitol
## 3 2 1 2
## Loniyan purwa Maanpur Mahapuruwa Man road
## 1 6 2 1
## Manakamana tol Mangalpur bazaer Mangalpur bazer vitra Manikapur
## 2 1 1 7
## Masjid tole Matera Mathighar maula tol
## 6 2 1 1
## Maula tol Milana tol Milantol Mohanpur
## 2 1 1 3
## Naharpurwa Nawalpur nirmal pokhari Nirmal pokhari
## 1 6 5 1
## Pabitra tol padeli Padha padhali
## 3 1 1 6
## Pakaudi Parajuli tol Paraspur Parbatinagar
## 1 1 30 8
## Pasupati patihani patihani bazar patlahara
## 5 2 3 3
## Patle Phokshing Pokhreal tole Pothedarpurwa
## 2 1 1 1
## Pulchowk Puraina Puraini Raam tole
## 3 17 3 5
## Radhakrishna Radhakrishns radhakrisna tol Radhapur
## 3 1 1 1
## Rajdevi tole Rajhanatol Rajhani tol Rajhena
## 1 4 2 2
## Rajhena tharugau Rajhenatol ramchok Ramchwok
## 1 1 1 1
## ramkrisna tol Ramwapur Rangashala Resham kothi
## 2 19 1 1
## Ryale Sagaramatha sangam tol Sangamtol
## 1 3 2 3
## Sanogau Santinagar santipur saudaha
## 3 2 3 3
## Saudakochhautaro saudha chautara School road Semragau
## 3 2 3 2
## Shantitol Shimragaun Shivanagar Shivthan
## 5 2 1 1
## Shreepur sibalaya Sibalaya sindasara
## 3 6 5 1
## Sirha rod Siva choka tol suikhet Suikhet
## 1 1 6 10
## Suiya Sukyatol Sundarchok Sunderbasti
## 11 1 1 1
## Sunderbsati Swarn tole swekhet swikhet
## 2 2 7 8
## swikot Syalghari Tadi bisauna tallachaure
## 2 1 1 1
## Tarigai Tarigain Tarigau tharugaun Tarigaun mainroad
## 1 2 2 1
## Telipatti mas Teliyanpur Thapa tol Thaple tilahar
## 3 2 5 2
## thapletilar Thapletilar Tharugau Thulobesi
## 1 1 2 1
## Thulobesi marga Thuti Tumki Urahari
## 1 2 1 1
## vimshen nagar Yakla bel Yamdi YAmdi
## 1 1 5 1
## yamdi tol
## 2
## [1] "Frequency table after encoding"
## baseline_child_settlement. Name of the Settlements
## 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645
## 1 2 2 3 1 11 1 1 2 3 3 1 5 1 2 1 3 2 2
## 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664
## 4 3 6 9 1 1 5 5 3 1 2 2 4 1 3 2 8 3 3
## 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683
## 2 1 1 3 1 1 2 3 2 1 1 1 2 1 6 3 1 1 1
## 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702
## 1 1 2 1 7 2 3 2 10 1 1 2 4 2 1 1 1 1 2
## 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721
## 3 2 2 1 1 2 1 1 1 6 1 4 2 2 1 2 1 1 5
## 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740
## 3 2 7 1 2 1 7 3 1 1 1 11 3 5 1 1 3 5 1
## 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759
## 3 2 3 30 3 1 1 1 3 6 1 3 1 1 1 1 1 1 2
## 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778
## 3 1 2 5 3 2 19 11 1 2 2 3 1 1 1 14479 1 2 5
## 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797
## 8 1 3 1 5 1 1 1 6 3 5 2 5 1 4 1 1 17 1
## 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816
## 3 1 3 3 1 5 1 2 2 2 1 1 3 2 2 3 1 1 13
## 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835
## 1 1 1 1 2 2 2 8 3 4 1 2 3 5 1 3 2 2 3
## 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854
## 1 2 1 6 1 5 1 6 1 6 1 2 7 10 2 1 1 1 1
## 855
## 7Indirect PII - Ordinal: Global recode or Top/bottom coding for extreme values
# Focus on variables with a "Lowest Freq" of 10 or less.
mydata <- top_recode ("baseline_s3q3", break_point=80, missing=999999) # Topcode cases age 80 or older## [1] "Frequency table before encoding"
