DOL-ILAB SDC - Section_4
Pablo Diego-Rosell, PhD
2021-08-27
- 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 <- "Section_4" # !!!Update filename
functions_vers <- "functions_1.8.R" # !!!Update helper functions fileSetup data, functions and create dictionary for dataset review
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
# !!!No small locationsIndirect 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 high income to the 99.5 percentile
percentile_99.5 <- floor(quantile(na.exclude(mydata$s4q22)[na.exclude(mydata$s4q22)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="s4q22", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## s4q22. In the past 12 months, how much did you or other members of your household spend
## 1 5 6 9 10 15 20 21 25 27 28 30 35 40 45 50 60 70
## 2 6 2 1 2 2 33 1 1 1 1 6 1 18 1 31 11 3
## 80 100 110 120 125 140 150 180 200 220 240 250 260 270 280 300 320 340
## 2 45 1 14 1 2 4 7 26 1 19 3 1 1 1 23 1 1
## 360 365 400 420 450 460 480 500 576 600 700 720 730 780 800 840 900 960
## 3 1 4 1 1 1 14 36 1 14 2 5 1 1 1 1 3 8
## 1000 1008 1056 1080 1095 1200 1440 1500 1560 1600 1650 1680 1800 1825 1920 2000 2080 2160
## 24 1 1 2 1 14 4 3 2 3 1 1 7 1 3 3 1 1
## 2400 2880 2980 3000 3360 3600 3640 3650 3840 4000 4200 4320 4800 4860 5000 5400 5760 6000
## 8 2 1 3 2 6 1 1 1 2 1 2 2 1 2 3 1 1
## 6480 6720 7200 7300 10000 10800 11000 12480 14400 21600 23520 24000 29120 36000 40000 <NA>
## 1 2 3 1 3 2 1 1 1 2 1 2 1 1 1 1786
## [1] "Frequency table after encoding"
## s4q22. In the past 12 months, how much did you or other members of your household spend
## 1 5 6 9 10 15 20
## 2 6 2 1 2 2 33
## 21 25 27 28 30 35 40
## 1 1 1 1 6 1 18
## 45 50 60 70 80 100 110
## 1 31 11 3 2 45 1
## 120 125 140 150 180 200 220
## 14 1 2 4 7 26 1
## 240 250 260 270 280 300 320
## 19 3 1 1 1 23 1
## 340 360 365 400 420 450 460
## 1 3 1 4 1 1 1
## 480 500 576 600 700 720 730
## 14 36 1 14 2 5 1
## 780 800 840 900 960 1000 1008
## 1 1 1 3 8 24 1
## 1056 1080 1095 1200 1440 1500 1560
## 1 2 1 14 4 3 2
## 1600 1650 1680 1800 1825 1920 2000
## 3 1 1 7 1 3 3
## 2080 2160 2400 2880 2980 3000 3360
## 1 1 8 2 1 3 2
## 3600 3640 3650 3840 4000 4200 4320
## 6 1 1 1 2 1 2
## 4800 4860 5000 5400 5760 6000 6480
## 2 1 2 3 1 1 1
## 6720 7200 7300 10000 10800 11000 12480
## 2 3 1 3 2 1 1
## 14400 21600 23520 24000 26329 or more <NA>
## 1 2 1 2 3 1786
percentile_99.5 <- floor(quantile(na.exclude(mydata$s4q23)[na.exclude(mydata$s4q23)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="s4q23", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## s4q23. In the past 12 months, how much did you or other members of your household win f
## 0 10 30 35 40 50 60 100 150 180 195 200 250 280 300 360 400 500
## 262 1 1 1 1 2 2 6 2 1 1 7 1 1 7 1 2 8
## 600 700 750 800 860 900 1000 1050 1100 1200 1300 1320 1400 1500 1600 1700 1750 1800
## 8 3 1 1 1 1 12 3 1 11 1 1 2 24 2 1 2 7
## 1900 2000 2120 2200 2400 2450 2500 2800 3000 3200 3500 3800 4000 4200 4500 4800 5000 5200
## 2 9 1 1 2 1 1 1 24 1 7 1 4 1 2 3 7 1
## 5250 5400 5500 5600 6000 6400 7000 7200 7800 8000 9000 9450 10000 10750 11400 12000 13000 15000
## 1 2 1 1 12 1 4 3 1 3 7 1 2 1 1 2 1 2
## 16000 30000 64800 86000 1e+05 <NA>
## 1 1 1 1 2 1786
## [1] "Frequency table after encoding"
## s4q23. In the past 12 months, how much did you or other members of your household win f
## 0 10 30 35 40 50 60
## 262 1 1 1 1 2 2
## 100 150 180 195 200 250 280
## 6 2 1 1 7 1 1
## 300 360 400 500 600 700 750
## 7 1 2 8 8 3 1
## 800 860 900 1000 1050 1100 1200
## 1 1 1 12 3 1 11
## 1300 1320 1400 1500 1600 1700 1750
## 1 1 2 24 2 1 2
## 1800 1900 2000 2120 2200 2400 2450
## 7 2 9 1 1 2 1
## 2500 2800 3000 3200 3500 3800 4000
## 1 1 24 1 7 1 4
## 4200 4500 4800 5000 5200 5250 5400
## 1 2 3 7 1 1 2
## 5500 5600 6000 6400 7000 7200 7800
## 1 1 12 1 4 3 1
## 8000 9000 9450 10000 10750 11400 12000
## 3 7 1 2 1 1 2
## 13000 15000 16000 30000 64800 74445 or more <NA>
## 1 2 1 1 1 3 1786
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("s4q1",
"s4q3_1",
"s4q3_2",
"s4q3_3",
"s4q3_4",
"s4q3_5",
"s4q4_1",
"s4q4_2",
"s4q4_3",
"s4q5",
"s4q6",
"s4q13",
"s4q14",
"s4q15",
"s4q16",
"s4q17",
"s4q18",
"s4q24",
"s4q25",
"s4q26",
"s4q27",
"s4q28",
"s4q29")
capture_tables (indirect_PII)
