DOL-ILAB SDC - Section_5
Pablo Diego-Rosell, PhD
2021-10-25
- 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_5" # !!!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
mydata$household_id <- zap_labels(mydata$household_id)Direct 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
# Top code high income to the 99.5 percentile
percentile_99.5 <- floor(quantile(na.exclude(mydata$m_s5q4)[na.exclude(mydata$m_s5q4)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="m_s5q4", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## m_s5q4. sEq4: How much cash did you receive? Magkano ang perang inyong natanggap?
## -998 500 600 1000 1600 1800 2200 2280 2400 2500 2600 2800 3000 3600 3800 4000 4300 4400 4600 4800 5000 5200 5400 5500 5600 6000 6200 6300 6400 6600 6800 6900
## 41 1 1 1 1 1 1 1 4 1 1 1 1 2 1 3 1 3 2 4 4 1 1 2 1 11 4 1 5 7 3 1
## 7000 7100 7200 7400 7500 7600 7700 7800 7900 8000 8200 8300 8400 8600 8700 8800 8900 9000 9100 9200 9300 9400 9500 9600 9680 9700 9800 9900 10000 10100 10146 10175
## 1 1 15 3 1 8 3 9 1 11 6 2 11 5 2 15 4 16 3 5 3 3 4 48 1 2 7 1 13 4 1 1
## 10200 10300 10400 10500 10600 10700 10800 10900 11000 11100 11200 11400 11500 11600 11700 11800 11900 12000 12100 12200 12300 12400 12500 12600 12700 12800 12900 13000 13100 13200 13300 13400
## 21 6 10 3 14 6 50 3 19 4 17 20 1 19 1 12 2 43 3 9 7 9 3 23 3 20 4 14 2 94 7 6
## 13500 13600 13700 13800 13900 14000 14100 14108 14200 14270 14300 14400 14500 14600 14700 14800 14900 15000 15100 15200 15300 15400 15500 15600 15700 15800 16000 16100 16200 16300 16400 16500
## 2 18 5 38 1 9 1 1 25 1 5 51 3 11 6 32 3 36 3 18 6 8 3 49 5 26 12 2 30 8 6 7
## 16520 16600 16700 16800 16900 17000 17100 17200 17300 17400 17600 17700 17800 17900 18000 18100 18200 18300 18400 18500 18600 18700 18800 19000 19100 19200 19300 19400 19500 19600 19700 19800
## 1 18 3 99 1 4 2 17 2 23 7 1 21 2 43 1 11 2 8 1 26 2 17 10 1 59 2 10 1 6 1 28
## 19900 20000 20100 20200 20300 20400 20500 20600 20700 20800 20900 21000 21200 21300 21400 21600 21700 21800 22000 22200 22300 22400 22600 22700 22800 22900 23200 23400 23600 23700 23800 24000
## 4 3 2 9 2 51 5 3 2 18 1 31 2 1 2 29 2 6 3 11 2 1 6 2 31 1 3 14 1 2 3 14
## 24200 24500 24600 24800 25000 25200 25600 25700 25800 26400 27000 27600 28200 28600 29200 30000 36000 44400 62800 71600 <NA>
