rm(list=ls(all=t))filename <- "bcsection7" # !!!Update filename
functions_vers <- "functions_1.7.R" # !!!Update helper functions filesource (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
dropvars <- c("dise")
mydata <- mydata[!names(mydata) %in% dropvars]
locvars <- c("q002_blckid", "q003_vill_id")
mydata <- encode_location (variables= locvars, missing=999999)## [1] "Frequency table before encoding"
## q002_blckid. 2. Tehsil/Block ID
## 1 2 3 4 5 6 7 8 9
## 210 152 192 414 100 197 158 426 550
## [1] "Frequency table after encoding"
## q002_blckid. 2. Tehsil/Block ID
## 279 280 281 282 283 284 285 286 287
## 426 152 192 550 414 100 210 158 197
## [1] "Frequency table before encoding"
## q003_vill_id. 3. Village ID
## 1 2 3 4 5 6 7 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
## 17 12 16 16 19 31 27 17 15 20 26 23 2 13 19 21 17 18 20 30 22 17 16 27 27 28 18
## 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55
## 15 15 24 27 21 13 30 17 17 21 28 16 16 18 16 26 21 24 21 17 17 17 13 18 28 25 27
## 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 80 81 82 83
## 19 14 21 12 22 9 17 19 18 28 15 19 22 26 14 16 21 16 25 23 17 23 31 30 17 22 17
## 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110
## 16 13 14 17 18 16 19 19 20 21 14 20 24 29 29 26 22 25 21 11 23 17 30 14 28 20 16
## 111 112 113 114 115 116 117 118 119 120 121 122
## 20 27 41 22 20 12 24 19 21 13 13 10
## [1] "Frequency table after encoding"
## q003_vill_id. 3. Village ID
## 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635
## 29 16 12 21 23 18 16 23 10 25 18 15 31 19 28 17 20 17 17 17 21 20 19 19 25 22 15
## 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662
## 31 17 22 28 22 24 17 14 24 26 30 21 20 26 13 29 11 19 27 28 15 14 24 21 19 14 20
## 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689
## 21 13 15 27 16 13 2 18 17 20 17 21 17 13 20 30 23 25 18 27 18 22 27 12 16 9 18
## 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716
## 17 26 27 17 17 17 21 21 41 17 21 28 20 12 14 16 21 24 13 30 30 19 16 17 27 22 14
## 717 718 719 720 721 722 723 724 725 726 727 728
## 19 16 19 16 13 22 16 23 26 28 16 16#!!! No Indirect PII - Ordinal#!!! No Indirect PII - Categoricalopen_ends <- c("q709_add_comment")
report_open (list_open_ends = open_ends)
# Review "verbatims.csv". Identify variables to be deleted or redacted and their row number
# Drop all, as actually verbatim data in Hindi
mydata <- mydata[!names(mydata) %in% "q709_add_comment"]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)