# Recode those with very specific values.
break_language <- c(1,2,3,4,5)
labels_language <- c("Tagalog"=1,
"Bikol/Bicolano" = 2,
"Other" = 3,
"Ilocano"=4,
"Other"=5)
mydata <- ordinal_recode (variable="s4q1", break_points=break_language, missing=999999, value_labels=labels_language)
## [1] "Frequency table before encoding"
## s4q1. What language do you normally speak at home? Ano ang wikang karaniwan mong sina
## 1. Tagalog 2. Bikol/Bicolano 3. Kapampangan 4. Ilocano
## 995 650 4 455
## 5. Masbate<f1>o/Masbatenon 6. Cebuano 7. Ibanag 8. Bisaya/Binisaya
## 1 2 3 6
## 9. Batangueno 10. Other Foreign 11. English <NA>
## 2 13 1 164
## recoded
## [1,2) [2,3) [3,4) [4,5) [5,1e+06)
## 1 995 0 0 0 0
## 2 0 650 0 0 0
## 3 0 0 4 0 0
## 4 0 0 0 455 0
## 5 0 0 0 0 1
## 6 0 0 0 0 2
## 7 0 0 0 0 3
## 8 0 0 0 0 6
## 9 0 0 0 0 2
## 10 0 0 0 0 13
## 11 0 0 0 0 1
## [1] "Frequency table after encoding"
## s4q1. What language do you normally speak at home? Ano ang wikang karaniwan mong sina
## Tagalog Bikol/Bicolano Other Ilocano <NA>
## 995 650 32 455 164
## [1] "Inspect value labels and relabel as necessary"
## Tagalog Bikol/Bicolano Other Ilocano Other
## 1 2 3 4 5break_eth <- c(1,2,3,4,6,7,11,12)
labels_eth <- c("Other"=1,
"Bicolano" = 2,
"Bisayan/Cebuano" = 3,
"Other"=4,
"Ilocano"=5,
"Other"=6,
"Tagalog"=7,
"Other"=8)
mydata <- ordinal_recode (variable="s4q3_1", break_points=break_eth, missing=999999, value_labels=labels_eth)
## [1] "Frequency table before encoding"
## s4q3_1. What is the ethnicity of this household? Ano ang lahi ng kasambahay na ito? (#1
## 1. Aklanon 2. Bicolano 3. Bisayan/Cebuano 4. Chavacano 5. Hiligaynon
## 1 807 151 1 1
## 6. Ilocano 7. Ilonggo 9. Maranao 10. Masbate<f1>o 11. Tagalog
## 520 26 1 6 596
## 12. Tausug 13. Waray 15. Other Foreign <NA>
## 1 16 14 155
## recoded
## [1,2) [2,3) [3,4) [4,6) [6,7) [7,11) [11,12) [12,1e+06)
## 1 1 0 0 0 0 0 0 0
## 2 0 807 0 0 0 0 0 0
## 3 0 0 151 0 0 0 0 0
## 4 0 0 0 1 0 0 0 0
## 5 0 0 0 1 0 0 0 0
## 6 0 0 0 0 520 0 0 0
## 7 0 0 0 0 0 26 0 0
## 9 0 0 0 0 0 1 0 0
## 10 0 0 0 0 0 6 0 0
## 11 0 0 0 0 0 0 596 0
## 12 0 0 0 0 0 0 0 1
## 13 0 0 0 0 0 0 0 16
## 15 0 0 0 0 0 0 0 14
## [1] "Frequency table after encoding"
## s4q3_1. What is the ethnicity of this household? Ano ang lahi ng kasambahay na ito? (#1
## Other Bicolano Bisayan/Cebuano Ilocano Tagalog <NA>
## 67 807 151 520 596 155
## [1] "Inspect value labels and relabel as necessary"
## Other Bicolano Bisayan/Cebuano Other Ilocano Other
## 1 2 3 4 5 6
## Tagalog Other
## 7 8break_eth <- c(1,6,7,11,12)
labels_eth <- c("Other"=1,
"Ilocano" = 2,
"Other" = 3,
"Tagalog"=4,
"Other"=5)
mydata <- ordinal_recode (variable="s4q3_2", break_points=break_eth, missing=999999, value_labels=labels_eth)
## [1] "Frequency table before encoding"
## s4q3_2. What is the ethnicity of this household? Ano ang lahi ng kasambahay na ito? (#2
## 2. Bicolano 3. Bisayan/Cebuano 6. Ilocano 7. Ilonggo 8. Kinaray-a
## 1 29 46 13 1
## 9. Maranao 10. Masbate<f1>o 11. Tagalog 13. Waray 15. Other Foreign
## 1 3 251 51 6
## <NA>
## 1894
## recoded
## [1,6) [6,7) [7,11) [11,12) [12,1e+06)
## 2 1 0 0 0 0
## 3 29 0 0 0 0
## 6 0 46 0 0 0
## 7 0 0 13 0 0
## 8 0 0 1 0 0
## 9 0 0 1 0 0
## 10 0 0 3 0 0
## 11 0 0 0 251 0
## 13 0 0 0 0 51
## 15 0 0 0 0 6
## [1] "Frequency table after encoding"
## s4q3_2. What is the ethnicity of this household? Ano ang lahi ng kasambahay na ito? (#2
## Other Ilocano Tagalog <NA>
## 105 46 251 1894
## [1] "Inspect value labels and relabel as necessary"
## Other Ilocano Other Tagalog Other
## 1 2 3 4 5break_religion <- c(1,2,3,4,5,6,7)
labels_religion <- c("Roman Catholic"=1,
"Other Christian" = 2,
"Aglipayan" = 3,
"Iglesia ni Cristo"=4,
"Other Christian"=5,
"Other Christian"=6,
"Other"=7)
mydata <- ordinal_recode (variable="s4q4_1", break_points=break_religion, missing=999999, value_labels=labels_religion)