## baseline_s3q3. Age in completed years at the time of survey:
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
## 123 177 214 228 333 464 404 424 434 350 498 368 589 412 450 451 446 246 406 169 229 126 193
## 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45
## 133 168 343 200 151 273 115 491 101 335 134 120 645 225 128 245 95 553 60 161 86 61 355
## 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68
## 66 59 101 45 243 46 86 33 30 199 49 47 52 23 252 27 62 42 23 163 28 22 38
## 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 92
## 26 138 21 49 17 5 40 16 12 15 3 32 5 14 9 6 12 5 3 2 1 7 1
## 93 94 95 96 97 <NA>
## 2 2 1 1 2 31
## [1] "Frequency table after encoding"
## baseline_s3q3. Age in completed years at the time of survey:
## 0 1 2 3 4 5 6 7 8 9
## 123 177 214 228 333 464 404 424 434 350
## 10 11 12 13 14 15 16 17 18 19
## 498 368 589 412 450 451 446 246 406 169
## 20 21 22 23 24 25 26 27 28 29
## 229 126 193 133 168 343 200 151 273 115
## 30 31 32 33 34 35 36 37 38 39
## 491 101 335 134 120 645 225 128 245 95
## 40 41 42 43 44 45 46 47 48 49
## 553 60 161 86 61 355 66 59 101 45
## 50 51 52 53 54 55 56 57 58 59
## 243 46 86 33 30 199 49 47 52 23
## 60 61 62 63 64 65 66 67 68 69
## 252 27 62 42 23 163 28 22 38 26
## 70 71 72 73 74 75 76 77 78 79
## 138 21 49 17 5 40 16 12 15 3
## 80 or more <NA>
## 105 31
mydata <- top_recode ("baseline_s4q7", break_point=60, missing=999999) # Topcode cases with 60 or farther ## [1] "Frequency table before encoding"
## baseline_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 18 20 25 26 28 30
## 45 142 185 87 22 825 27 27 21 7 915 2 39 4 3 918 3 3 537 177 1 3 868
## 35 38 40 45 46 50 55 60 70 75 80 90 98 120 160 <NA>
## 34 7 14 114 2 4 10 186 1 2 2 12 1 6 2 9868
## [1] "Frequency table after encoding"
## baseline_s4q7. How far is the school from home?
## 0 1 2 3 4 5 6 7 8 9
## 45 142 185 87 22 825 27 27 21 7
## 10 11 12 13 14 15 16 18 20 25
## 915 2 39 4 3 918 3 3 537 177
## 26 28 30 35 38 40 45 46 50 55
## 1 3 868 34 7 14 114 2 4 10
## 60 or more <NA>
## 212 9868
percentile_99.5 <- floor(quantile(mydata$baseline_s4q9, probs = c(0.995), na.rm=T))
mydata <- top_recode (variable="baseline_s4q9", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## baseline_s4q9. How much are the school fees in a year?
## 0 5 8 12 24 25 50 60 75 84 95 100 150 180
## 464 1 1 1 1 1 2 4 6 1 2 10 42 1
## 200 250 300 308 315 330 350 400 450 500 600 650 700 750
## 13 3 39 1 1 1 5 25 6 98 13 4 6 4
## 800 825 900 1000 1050 1100 1152 1200 1260 1296 1300 1320 1400 1500
## 10 1 3 154 1 5 1 38 1 1 3 1 4 50
## 1580 1600 1700 1800 2000 2100 2200 2400 2500 2800 3000 3500 3600 4000
## 1 2 2 10 140 1 1 18 28 1 67 5 28 61
## 4200 4400 4500 4800 5000 5200 5400 5500 6000 6200 6500 6600 7000 7200
## 6 1 4 31 135 2 5 9 113 2 4 11 27 55
## 7272 7400 7500 7600 7800 8000 8200 8400 8484 8500 8640 9000 9500 9600
## 1 1 2 1 3 98 1 28 1 1 1 