DOL-ILAB SDC - Section_9
Pablo Diego-Rosell, PhD
2021-10-15
- 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_9" # !!!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.
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q2)[na.exclude(mydata$eh_s9q2)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q2", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q2. How many units of agricultural land, including garden plots does your household
## -998 1 2 3 4 5 6 7 8 9 10 12 13 15 16 17 18 20 25 30 32 34 35 36 40 45 50 60
## 11 89 29 18 5 33 8 5 3 2 17 1 1 9 4 1 2 13 5 8 1 1 1 3 3 1 11 1
## 70 75 80 90 100 110 125 135 150 162 200 230 250 283 300 400 450 500 576 600 700 800 900 992 1000 1200 1250 2000
## 2 1 3 2 11 1 1 1 4 1 5 1 6 1 5 4 1 11 1 1 1 2 2 1 3 1 1 4
## 2072 2233 2500 2900 3024 3300 4000 4730 5000 5681 6000 7500 8000 10000 10500 12500 15000 16000 18000 22000 25000 <NA>
## 1 1 17 1 1 2 1 1 23 1 2 1 1 4 1 1 2 1 1 1 1 1859
## [1] "Frequency table after encoding"
## eh_s9q2. How many units of agricultural land, including garden plots does your household
## -998 1 2 3 4 5 6 7 8 9 10 12
## 11 89 29 18 5 33 8 5 3 2 17 1
## 13 15 16 17 18 20 25 30 32 34 35 36
## 1 9 4 1 2 13 5 8 1 1 1 3
## 40 45 50 60 70 75 80 90 100 110 125 135
## 3 1 11 1 2 1 3 2 11 1 1 1
## 150 162 200 230 250 283 300 400 450 500 576 600
## 4 1 5 1 6 1 5 4 1 11 1 1
## 700 800 900 992 1000 1200 1250 2000 2072 2233 2500 2900
## 1 2 2 1 3 1 1 4 1 1 17 1
## 3024 3300 4000 4730 5000 5681 6000 7500 8000 10000 10500 12500
## 1 2 1 1 23 1 2 1 1 4 1 1
## 15000 16000 17720 or more <NA>
## 2 1 3 1859
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q7)[na.exclude(mydata$eh_s9q7)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q7", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q7. Q347: House Bahay
## 0 1 2 3 4 5000
## 189 2028 66 3 1 1
## [1] "Frequency table after encoding"
## eh_s9q7. Q347: House Bahay
## 0 1 2 or more
## 189 2028 71
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q8)[na.exclude(mydata$eh_s9q8)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q8", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q8. Large or small livestock such as pigs, goats, carabao, cows, etc. Malaki o mali
## 0 1 2 3 4 5 6 7 8 9 10 11 12 14 16 22 29 30
## 1699 236 154 84 31 31 11 10 8 7 3 2 2 2 2 2 1 3
## [1] "Frequency table after encoding"
## eh_s9q8. Large or small livestock such as pigs, goats, carabao, cows, etc. Malaki o mali
## 0 1 2 3 4 5 6 7 8 9 10 11 or more
## 1699 236 154 84 31 31 11 10 8 7 3 14
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q9)[na.exclude(mydata$eh_s9q9)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q9", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q9. Birds, poultry, roosters, fighting cocks, ducks. Mga Ibon, manok, tandang, itik
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 32 33
## 1005 90 121 91 94 72 66 58 57 40 109 31 30 29 19 62 16 9 20 11 67 12 6 10 6 17 9 3 6 2 28 8 1
## 34 35 36 37 38 39 40 42 43 45 48 50 55 57 60 63 65 68 73 80 95 100 120 140 200
## 4 6 7 3 1 1 12 5 4 3 1 14 4 1 4 1 1 1 2 1 1 3 1 1 1
## [1] "Frequency table after encoding"
## eh_s9q9. Birds, poultry, roosters, fighting cocks, ducks. Mga Ibon, manok, tandang, itik
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
## 1005 90 121 91 94 72 66 58 57 40 109 31 30 29 19
## 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
## 62 16 9 20 11 67 12 6 10 6 17 9 3 6 2
## 30 32 33 34 35 36 37 38 39 40 42 43 45 48 50
## 28 8 1 4 6 7 3 1 1 12 5 4 3 1 14
## 55 57 60 63 64 or more
## 4 1 4 1 12
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q10)[na.exclude(mydata$eh_s9q10)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q10", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q10. Boats Bangka
## 0 1 2 3
## 2198 83 6 1
## [1] "Frequency table after encoding"
## eh_s9q10. Boats Bangka
## 0 1 or more
## 2198 90
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q12)[na.exclude(mydata$eh_s9q12)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q12", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q12. Q349: Cell Phone Cellphone
## 0 1 2 3 4 5 6 7 8 9 16
## 121 591 717 468 242 89 32 14 8 5 1
## [1] "Frequency table after encoding"
## eh_s9q12. Q349: Cell Phone Cellphone
## 0 1 2 3 4 5 6 7 8 or more
## 121 591 717 468 242 89 32 14 14
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q13)[na.exclude(mydata$eh_s9q13)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q13", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q13. Q350: Sofa Sofa
## 0 1 2 3 4 5 6
## 1648 493 81 51 9 4 2
## [1] "Frequency table after encoding"
## eh_s9q13. Q350: Sofa Sofa
## 0 1 2 3 4 or more
## 1648 493 81 51 15
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q14)[na.exclude(mydata$eh_s9q14)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q14", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q14. Q351: Chairs Mga silya
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 20 26
## 470 271 351 326 320 157 194 66 65 20 19 6 12 3 4 2 1 1
## [1] "Frequency table after encoding"
## eh_s9q14. Q351: Chairs Mga silya
## 0 1 2 3 4 5 6 7 8 9 10 11 12 or more
## 470 271 351 326 320 157 194 66 65 20 19 6 23
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q15)[na.exclude(mydata$eh_s9q15)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q15", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q15. Q352: Table Lamesa
## 0 1 2 3 4 5 6
## 201 1383 508 147 37 10 2
## [1] "Frequency table after encoding"
## eh_s9q15. Q352: Table Lamesa
## 0 1 2 3 4 or more
## 201 1383 508 147 49
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q16)[na.exclude(mydata$eh_s9q16)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q16", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q16. Q353: Clock/Watch Relo
## 0 1 2 3 4 5 6 7 8 9 10 12
## 865 886 308 123 61 26 9 5 2 1 1 1
## [1] "Frequency table after encoding"
## eh_s9q16. Q353: Clock/Watch Relo
## 0 1 2 3 4 5 6 or more
## 865 886 308 123 61 26 19
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q17)[na.exclude(mydata$eh_s9q17)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q17", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q17. Other jewelry. Mga alahas