## [1] "Frequency table before encoding"
## s4q4_1. What is your religion? Ano ang iyong relihiyon? (#1/10)
## 1. Roman Catholic 2. Protestant 3. Aglipayan 4. Iglesia ni Cristo 5. Evangelical
## 1954 4 51 68 4
## 6. Other Christian 7. Muslim <NA>
## 137 3 75
## recoded
## [1,2) [2,3) [3,4) [4,5) [5,6) [6,7) [7,1e+06)
## 1 1954 0 0 0 0 0 0
## 2 0 4 0 0 0 0 0
## 3 0 0 51 0 0 0 0
## 4 0 0 0 68 0 0 0
## 5 0 0 0 0 4 0 0
## 6 0 0 0 0 0 137 0
## 7 0 0 0 0 0 0 3
## [1] "Frequency table after encoding"
## s4q4_1. What is your religion? Ano ang iyong relihiyon? (#1/10)
## Roman Catholic Other Christian Aglipayan Iglesia ni Cristo Other <NA>
## 1954 145 51 68 3 75
## [1] "Inspect value labels and relabel as necessary"
## Roman Catholic Other Christian Aglipayan Iglesia ni Cristo Other Christian Other Christian
## 1 2 3 4 5 6
## Other
## 7break_religion <- c(1,2,3,4,5,6,7)
labels_religion <- c("Roman Catholic"=1,
"Other Christian" = 2,
"Other Christian" = 3,
"Other Christian"=4,
"Other Christian"=5,
"Other Christian"=6,
"Other"=7)
mydata <- ordinal_recode (variable="s4q4_2", break_points=break_religion, missing=999999, value_labels=labels_religion)
## [1] "Frequency table before encoding"
## s4q4_2. What is your religion? Ano ang iyong relihiyon? (#2/10)
## 3. Aglipayan 4. Iglesia ni Cristo 6. Other Christian 7. Muslim <NA>
## 5 13 19 2 2257
## recoded
## [1,2) [2,3) [3,4) [4,5) [5,6) [6,7) [7,1e+06)
## 3 0 0 5 0 0 0 0
## 4 0 0 0 13 0 0 0
## 6 0 0 0 0 0 19 0
## 7 0 0 0 0 0 0 2
## [1] "Frequency table after encoding"
## s4q4_2. What is your religion? Ano ang iyong relihiyon? (#2/10)
## Other Christian Other <NA>
## 37 2 2257
## [1] "Inspect value labels and relabel as necessary"
## Roman Catholic Other Christian Other Christian Other Christian Other Christian Other Christian
## 1 2 3 4 5 6
## Other
## 7break_water <- c(1,2,3,4,5,6,7,9)
labels_water <- c("Own Use Faucet, community water system"=1,
"Shared Faucet, community water system" = 2,
"Own Use Tube or pipe Well" = 3,
"Shared Tube or pipe well"=4,
"Dug Well"=5,
"Spring, River Stream"=6,
"Other"=7,
"Bottled Water"=8)
mydata <- ordinal_recode (variable="s4q13", break_points=break_water, missing=999999, value_labels=labels_water)
## [1] "Frequency table before encoding"
## s4q13. What is the household's main source of drinking water? Ano ang pangunahing pina
## 1. Own Use Faucet, community water system 2. Shared Faucet, community water system
## 460 467
## 3. Own Use Tube or pipe Well 4. Shared Tube or pipe well
## 132 480
## 5. Dug Well 6. Spring, River Stream
## 255 252
## 7. Collected Rainfall 8. Peddler - rationed water
## 1 29
## 9. Bottled Water <NA>
## 123 97
## recoded
## [1,2) [2,3) [3,4) [4,5) [5,6) [6,7) [7,9) [9,1e+06)
## 1 460 0 0 0 0 0 0 0
## 2 0 467 0 0 0 0 0 0
## 3 0 0 132 0 0 0 0 0
## 4 0 0 0 480 0 0 0 0
## 5 0 0 0 0 255 0 0 0
## 6 0 0 0 0 0 252 0 0
## 7 0 0 0 0 0 0 1 0
## 8 0 0 0 0 0 0 29 0
## 9 0 0 0 0 0 0 0 123
## [1] "Frequency table after encoding"
## s4q13. What is the household's main source of drinking water? Ano ang pangunahing pina
## Own Use Faucet, community water system Shared Faucet, community water system
## 460 467
## Own Use Tube or pipe Well Shared Tube or pipe well
## 132 480
## Dug Well Spring, River Stream
## 255 252
## Other Bottled Water
## 30 123
## <NA>
## 97
## [1] "Inspect value labels and relabel as necessary"
## Own Use Faucet, community water system Shared Faucet, community water system
## 1 2
## Own Use Tube or pipe Well Shared Tube or pipe well
## 3 4
## Dug Well Spring, River Stream
## 5 6
## Other Bottled Water
## 7 8break_fuel <- c(1,2,3,7,8,9)
labels_fuel <- c("Other"=1,
"LPG" = 2,
"Other" = 3,
"Charcoal"=4,
"Wood"=5,
"Other"=6)
mydata <- ordinal_recode (variable="s4q14", break_points=break_fuel, missing=999999, value_labels=labels_fuel)