30 2 38
## 9800 9840 10000 10200 10560 10680 10800 10850 10900 11000 11400 11600 12000 12200
## 1 1 167 7 1 1 17 1 2 16 1 1 267 1
## 12500 12600 13000 13200 13500 13925 14000 14300 14400 14500 15000 15300 15600 16000
## 3 4 30 8 2 1 39 1 44 3 232 1 13 34
## 16200 16320 16800 17000 17200 17400 18000 18600 19000 19200 20000 20100 20400 20500
## 1 1 9 12 1 2 111 1 7 8 148 1 8 1
## 20604 21000 21600 22000 22800 23000 23160 23400 24000 25000 25800 25920 26000 26400
## 1 12 7 20 2 8 1 2 117 132 1 1 12 1
## 26600 26850 27000 27520 27600 28000 29000 29212 30000 31200 32000 32400 33000 33600
## 1 1 5 1 1 11 5 1 96 4 7 3 1 4
## 34000 35000 36000 36100 40000 42000 43000 43200 44000 45000 46000 48000 50000 52000
## 1 35 78 1 43 7 3 2 1 7 1 9 22 1
## 55000 60000 66000 67200 70000 72000 78000 80000 84000 85000 96000 98000 1e+05 102000
## 2 30 2 1 6 3 3 7 3 1 10 1 9 1
## 108000 110000 115000 120000 125000 130000 150000 161600 186000 2e+05 240000 250000 3e+05 360000
## 1 3 1 9 1 1 8 1 1 7 3 1 4 1
## 390000 470000 480000 1010000 1560000 <NA>
## 1 1 1 1 2 11113
## [1] "Frequency table after encoding"
## baseline_s4q9. How much are the school fees in a year?
## 0 5 8 12 24 25 50 60
## 464 1 1 1 1 1 2 4
## 75 84 95 100 150 180 200 250
## 6 1 2 10 42 1 13 3
## 300 308 315 330 350 400 450 500
## 39 1 1 1 5 25 6 98
## 600 650 700 750 800 825 900 1000
## 13 4 6 4 10 1 3 154
## 1050 1100 1152 1200 1260 1296 1300 1320
## 1 5 1 38 1 1 3 1
## 1400 1500 1580 1600 1700 1800 2000 2100
## 4 50 1 2 2 10 140 1
## 2200 2400 2500 2800 3000 3500 3600 4000
## 1 18 28 1 67 5 28 61
## 4200 4400 4500 4800 5000 5200 5400 5500
## 6 1 4 31 135 2 5 9
## 6000 6200 6500 6600 7000 7200 7272 7400
## 113 2 4 11 27 55 1 1
## 7500 7600 7800 8000 8200 8400 8484 8500
## 2 1 3 98 1 28 1 1
## 8640 9000 9500 9600 9800 9840 10000 10200
## 1 30 2 38 1 1 167 7
## 10560 10680 10800 10850 10900 11000 11400 11600
## 1 1 17 1 2 16 1 1
## 12000 12200 12500 12600 13000 13200 13500 13925
## 267 1 3 4 30 8 2 1
## 14000 14300 14400 14500 15000 15300 15600 16000
## 39 1 44 3 232 1 13 34
## 16200 16320 16800 17000 17200 17400 18000 18600
## 1 1 9 12 1 2 111 1
## 19000 19200 20000 20100 20400 20500 20604 21000
## 7 8 148 1 8 1 1 12
## 21600 22000 22800 23000 23160 23400 24000 25000
## 7 20 2 8 1 2 117 132
## 25800 25920 26000 26400 26600 26850 27000 27520
## 1 1 12 1 1 1 5 1
## 27600 28000 29000 29212 30000 31200 32000 32400
## 1 11 5 1 96 4 7 3
## 33000 33600 34000 35000 36000 36100 40000 42000
## 1 4 1 35 78 1 43 7
## 43000 43200 44000 45000 46000 48000 50000 52000
## 3 2 1 7 1 9 22 1
## 55000 60000 66000 67200 70000 72000 78000 80000
## 2 30 2 1 6 3 3 7
## 84000 85000 96000 98000 1e+05 102000 108000 110000
## 3 1 10 1 9 1 1 3
## 115000 120000 125000 130000 150000 161600 186000 2e+05 or more
## 1 9 1 1 8 1 1 22
## <NA>
## 11113
percentile_99.5 <- floor(quantile(mydata$baseline_s4q10, probs = c(0.995),na.rm=T))
mydata <- top_recode (variable="baseline_s4q10", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## baseline_s4q10. How much are other costs associated to schooling with in a year?