## -998 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 20 26 30
## 1 1477 211 183 160 86 54 46 20 10 16 11 3 2 3 2 1 1 1
## [1] "Frequency table after encoding"
## eh_s9q17. Other jewelry. Mga alahas
## -998 0 1 2 3 4 5 6 7 8 9 10 11 or more
## 1 1477 211 183 160 86 54 46 20 10 16 11 13
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q18)[na.exclude(mydata$eh_s9q18)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q18", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q18. Q354: Bicycle Bisekleta
## 0 1 2 3 4 5 6 7
## 1695 493 77 17 3 1 1 1
## [1] "Frequency table after encoding"
## eh_s9q18. Q354: Bicycle Bisekleta
## 0 1 2 3 or more
## 1695 493 77 23
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q19)[na.exclude(mydata$eh_s9q19)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q19", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q19. Q355: Tricycle Tricycle
## 0 1 2 3 5
## 2034 244 6 2 2
## [1] "Frequency table after encoding"
## eh_s9q19. Q355: Tricycle Tricycle
## 0 1 or more
## 2034 254
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q20)[na.exclude(mydata$eh_s9q20)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q20", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q20. Q356: Motorbike Motor
## 0 1 2 3 4 7
## 1636 584 58 6 3 1
## [1] "Frequency table after encoding"
## eh_s9q20. Q356: Motorbike Motor
## 0 1 2 or more
## 1636 584 68
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q21)[na.exclude(mydata$eh_s9q21)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q21", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q21. Q357: Motorized Boat/Banca Bangkang de-makina/ bangka
## 0 1 2
## 2164 115 9
## [1] "Frequency table after encoding"
## eh_s9q21. Q357: Motorized Boat/Banca Bangkang de-makina/ bangka
## 0 1 or more
## 2164 124
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q22)[na.exclude(mydata$eh_s9q22)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q22", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q22. Q358: Other Motorized Vehicle Iba pang sasakyang de-motor
## 0 1
## 2269 19
## [1] "Frequency table after encoding"
## eh_s9q22. Q358: Other Motorized Vehicle Iba pang sasakyang de-motor
## 0 1 or more
## 2269 19
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q23)[na.exclude(mydata$eh_s9q23)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q23", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q23. Q359: Radio, Tape, or CD Player Radyo, Tape o CD Player
## 0 1 2 3 5
## 1297 944 42 3 2
## [1] "Frequency table after encoding"
## eh_s9q23. Q359: Radio, Tape, or CD Player Radyo, Tape o CD Player
## 0 1 2 or more
## 1297 944 47
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q24)[na.exclude(mydata$eh_s9q24)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q24", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q24. Q360: Beds Mga kama
## 0 1 2 3 4 5 6
## 1115 656 373 125 16 1 2
## [1] "Frequency table after encoding"
## eh_s9q24. Q360: Beds Mga kama
## 0 1 2 3 4 or more
## 1115 656 373 125 19
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q25)[na.exclude(mydata$eh_s9q25)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q25", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q25. Q361: Mattresses Mga kutson ng kama
## 0 1 2 3 4 5 6
## 1057 731 370 100 26 3 1
## [1] "Frequency table after encoding"
## eh_s9q25. Q361: Mattresses Mga kutson ng kama
## 0 1 2 3 4 or more
## 1057 731 370 100 30
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q26)[na.exclude(mydata$eh_s9q26)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q26", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q26. Q362: Solar Panel Solar Panel
## 0 1 3
## 2243 44 1
## [1] "Frequency table after encoding"
## eh_s9q26. Q362: Solar Panel Solar Panel
## 0 1 or more
## 2243 45
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q28)[na.exclude(mydata$eh_s9q28)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q28", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q28. Q364: Television TV
## 0 1 2 3 4 20
## 494 1683 104 5 1 1
## [1] "Frequency table after encoding"
## eh_s9q28. Q364: Television TV
## 0 1 2 or more
## 494 1683 111
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q29)[na.exclude(mydata$eh_s9q29)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q29", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q29. Q365: VCR/DVD VCR/DVD
## 0 1 2 3
## 1613 648 26 1
## [1] "Frequency table after encoding"
## eh_s9q29. Q365: VCR/DVD VCR/DVD
## 0 1 2 or more
## 1613 648 27
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q30)[na.exclude(mydata$eh_s9q30)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q30", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q30. Q366: Computer Computer
## 0 1 2 3 4 7
## 2208 71 6 1 1 1
## [1] "Frequency table after encoding"
## eh_s9q30. Q366: Computer Computer
## 0 1 or more
## 2208 80
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q31)[na.exclude(mydata$eh_s9q31)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q31", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q31. Q368: Wheelbarrow Kareta
## 0 1
## 2266 22
## [1] "Frequency table after encoding"
## eh_s9q31. Q368: Wheelbarrow Kareta
## 0 1 or more
## 2266 22
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q32)[na.exclude(mydata$eh_s9q32)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q32", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q32. Q369: Cart Kariton
## 0 1 2
## 2193 92 3
## [1] "Frequency table after encoding"
## eh_s9q32. Q369: Cart Kariton
## 0 1 or more
## 2193 95
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q33)[na.exclude(mydata$eh_s9q33)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q33", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q33. Q370: Kerosene or propane stove Kerosene o propane stove
## 0 1 2 3 6 11
## 1592 680 12 2 1 1
## [1] "Frequency table after encoding"
## eh_s9q33. Q370: Kerosene or propane stove Kerosene o propane stove
## 0 1 2 or more
## 1592 680 16
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q34)[na.exclude(mydata$eh_s9q34)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q34", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q34. Q371: Stove with Oven/Gas Range Stove na may oven/gas range
## 0 1 2 3
## 1896 382 7 3
## [1] "Frequency table after encoding"
## eh_s9q34. Q371: Stove with Oven/Gas Range Stove na may oven/gas range
## 0 1 or more
## 1896 392
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q35)[na.exclude(mydata$eh_s9q35)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q35", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q35. Q372: Refrigerator Refrigerator