## 1 1 4 1 2 12 1 1 6 4 1 2 2 1 1 1 1 1 1 1 334
## [1] "Frequency table after encoding"
## m_s5q4. sEq4: How much cash did you receive? Magkano ang perang inyong natanggap?
## -998 500 600 1000 1600 1800 2200 2280 2400 2500 2600 2800 3000
## 41 1 1 1 1 1 1 1 4 1 1 1 1
## 3600 3800 4000 4300 4400 4600 4800 5000 5200 5400 5500 5600 6000
## 2 1 3 1 3 2 4 4 1 1 2 1 11
## 6200 6300 6400 6600 6800 6900 7000 7100 7200 7400 7500 7600 7700
## 4 1 5 7 3 1 1 1 15 3 1 8 3
## 7800 7900 8000 8200 8300 8400 8600 8700 8800 8900 9000 9100 9200
## 9 1 11 6 2 11 5 2 15 4 16 3 5
## 9300 9400 9500 9600 9680 9700 9800 9900 10000 10100 10146 10175 10200
## 3 3 4 48 1 2 7 1 13 4 1 1 21
## 10300 10400 10500 10600 10700 10800 10900 11000 11100 11200 11400 11500 11600
## 6 10 3 14 6 50 3 19 4 17 20 1 19
## 11700 11800 11900 12000 12100 12200 12300 12400 12500 12600 12700 12800 12900
## 1 12 2 43 3 9 7 9 3 23 3 20 4
## 13000 13100 13200 13300 13400 13500 13600 13700 13800 13900 14000 14100 14108
## 14 2 94 7 6 2 18 5 38 1 9 1 1
## 14200 14270 14300 14400 14500 14600 14700 14800 14900 15000 15100 15200 15300
## 25 1 5 51 3 11 6 32 3 36 3 18 6
## 15400 15500 15600 15700 15800 16000 16100 16200 16300 16400 16500 16520 16600
## 8 3 49 5 26 12 2 30 8 6 7 1 18
## 16700 16800 16900 17000 17100 17200 17300 17400 17600 17700 17800 17900 18000
## 3 99 1 4 2 17 2 23 7 1 21 2 43
## 18100 18200 18300 18400 18500 18600 18700 18800 19000 19100 19200 19300 19400
## 1 11 2 8 1 26 2 17 10 1 59 2 10
## 19500 19600 19700 19800 19900 20000 20100 20200 20300 20400 20500 20600 20700
## 1 6 1 28 4 3 2 9 2 51 5 3 2
## 20800 20900 21000 21200 21300 21400 21600 21700 21800 22000 22200 22300 22400
## 18 1 31 2 1 2 29 2 6 3 11 2 1
## 22600 22700 22800 22900 23200 23400 23600 23700 23800 24000 24200 24500 24600
## 6 2 31 1 3 14 1 2 3 14 1 1 4
## 24800 25000 25200 25600 25700 25800 26400 27000 27600 or more <NA>
## 1 2 12 1 1 6 4 1 11 334
percentile_99.5 <- floor(quantile(na.exclude(mydata$m_s5q5)[na.exclude(mydata$m_s5q5)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="m_s5q5", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## m_s5q5. sEq5: What was the value of the in-kind transfer? Ano ang halaga ng hindi pera
## 200 300 500 600 800 1000 1200 2000 3000 3600 5000 6400 8900 9000 <NA>
## 1 2 2 2 1 1 1 2 1 1 1 1 1 1 2267
## [1] "Frequency table after encoding"
## m_s5q5. sEq5: What was the value of the in-kind transfer? Ano ang halaga ng hindi pera
## 200 300 500 600 800 1000 1200 2000 3000 3600 5000 6400 8900 8991 or more
## 1 2 2 2 1 1 1 2 1 1 1 1 1 1
## <NA>
## 2267
percentile_99.5 <- floor(quantile(na.exclude(mydata$m_s5q9)[na.exclude(mydata$m_s5q9)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="m_s5q9", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## m_s5q9. sEq9: How much cash did you receive? Magkano ang perang inyong natanggap?
