DOL-ILAB SDC - Admin Data - Ecuador_Public Use
Pablo Diego-Rosell, PhD
2021-05-01
- Setup filenames
- Setup 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
filename <- "Admin Data - Ecuador_Public Use" # !!!Update filename
functions_vers <- "functions_1.7.R" # !!!Update helper functions fileSetup data, functions and create dictionary for dataset review
source (functions_vers)## --------
## This is sdcMicro v5.6.0.
## For references, please have a look at citation('sdcMicro')
## Note: since version 5.0.0, the graphical user-interface is a shiny-app that can be started with sdcApp().
## Please submit suggestions and bugs at: https://github.com/sdcTools/sdcMicro/issues
## --------##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union## Loading required package: sp## Checking rgeos availability: TRUE##
## Attaching package: 'raster'## The following object is masked from 'package:dplyr':
##
## select## The following object is masked from 'package:sdcMicro':
##
## freq## rgdal: version: 1.5-23, (SVN revision 1121)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 3.2.1, released 2020/12/29
## Path to GDAL shared files: C:/Users/C_Pablo_Diego-Rosell/Documents/R/R-3.6.3/library/rgdal/gdal
## GDAL binary built with GEOS: TRUE
## Loaded PROJ runtime: Rel. 7.2.1, January 1st, 2021, [PJ_VERSION: 721]
## Path to PROJ shared files: C:/Users/C_Pablo_Diego-Rosell/Documents/R/R-3.6.3/library/rgdal/proj
## PROJ CDN enabled: FALSE
## Linking to sp version:1.4-5
## To mute warnings of possible GDAL/OSR exportToProj4() degradation,
## use options("rgdal_show_exportToProj4_warnings"="none") before loading rgdal.
## Overwritten PROJ_LIB was C:/Users/C_Pablo_Diego-Rosell/Documents/R/R-3.6.3/library/rgdal/proj## Loading required package: spatstat.data## Loading required package: spatstat.geom## spatstat.geom 2.0-1##
## Attaching package: 'spatstat.geom'## The following objects are masked from 'package:raster':
##
## area, rotate, shift## Loading required package: spatstat.core## Loading required package: nlme##
## Attaching package: 'nlme'## The following object is masked from 'package:raster':
##
## getData## The following object is masked from 'package:dplyr':
##
## collapse## Loading required package: rpart## spatstat.core 2.0-0## Loading required package: spatstat.linnet## spatstat.linnet 2.1-1##
## spatstat 2.0-1 (nickname: 'Caution: contains small parts')
## For an introduction to spatstat, type 'beginner'## rgeos version: 0.5-5, (SVN revision 640)
## GEOS runtime version: 3.8.0-CAPI-1.13.1
## Linking to sp version: 1.4-4
## Polygon checking: TRUE##
## Spatial Point Pattern Analysis Code in S-Plus
##
## Version 2 - Spatial and Space-Time analysis##
## Attaching package: 'splancs'## The following object is masked from 'package:raster':
##
## zoom## The following object is masked from 'package:dplyr':
##
## tribble## Loading required package: spam## Loading required package: dotCall64## Loading required package: grid## Spam version 2.6-0 (2020-12-14) is loaded.