## 0 1 2
## 1861 415 12
## [1] "Frequency table after encoding"
## eh_s9q35. Q372: Refrigerator Refrigerator
## 0 1 or more
## 1861 427
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q36)[na.exclude(mydata$eh_s9q36)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q36", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q36. Q373: Clothes Washing Machine Washing Machine
## 0 1 2 3
## 1594 680 13 1
## [1] "Frequency table after encoding"
## eh_s9q36. Q373: Clothes Washing Machine Washing Machine
## 0 1 2 or more
## 1594 680 14
mydata <- top_recode (variable="eh_s9q37", break_point=1, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q37. Q374: Air Conditioner Air Con
## 0 1 2
## 2276 11 1
## [1] "Frequency table after encoding"
## eh_s9q37. Q374: Air Conditioner Air Con
## 0 1 or more
## 2276 12
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q38)[na.exclude(mydata$eh_s9q38)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q38", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q38. Q375: Electric Fan Electric Fan
## 0 1 2 3 4 5 6
## 539 1030 538 135 38 6 2
## [1] "Frequency table after encoding"
## eh_s9q38. Q375: Electric Fan Electric Fan
## 0 1 2 3 4 or more
## 539 1030 538 135 46
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q39)[na.exclude(mydata$eh_s9q39)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q39", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q39. Q377: Pedicab Pedicab
## 0 1 2
## 2191 91 6
## [1] "Frequency table after encoding"
## eh_s9q39. Q377: Pedicab Pedicab
## 0 1 or more
## 2191 97
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q40)[na.exclude(mydata$eh_s9q40)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q40", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q40. Q378: Rice Stocks [Un-milled dry rice] Palay
## 0 1 2 3 4 5 6 7 8 9 10 12 13 14 15 17 18 20 22 23 28 30 32 34 50 90 280
## 2023 51 34 29 15 28 13 9 12 11 22 5 4 4 10 1 3 3 1 2 1 2 1 1 1 1 1
## [1] "Frequency table after encoding"
## eh_s9q40. Q378: Rice Stocks [Un-milled dry rice] Palay
## 0 1 2 3 4 5 6 7 8 9 10 12 13 14 15
## 2023 51 34 29 15 28 13 9 12 11 22 5 4 4 10
## 17 18 20 or more
## 1 3 14
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q41)[na.exclude(mydata$eh_s9q41)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q41", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q41. Q381: House Bahay
## -999 -998 0 1 5 10 20 25 40 50 60 80 150 200 300 500 700 800 1000 1800 2000
## 1 22 2 2 1 1 2 1 1 3 1 1 1 2 1 5 1 1 16 1 22
## 2500 3000 4000 5000 6000 6500 7000 8000 9000 10000 11000 11008 12000 13008 13500 14000 15000 17000 20000 22000 25000
## 2 22 9 89 14 1 20 10 3 195 4 1 8 1 1 1 93 1 183 1 46
## 26000 27000 28000 30000 31500 35000 38000 40000 42000 45000 50000 53500 55000 56500 60000 63000 64500 65000 66000 70000 75000
## 2 2 1 193 1 23 1 65 1 6 299 1 3 1 52 1 1 3 1 55 7
## 80000 82000 85000 90000 95000 1e+05 102000 105000 110000 120000 130000 140000 150000 160000 170000 180000 2e+05 205000 207000 210000 225000
## 48 1 2 13 1 201 1 1 2 9 9 1 77 1 2 2 70 1 1 1 1
## 250000 255000 270000 290000 3e+05 320000 350000 385000 4e+05 450000 5e+05 550000 6e+05 8e+05 1e+06 1300000 1500000 1650000 2e+06 2500000 5e+06
## 14 1 1 1 50 1 7 1 12 1 25 2 3 4 10 1 1 1 3 1 1
## 4.5e+07 <NA>
## 1 189
## [1] "Frequency table after encoding"
## eh_s9q41. Q381: House Bahay
## -999 -998 0 1 5 10 20 25 40 50 60 80
## 1 22 2 2 1 1 2 1 1 3 1 1
## 150 200 300 500 700 800 1000 1800 2000 2500 3000 4000
## 1 2 1 5 1 1 16 1 22 2 22 9
## 5000 6000 6500 7000 8000 9000 10000 11000 11008 12000 13008 13500
## 89 14 1 20 10 3 195 4 1 8 1 1
## 14000 15000 17000 20000 22000 25000 26000 27000 28000 30000 31500 35000
## 1 93 1 183 1 46 2 2 1 193 1 23
## 38000 40000 42000 45000 50000 53500 55000 56500 60000 63000 64500 65000
## 1 65 1 6 299 1 3 1 52 1 1 3
## 66000 70000 75000 80000 82000 85000 90000 95000 1e+05 102000 105000 110000
## 1 55 7 48 1 2 13 1 201 1 1 2
## 120000 130000 140000 150000 160000 170000 180000 2e+05 205000 207000 210000 225000
## 9 9 1 77 1 2 2 70 1 1 1 1
## 250000 255000 270000 290000 3e+05 320000 350000 385000 4e+05 450000 5e+05 550000
## 14 1 1 1 50 1 7 1 12 1 25 2
## 6e+05 8e+05 1e+06 or more <NA>
## 3 4 19 189
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q42)[na.exclude(mydata$eh_s9q42)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q42", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q42. Other landholdings including agricultural land or garden plots.
## -999 -998 0 1 100 126 300 500 900 1000 1500 2000 3000 4000 5000 6000 7000 7500
## 1 14 5 1 1 1 1 8 1 10 3 11 7 2 42 2 2 1
## 8000 10000 11400 15000 17500 18000 19500 20000 25000 30000 35000 36000 40000 45000 46000 47500 48000 50000
## 2 20 1 13 1 1 1 26 9 19 1 1 8 2 1 1 1 39
## 52000 60000 70000 75000 80000 87500 90000 1e+05 110000 115000 120000 130000 150000 180000 2e+05 250000 267600 3e+05
## 1 7 3 3 2 1 1 38 1 1 3 1 15 4 17 5 1 13
## 350000 380000 4e+05 5e+05 6e+05 7e+05 8e+05 1e+06 1300000 1500000 1800000 2e+06 3e+06 3300000 3700000 10050000 1.5e+07 1.6e+07
## 3 1 5 16 1 1 1 4 1 3 1 1 4 1 1 1 1 1
## 2e+07 3e+07 1e+08 3.75e+08 <NA>
## 1 2 1 1 1859
## [1] "Frequency table after encoding"
## eh_s9q42. Other landholdings including agricultural land or garden plots.
## -999 -998 0 1 100 126 300 500 900 1000 1500 2000
## 1 14 5 1 1 1 1 8 1 10 3 11
## 3000 4000 5000 6000 7000 7500 8000 10000 11400 15000 17500 18000
## 7 2 42 2 2 1 2 20 1 13 1 1
## 19500 20000 25000 30000 35000 36000 40000 45000 46000 47500 48000 50000
## 1 26 9 19 1 1 8 2 1 1 1 39
## 52000 60000 70000 75000 80000 87500 90000 1e+05 110000 115000 120000 130000
## 1 7 3 3 2 1 1 38 1 1 3 1
## 150000 180000 2e+05 250000 267600 3e+05 350000 380000 4e+05 5e+05 6e+05 7e+05
## 15 4 17 5 1 13 3 1 5 16 1 1
## 8e+05 1e+06 1300000 1500000 1800000 2e+06 3e+06 3300000 3700000 10050000 1.5e+07 1.6e+07
## 1 4 1 3 1 1 4 1 1 1 1 1
## 2e+07 3e+07 or more <NA>
## 1 4 1859
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q43)[na.exclude(mydata$eh_s9q43)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q43", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q43. Agricultural farm tools and equipment.