## -998 100 150 260 500 700 1200 1500 3000 4000 5000 5115 6000 6350 7000 7500 8000 10000 13200 15000 20000 <NA>
## 1 1 1 1 1 1 1 2 1 2 34 1 1 1 4 1 2 36 1 1 2 2189
## [1] "Frequency table after encoding"
## m_s5q9. sEq9: How much cash did you receive? Magkano ang perang inyong natanggap?
## -998 100 150 260 500 700 1200 1500 3000 4000 5000 5115 6000
## 1 1 1 1 1 1 1 2 1 2 34 1 1
## 6350 7000 7500 8000 10000 13200 15000 20000 or more <NA>
## 1 4 1 2 36 1 1 2 2189
percentile_99.5 <- floor(quantile(na.exclude(mydata$m_s5q10)[na.exclude(mydata$m_s5q10)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="m_s5q10", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## m_s5q10. sEq10: What was the value of the in-kind transfer? Ano ang halaga ng hindi pera
## -998 130 150 168 180 200 225 240 250 270 280 300 350 500 600 900 1000 1200 1285 1500 1636 1983 2000 2500 3000 3800 5000 5320 5340 5345 5500 5556
## 1 3 1 1 1 2 1 1 1 1 1 1 1 13 1 1 3 2 1 1 1 1 1 1 1 1 3 1 1 1 1 1
## 6000 7884 8350 10000 10500 14000 15000 16000 17970 18230 18340 20000 28000 37000 75000 <NA>
## 2 1 1 6 1 3 1 1 1 1 1 2 1 1 1 2209
## [1] "Frequency table after encoding"
## m_s5q10. sEq10: What was the value of the in-kind transfer? Ano ang halaga ng hindi pera
## -998 130 150 168 180 200 225 240 250 270 280 300 350
## 1 3 1 1 1 2 1 1 1 1 1 1 1
## 500 600 900 1000 1200 1285 1500 1636 1983 2000 2500 3000 3800
## 13 1 1 3 2 1 1 1 1 1 1 1 1
## 5000 5320 5340 5345 5500 5556 6000 7884 8350 10000 10500 14000 15000
## 3 1 1 1 1 1 2 1 1 6 1 3 1
## 16000 17970 18230 18340 20000 28000 37000 60750 or more <NA>
## 1 1 1 1 2 1 1 1 2209
percentile_99.5 <- floor(quantile(na.exclude(mydata$m_s5q12)[na.exclude(mydata$m_s5q12)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="m_s5q12", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## m_s5q12. sEq12: How much money in pesos did your household receive in the past 12 months
## 2000 3000 3300 4000 5000 6000 8000 13200 14400 15400 18000 19800 20000 21000 21600 21870 22000 23000 24000 26400 27500 28000 28800 30000 40800 52945 63600
## 1 1 1 1 1 2 2 1 3 1 1 1 2 1 1 1 2 1 1 2 1 1 2 1 1 1 1
## 120000 <NA>
## 1 2249
## [1] "Frequency table after encoding"
## m_s5q12. sEq12: How much money in pesos did your household receive in the past 12 months
## 2000 3000 3300 4000 5000 6000 8000 13200 14400 15400 18000 19800
## 1 1 1 1 1 2 2 1 3 1 1 1
## 20000 21000 21600 21870 22000 23000 24000 26400 27500 28000 28800 30000
## 2 1 1 1 2 1 1 2 1 1 2 1
## 40800 52945 63600 110130 or more <NA>
## 1 1 1 1 2249
percentile_99.5 <- floor(quantile(na.exclude(mydata$m_s5q13)[na.exclude(mydata$m_s5q13)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="m_s5q13", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## m_s5q13. sEq13: How much did your household receive in benefits each month? Magkano po a