## [1] "Frequency table before encoding"
## s4q14. What type of fuel does your household mainly use for cooking? Anoang mga uri ng
## 1. Electricity 2. LPG 3. Natural Gas 4. Biogas
## 23 270 2 2
## 5. Kerosene 6. Coal, Lignite 7. Charcoal 8. Wood
## 12 3 407 1561
## 9. Straw, Shrubs, Grass <NA>
## 9 7
## recoded
## [1,2) [2,3) [3,7) [7,8) [8,9) [9,1e+06)
## 1 23 0 0 0 0 0
## 2 0 270 0 0 0 0
## 3 0 0 2 0 0 0
## 4 0 0 2 0 0 0
## 5 0 0 12 0 0 0
## 6 0 0 3 0 0 0
## 7 0 0 0 407 0 0
## 8 0 0 0 0 1561 0
## 9 0 0 0 0 0 9
## [1] "Frequency table after encoding"
## s4q14. What type of fuel does your household mainly use for cooking? Anoang mga uri ng
## Other LPG Charcoal Wood <NA>
## 51 270 407 1561 7
## [1] "Inspect value labels and relabel as necessary"
## Other LPG Other Charcoal Wood Other
## 1 2 3 4 5 6break_material <- c(1,2,3,4,5,6)
labels_material <- c("Strong Materials (Tile, Concrete, Brick, Stone, Wood, Plywood)"=1,
"Light Materials (Cogon, Nipa, Anahaw, Bamboo)" = 2,
"Salvaged or Make Shift Materials" = 3,
"Mixed, predominantly strong"=4,
"Mixed, predominantly light"=5,
"Other"=6)
mydata <- ordinal_recode (variable="s4q15", break_points=break_material, missing=999999, value_labels=labels_material)
## [1] "Frequency table before encoding"
## s4q15. What type of construction materials are the outer walls made of? Anong uri ng m
## 1. Strong Materials (Tile, Concrete, Brick, Stone, Wood, Plywood)
## 837
## 2. Light Materials (Cogon, Nipa, Anahaw, Bamboo)
## 657
## 3. Salvaged or Make Shift Materials
## 64
## 4. Mixed, predominantly strong
## 418
## 5. Mixed, predominantly light
## 300
## 6. Mixed, predominantly salvaged
## 19
## <NA>
## 1
## recoded
## [1,2) [2,3) [3,4) [4,5) [5,6) [6,1e+06)
## 1 837 0 0 0 0 0
## 2 0 657 0 0 0 0
## 3 0 0 64 0 0 0
## 4 0 0 0 418 0 0
## 5 0 0 0 0 300 0
## 6 0 0 0 0 0 19
## [1] "Frequency table after encoding"
## s4q15. What type of construction materials are the outer walls made of? Anong uri ng m
## Strong Materials (Tile, Concrete, Brick, Stone, Wood, Plywood)
## 837
## Light Materials (Cogon, Nipa, Anahaw, Bamboo)
## 657
## Salvaged or Make Shift Materials
## 64
## Mixed, predominantly strong
## 418
## Mixed, predominantly light
## 300
## Other
## 19
## <NA>
## 1
## [1] "Inspect value labels and relabel as necessary"
## Strong Materials (Tile, Concrete, Brick, Stone, Wood, Plywood)
## 1
## Light Materials (Cogon, Nipa, Anahaw, Bamboo)
## 2
## Salvaged or Make Shift Materials
## 3
## Mixed, predominantly strong
## 4
## Mixed, predominantly light
## 5
## Other
## 6break_status <- c(1,2,3,4,5,6,7)
labels_status <- c("Own House and Lot"=1,
"Rent house or room including lot" = 2,
"Own house but rented lot" = 3,
"Own house, rent-free lot with consent of owner"=4,
"Own house, rent-free lot without known consent of owner"=5,
"Rent-free house and lot with consent of owner"=6,
"Other"=7)
mydata <- ordinal_recode (variable="s4q16", break_points=break_status, missing=999999, value_labels=labels_status)
## [1] "Frequency table before encoding"
## s4q16. What is the tenure status of the property occupied by the household? Ano ang ko
## 1. Own House and Lot
## 717
## 2. Rent house or room including lot
## 50
## 3. Own house but rented lot
## 82
## 4. Own house, rent-free lot with consent of owner
## 1051
## 5. Own house, rent-free lot without known consent of owner
## 75
## 6. Rent-free house and lot with consent of owner
## 302
## 7. Rent-free house and lot without consent of owner
## 8
## <NA>
## 11
## recoded
## [1,2) [2,3) [3,4) [4,5) [5,6) [6,7) [7,1e+06)
## 1 717 0 0 0 0 0 0
## 2 0 50 0 0 0 0 0
## 3 0 0 82 0 0 0 0
## 4 0 0 0 1051 0 0 0
## 5 0 0 0 0 75 0 0
## 6 0 0 0 0 0 302 0
## 7 0 0 0 0 0 0 8
## [1] "Frequency table after encoding"
## s4q16. What is the tenure status of the property occupied by the household? Ano ang ko
## Own House and Lot
## 717
## Rent house or room including lot
## 50
## Own house but rented lot
## 82
## Own house, rent-free lot with consent of owner
## 1051
## Own house, rent-free lot without known consent of owner
## 75
## Rent-free house and lot with consent of owner
## 302
## Other
## 8
## <NA>
## 11
## [1] "Inspect value labels and relabel as necessary"
## Own House and Lot
## 1
## Rent house or room including lot
## 2
## Own house but rented lot
## 3
## Own house, rent-free lot with consent of owner
## 4
## Own house, rent-free lot without known consent of owner
## 5
## Rent-free house and lot with consent of owner
## 6
## Other
## 7Matching and crosstabulations: Run automated PII check
# !!! Insufficient demographic dataOpen-ends: review responses for any sensitive information, redact as necessary
# !!! Identify open-end variables here:
open_ends <- c("s4q1other",