## 0 10 20 50 60 70 100 120 140 150 180 200 250 270 280 300 320 335 340 350 400 450 470 500
## 12 1 1 7 1 1 13 1 1 14 3 19 17 1 1 25 1 1 1 5 7 2 2 33
## 550 600 650 680 700 750 800 900 1000 1150 1200 1250 1300 1350 1500 1800 2000 2100 2200 2250 2300 2350 2400 2500
## 1 8 1 1 9 3 7 3 42 2 3 1 3 1 26 4 29 1 1 1 2 1 1 16
## 2550 2600 3000 3100 3300 3500 3600 3700 4000 4200 4500 5000 5100 5400 6000 6100 6180 6500 7000 8000 9000 10000 10500 10560
## 1 1 20 1 1 6 2 1 7 1 5 25 1 1 6 1 1 1 8 6 1 17 1 1
## 10700 11000 12000 14000 14100 15000 15500 16000 18150 20000 22000 24000 25000 26000 27000 28000 30000 35000 38000 41000 45000 50000 52350 55000
## 1 1 2 1 1 7 2 1 1 7 1 1 1 2 1 2 7 2 1 1 1 4 1 1
## 57000 62500 75000 80000 1e+05 150000 3e+05 <NA>
## 1 1 1 2 4 1 1 1774
## [1] "Frequency table after encoding"
## eh_s9q43. Agricultural farm tools and equipment.
## 0 10 20 50 60 70 100 120 140 150 180 200
## 12 1 1 7 1 1 13 1 1 14 3 19
## 250 270 280 300 320 335 340 350 400 450 470 500
## 17 1 1 25 1 1 1 5 7 2 2 33
## 550 600 650 680 700 750 800 900 1000 1150 1200 1250
## 1 8 1 1 9 3 7 3 42 2 3 1
## 1300 1350 1500 1800 2000 2100 2200 2250 2300 2350 2400 2500
## 3 1 26 4 29 1 1 1 2 1 1 16
## 2550 2600 3000 3100 3300 3500 3600 3700 4000 4200 4500 5000
## 1 1 20 1 1 6 2 1 7 1 5 25
## 5100 5400 6000 6100 6180 6500 7000 8000 9000 10000 10500 10560
## 1 1 6 1 1 1 8 6 1 17 1 1
## 10700 11000 12000 14000 14100 15000 15500 16000 18150 20000 22000 24000
## 1 1 2 1 1 7 2 1 1 7 1 1
## 25000 26000 27000 28000 30000 35000 38000 41000 45000 50000 52350 55000
## 1 2 1 2 7 2 1 1 1 4 1 1
## 57000 62500 75000 80000 1e+05 or more <NA>
## 1 1 1 2 6 1774
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q44)[na.exclude(mydata$eh_s9q44)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q44", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q44. Large or small livestock. Malaki o maliit na hayupan
## -998 0 100 300 350 600 1000 1200 1500 2000 2200 2300 2500 2800 3000 3500 4000 4200 4500 4600 4800 5000 5700 6000
## 2 1 1 2 1 2 5 3 8 14 1 1 5 1 19 5 14 1 6 1 1 21 1 20
## 6400 6600 6800 7000 7150 7500 8000 9000 9450 9600 9900 10000 10500 11000 11200 11500 11700 12000 12500 13000 14000 15000 15500 16000
## 1 1 1 18 1 2 15 11 1 1 1 29 1 2 1 2 1 13 1 6 5 31 2 6
## 17000 17500 18000 19000 20000 21000 21500 22000 23000 23500 24000 24500 25000 25200 26000 26400 27000 27500 28700 29000 30000 31000 32000 32400
## 1 2 11 2 41 1 1 2 3 1 3 1 29 1 2 1 2 1 1 1 31 1 4 1
## 32500 33000 34000 35000 36000 37000 38000 39500 40000 40500 41000 42000 42500 43000 43500 45000 46000 47000 48000 49000 50000 50500 53000 54000
## 1 5 3 11 3 1 7 1 11 1 2 1 1 2 1 8 2 3 3 1 10 1 1 1
## 55000 56000 57000 57500 58000 58500 59000 60000 62000 63000 64000 67000 70000 72500 74000 75000 77000 80000 81000 83000 85000 87000 90000 1e+05
## 4 3 1 1 1 1 1 14 1 1 1 2 2 1 1 3 1 2 1 1 1 1 6 4
## 104000 107500 111000 114000 114800 116000 120000 126000 130000 150000 157000 162000 180000 2e+05 220000 256000 <NA>
## 1 1 1 1 1 2 1 1 1 2 1 1 1 3 1 1 1699
## [1] "Frequency table after encoding"
## eh_s9q44. Large or small livestock. Malaki o maliit na hayupan
## -998 0 100 300 350 600 1000 1200 1500 2000 2200 2300
## 2 1 1 2 1 2 5 3 8 14 1 1
## 2500 2800 3000 3500 4000 4200 4500 4600 4800 5000 5700 6000
## 5 1 19 5 14 1 6 1 1 21 1 20
## 6400 6600 6800 7000 7150 7500 8000 9000 9450 9600 9900 10000
## 1 1 1 18 1 2 15 11 1 1 1 29
## 10500 11000 11200 11500 11700 12000 12500 13000 14000 15000 15500 16000
## 1 2 1 2 1 13 1 6 5 31 2 6
## 17000 17500 18000 19000 20000 21000 21500 22000 23000 23500 24000 24500
## 1 2 11 2 41 1 1 2 3 1 3 1
## 25000 25200 26000 26400 27000 27500 28700 29000 30000 31000 32000 32400
## 29 1 2 1 2 1 1 1 31 1 4 1
## 32500 33000 34000 35000 36000 37000 38000 39500 40000 40500 41000 42000
## 1 5 3 11 3 1 7 1 11 1 2 1
## 42500 43000 43500 45000 46000 47000 48000 49000 50000 50500 53000 54000
## 1 2 1 8 2 3 3 1 10 1 1 1
## 55000 56000 57000 57500 58000 58500 59000 60000 62000 63000 64000 67000
## 4 3 1 1 1 1 1 14 1 1 1 2
## 70000 72500 74000 75000 77000 80000 81000 83000 85000 87000 90000 1e+05
## 2 1 1 3 1 2 1 1 1 1 6 4
## 104000 107500 111000 114000 114800 116000 120000 126000 130000 150000 157000 162000
## 1 1 1 1 1 2 1 1 1 2 1 1
## 180000 2e+05 or more <NA>
## 1 5 1699
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q45)[na.exclude(mydata$eh_s9q45)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q45", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q45. Birds, poultry, roosters, ducks. Mga Ibon, manok, tandang, itik