## -998 1000 1100 1200 1500 1510 1700 1800 2000 2400 2817 3337 4800 5300 10000 <NA>
## 1 1 1 1 1 1 3 1 1 3 1 1 1 1 1 2266
## [1] "Frequency table after encoding"
## m_s5q13. sEq13: How much did your household receive in benefits each month? Magkano po a
## -998 1000 1100 1200 1500 1510 1700 1800 2000 2400 2817 3337 4800 5300
## 1 1 1 1 1 1 3 1 1 3 1 1 1 1
## 9577 or more <NA>
## 1 2266
percentile_99.5 <- floor(quantile(na.exclude(mydata$m_s5q15)[na.exclude(mydata$m_s5q15)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="m_s5q15", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## m_s5q15. sEq15: What was the value of the in-kind transfer you received? Magkano po ang
## -998 200 250 300 400 500 700 1000 2000 2555 2635 2650 2850 3000 4000 4300 4800 5000 5200 5400 5500 5600 5742 5782 5800 5900 5907 6000 6500 6700 6800 6900
## 8 1 1 2 1 3 1 1 2 1 1 1 1 1 1 2 1 18 1 1 1 1 1 1 1 1 1 15 4 1 1 1
## 6980 7000 7100 7200 7420 7500 7600 7800 7900 8000 8040 8075 8200 8273 8330 8400 8500 8920 9000 9245 9400 9500 9800 9890 9900 9935 9950 9952 9953 9954 9972 10000
## 1 21 2 1 1 3 1 3 2 28 1 1 1 1 1 1 1 1 25 1 1 7 4 1 2 1 1 5 1 1 1 540
## 11200 12000 15000 15600 20000 20800 21200 25800 35000 <NA>
## 1 1 2 1 1 1 1 1 1 1535
## [1] "Frequency table after encoding"
## m_s5q15. sEq15: What was the value of the in-kind transfer you received? Magkano po ang
## -998 200 250 300 400 500 700 1000 2000 2555 2635 2650 2850
## 8 1 1 2 1 3 1 1 2 1 1 1 1
## 3000 4000 4300 4800 5000 5200 5400 5500 5600 5742 5782 5800 5900
## 1 1 2 1 18 1 1 1 1 1 1 1 1
## 5907 6000 6500 6700 6800 6900 6980 7000 7100 7200 7420 7500 7600
## 1 15 4 1 1 1 1 21 2 1 1 3 1
## 7800 7900 8000 8040 8075 8200 8273 8330 8400 8500 8920 9000 9245
## 3 2 28 1 1 1 1 1 1 1 1 25 1
## 9400 9500 9800 9890 9900 9935 9950 9952 9953 9954 9972 10000 11200
## 1 7 4 1 2 1 1 5 1 1 1 540 1
## 12000 15000 15600 20000 20203 or more <NA>
## 1 2 1 1 4 1535
percentile_99.5 <- floor(quantile(na.exclude(mydata$m_s5q17)[na.exclude(mydata$m_s5q17)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="m_s5q17", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## m_s5q17. sEq17: How much did your household receive in benefits in the last 12 months? M
## -998 1500 2600 3000 3600 3700 4000 4600 5000 6000 7000 7500 8563 10000 12000 15000 18000 19000 25500 28000 30000 62000 <NA>
## 1 1 1 1 1 1 3 1 1 1 2 1 1 2 1 1 1 1 1 1 1 1 2259
## [1] "Frequency table after encoding"
## m_s5q17. sEq17: How much did your household receive in benefits in the last 12 months? M
## -998 1500 2600 3000 3600 3700 4000 4600 5000 6000 7000 7500 8563
## 1 1 1 1 1 1 3 1 1 1 2 1 1
## 10000 12000 15000 18000 19000 25500 28000 30000 58000 or more <NA>
## 2 1 1 1 1 1 1 1 1 2259
percentile_99.5 <- floor(quantile(na.exclude(mydata$m_s5q19)[na.exclude(mydata$m_s5q19)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="m_s5q19", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## m_s5q19. sEq21: How much did your household receive in total from the government or NGOs
