DOL-ILAB SDC - bhsections67
Pablo Diego-Rosell, PhD
2021-05-04
- Setup filenames, data, functions and create dictionary for dataset review
- Direct PII: variables to be removed
- Direct PII-team: Encode field team names
- Small locations: Encode locations with pop <100,000 using random large numbers
- Indirect PII - Ordinal: Global recode or Top/bottom coding for extreme values
- Indirect PII - Categorical: Recode, encode, 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, data, functions and create dictionary for dataset review
filename <- "bhsections67" # !!!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 Direct PII: variables to be removed
# !!! No Direct PIIDirect PII-team: Encode field team names
# !!! 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
dropvars <- c("dise")
mydata <- mydata[!names(mydata) %in% dropvars]
locvars <- c("q006_block_id", "q007_vlg_id")
mydata <- encode_location (variables= locvars, missing=999999)## [1] "Frequency table before encoding"
## q006_block_id. 6 Block Code
## 1 2 3 4 5 6 7 8 9 <NA>
## 194 155 195 407 98 190 143 422 516 33
## [1] "Frequency table after encoding"
## q006_block_id. 6 Block Code
## 279 280 281 282 283 284 285 286 287 <NA>
## 422 155 194 190 195 516 143 98 407 33
## [1] "Frequency table before encoding"
## q007_vlg_id. 7 Village Code
## 1 2 3 4 5 6 7 9 10 11 12 13 15 16 17 18 19 20 21
## 16 16 16 15 20 31 28 17 15 20 24 24 15 18 21 17 17 18 30
## 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
## 22 18 17 32 27 26 18 14 15 24 24 22 16 29 18 17 22 27 17
## 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59
## 16 18 17 28 20 24 21 19 17 17 16 18 26 24 27 18 17 21 13
## 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78
## 24 22 16 18 18 29 16 18 21 25 13 16 19 16 23 23 17 22 29
## 80 81 82 83 84 85 87 88 89 90 91 92 93 94 95 96 97 98 99
## 30 17 22 17 17 13 16 22 15 19 19 19 21 13 17 22 28 21 25
## 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118
## 18 24 21 15 19 14 31 16 27 21 17 21 26 14 24 19 16 21 22
## 119 <NA>
## 16 33
## [1] "Frequency table after encoding"
## q007_vlg_id. 7 Village Code
## 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283
## 19 19 18 17 18 17 15 24 15 15 22 16 22 18 22 24 21 16 17
## 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302
## 15 26 20 24 21 17 21 17 16 18 30 18 16 19 24 25 27 26 21
## 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321
## 26 18 17 28 19 29 24 28 27 20 16 21 16 24 23 14 14 30 16
## 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340
## 13 13 17 19 21 15 16 13 16 22 17 17 23 18 24 16 16 17 15
## 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359
## 27 18 22 16 29 28 14 29 17 31 17 18 20 17 21 25 32 21 18
## 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378
## 17 19 22 22 17 19 24 24 18 31 21 21 13 17 27 16 18 22 16
## 379 <NA>
## 22 33Indirect PII - Ordinal: Global recode or Top/bottom coding for extreme values
# Focus on variables with a "Lowest Freq" in dictionary of 30 or less.
mydata <- top_recode (variable="q704_distance", break_point=3, missing=NA)## [1] "Frequency table before encoding"
## q704_distance. 704 Travel distance from residence to associated school (DISE Code) in question
## 0 1 2 3 4 5 7 10 61
## 1839 389 90 21 7 4 1 1 1
## [1] "Frequency table after encoding"
## q704_distance. 704 Travel distance from residence to associated school (DISE Code) in question
## 0 1 2 3 or more
## 1839 389 90 35
mydata <- top_recode (variable="q705_time", break_point=60, missing=NA)## [1] "Frequency table before encoding"
## q705_time. 705 Travel time from residence to associated school (DISE Code) in question 001
## 0 1 2 3 4 5 7 8 10 15 18 20 25 30 40 45 50 55 60 75 90 120 180
## 40 22 20 6 2 362 4 2 715 457 3 223 38 356 7 18 2 2 63 4 3 3 1
## [1] "Frequency table after encoding"
## q705_time. 705 Travel time from residence to associated school (DISE Code) in question 001
## 0 1 2 3 4 5 7 8
## 40 22 20 6 2 362 4 2
## 10 15 18 20 25 30 40 45
## 715 457 3 223 38 356 7 18
## 50 55 60 or more
## 2 2 74
Indirect PII - Categorical: Recode, encode, or Top/bottom coding for extreme values
# !!! No Indirect PII categorical
dropvars <- c("q606_rel1", "q606_rel2")
mydata <- mydata[!names(mydata) %in% dropvars]Matching and crosstabulations: Run automated PII check
# !!! No direct demographic variables available in datasetOpen-ends: review responses for any sensitive information, redact as necessary
mydata <- mydata[!names(mydata) %in% "q708_remarks"]GPS data: Displace
# !!! No GPS dataSave 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)