## -998 0 10 20 25 32 50 60 65 70 75 80 90 100 120 125 150 180 190 200 210 240 250 260 280 286 300 320
## 6 10 1 2 1 1 7 1 1 1 1 2 2 26 1 1 28 2 1 47 1 3 10 1 2 1 57 1
## 350 360 390 400 405 410 420 430 440 450 480 490 500 510 515 540 550 600 620 630 650 675 700 720 725 750 755 770
## 4 2 3 27 1 1 3 1 1 27 2 2 96 1 1 1 3 56 1 1 1 1 26 5 1 9 1 1
## 780 800 820 840 850 860 870 875 890 900 950 960 975 1000 1040 1050 1080 1100 1110 1180 1200 1225 1250 1280 1300 1320 1330 1350
## 1 31 1 1 4 1 2 1 1 16 1 1 1 80 1 5 3 5 1 1 17 1 2 1 9 1 1 4
## 1360 1375 1400 1440 1450 1475 1500 1530 1550 1560 1570 1600 1650 1680 1700 1720 1740 1750 1760 1800 1825 1850 1875 1900 1920 1925 1930 1950
## 1 2 12 3 1 1 65 1 1 1 1 6 1 2 4 1 1 5 1 13 1 1 1 2 1 1 1 2
## 2000 2020 2050 2100 2150 2180 2200 2225 2250 2300 2320 2370 2400 2450 2500 2525 2550 2580 2600 2625 2800 2880 2900 2920 3000 3050 3100 3115
## 80 1 1 5 1 1 2 1 8 4 1 1 6 1 27 1 1 1 1 1 2 1 2 1 69 1 1 1
## 3125 3150 3200 3250 3300 3375 3400 3500 3550 3600 3700 3740 3750 3800 3840 4000 4090 4125 4150 4200 4300 4350 4400 4440 4450 4500 4600 4700
## 1 4 3 1 4 1 2 6 1 2 2 1 3 1 1 16 1 1 2 1 1 2 1 1 1 8 2 3
## 4800 5000 5080 5200 5250 5300 5400 5420 5500 5730 6000 6250 6500 6550 6750 6800 7000 7500 7600 8000 8100 8600 9000 9500 10000 10500 11000 11650
## 2 32 1 1 1 1 1 1 7 1 16 1 2 1 1 1 5 3 1 5 1 1 3 1 24 1 2 1
## 12000 13000 14000 15000 15500 16000 16050 16900 18000 19000 20000 21000 25000 29500 30000 35000 37500 40000 50000 57500 60000 75000 85000 1e+05 <NA>
## 5 2 3 7 1 1 1 1 1 1 3 1 1 1 2 2 1 4 2 1 1 1 1 1 1005
## [1] "Frequency table after encoding"
## eh_s9q45. Birds, poultry, roosters, ducks. Mga Ibon, manok, tandang, itik
## -998 0 10 20 25 32 50 60 65 70 75 80
## 6 10 1 2 1 1 7 1 1 1 1 2
## 90 100 120 125 150 180 190 200 210 240 250 260
## 2 26 1 1 28 2 1 47 1 3 10 1
## 280 286 300 320 350 360 390 400 405 410 420 430
## 2 1 57 1 4 2 3 27 1 1 3 1
## 440 450 480 490 500 510 515 540 550 600 620 630
## 1 27 2 2 96 1 1 1 3 56 1 1
## 650 675 700 720 725 750 755 770 780 800 820 840
## 1 1 26 5 1 9 1 1 1 31 1 1
## 850 860 870 875 890 900 950 960 975 1000 1040 1050
## 4 1 2 1 1 16 1 1 1 80 1 5
## 1080 1100 1110 1180 1200 1225 1250 1280 1300 1320 1330 1350
## 3 5 1 1 17 1 2 1 9 1 1 4
## 1360 1375 1400 1440 1450 1475 1500 1530 1550 1560 1570 1600
## 1 2 12 3 1 1 65 1 1 1 1 6
## 1650 1680 1700 1720 1740 1750 1760 1800 1825 1850 1875 1900
## 1 2 4 1 1 5 1 13 1 1 1 2
## 1920 1925 1930 1950 2000 2020 2050 2100 2150 2180 2200 2225
## 1 1 1 2 80 1 1 5 1 1 2 1
## 2250 2300 2320 2370 2400 2450 2500 2525 2550 2580 2600 2625
## 8 4 1 1 6 1 27 1 1 1 1 1
## 2800 2880 2900 2920 3000 3050 3100 3115 3125 3150 3200 3250
## 2 1 2 1 69 1 1 1 1 4 3 1
## 3300 3375 3400 3500 3550 3600 3700 3740 3750 3800 3840 4000
## 4 1 2 6 1 2 2 1 3 1 1 16
## 4090 4125 4150 4200 4300 4350 4400 4440 4450 4500 4600 4700
## 1 1 2 1 1 2 1 1 1 8 2 3
## 4800 5000 5080 5200 5250 5300 5400 5420 5500 5730 6000 6250
## 2 32 1 1 1 1 1 1 7 1 16 1
## 6500 6550 6750 6800 7000 7500 7600 8000 8100 8600 9000 9500
## 2 1 1 1 5 3 1 5 1 1 3 1
## 10000 10500 11000 11650 12000 13000 14000 15000 15500 16000 16050 16900
## 24 1 2 1 5 2 3 7 1 1 1 1
## 18000 19000 20000 21000 25000 29500 30000 35000 37500 40000 45899 or more <NA>
## 1 1 3 1 1 1 2 2 1 4 7 1005
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q46)[na.exclude(mydata$eh_s9q46)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q46", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q46. Boats Bangka
## 450 500 700 800 1000 1500 2000 2500 3000 3500 4000 4500 5000 6000 7000 7500 8000 10000 12000 14000 15000 18000 20000 25000 30000 40000 43000 50000
## 1 2 1 1 5 1 5 1 8 1 4 1 12 4 8 1 4 8 1 1 6 1 4 2 1 1 1 3
## 51000 <NA>
## 1 2198
## [1] "Frequency table after encoding"
## eh_s9q46. Boats Bangka
## 450 500 700 800 1000 1500 2000 2500 3000 3500 4000 4500
## 1 2 1 1 5 1 5 1 8 1 4 1
## 5000 6000 7000 7500 8000 10000 12000 14000 15000 18000 20000 25000
## 12 4 8 1 4 8 1 1 6 1 4 2
## 30000 40000 43000 50000 50554 or more <NA>
## 1 1 1 3 1 2198
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q47)[na.exclude(mydata$eh_s9q47)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q47", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q47. Nets and other fishing equipment. Lambat at ibang kagamitan sa pangingisda