## -998 0 150 180 200 270 300 350 400 500 600 900 1000 1200 1500 1636 1800 2000 2400 2500 2560 2600 2800 2970 3000 3600 3700
## 40 168 1 1 5 1 2 1 1 6 1 1 2 1 2 1 2 4 2 1 2 1 1 1 2 1 1
## 4000 4100 4300 4400 4500 4600 4800 5000 5200 5400 5500 5900 6000 6200 6280 6400 6500 6600 6700 7000 7200 7285 7400 7500 7600 7700 7800
## 3 1 1 1 2 3 2 12 1 1 1 1 13 3 1 4 2 4 1 9 12 1 1 1 9 2 8
## 8000 8200 8300 8400 8500 8563 8600 8700 8800 8900 9000 9100 9200 9400 9500 9600 9700 9800 9900 9952 10000 10175 10200 10300 10400 10500 10600
## 16 2 2 10 1 1 4 2 8 3 15 3 4 1 4 28 1 3 1 1 66 1 17 4 7 7 8
## 10700 10740 10800 10900 11000 11100 11200 11300 11400 11500 11600 11700 11800 11900 12000 12100 12130 12200 12300 12400 12500 12600 12700 12782 12800 12900 13000
## 2 1 22 3 15 1 11 1 9 2 10 1 7 2 27 2 1 3 5 9 5 8 1 1 9 4 13
## 13050 13200 13300 13400 13500 13600 13700 13730 13800 13900 14000 14100 14200 14300 14400 14450 14500 14600 14700 14800 14870 15000 15100 15168 15200 15300 15400
## 1 55 4 5 3 9 4 1 24 1 6 1 9 2 28 1 2 10 7 15 1 19 5 1 10 5 5
## 15500 15600 15700 15800 16000 16150 16200 16300 16400 16500 16520 16600 16700 16800 16900 17000 17056 17100 17200 17300 17400 17500 17600 17700 17800 17900 17980
## 3 36 2 14 7 1 15 9 9 4 1 13 2 45 3 7 1 4 12 2 13 1 8 4 18 3 1
## 18000 18100 18200 18300 18400 18500 18600 18700 18800 19000 19100 19200 19250 19300 19355 19400 19450 19500 19600 19680 19700 19800 19850 20000 20100 20200 20300
## 30 2 9 4 6 3 14 4 14 10 4 39 1 3 1 9 1 3 9 1 5 23 1 14 4 10 4
## 20400 20500 20560 20600 20700 20800 21000 21100 21200 21275 21300 21400 21500 21554 21600 21700 21800 21900 22000 22100 22200 22250 22266 22300 22345 22400 22500
## 26 4 1 15 6 20 15 2 7 1 2 7 2 1 26 5 6 2 5 2 14 1 1 5 1 3 2
## 22600 22700 22800 22900 23000 23050 23100 23152 23200 23300 23400 23500 23600 23650 23700 23800 23835 23900 24000 24073 24100 24140 24200 24220 24250 24300 24400
## 15 2 25 4 13 1 1 1 19 4 6 4 5 1 3 12 1 3 16 1 1 1 7 1 1 5 17
## 24500 24600 24700 24752 24800 24900 25000 25100 25200 25260 25300 25400 25500 25600 25700 25730 25800 26000 26200 26300 26400 26500 26530 26550 26600 26700 26750
## 4 3 1 1 6 2 8 5 13 1 4 5 2 7 2 1 14 2 9 4 4 1 1 1 5 2 1
## 26800 26900 26950 27000 27100 27200 27300 27320 27350 27400 27600 27742 27800 27900 28000 28100 28200 28300 28400 28600 28700 28800 29000 29200 29250 29300 29356
## 19 2 1 8 3 7 3 1 1 5 1 1 8 1 10 1 6 3 6 10 2 7 2 11 1 1 1
## 29400 29500 29600 29700 29800 29900 29925 30000 30050 30100 30184 30200 30300 30400 30500 30550 30600 30688 30700 30800 31000 31100 31150 31200 31215 31257 31300
## 5 1 2 1 2 4 1 2 1 2 1 1 1 12 2 1 2 1 2 8 4 1 2 1 1 1 1
## 31400 31500 31600 31800 31900 32000 32120 32200 32300 32400 32600 32800 32845 32900 33200 33300 33400 33500 33550 33600 33656 33800 33900 34000 34050 34100 34300
## 2 3 6 9 1 2 1 2 2 1 3 6 1 2 3 1 4 