## Type 'help( Spam)' or 'demo( spam)' for a short introduction
## and overview of this package.
## Help for individual functions is also obtained by adding the
## suffix '.spam' to the function name, e.g. 'help( chol.spam)'.##
## Attaching package: 'spam'## The following objects are masked from 'package:base':
##
## backsolve, forwardsolve## See https://github.com/NCAR/Fields for
## an extensive vignette, other supplements and source code##
## Attaching package: 'geosphere'## The following object is masked from 'package:spatstat.geom':
##
## perimeter##
## Attaching package: 'tibble'## The following object is masked from 'package:splancs':
##
## tribbleVisually 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 .csv 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 field team names
# !!!No Direct PII-teamSmall locations: Encode locations with pop <100,000 using random large numbers
# !!!No small locationIndirect PII - Ordinal: Global recode or Top/bottom coding for extreme values
# Focus on variables with a "Lowest Freq" in dictionary of 30 or less. # Top code household composition variables with large and unusual numbers
mydata <- top_recode ("b_hhsize_2016_masked", break_point=10, missing=c(888, 999999)) # Topcode cases with 10 or more individuals.## [1] "Frequency table before encoding"
## b_hhsize_2016_masked. Household Size
## 2 or less 3 4 5 6 7 8 9 10 11+ <NA>
## 27 81 117 186 112 73 33 18 14 18 47
## [1] "Frequency table after encoding"
## b_hhsize_2016_masked. Household Size
## 2 or less 3 4 5 6 7 8 9 10 or more <NA>
## 27 81 117 186 112 73 33 18 32 47
mydata <- top_recode ("b_hhsize_2016_imp", break_point=10, missing=c(888, 999999)) # Topcode cases with 10 or more individuals.## [1] "Frequency table before encoding"
## b_hhsize_2016_imp. Imputed Version Of B_Hhsize_2016
## 1 2 3 4 5 5.42120742797852
## 2 25 81 117 186 47
## 6 7 8 9 10 11
## 112 73 33 18 14 6
## 12 13 14 16 17 29
## 7 1 1 1 1 1
## [1] "Frequency table after encoding"
## b_hhsize_2016_imp. Imputed Version Of B_Hhsize_2016
## 1 2 3 4 5 5.42120742797852
## 2 25 81 117 186 47
## 6 7 8 9 10 or more
## 112 73 33 18 32
Indirect PII - Categorical: Recode, encode, or Top/bottom coding for extreme values
# !!!Include relevant variables in list below (Indirect PII - Categorical, and Ordinal if not processed yet)
indirect_PII <- c("female",
"age_at_base",
"e_fasis",
"e_ffinjust",
"e_ffjust",
"e_pdfinal",
"e_promotion_status",
"e_qanual",
"b_white_2016",
"b_mestizo_2016",
"b_afro_2016",
"b_montub_2016",
"b_indig_2016",
"b_ownchild_2016",
"b_married_2016",
"b_mspan_2016",
"b_mnat_2016",
"b_fspan_2016",
"b_fnat_2016",
"b_sameage_2016",
"b_younger_2016",
"b_older_2016",
"b_yearsout_2016",
"b_othrace_2016",
"b_afro_2016_imp",
"b_fspan_2016_imp",
"b_married_2016_imp",
"b_mestizo_2016_imp",
"b_mspan_2016_imp",
"b_older_2016_imp",
"b_othrace_2016_imp",
"b_ownchild_2016_imp",
"b_sameage_2016_imp",
"b_white_2016_imp",
"b_yearsout_2016_imp",
"b_younger_2016_imp")
capture_tables (indirect_PII)
# Recode those with very specific values.
# Combine races' variables in other races' variable in order to reduce risk of re-identification.
mydata$b_othrace_2016 <- cbind(mydata$b_montub_2016,mydata$b_indig_2016, mydata$b_othrace_2016)
mydata <- mydata[!names(mydata) %in% c("b_indig_2016", "b_montub_2016")]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('female', 'age_at_base', 'b_white_2016') ##!!! Replace with candidate categorical demo vars
# weight variable (add if available)
# selectedWeightVar = c('projwt') ##!!! Replace with weight var
# household id variable (cluster)
# selectedHouseholdID = c('wpid') ##!!! Replace with household id
# creating the sdcMicro object with the assigned variables
sdcInitial <- createSdcObj(dat = mydata, keyVars = selectedKeyVars)
sdcInitial## The input dataset consists of 726 rows and 74 variables.
## --> Categorical key variables: female, age_at_base, b_white_2016
## ----------------------------------------------------------------------## 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)
## female 2 (2) 363.000 (363.000) 275 (275)
## age_at_base 4 (4) 181.500 (181.500) 44 (44)
## b_white_2016 3 (3) 341.500 (341.500) 51 (51)## ----------------------------------------------------------------------## Infos on 2/3-Anonymity:
##
## Number of observations violating
## - 2-anonymity: 0 (0.000%)
## - 3-anonymity: 0 (0.000%)
## - 5-anonymity: 0 (0.000%)
##
## ----------------------------------------------------------------------# All cases meet 2-anonimityOpen-ends: review responses for any sensitive information, redact as necessary
# !!! Identify open-end variables here:
open_ends <- c("e_fecom")
report_open (list_open_ends = open_ends)
mydata <- mydata[!names(mydata) %in% open_ends]
# Review "verbatims.csv". Identify variables to be deleted or redacted and their row number 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)