"s4q1whynoresponse",
"s4q3other",
"s4q3whynoresponse",
"s4q4other",
"s4q4whynoresponse",
"s4q5_other",
"s4q5whynoresponse",
"s4q6whynoresponse",
"s4q7whynoresponse",
"s4q9whynoresponse",
"s4q10whynoresponse",
"s4q11whynoresponse",
"s4q12whynoresponse",
"s4q13other",
"s4q13whynoresponse",
"s4q14other",
"s4q14whynoresponse",
"s4q15whynoresponse",
"s4q16whynoresponse",
"s4q17whynoresponse",
"s4q18whynoresponse",
"s4q19whynoresponse",
"s4q20whynoresponse",
"s4q21whynoresponse",
"s4q22whynoresponse",
"s4q23whynoresponse",
"s4q24whynoresponse",
"s4q25whynoresponse",
"s4q26whynoresponse",
"s4q27whynoresponse",
"s4q28whynoresponse",
"s4q29whynoresponse")
report_open (list_open_ends = open_ends)
# Review "verbatims.csv". Identify variables to be deleted or redacted and their row number
mydata$s4q1other[58] <- "Other"
mydata$s4q1other[62] <- "Other"
mydata$s4q1other[63] <- "Other"
mydata$s4q1other[64] <- "Other"
mydata$s4q1other[67] <- "Other"
mydata$s4q1other[70] <- "Other"
mydata$s4q1other[73] <- "Other"
mydata$s4q1other[75] <- "Other"
mydata$s4q1other[76] <- "Other"
mydata$s4q1other[79] <- "Other"
mydata$s4q1other[85] <- "Other"
mydata$s4q1other[92] <- "Other"
mydata$s4q1other[94] <- "Other"
mydata$s4q1other[98] <- "Other"
mydata$s4q1other[128] <- "Other"
mydata$s4q1other[130] <- "Other"
mydata$s4q1other[134] <- "Other"
mydata$s4q1other[135] <- "Other"
mydata$s4q1other[137] <- "Other"
mydata$s4q1other[139] <- "Other"
mydata$s4q1other[140] <- "Other"
mydata$s4q1other[142] <- "Other"
mydata$s4q1other[143] <- "Other"
mydata$s4q1other[146] <- "Other"
mydata$s4q1other[147] <- "Other"
mydata$s4q1other[154] <- "Other"
mydata$s4q1other[162] <- "Other"
mydata$s4q1other[167] <- "Other"
mydata$s4q1other[171] <- "Other"
mydata$s4q1other[180] <- "Other"
mydata$s4q1other[184] <- "Other"
mydata$s4q1other[189] <- "Other"
mydata$s4q1other[193] <- "Other"
mydata$s4q1other[198] <- "Other"
mydata$s4q1other[200] <- "Other"
mydata$s4q1other[201] <- "Other"
mydata$s4q1other[203] <- "Other"
mydata$s4q1other[204] <- "Other"
mydata$s4q1other[208] <- "Other"
mydata$s4q1other[209] <- "Other"
mydata$s4q1other[210] <- "Other"
mydata$s4q1other[211] <- "Other"
mydata$s4q1other[212] <- "Other"
mydata$s4q1other[213] <- "Other"
mydata$s4q1other[215] <- "Other"
mydata$s4q1other[216] <- "Other"
mydata$s4q1other[218] <- "Other"
mydata$s4q1other[219] <- "Other"
mydata$s4q1other[222] <- "Other"
mydata$s4q1other[243] <- "Other"
mydata$s4q1other[246] <- "Other"
mydata$s4q1other[247] <- "Other"
mydata$s4q1other[248] <- "Other"
mydata$s4q1other[251] <- "Other"
mydata$s4q1other[253] <- "Other"
mydata$s4q1other[255] <- "Other"
mydata$s4q1other[259] <- "Other"
mydata$s4q1other[260] <- "Other"
mydata$s4q1other[261] <- "Other"
mydata$s4q1other[265] <- "Other"
mydata$s4q1other[266] <- "Other"
mydata$s4q1other[273] <- "Other"
mydata$s4q1other[276] <- "Other"
mydata$s4q1other[278] <- "Other"
mydata$s4q1other[323] <- "Other"
mydata$s4q1other[325] <- "Other"
mydata$s4q1other[326] <- "Other"
mydata$s4q1other[327] <- "Other"
mydata$s4q1other[330] <- "Other"
mydata$s4q1other[332] <- "Other"
mydata$s4q1other[333] <- "Other"
mydata$s4q1other[334] <- "Other"
mydata$s4q1other[335] <- "Other"
mydata$s4q1other[336] <- "Other"
mydata$s4q1other[338] <- "Other"
mydata$s4q1other[339] <- "Other"
mydata$s4q1other[340] <- "Other"
mydata$s4q1other[341] <- "Other"
mydata$s4q1other[342] <- "Other"
mydata$s4q1other[344] <- "Other"
mydata$s4q1other[345] <- "Other"
mydata$s4q1other[347] <- "Other"
mydata$s4q1other[348] <- "Other"
mydata$s4q1other[349] <- "Other"
mydata$s4q1other[350] <- "Other"
mydata$s4q1other[357] <- "Other"
mydata$s4q1other[366] <- "Other"
mydata$s4q1other[368] <- "Other"
mydata$s4q1other[369] <- "Other"
mydata$s4q1other[370] <- "Other"