1 1 1 1 5 1 3 1 1 1
## 34400 34600 34725 34800 35000 35200 35300 35400 35600 35800 35900 36000 36200 36300 36500 36600 36649 36800 36900 37100 37300 37400 37500 37600 37800 38000 38135
## 1 2 1 2 2 3 2 1 3 5 2 4 1 2 1 1 1 1 1 2 1 1 1 1 2 1 1
## 38156 38200 38600 38800 39200 39400 39570 39600 39800 39898 40000 40300 40500 40600 41000 41400 41600 41753 42400 43000 43500 43600 44000 44400 44800 45000 45400
## 1 1 1 2 1 2 1 1 1 1 3 1 1 1 3 2 1 1 2 1 1 1 1 2 1 1 1
## 45800 46000 46300 46733 46800 47400 47800 48000 48200 48450 48630 48800 49200 49400 49600 50000 50700 51000 51050 51140 52100 52400 52900 53000 53410 54000 54300
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 54900 55500 56400 57000 57600 62000 65400 68245 72772 73200 76800 81600 87200 88200 104200 110400 130200 186000
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1] "Frequency table after encoding"
## m_s5q19. sEq21: How much did your household receive in total from the government or NGOs
## -998 0 150 180 200 270 300 350 400 500 600 900 1000
## 40 168 1 1 5 1 2 1 1 6 1 1 2
## 1200 1500 1636 1800 2000 2400 2500 2560 2600 2800 2970 3000 3600
## 1 2 1 2 4 2 1 2 1 1 1 2 1
## 3700 4000 4100 4300 4400 4500 4600 4800 5000 5200 5400 5500 5900
## 1 3 1 1 1 2 3 2 12 1 1 1 1
## 6000 6200 6280 6400 6500 6600 6700 7000 7200 7285 7400 7500 7600
## 13 3 1 4 2 4 1 9 12 1 1 1 9
## 7700 7800 8000 8200 8300 8400 8500 8563 8600 8700 8800 8900 9000
## 2 8 16 2 2 10 1 1 4 2 8 3 15
## 9100 9200 9400 9500 9600 9700 9800 9900 9952 10000 10175 10200 10300
## 3 4 1 4 28 1 3 1 1 66 1 17 4
## 10400 10500 10600 10700 10740 10800 10900 11000 11100 11200 11300 11400 11500
## 7 7 8 2 1 22 3 15 1 11 1 9 2
## 11600 11700 11800 11900 12000 12100 12130 12200 12300 12400 12500 12600 12700
## 10 1 7 2 27 2 1 3 5 9 5 8 1
## 12782 12800 12900 13000 13050 13200 13300 13400 13500 13600 13700 13730 13800
## 1 9 4 13 1 55 4 5 3 9 4 1 24
## 13900 14000 14100 14200 14300 14400 14450 14500 14600 14700 14800 14870 15000
## 1 6 1 9 2 28 1 2 10 7 15 1 19
## 15100 15168 15200 15300 15400 15500 15600 15700 15800 16000 16150 16200 16300
## 5 1 10 5 5 3 36 2 14 7 1 15 9
## 16400 16500 16520 16600 16700 16800 16900 17000 17056 17100 17200 17300 17400
## 9 4 1 13 2 45 3 7 1 4 12 2 13
## 17500 17600 17700 17800 17900 17980 18000 18100 18200 18300 18400 18500 18600
## 1 8 4 18 3 1 30 2 9 4 6 3 14
## 18700 18800 19000 19100 19200 19250 19300 19355 19400 19450 19500 19600 19680
## 4 14 10 4 39 1 3 1 9 1 3 9 1
## 19700 19800 19850 20000 20100 20200 20300 20400 20500 20560 20600 20700 20800
## 5 23 1 14 4 10 4 26 4 1 15 6 20
## 21000 21100 21200 21275 21300 21400 21500 21554 21600 21700 21800 21900 22000
## 15 2 7 1 2 7 2 1 26 5 6 2 5
## 22100 22200 22250 22266 22300 22345 22400 22500 22600 22700 22800 22900 23000
## 2 14 1 1 5 1 3 2 15 2 25 4 13
## 23050 23100 23152 23200 23300 23400 23500 23600 23650 23700 23800 23835 23900
## 1 