## -998 0 10 20 30 50 75 100 150 200 250 300 350 380 400 450 500 600 620 650 680 700 800 850
## 1 5 1 2 2 6 2 13 1 10 1 10 2 1 1 1 24 1 1 1 2 2 6 1
## 1000 1030 1200 1500 1520 1600 1700 1800 1900 1990 2000 2100 2300 2500 2700 2800 3000 3200 3500 3700 4000 4200 4500 5000
## 24 1 4 25 1 3 4 1 1 1 21 1 1 6 1 1 24 1 7 1 12 1 1 21
## 5250 5500 6000 7000 8000 8700 9500 10000 11000 11800 12000 13000 14000 15000 16000 18000 20000 25000 26000 30000 40000 50000 60000 72000
## 1 1 8 4 4 1 1 29 2 1 3 1 1 13 2 1 10 5 1 4 2 3 1 1
## 1e+05 150000 2e+05 <NA>
## 2 1 1 1923
## [1] "Frequency table after encoding"
## eh_s9q47. Nets and other fishing equipment. Lambat at ibang kagamitan sa pangingisda
## -998 0 10 20 30 50 75 100 150 200 250
## 1 5 1 2 2 6 2 13 1 10 1
## 300 350 380 400 450 500 600 620 650 680 700
## 10 2 1 1 1 24 1 1 1 2 2
## 800 850 1000 1030 1200 1500 1520 1600 1700 1800 1900
## 6 1 24 1 4 25 1 3 4 1 1
## 1990 2000 2100 2300 2500 2700 2800 3000 3200 3500 3700
## 1 21 1 1 6 1 1 24 1 7 1
## 4000 4200 4500 5000 5250 5500 6000 7000 8000 8700 9500
## 12 1 1 21 1 1 8 4 4 1 1
## 10000 11000 11800 12000 13000 14000 15000 16000 18000 20000 25000
## 29 2 1 3 1 1 13 2 1 10 5
## 26000 30000 40000 50000 60000 72000 1e+05 109000 or more <NA>
## 1 4 2 3 1 1 2 2 1923
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q48)[na.exclude(mydata$eh_s9q48)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q48", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q48. Other business inventory and assets. Ibang imbentaryo ng kalakal at mga ari-ari
## -998 0 8 10 15 20 25 34 35 58 60 100 120 130 150 200 210 250 300 350 400 450 500 600 650 700 750 800
## 1 6 2 1 1 5 2 1 1 1 1 6 1 1 2 5 1 2 3 1 2 2 10 2 1 3 1 3
## 850 900 1000 1200 1500 1700 2000 2500 3000 4000 4200 4500 4800 5000 5800 7000 8000 9000 9500 10000 12000 13000 15000 22000 30000 35000 50000 70000
## 1 1 5 1 4 1 8 2 1 5 1 1 1 10 1 1 1 2 1 5 2 1 2 1 4 1 2 1
## 80000 <NA>
## 2 2151
## [1] "Frequency table after encoding"
## eh_s9q48. Other business inventory and assets. Ibang imbentaryo ng kalakal at mga ari-ari
## -998 0 8 10 15 20 25 34 35 58 60 100
## 1 6 2 1 1 5 2 1 1 1 1 6
## 120 130 150 200 210 250 300 350 400 450 500 600
## 1 1 2 5 1 2 3 1 2 2 10 2
## 650 700 750 800 850 900 1000 1200 1500 1700 2000 2500
## 1 3 1 3 1 1 5 1 4 1 8 2
## 3000 4000 4200 4500 4800 5000 5800 7000 8000 9000 9500 10000
## 1 5 1 1 1 10 1 1 1 2 1 5
## 12000 13000 15000 22000 30000 35000 50000 70000 80000 or more <NA>
## 2 1 2 1 4 1 2 1 2 2151
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q50)[na.exclude(mydata$eh_s9q50)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q50", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q50. Q383: Cell Phone Cellphone
## -998 1 3 7 10 40 96 100 150 200 250 300 350 375 380 385 399 400 450 499 500 548 550 589
## 10 1 2 1 1 1 1 11 1 23 4 54 5 1 1 1 3 30 2 3 162 1 3 1
## 599 600 699 700 792 798 800 848 900 978 980 998 999 1000 1050 1100 1200 1250 1290 1300 1380 1400 1470 1480
## 1 35 1 38 1 1 28 1 22 1 1 1 1 180 1 4 32 1 1 11 1 15 1 1
## 1500 1600 1699 1700 1750 1800 1849 1850 1900 1914 1950 1999 2000 2030 2050 2100 2150 2200 2250 2300 2400 2480 2500 2508
## 157 15 1 13 3 28 1 1 13 1 2 1 139 1 1 12 1 15 2 7 11 1 56 1
## 2550 2600 2700 2799 2800 2880 2900 2950 3000 3050 3100 3150 3200 3250 3300 3400 3500 3599 3600 3696 3700 3800 3900 3950
## 1 7 12 1 9 1 5 1 154 1 3 2 8 1 9 5 39 1 8 1 5 7 5 1
## 4000 4100 4200 4300 4400 4495 4500 4600 4700 4800 4900 5000 5099 5100 5200 5300 5400 5500 5600 5700 5800 5900 6000 6100
## 101 3 4 5 2 1 22 2 3 7 5 90 1 5 2 2 4 10 1 4 1 1 59 1
## 6200 6300 6400 6500 6600 6700 6800 7000 7200 7400 7500 7600 7700 7800 8000 8200 8400 8450 8500 8600 8700 8800 8900 9000
## 2 3 3 5 2 1 2 27 2 1 6 1 1 1 23 1 1 1 7 1 1 1 1 25
## 9050 9200 9400 9420 9460 9500 10000 10200 10250 10300 10400 10500 10600 10900 11000 11100 11200 11300 11500 11600 12000 12100 12200 12500
## 1 3 2 1 1 5 43 1 1 1 1 3 2 1 11 1 1 1 5 1 12 1 1 2
## 13000 13500 14000 14400 14700 14999 15000 15500 15600 16000 16300 16600 16800 17000 17399 17800 18000 18200 19000 20000 20003 20700 20900 21000
## 8 2 8 2 1 1 28 1 1 8 1 1 1 3 1 1 4 1 2 4 1 1 1 2
## 21500 22000 22500 22800 23150 23700 24000 24800 25000 25500 26000 26700 27000 28000 29000 30000 32000 32500 33500 34000 34200 34450 35000 37000
## 2 1 2 1 1 1 1 1 7 1 1 1 1 2 1 7 3 1 2 1 1 1 3 1
## 39000 40000 40500 40600 50000 104000 <NA>
## 1 4 1 1 1 1 121
## [1] "Frequency table after encoding"
## eh_s9q50. Q383: Cell Phone Cellphone
## -998 1 3 7 10 40 96 100 150 200 250 300
## 10 1 2 1 1 1 1 11 1 23 4 54
## 350 375 380 385 399 400 450 499 500 548 550 589
## 5 1 1 1 3 30 2 3 162 1 3 1
## 599 600 699 700 792 798 800 848 900 978 980 998
## 1 35 1 38 1 1 28 1 22 1 1 1
## 999 1000 1050 1100 1200 1250 1290 1300 1380 1400 1470 1480
## 1 180 1 4 32 1 1 11 1 15 1 1
## 1500 1600 1699 1700 1750 1800 1849 1850 1900 1914 1950 1999
## 157 15 1 13 3 28 1 1 13 1 2 1
## 2000 2030 2050 2100 2150 2200 2250 2300 2400 2480 2500 2508
## 139 1 1 12 1 15 2 7 11 1 56 1
## 2550 2600 2700 2799 2800 2880 2900 2950 3000 3050 3100 3150
## 1 7 12 1 9 1 5 1 154 1 3 2
## 3200 3250 3300 3400 3500 3599 3600 3696 3700 3800 3900 3950
## 8 1 9 5 39 1 8 1 5 7 5 1
## 4000 4100 4200 4300 4400 4495 4500 4600 4700 4800 4900 5000
## 101 3 4 5 2 1 22 2 3 7 5 90
## 5099 5100 5200 5300 5400 