mydata$s4q1other[371] <- "Other"
mydata$s4q1other[372] <- "Other"
mydata$s4q1other[377] <- "Other"
mydata$s4q1other[388] <- "Other"
mydata$s4q1other[395] <- "Other"
mydata$s4q1other[396] <- "Other"
mydata$s4q1other[399] <- "Other"
mydata$s4q1other[400] <- "Other"
mydata$s4q1other[403] <- "Other"
mydata$s4q1other[456] <- "Other"
mydata$s4q1other[632] <- "Other"
mydata$s4q1other[633] <- "Other"
mydata$s4q1other[638] <- "Other"
mydata$s4q1other[640] <- "Other"
mydata$s4q1other[641] <- "Other"
mydata$s4q1other[642] <- "Other"
mydata$s4q1other[687] <- "Other"
mydata$s4q1other[688] <- "Other"
mydata$s4q1other[689] <- "Other"
mydata$s4q1other[694] <- "Other"
mydata$s4q1other[699] <- "Other"
mydata$s4q1other[703] <- "Other"
mydata$s4q1other[705] <- "Other"
mydata$s4q1other[706] <- "Other"
mydata$s4q1other[707] <- "Other"
mydata$s4q1other[708] <- "Other"
mydata$s4q1other[709] <- "Other"
mydata$s4q1other[712] <- "Other"
mydata$s4q1other[713] <- "Other"
mydata$s4q1other[1456] <- "Other"
mydata$s4q1whynoresponse[697] <- "Other"
mydata$s4q3other[64] <- "Other"
mydata$s4q3other[70] <- "Other"
mydata$s4q3other[71] <- "Other"
mydata$s4q3other[73] <- "Other"
mydata$s4q3other[75] <- "Other"
mydata$s4q3other[76] <- "Other"
mydata$s4q3other[94] <- "Other"
mydata$s4q3other[98] <- "Other"
mydata$s4q3other[127] <- "Other"
mydata$s4q3other[128] <- "Other"
mydata$s4q3other[129] <- "Other"
mydata$s4q3other[130] <- "Other"
mydata$s4q3other[133] <- "Other"
mydata$s4q3other[134] <- "Other"
mydata$s4q3other[141] <- "Other"
mydata$s4q3other[142] <- "Other"
mydata$s4q3other[143] <- "Other"
mydata$s4q3other[144] <- "Other"
mydata$s4q3other[146] <- "Other"
mydata$s4q3other[147] <- "Other"
mydata$s4q3other[148] <- "Other"
mydata$s4q3other[149] <- "Other"
mydata$s4q3other[154] <- "Other"
mydata$s4q3other[157] <- "Other"
mydata$s4q3other[163] <- "Other"
mydata$s4q3other[166] <- "Other"
mydata$s4q3other[167] <- "Other"
mydata$s4q3other[168] <- "Other"
mydata$s4q3other[170] <- "Other"
mydata$s4q3other[171] <- "Other"
mydata$s4q3other[172] <- "Other"
mydata$s4q3other[173] <- "Other"
mydata$s4q3other[174] <- "Other"
mydata$s4q3other[175] <- "Other"
mydata$s4q3other[176] <- "Other"
mydata$s4q3other[177] <- "Other"
mydata$s4q3other[178] <- "Other"
mydata$s4q3other[180] <- "Other"
mydata$s4q3other[181] <- "Other"
mydata$s4q3other[186] <- "Other"
mydata$s4q3other[187] <- "Other"
mydata$s4q3other[189] <- "Other"
mydata$s4q3other[190] <- "Other"
mydata$s4q3other[192] <- "Other"
mydata$s4q3other[193] <- "Other"
mydata$s4q3other[196] <- "Other"
mydata$s4q3other[200] <- "Other"
mydata$s4q3other[201] <- "Other"
mydata$s4q3other[203] <- "Other"
mydata$s4q3other[206] <- "Other"
mydata$s4q3other[209] <- "Other"
mydata$s4q3other[211] <- "Other"
mydata$s4q3other[212] <- "Other"
mydata$s4q3other[213] <- "Other"
mydata$s4q3other[215] <- "Other"
mydata$s4q3other[216] <- "Other"
mydata$s4q3other[217] <- "Other"
mydata$s4q3other[218] <- "Other"
mydata$s4q3other[219] <- "Other"
mydata$s4q3other[222] <- "Other"
mydata$s4q3other[224] <- "Other"
mydata$s4q3other[240] <- "Other"
mydata$s4q3other[242] <- "Other"
mydata$s4q3other[243] <- "Other"
mydata$s4q3other[245] <- "Other"
mydata$s4q3other[246] <- "Other"
mydata$s4q3other[248] <- "Other"
mydata$s4q3other[252] <- "Other"
mydata$s4q3other[253] <- "Other"
mydata$s4q3other[259] <- "Other"
mydata$s4q3other[260] <- "Other"
mydata$s4q3other[261] <- "Other"
mydata$s4q3other[266] <- "Other"
mydata$s4q3other[276] <- "Other"
mydata$s4q3other[325] <- "Other"
mydata$s4q3other[327] <- "Other"
mydata$s4q3other[329] <- "Other"
mydata$s4q3other[330] <- "Other"
mydata$s4q3other[332] <- "Other"
mydata$s4q3other[333] <- "Other"
mydata$s4q3other[334] <- "Other"
mydata$s4q3other[336] <- "Other"
mydata$s4q3other[338] <- "Other"
mydata$s4q3other[340] <- "Other"
mydata$s4q3other[341] <- "Other"
mydata$s4q3other[342] <- "Other"
mydata$s4q3other[344] <- "Other"
mydata$s4q3other[345] <- "Other"
mydata$s4q3other[347] <- "Other"
mydata$s4q3other[349] <- "Other"
mydata$s4q3other[350] <- "Other"