## 0 25 50 95 96 100 150 200 250 300 350 400 500 508 550 600
## 66 1 1 3 1 11 2 21 1 11 1 12 96 1 1 23
## 700 800 900 1000 1100 1200 1300 1500 1800 1900 2000 2400 2500 2800 3000 3500
## 11 22 9 275 1 14 3 123 2 1 376 1 42 1 269 2
## 3600 3700 3800 4000 4500 4800 5000 5500 5550 6000 6500 7000 7200 7500 8000 9000
## 8 1 1 179 5 2 622 1 1 162 2 61 6 1 132 23
## 10000 11000 12000 13000 14000 15000 16000 17000 18000 19000 20000 22000 23000 24000 25000 25200
## 398 2 105 6 1 279 6 6 16 1 135 3 3 41 45 1
## 26000 27000 28000 30000 32000 35000 36000 40000 48000 50000 54000 60000 70000 75000 80000 84000
## 2 1 1 48 1 12 18 16 5 15 1 15 2 3 1 1
## 89000 96000 1e+05 120000 144000 180000 2e+05 240000 480000 <NA>
## 1 1 9 7 1 1 1 2 1 11301
## [1] "Frequency table after encoding"
## baseline_s4q10. How much are other costs associated to schooling with in a year?
## 0 25 50 95 96 100 150 200
## 66 1 1 3 1 11 2 21
## 250 300 350 400 500 508 550 600
## 1 11 1 12 96 1 1 23
## 700 800 900 1000 1100 1200 1300 1500
## 11 22 9 275 1 14 3 123
## 1800 1900 2000 2400 2500 2800 3000 3500
## 2 1 376 1 42 1 269 2
## 3600 3700 3800 4000 4500 4800 5000 5500
## 8 1 1 179 5 2 622 1
## 5550 6000 6500 7000 7200 7500 8000 9000
## 1 162 2 61 6 1 132 23
## 10000 11000 12000 13000 14000 15000 16000 17000
## 398 2 105 6 1 279 6 6
## 18000 19000 20000 22000 23000 24000 25000 25200
## 16 1 135 3 3 41 45 1
## 26000 27000 28000 30000 32000 35000 36000 40000
## 2 1 1 48 1 12 18 16
## 48000 50000 54000 60000 70000 75000 80000 84000
## 5 15 1 15 2 3 1 1
## 89000 96000 1e+05 or more <NA>
## 1 1 22 11301
mydata <- top_recode ("baseline_child6", break_point=5, missing=999999) # Topcode cases with 5 or more ## [1] "Frequency table before encoding"
## baseline_child6. Number of children under 6 in the household
## 0 1 2 3 4 5 6 7 8
## 8724 3879 1593 607 202 55 15 32 19
## [1] "Frequency table after encoding"
## baseline_child6. Number of children under 6 in the household
## 0 1 2 3 4 5 or more
## 8724 3879 1593 607 202 121
mydata <- top_recode ("baseline_s17q7", break_point=60, missing=999999) # Topcode cases with 60 or farther ## [1] "Frequency table before encoding"