5500 5600 5700 5800 5900 6000 6100
## 1 5 2 2 4 10 1 4 1 1 59 1
## 6200 6300 6400 6500 6600 6700 6800 7000 7200 7400 7500 7600
## 2 3 3 5 2 1 2 27 2 1 6 1
## 7700 7800 8000 8200 8400 8450 8500 8600 8700 8800 8900 9000
## 1 1 23 1 1 1 7 1 1 1 1 25
## 9050 9200 9400 9420 9460 9500 10000 10200 10250 10300 10400 10500
## 1 3 2 1 1 5 43 1 1 1 1 3
## 10600 10900 11000 11100 11200 11300 11500 11600 12000 12100 12200 12500
## 2 1 11 1 1 1 5 1 12 1 1 2
## 13000 13500 14000 14400 14700 14999 15000 15500 15600 16000 16300 16600
## 8 2 8 2 1 1 28 1 1 8 1 1
## 16800 17000 17399 17800 18000 18200 19000 20000 20003 20700 20900 21000
## 1 3 1 1 4 1 2 4 1 1 1 2
## 21500 22000 22500 22800 23150 23700 24000 24800 25000 25500 26000 26700
## 2 1 2 1 1 1 1 1 7 1 1 1
## 27000 28000 29000 30000 32000 32500 33500 34000 34200 34450 35000 or more <NA>
## 1 2 1 7 3 1 2 1 1 1 13 121
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q51)[na.exclude(mydata$eh_s9q51)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q51", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q51. Q384: Sofa Sofa
## -998 0 20 30 50 75 100 150 200 208 250 300 400 450 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1635 1800
## 4 5 2 1 5 1 21 4 32 1 5 25 4 1 77 5 8 12 5 61 1 8 3 1 61 1 1 2
## 1900 2000 2100 2200 2300 2400 2500 2600 2700 2800 2900 3000 3100 3200 3300 3400 3500 3700 3800 3900 4000 4400 4500 4600 4800 5000 5500 5700
## 1 56 2 1 5 3 23 1 4 4 1 27 1 3 1 4 8 2 2 1 18 1 6 1 1 30 1 1
## 5900 6000 6500 6600 6800 7000 7200 7500 7900 8000 9000 9500 10000 11000 12000 15000 16000 18000 19000 20000 24000 25000 30000 50000 <NA>
## 1 11 3 1 1 7 1 2 1 5 4 1 12 2 7 5 2 1 1 1 1 1 2 1 1648
## [1] "Frequency table after encoding"
## eh_s9q51. Q384: Sofa Sofa
## -998 0 20 30 50 75 100 150 200 208 250 300
## 4 5 2 1 5 1 21 4 32 1 5 25
## 400 450 500 600 700 800 900 1000 1100 1200 1300 1400
## 4 1 77 5 8 12 5 61 1 8 3 1
## 1500 1600 1635 1800 1900 2000 2100 2200 2300 2400 2500 2600
## 61 1 1 2 1 56 2 1 5 3 23 1
## 2700 2800 2900 3000 3100 3200 3300 3400 3500 3700 3800 3900
## 4 4 1 27 1 3 1 4 8 2 2 1
## 4000 4400 4500 4600 4800 5000 5500 5700 5900 6000 6500 6600
## 18 1 6 1 1 30 1 1 1 11 3 1
## 6800 7000 7200 7500 7900 8000 9000 9500 10000 11000 12000 15000
## 1 7 1 2 1 5 4 1 12 2 7 5
## 16000 18000 19000 20000 24000 24804 or more <NA>
## 2 1 1 1 1 4 1648
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q52)[na.exclude(mydata$eh_s9q52)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q52", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q52. Q385: Chairs Mga silya
## -998 0 4 5 10 15 20 24 25 30 40 50 58 60 70 75 80 90 100 110 120 125 130 135 140 150 160 180
## 15 13 1 2 8 2 20 1 1 8 15 63 1 8 5 3 5 4 155 1 8 1 1 1 1 72 5 1
## 190 195 200 210 220 225 226 230 240 250 260 270 275 280 285 300 320 330 340 350 360 380 400 405 410 420 435 450
## 1 1 146 1 1 3 1 1 2 28 3 1 2 3 1 141 3 2 1 9 6 1 83 1 1 1 1 30
## 460 480 500 510 520 540 550 575 600 620 630 640 650 680 690 694 700 720 750 800 825 840 850 860 875 900 920 950
## 2 4 146 1 2 2 2 1 87 1 2 2 5 1 2 1 22 2 19 59 1 2 3 1 1 23 1 1
## 960 969 1000 1020 1050 1060 1072 1080 1100 1120 1140 1150 1200 1220 1240 1250 1280 1300 1325 1350 1400 1450 1500 1520 1560 1600 1620 1650
## 2 1 120 1 6 1 1 2 2 1 1 1 39 1 1 7 1 3 1 1 13 1 67 1 1 9 1 3
## 1680 1700 1750 1790 1800 1860 1900 1920 1980 2000 2100 2200 2240 2250 2280 2300 2340 2350 2400 2450 2500 2600 2700 2800 2880 2900 3000 3100
## 6 8 1 1 24 1 1 1 1 37 4 5 1 1 2 1 1 1 7 2 24 4 6 6 1 2 20 1
## 3225 3500 3600 3700 3800 3990 4000 4500 4600 4800 5000 5400 5500 6000 6500 7000 7300 7500 8000 8400 10000 11000 15000 16000 20000 <NA>
## 1 6 1 4 2 1 12 3 1 1 12 1 1 4 2 1 1 1 1 1 5 1 2 1 1 470
## [1] "Frequency table after encoding"
## eh_s9q52. Q385: Chairs Mga silya
## -998 0 4 5 10 15 20 24 25 30 40 50
## 15 13 1 2 8 2 20 1 1 8 15 63
## 58 60 70 75 80 90 100 110 120 125 130 135
## 1 8 5 3 5 4 155 1 8 1 1 1
## 140 150 160 180 190 195 200 210 220 225 226 230
## 1 72 5 1 1 1 146 1 1 3 1 1
## 240 250 260 270 275 280 285 300 320 330 340 350
## 2 28 3 1 2 3 1 141 3 2 1 9
## 360 380 400 405 410 420 435 450 460 480 500 510
## 6 1 83 1 1 1 1 30 2 4 146 1
## 520 540 550 575 600 620 630 640 650 680 690 694
## 2 2 2 1 87 1 2 2 5 1 2 1
## 700 720 750 800 825 840 850 860 875 900 920 950
## 22 2 19 59 1 2 3 1 1 23 1 1
## 960 969 1000 1020 1050 1060 1072 1080 1100 1120 1140 1150
## 2 1 120 1 6 1 1 2 2 1 1 1
## 1200 1220 1240 1250 1280 1300 1325 1350 1400 1450 1500 1520
## 39 1 1 7 1 3 1 1 13 1 67 1
## 1560 1600 1620 1650 1680 1700 1750 1790 1800 1860 1900 1920
## 1 9 1 3 6 8 1 1 24 1 1 1
## 1980 2000 2100 2200 2240 2250 2280 2300 2340 2350 2400 2450
## 1 37 4 5 1 1 2 1 1 1 7 2
## 2500 2600 2700 2800 2880 2900 3000 3100 3225 3500 3600 3700
## 24 4 6 6 1 2 20 1 1 6 1 4
## 3800 3990 4000 4500 4600 4800 5000 5400 5500 6000 6500 7000
## 2 1 12 3 1 1 12 1 1 4 2 1
## 7300 7500 8000 8400 9863 or more <NA>
## 1 1 1 1 10 470
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q53)[na.exclude(mydata$eh_s9q53)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q53", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q53. Q386: Table Lamesa