1 1 19 4 6 4 5 1 3 12 1 3
## 24000 24073 24100 24140 24200 24220 24250 24300 24400 24500 24600 24700 24752
## 16 1 1 1 7 1 1 5 17 4 3 1 1
## 24800 24900 25000 25100 25200 25260 25300 25400 25500 25600 25700 25730 25800
## 6 2 8 5 13 1 4 5 2 7 2 1 14
## 26000 26200 26300 26400 26500 26530 26550 26600 26700 26750 26800 26900 26950
## 2 9 4 4 1 1 1 5 2 1 19 2 1
## 27000 27100 27200 27300 27320 27350 27400 27600 27742 27800 27900 28000 28100
## 8 3 7 3 1 1 5 1 1 8 1 10 1
## 28200 28300 28400 28600 28700 28800 29000 29200 29250 29300 29356 29400 29500
## 6 3 6 10 2 7 2 11 1 1 1 5 1
## 29600 29700 29800 29900 29925 30000 30050 30100 30184 30200 30300 30400 30500
## 2 1 2 4 1 2 1 2 1 1 1 12 2
## 30550 30600 30688 30700 30800 31000 31100 31150 31200 31215 31257 31300 31400
## 1 2 1 2 8 4 1 2 1 1 1 1 2
## 31500 31600 31800 31900 32000 32120 32200 32300 32400 32600 32800 32845 32900
## 3 6 9 1 2 1 2 2 1 3 6 1 2
## 33200 33300 33400 33500 33550 33600 33656 33800 33900 34000 34050 34100 34300
## 3 1 4 1 1 1 1 5 1 3 1 1 1
## 34400 34600 34725 34800 35000 35200 35300 35400 35600 35800 35900 36000 36200
## 1 2 1 2 2 3 2 1 3 5 2 4 1
## 36300 36500 36600 36649 36800 36900 37100 37300 37400 37500 37600 37800 38000
## 2 1 1 1 1 1 2 1 1 1 1 2 1
## 38135 38156 38200 38600 38800 39200 39400 39570 39600 39800 39898 40000 40300
## 1 1 1 1 2 1 2 1 1 1 1 3 1
## 40500 40600 41000 41400 41600 41753 42400 43000 43500 43600 44000 44400 44800
## 1 1 3 2 1 1 2 1 1 1 1 2 1
## 45000 45400 45800 46000 46300 46733 46800 47400 47800 48000 48200 48450 48630
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 48800 49200 49400 49600 50000 50700 51000 51050 51140 52100 52400 52900 53000
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 53410 54000 54300 54900 55500 56400 57000 57600 62000 63971 or more
## 1 1 1 1 1 1 1 1 1 12
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("m_s5q2",
"m_s5q3",
"m_s5q6",
"m_s5q8",
"m_s5q11",
"m_s5q14",
"m_s5q16",
"m_s5q18",
"m_s5q21")
capture_tables (indirect_PII)
# Recode those with very specific values.
# !!!No very specific valuesMatching and crosstabulations: Run automated PII check
Open-ends: review responses for any sensitive information, redact as necessary
# !!! Identify open-end variables here:
open_ends <- c("m_s5q20",
"m_s5q20_other",
"m_endnote5")
report_open (list_open_ends = open_ends)
# Review "verbatims.csv". Identify variables to be deleted or redacted and their row number
mydata$m_s5q20[122] <- "Other"
mydata$m_s5q20[425] <- "Other"
mydata$m_s5q20[654] <- "Other"
mydata$m_s5q20[755] <- "Other"
mydata$m_s5q20[791] <- "Other"
mydata$m_s5q20[847] <- "Other"
mydata$m_s5q20[1003] <- "Other"
mydata$m_s5q20[1730] <- "Other"
mydata$m_endnote5[41] <- "4Ps benefits were received by [name] and he'll be the one to distribute it to his siblings who's studying in high school. So the respondent does not have an idea about the amount."