## baseline_s17q7. 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 18
## 52 102 158 69 14 814 24 28 30 5 827 6 39 5 8 906 3 7 12
## 20 24 25 26 28 30 32 35 38 40 45 50 55 60 65 68 75 80 90
## 485 1 171 1 2 832 1 48 7 28 96 6 7 195 1 1 2 3 12
## 100 115 120 160 198 330 <NA>
## 1 1 8 1 2 2 10103
## [1] "Frequency table after encoding"
## baseline_s17q7. How far is the school from home?
## 0 1 2 3 4 5 6 7 8 9
## 52 102 158 69 14 814 24 28 30 5
## 10 11 12 13 14 15 16 17 18 20
## 827 6 39 5 8 906 3 7 12 485
## 24 25 26 28 30 32 35 38 40 45
## 1 171 1 2 832 1 48 7 28 96
## 50 55 60 or more <NA>
## 6 7 229 10103
# !!!Include relevant variables in list below
indirect_PII <- c("baseline_s17q5",
"baseline_s17q6",
"baseline_s17q6_3",
"baseline_s17q6_4",
"baseline_s17q6_6",
"baseline_s17q6_7",
"baseline_s17q9",
"baseline_s18q1",
"baseline_s18q3",
"baseline_s19q2a",
"baseline_s19q2b",
"baseline_s19q2d",
"baseline_s19q2f",
"baseline_s19q2g",
"baseline_s19q3",
"baseline_s19q4a",
"baseline_s19q8",
"baseline_s19q9",
"baseline_s19q10",
"baseline_s19q10_4",
"baseline_s19q10_7",
"baseline_s19q10_8",
"baseline_s19q10_10",
"baseline_s19q10_11",
"baseline_s19q10_96",
"baseline_s19q10_99",
"baseline_s19q11",
"baseline_s20q3",
"baseline_s20q8",
"baseline_s5q6c",
"baseline_s19q4c",
"baseline_s5q6_2c",
"baseline_s19q4bc",
"baseline_s4q6_10",
"baseline_s17q6_10",
"baseline_ppi",
"baseline_madrassa",
"baseline_benboyt",
"baseline_bengirlt")
capture_tables (indirect_PII)
# Recode those with very specific values where more than half of the sample have actual data.
break_rel <- c(1,2,3,4,999)
labels_rel <- c("Hindu" = 1,
"Muslim" = 2,
"Buddhist" = 3,
"Other" = 4)
mydata <- ordinal_recode (variable="baseline_hhheadreligion", break_points=break_rel, missing=999999, value_labels=labels_rel)
## [1] "Frequency table before encoding"
## baseline_hhheadreligion. Religion of head of household?
## Hindu Muslim Buddhist Christian Sikh Jain
## 12672 1972 320 84 25 12
## Kirat No religion Other (Specify) <NA>
## 3 5 7 26
## recoded
## [1,2) [2,3) [3,4) [4,999) [999,1e+06)
## 1 12672 0 0 0 0
## 2 0 1972 0 0 0
## 3 0 0 320 0 0
## 4 0 0 0 84 0
## 5 0 0 0 25 0
## 6 0 0 0 12 0
## 7 0 0 0 3 0
## 8 0 0 0 5 0
## 96 0 0 0 7 0
## [1] "Frequency table after encoding"
## baseline_hhheadreligion. Religion of head of household?
## Hindu Muslim Buddhist Other <NA>
## 12672 1972 320 136 26
## [1] "Inspect value labels and relabel as necessary"
## Hindu Muslim Buddhist Other
## 1 2 3 4break_lan <- c(1,2,3,4,5,6,7,8,999)
labels_lan <- c("Nepali" = 1,
"Others" = 2,
"Others" = 3,
"Bhojpuri" = 4,
"Maithali" = 5,
"Tharu" = 6,
"Abadhi" = 7,
"Others" = 8)
mydata <- ordinal_recode (variable="baseline_hhheadmthrtongue", break_points=break_lan, missing=999999, value_labels=labels_lan)
