rm(list=ls(all=t))
filename <- "Section_0" # !!!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
# !!!No Small locations
# Focus on variables with a "Lowest Freq" in dictionary of 30 or less.
mydata <- top_recode (variable="age0", 80, missing=NA)
## [1] "Frequency table before encoding"
## age0. Age at Baseline, Corrected
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
## 386 258 288 297 318 755 821 159 92 616 611 610 689 563 615 551 472 411 323 270 221 202 152
## 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46
## 106 90 75 81 66 73 95 88 104 124 131 162 183 163 213 188 211 174 226 219 159 218 167
## 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69
## 176 158 159 144 101 124 109 102 86 71 57 70 57 41 25 33 21 16 21 27 20 17 18
## 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 88 89 90 91 92 <NA>
## 14 7 10 13 6 10 10 9 11 6 3 8 6 4 3 3 4 2 2 2 1 1 118
## [1] "Frequency table after encoding"
## age0. Age at Baseline, Corrected
## 1 2 3 4 5 6 7 8 9 10
## 386 258 288 297 318 755 821 159 92 616
## 11 12 13 14 15 16 17 18 19 20
## 611 610 689 563 615 551 472 411 323 270
## 21 22 23 24 25 26 27 28 29 30
## 221 202 152 106 90 75 81 66 73 95
## 31 32 33 34 35 36 37 38 39 40
## 88 104 124 131 162 183 163 213 188 211
## 41 42 43 44 45 46 47 48 49 50
## 174 226 219 159 218 167 176 158 159 144
## 51 52 53 54 55 56 57 58 59 60
## 101 124 109 102 86 71 57 70 57 41
## 61 62 63 64 65 66 67 68 69 70
## 25 33 21 16 21 27 20 17 18 14
## 71 72 73 74 75 76 77 78 79 80 or more
## 7 10 13 6 10 10 9 11 6 39
## <NA>
## 118
# !!!No Indirect PII - Categorical
# 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('c_gender', 'age0') ##!!! 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 14902 rows and 8 variables.
## --> Categorical key variables: c_gender, age0
## ----------------------------------------------------------------------
## 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)
## c_gender 4 (4) 3725.500 (3725.500) 3 (3)
## age0 81 (81) 184.800 (184.800) 6 (6)
## ----------------------------------------------------------------------
## Infos on 2/3-Anonymity:
##
## Number of observations violating
## - 2-anonymity: 0 (0.000%)
## - 3-anonymity: 2 (0.013%)
## - 5-anonymity: 3 (0.020%)
##
## ----------------------------------------------------------------------
# !!!Any observations violating 2-anonymity
# !!!No Open-ends
# !!!No GPS data
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)