mydata$s4q3other[361] <- "Other"
mydata$s4q3other[366] <- "Other"
mydata$s4q3other[369] <- "Other"
mydata$s4q3other[370] <- "Other"
mydata$s4q3other[372] <- "Other"
mydata$s4q3other[373] <- "Other"
mydata$s4q3other[374] <- "Other"
mydata$s4q3other[375] <- "Other"
mydata$s4q3other[376] <- "Other"
mydata$s4q3other[377] <- "Other"
mydata$s4q3other[388] <- "Other"
mydata$s4q3other[395] <- "Other"
mydata$s4q3other[399] <- "Other"
mydata$s4q3other[400] <- "Other"
mydata$s4q3other[403] <- "Other"
mydata$s4q3other[452] <- "Other"
mydata$s4q3other[632] <- "Other"
mydata$s4q3other[633] <- "Other"
mydata$s4q3other[638] <- "Other"
mydata$s4q3other[640] <- "Other"
mydata$s4q3other[641] <- "Other"
mydata$s4q3other[642] <- "Other"
mydata$s4q3other[673] <- "Other"
mydata$s4q3other[688] <- "Other"
mydata$s4q3other[695] <- "Other"
mydata$s4q3other[699] <- "Other"
mydata$s4q3other[703] <- "Other"
mydata$s4q3other[705] <- "Other"
mydata$s4q3other[706] <- "Other"
mydata$s4q3other[707] <- "Other"
mydata$s4q3other[708] <- "Other"
mydata$s4q3other[709] <- "Other"
mydata$s4q3other[712] <- "Other"
mydata$s4q3other[779] <- "Other"
mydata$s4q3other[826] <- "Other"
mydata$s4q3other[841] <- "Other"
mydata$s4q3other[948] <- "Other"
mydata$s4q3other[1015] <- "Other"
mydata$s4q3other[1039] <- "Other"
mydata$s4q3other[1053] <- "Other"
mydata$s4q3other[1054] <- "Other"
mydata$s4q3other[1077] <- "Other"
mydata$s4q3other[1083] <- "Other"
mydata$s4q3other[1084] <- "Other"
mydata$s4q3other[1088] <- "Other"
mydata$s4q3other[1096] <- "Other"
mydata$s4q3other[1100] <- "Other"
mydata$s4q3other[1101] <- "Other"
mydata$s4q3other[1107] <- "Other"
mydata$s4q3other[1111] <- "Other"
mydata$s4q3other[1112] <- "Other"
mydata$s4q3other[1114] <- "Other"
mydata$s4q3other[1118] <- "Other"
mydata$s4q3other[1119] <- "Other"
mydata$s4q3other[1120] <- "Other"
mydata$s4q3other[1456] <- "Other"
mydata$s4q3other[1471] <- "Other"
mydata$s4q3other[1479] <- "Other"
mydata$s4q3other[1495] <- "Other"
mydata$s4q3other[1500] <- "Other"
mydata$s4q3whynoresponse[1087] <- "Other"
mydata$s4q3whynoresponse[1089] <- "Other"
mydata$s4q3whynoresponse[1211] <- "Other"
mydata$s4q4other[10] <- "Other"
mydata$s4q4other[28] <- "Other"
mydata$s4q4other[30] <- "Other"
mydata$s4q4other[69] <- "Other"
mydata$s4q4other[71] <- "Other"
mydata$s4q4other[72] <- "Other"
mydata$s4q4other[85] <- "Other"
mydata$s4q4other[88] <- "Other"
mydata$s4q4other[89] <- "Other"
mydata$s4q4other[96] <- "Other"
mydata$s4q4other[97] <- "Other"
mydata$s4q4other[98] <- "Other"
mydata$s4q4other[117] <- "Other"
mydata$s4q4other[125] <- "Other"
mydata$s4q4other[199] <- "Other"
mydata$s4q4other[236] <- "Other"
mydata$s4q4other[382] <- "Other"
mydata$s4q4other[387] <- "Other"
mydata$s4q4other[450] <- "Other"
mydata$s4q4other[508] <- "Other"
mydata$s4q4other[511] <- "Other"
mydata$s4q4other[540] <- "Other"
mydata$s4q4other[544] <- "Other"
mydata$s4q4other[545] <- "Other"
mydata$s4q4other[549] <- "Other"
mydata$s4q4other[558] <- "Other"
mydata$s4q4other[560] <- "Other"
mydata$s4q4other[563] <- "Other"
mydata$s4q4other[564] <- "Other"
mydata$s4q4other[567] <- "Other"
mydata$s4q4other[576] <- "Other"
mydata$s4q4other[585] <- "Other"
mydata$s4q4other[592] <- "Other"
mydata$s4q4other[600] <- "Other"
mydata$s4q4other[606] <- "Other"
mydata$s4q4other[612] <- "Other"
mydata$s4q4other[613] <- "Other"
mydata$s4q4other[620] <- "Other"
mydata$s4q4other[625] <- "Other"
mydata$s4q4other[632] <- "Other"
mydata$s4q4other[634] <- "Other"
mydata$s4q4other[635] <- "Other"
mydata$s4q4other[640] <- "Other"
mydata$s4q4other[644] <- "Other"
mydata$s4q4other[645] <- "Other"
mydata$s4q4other[647] <- "Other"
mydata$s4q4other[648] <- "Other"
mydata$s4q4other[649] <- "Other"
mydata$s4q4other[668] <- "Other"
mydata$s4q4other[701] <- "Other"
mydata$s4q4other[707] <- "Other"
mydata$s4q4other[712] <- "Other"
mydata$s4q4other[719] <- "Other"
mydata$s4q4other[726] <- "Other"
mydata$s4q4other[815] <- "Other"
mydata$s4q4other[849] <- "Other"
mydata$s4q4other[929] <- "Other"
mydata$s4q4other[932] <- "Other"