## [1] "Frequency table before encoding"
## baseline_hhheadmthrtongue. What is the mother tongue of head of household?
## Nepali Newari Tamang Bhojpuri Maithali Tharu
## 5050 127 70 4719 1574 638
## Abadhi Gurung Magar Darai Hindi Madwadi
## 2372 168 59 42 132 40
## Other (Specify) <NA>
## 104 31
## recoded
## [1,2) [2,3) [3,4) [4,5) [5,6) [6,7) [7,8) [8,999) [999,1e+06)
## 1 5050 0 0 0 0 0 0 0 0
## 2 0 127 0 0 0 0 0 0 0
## 3 0 0 70 0 0 0 0 0 0
## 4 0 0 0 4719 0 0 0 0 0
## 5 0 0 0 0 1574 0 0 0 0
## 6 0 0 0 0 0 638 0 0 0
## 7 0 0 0 0 0 0 2372 0 0
## 8 0 0 0 0 0 0 0 168 0
## 9 0 0 0 0 0 0 0 59 0
## 10 0 0 0 0 0 0 0 42 0
## 11 0 0 0 0 0 0 0 132 0
## 12 0 0 0 0 0 0 0 40 0
## 96 0 0 0 0 0 0 0 104 0
## [1] "Frequency table after encoding"
## baseline_hhheadmthrtongue. What is the mother tongue of head of household?
## Nepali Others Bhojpuri Maithali Tharu Abadhi <NA>
## 5050 742 4719 1574 638 2372 31
## [1] "Inspect value labels and relabel as necessary"
## Nepali Others Others Bhojpuri Maithali Tharu Abadhi Others
## 1 2 3 4 5 6 7 8mydata <- top_recode ("baseline_s3q0", break_point=15, missing=999999) # Topcode cases with 15 or more## [1] "Frequency table before encoding"
## baseline_s3q0. Household Size
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 26 46 <NA>
## 7 211 1496 3107 3288 2562 1395 1083 452 411 244 276 162 94 120 65 34 18 53 26 4 18
## [1] "Frequency table after encoding"
## baseline_s3q0. Household Size
## 1 2 3 4 5 6 7 8 9 10
## 7 211 1496 3107 3288 2562 1395 1083 452 411
## 11 12 13 14 15 or more <NA>
## 244 276 162 94 320 18
# Group as "Others" labels with low frequencies
val_label(mydata$baseline_s4q11, 2) <- "Others"
val_label(mydata$baseline_s4q11, 3) <- "Others"
val_label(mydata$baseline_s4q11, 5) <- "Others"
val_label(mydata$baseline_s4q11, 6) <- "Others"
val_label(mydata$baseline_s4q11, 7) <- "Others"
val_label(mydata$baseline_s4q11, 9) <- "Others"
val_label(mydata$baseline_s4q11, 10) <- "Others"
val_label(mydata$baseline_s4q11, 12) <- "Others"
val_label(mydata$baseline_s4q11, 13) <- "Others"
val_label(mydata$baseline_s4q11, 14) <- "Others"
val_label(mydata$baseline_s4q11, 16) <- "Others"
val_label(mydata$baseline_s4q11, 17) <- "Others"
val_label(mydata$baseline_s17q9, 2) <- "Others"
val_label(mydata$baseline_s17q9, 3) <- "Others"
val_label(mydata$baseline_s17q9, 5) <- "Others"
val_label(mydata$baseline_s17q9, 6) <- "Others"
val_label(mydata$baseline_s17q9, 7) <- "Others"
val_label(mydata$baseline_s17q9, 9) <- "Others"
val_label(mydata$baseline_s17q9, 10) <- "Others"
val_label(mydata$baseline_s17q9, 12) <- "Others"
val_label(mydata$baseline_s17q9, 13) <- "Others"
val_label(mydata$baseline_s17q9, 14) <- "Others"
val_label(mydata$baseline_s17q9, 16) <- "Others"
val_label(mydata$baseline_s17q9, 17) <- "Others"
# Drop variables recoded into others
dropvars <- c("baseline_lang2",
"baseline_lang3",
"baseline_lang9",
"baseline_lang10",
"baseline_lang11",
"baseline_lang12",
"baseline_rel4",
"baseline_rel5",
"baseline_rel6",
"baseline_rel7",
"baseline_rel8")
mydata <- mydata[!names(mydata) %in% dropvars]Matching and crosstabulations: Run automated PII check
# 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
selectedKeyVars = c('baseline_s3q2', 'baseline_s3q8', 'baseline_s3q3') ##!!! Replace with candidate categorical demo vars
# weight variable
# !!! No weight
# household id variable (cluster)
selectedHouseholdID = c('baseline_hhid') ##!!! Replace with household id
# creating the sdcMicro object with the assigned variables
sdcInitial <- createSdcObj(dat = mydata, keyVars = selectedKeyVars, hhId = selectedHouseholdID)
sdcInitial## The input dataset consists of 15126 rows and 530 variables.