mydata$m_endnote5[63] <- "Only one student is beneficiary of 4Ps, that's why they only received a small amount. While the items from DOLE were left to [name 1] (other DOLE benefeciary)by [name 2] when sh's gone to Manila to worked. And [name 3] is the one who gets the money of the sold items."
mydata$m_endnote5[94] <- "[name] also conducted training regarding livelihood"
mydata$m_endnote5[303] <- "[Tagalog]"
mydata$m_endnote5[380] <- "[Tagalog]"
mydata$m_endnote5[854] <- "[Tagalog]"
mydata$m_endnote5[1075] <- "[Tagalog]"
mydata$m_endnote5[1764] <- "[Tagalog]"
mydata$m_endnote5[126] <- "The responses pertaining to 4Ps benefits were answered by respondent's wife, [name]"
mydata$m_endnote5[165] <- "The ATM is in his daughter [name] living in [small location]. He dont know how much they received in 4Ps."
mydata$m_endnote5[185] <- "Respondent is not a 4Ps member but her 3 step children ([name], [name] and [name]) received a cash benefits from the 4Ps because they were covered from their aunt who was a member of 4Ps. She dont know how much did they received because of some family conflict between her and the aunt."
mydata$m_endnote5[214] <- "Beforethe respondent's father-in-law died, [name], got hospitalized. And the bill amounting to 4000 had been paid by his Senior Citizen membership axcording to the respondent. While the respondent had attended 4 times to a DoLE meeting and until now she's waiting for the 10000 worth of grocery items."
mydata$m_endnote5[330] <- "He is 4ps beneficiary but he did not get the pay out for the past months because he said he is far from Brgy [small location]"
mydata$m_endnote5[352] <- "In this section their family recieved benefits from 4P's, SLP she used to buy 2 piglets and groceries or relief goods from Local Government Unit of [small location], from Brgy. [small location] and UNICEF."
mydata$m_endnote5[1035] <- "Street vendor [language]"
mydata$m_endnote5[1067] <- "They received a grocery last Decmber 2016 from the [small location] local government."
mydata$m_endnote5[1075] <- "[name] receive educ asst at [small location] municipality"
mydata$m_endnote5[1124] <- "[name] from 4Ps, household also received a 4pcs. Of plywood from [small location] local gov't. But respondent does not know its amount."
mydata$m_endnote5[1220] <- "Household received 10000(in kind) from DOLE, 9800 from 4Ps and another 15000 from [small location] government as educational assistance for [name] and [name], and another 5000 from [small location] brgy. Government. In total, they received a 39800 of benefits for the last 12 months."
mydata$m_endnote5[1382] <- "[name] from 4Ps, [name] also receiving 5000 as an educational assistance for her study."
mydata$m_endnote5[1468] <- "Up [small location] have more help in the household than dept of agriculture."
mydata$m_endnote5[1477] <- "[small location]"
mydata$m_endnote5[1479] <- "DOLE conducted seminar on how to manage business which held at the municipal of [small location]"
mydata$m_endnote5[1514] <- "DOLE conducted seminars on how to manage a sari sari store held at Municpality of [small location]. They claimed lum sum in SSS and the monthly pension will start on 2018, monthly pension is 3,337"
mydata$m_endnote5[1727] <- "The respondents of barangay [small location] received a motorboat amounted to 35,000 from DOLE.the motorboat leader has a policy for the members,.. One person for one week."
mydata$m_endnote5[1764] <- "[language]"
mydata$m_endnote5[2239] <- "4Ps Scholarship [name]"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)