mydata$s4q4other[946] <- "Other"
mydata$s4q4other[981] <- "Other"
mydata$s4q4other[995] <- "Other"
mydata$s4q4other[1002] <- "Other"
mydata$s4q4other[1034] <- "Other"
mydata$s4q4other[1152] <- "Other"
mydata$s4q4other[1222] <- "Other"
mydata$s4q4other[1260] <- "Other"
mydata$s4q4other[1348] <- "Other"
mydata$s4q4other[1352] <- "Other"
mydata$s4q4other[2071] <- "Other"
mydata$s4q4other[2091] <- "Other"
mydata$s4q4other[2258] <- "Other"
mydata$s4q4whynoresponse[87] <- "Other"
mydata$s4q4whynoresponse[657] <- "Other"
mydata$s4q5_other[5] <- "Other"
mydata$s4q5_other[29] <- "Other"
mydata$s4q5_other[34] <- "Other"
mydata$s4q5_other[43] <- "Other"
mydata$s4q5_other[50] <- "Other"
mydata$s4q5_other[73] <- "Other"
mydata$s4q5_other[86] <- "Other"
mydata$s4q5_other[178] <- "Other"
mydata$s4q5_other[225] <- "Other"
mydata$s4q5_other[316] <- "Other"
mydata$s4q5_other[402] <- "Other"
mydata$s4q5_other[537] <- "Other"
mydata$s4q5_other[582] <- "Other"
mydata$s4q5_other[593] <- "Other"
mydata$s4q5_other[709] <- "Other"
mydata$s4q5_other[821] <- "Other"
mydata$s4q5_other[843] <- "Other"
mydata$s4q5_other[844] <- "Other"
mydata$s4q5_other[849] <- "Other"
mydata$s4q5_other[883] <- "Other"
mydata$s4q5_other[887] <- "Other"
mydata$s4q5_other[889] <- "Other"
mydata$s4q5_other[911] <- "Other"
mydata$s4q5_other[913] <- "Other"
mydata$s4q5_other[914] <- "Other"
mydata$s4q5_other[1018] <- "Other"
mydata$s4q5_other[1061] <- "Other"
mydata$s4q5_other[1063] <- "Other"
mydata$s4q5_other[1065] <- "Other"
mydata$s4q5_other[1070] <- "Other"
mydata$s4q5_other[1071] <- "Other"
mydata$s4q5_other[1073] <- "Other"
mydata$s4q5_other[1076] <- "Other"
mydata$s4q5_other[1083] <- "Other"
mydata$s4q5_other[1094] <- "Other"
mydata$s4q5_other[1095] <- "Other"
mydata$s4q5_other[1100] <- "Other"
mydata$s4q5_other[1102] <- "Other"
mydata$s4q5_other[1104] <- "Other"
mydata$s4q5_other[1116] <- "Other"
mydata$s4q5_other[1126] <- "Other"
mydata$s4q5_other[1153] <- "Other"
mydata$s4q5_other[1162] <- "Other"
mydata$s4q5_other[1183] <- "Other"
mydata$s4q5_other[1217] <- "Other"
mydata$s4q5_other[1232] <- "Other"
mydata$s4q5_other[1294] <- "Other"
mydata$s4q5_other[1300] <- "Other"
mydata$s4q5_other[1326] <- "Other"
mydata$s4q5_other[1590] <- "Other"
mydata$s4q5_other[1735] <- "Other"
mydata$s4q5_other[1745] <- "Other"
mydata$s4q5_other[1806] <- "Other"
mydata$s4q5_other[1811] <- "Other"
mydata$s4q5_other[1884] <- "Other"
mydata$s4q5_other[1983] <- "Other"
mydata$s4q5_other[2059] <- "Other"
mydata$s4q5_other[2105] <- "Other"
mydata$s4q5_other[2190] <- "Other"
mydata$s4q5whynoresponse[1110] <- "Other"
mydata$s4q7whynoresponse[1103] <- "[time redacted]"
mydata$s4q9whynoresponse[1103] <- "[time redacted]"
mydata$s4q10whynoresponse[925] <- "[time redacted]"
mydata$s4q10whynoresponse[1124] <- "[time redacted]"
mydata$s4q13other[716] <- "Other"
mydata$s4q13other[937] <- "Other"
mydata$s4q13other[943] <- "Other"
mydata$s4q13other[1042] <- "Other"
mydata$s4q13other[1045] <- "Other"
mydata$s4q13other[1377] <- "Tagalo"
mydata$s4q13other[1420] <- "Tagalo"
mydata$s4q13other[1521] <- "Other"
mydata$s4q13other[1979] <- "Other"
mydata$s4q14other[330] <- "Other"
mydata$s4q14other[1398] <- "Other"
mydata$s4q14other[1492] <- "Other"
mydata$s4q14other[1897] <- "Other"
mydata$s4q14other[2090] <- "Other"
mydata$s4q14other[2093] <- "Other"
mydata$s4q16whynoresponse[1320] <- "Other"
mydata$s4q16whynoresponse[1823] <- "Other"
mydata$s4q18whynoresponse[1060] <- "Other"
mydata$s4q18whynoresponse[1842] <- "Other"
mydata$s4q20whynoresponse[1080] <- "[name] together with her husband decide on those large or frequent purchases."
mydata$s4q22whynoresponse[1230] <- "Other"
mydata$s4q22whynoresponse[1257] <- "Her husband are [work] and everyday he had only an income of [amount] pesos a day."
mydata$s4q23whynoresponse[776] <- "She does not know because her son [situation]"
mydata$s4q23whynoresponse[1230] <- "Other"GPS data: Displace
# !!!No GPS dataSave processed data in Stata and SPSS format
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)