## --> Categorical key variables: baseline_s3q2, baseline_s3q8, baseline_s3q3
## --> Cluster/Household-Id variable: baseline_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)
## baseline_s3q2 3 (3) 7557.500 (7557.500) 7351 (7351)
## baseline_s3q8 8 (8) 1991.000 (1991.000) 74 (74)
## baseline_s3q3 82 (82) 186.358 (186.358) 3 (3)## ----------------------------------------------------------------------## Infos on 2/3-Anonymity:
##
## Number of observations violating
## - 2-anonymity: 16 (0.106%)
## - 3-anonymity: 43 (0.284%)
## - 5-anonymity: 247 (1.633%)
##
## ----------------------------------------------------------------------Show values of key variable of records that violate k-anonymity
notAnon <- sdcInitial@risk$individual[,2] < 2 # for 2-anonymity
mydata[notAnon,selectedKeyVars]## # A tibble: 16 x 3
## baseline_s3q2 baseline_s3q8 baseline_s3q3
## <dbl+lbl> <dbl+lbl> <dbl+lbl>
## 1 1 [Female] 2 [Lower Secondary] 68
## 2 1 [Female] 6 [Madrassa] 18
## 3 1 [Female] 2 [Lower Secondary] 44
## 4 1 [Female] 2 [Lower Secondary] 70
## 5 1 [Female] 6 [Madrassa] 34
## 6 1 [Female] 4 [High Secondary] 46
## 7 1 [Female] 2 [Lower Secondary] 63
## 8 1 [Female] 4 [High Secondary] 72
## 9 1 [Female] 4 [High Secondary] 57
## 10 1 [Female] 2 [Lower Secondary] 73
## 11 1 [Female] 4 [High Secondary] 41
## 12 1 [Female] 2 [Lower Secondary] 57
## 13 1 [Female] 5 [University + up] 46
## 14 1 [Female] 4 [High Secondary] 44
## 15 1 [Female] 2 [Lower Secondary] 75
## 16 1 [Female] 5 [University + up] 42sdcFinal <- localSuppression(sdcInitial)
# Recombining anonymized variables
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## baseline_s3q2 baseline_s3q8 baseline_s3q3
## 19 1 2 NA
## 1380 1 6 NA
## 3587 1 2 NA
## 4728 1 2 NA
## 5485 1 6 NA
## 6287 1 4 NA
## 6822 1 2 NA
## 7593 1 4 NA
## 8076 1 4 NA
## 8117 1 2 NA
## 9014 1 4 NA
## 11700 1 2 NA
## 13295 1 5 NA
## 13402 1 4 NA
## 14510 1 2 NA
## 14813 1 5 NAmydata [notAnon,"baseline_s3q3"] <- NA
#Check that 2-anonimity is now maintained
createSdcObj(dat = mydata, keyVars = selectedKeyVars, hhId = selectedHouseholdID)## The input dataset consists of 15126 rows and 530 variables.
## --> Categorical key variables: baseline_s3q2, baseline_s3q8, baseline_s3q3
## --> Cluster/Household-Id variable: baseline_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)
## baseline_s3q2 3 (3) 7557.500 (7557.500) 7351 (7351)
## baseline_s3q8 8 (8) 1991.000 (1991.000) 74 (74)
## baseline_s3q3 82 (82) 186.160 (186.160) 3 (3)## ----------------------------------------------------------------------## Infos on 2/3-Anonymity:
##
## Number of observations violating
## - 2-anonymity: 0 (0.000%)
## - 3-anonymity: 3 (0.020%)
## - 5-anonymity: 138 (0.912%)
##
## ----------------------------------------------------------------------Open-ends: review responses for any sensitive information, redact as necessary
# !!! Identify open-end variables here:
open_ends <- c("baseline_s2q4other",
"baseline_s2q5other",
"baseline_s2q6other",
"baseline_s9q1_1other",
"baseline_s9q2_2other",
"baseline_s9q2_1other",
"baseline_s9q1_2other",
"baseline_s9q5other",
"baseline_s9q6other",
"baseline_s10q3other",
"baseline_s10q5other",
"baseline_s10q8other",
"baseline_s10q10other",
"baseline_s11q3other",
"baseline_s11q6other",
"baseline_s13q1other",
"baseline_s3q1other",
"baseline_s3q5other",
"baseline_s4q6other",
"baseline_s4q11other",
"baseline_s5q6",
"baseline_s5q6_2",
"baseline_s5q13other",
"baseline_s5q14other",
"baseline_s6q8other",
"baseline_s17q6other",
"baseline_s17q9other",
"baseline_s18q3other",
"baseline_s19q4",
"baseline_s19q4b",
"baseline_s19q11other",
"baseline_s20q8other")
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.GPS data: Displace
# !!! No GPS data. Save processed data in Stata and SPSS format
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)