## -998 0 2 4 5 10 12 20 22 28 30 35 40 50 60 70 80 100 120 150 180 200 208 210 220 225 230 250
## 14 24 1 1 1 3 1 15 1 1 11 1 1 94 3 1 3 242 1 88 2 290 1 1 1 1 1 25
## 300 350 360 380 400 450 480 500 550 600 650 670 700 750 800 850 900 950 998 1000 1010 1050 1100 1150 1200 1300 1360 1400
## 196 15 2 1 69 9 1 282 9 79 3 1 44 7 35 4 11 1 1 183 1 1 5 1 22 4 1 5
## 1500 1600 1700 1750 1800 1840 2000 2100 2200 2400 2500 2550 2700 3000 3200 3500 3550 3800 4000 4500 5000 5300 5400 5500 6000 6500 7000 8000
## 74 4 4 1 6 1 45 1 2 4 14 1 1 29 3 5 1 2 11 1 15 1 1 1 10 1 3 2
## 9000 10000 10500 11800 12000 13000 15000 17000 18000 20000 27000 44500 70000 <NA>
## 1 5 1 1 3 1 2 2 1 1 1 1 1 201
## [1] "Frequency table after encoding"
## eh_s9q53. Q386: Table Lamesa
## -998 0 2 4 5 10 12 20 22 28 30 35
## 14 24 1 1 1 3 1 15 1 1 11 1
## 40 50 60 70 80 100 120 150 180 200 208 210
## 1 94 3 1 3 242 1 88 2 290 1 1
## 220 225 230 250 300 350 360 380 400 450 480 500
## 1 1 1 25 196 15 2 1 69 9 1 282
## 550 600 650 670 700 750 800 850 900 950 998 1000
## 9 79 3 1 44 7 35 4 11 1 1 183
## 1010 1050 1100 1150 1200 1300 1360 1400 1500 1600 1700 1750
## 1 1 5 1 22 4 1 5 74 4 4 1
## 1800 1840 2000 2100 2200 2400 2500 2550 2700 3000 3200 3500
## 6 1 45 1 2 4 14 1 1 29 3 5
## 3550 3800 4000 4500 5000 5300 5400 5500 6000 6500 7000 8000
## 1 2 11 1 15 1 1 1 10 1 3 2
## 9000 10000 10500 11800 12000 or more <NA>
## 1 5 1 1 13 201
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q54)[na.exclude(mydata$eh_s9q54)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q54", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q54. Q387: Clock/Watch Relo
## -999 -998 0 1 5 10 15 20 25 28 30 35 40 45 50 55 60 65 70 75 80 85 89 90 95 100 110 120
## 1 5 12 1 1 1 1 12 1 1 14 2 2 1 109 2 14 7 5 11 15 4 1 3 1 261 3 36
## 125 130 135 139 140 150 160 170 175 180 198 200 205 220 230 240 250 255 265 289 290 299 300 310 315 320 330 350
## 1 9 2 1 3 177 4 1 4 6 1 128 1 1 2 3 40 1 1 1 1 1 91 1 1 1 1 15
## 360 380 390 400 420 430 450 475 490 500 550 580 600 648 650 660 700 720 725 750 800 805 850 870 900 1000 1050 1080
## 2 1 2 31 2 1 7 1 1 69 4 1 25 1 3 1 11 1 1 5 12 1 2 1 3 44 2 1
## 1100 1108 1150 1200 1250 1300 1400 1450 1500 1600 1650 1700 1800 1900 1920 2000 2020 2050 2150 2250 2300 2350 2450 2500 2600 2650 2700 2799
## 5 1 4 4 1 2 1 1 31 4 3 2 2 1 1 17 1 1 1 1 1 1 1 5 1 1 1 1
## 2800 2900 3000 3150 3200 3500 3750 4000 4120 4150 4400 4500 4650 5000 5200 5250 5500 6000 7000 7550 8050 8200 8400 9000 10000 10200 10300 11700
## 1 1 14 1 1 3 1 8 1 1 1 2 1 7 1 1 1 3 2 1 1 1 1 2 1 1 1 1
## 15000 17700 18780 20000 <NA>
## 1 1 1 1 865
## [1] "Frequency table after encoding"
## eh_s9q54. Q387: Clock/Watch Relo
## -999 -998 0 1 5 10 15 20 25 28 30 35
## 1 5 12 1 1 1 1 12 1 1 14 2
## 40 45 50 55 60 65 70 75 80 85 89 90
## 2 1 109 2 14 7 5 11 15 4 1 3
## 95 100 110 120 125 130 135 139 140 150 160 170
## 1 261 3 36 1 9 2 1 3 177 4 1
## 175 180 198 200 205 220 230 240 250 255 265 289
## 4 6 1 128 1 1 2 3 40 1 1 1
## 290 299 300 310 315 320 330 350 360 380 390 400
## 1 1 91 1 1 1 1 15 2 1 2 31
## 420 430 450 475 490 500 550 580 600 648 650 660
## 2 1 7 1 1 69 4 1 25 1 3 1
## 700 720 725 750 800 805 850 870 900 1000 1050 1080
## 11 1 1 5 12 1 2 1 3 44 2 1
## 1100 1108 1150 1200 1250 1300 1400 1450 1500 1600 1650 1700
## 5 1 4 4 1 2 1 1 31 4 3 2
## 1800 1900 1920 2000 2020 2050 2150 2250 2300 2350 2450 2500
## 2 1 1 17 1 1 1 1 1 1 1 5
## 2600 2650 2700 2799 2800 2900 3000 3150 3200 3500 3750 4000
## 1 1 1 1 1 1 14 1 1 3 1 8
## 4120 4150 4400 4500 4650 5000 5200 5250 5500 6000 7000 7550
## 1 1 1 2 1 7 1 1 1 3 2 1
## 8050 8200 8400 9000 9890 or more <NA>
## 1 1 1 2 8 865
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q55)[na.exclude(mydata$eh_s9q55)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q55", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q55. Q388: Bicycle Bisekleta
## -998 0 1 21 30 50 100 120 150 200 250 300 400 500 600 700 750 800 900 1000 1008 1100 1200 1300 1400 1500 1600 1700
## 1 3 1 1 1 2 4 1 1 9 2 22 5 74 9 17 2 19 3 77 1 1 15 2 1 64 3 3
## 1800 1900 2000 2025 2200 2300 2400 2500 2600 2700 2800 3000 3200 3400 3500 3600 3700 3800 4000 4200 4500 4700 5000 5400 5500 5800 6000 6500
## 10 1 47 1 3 4 4 34 2 8 7 36 2 2 12 1 1 1 12 1 2 1 22 1 1 1 1 3
## 6700 7000 7500 8000 8500 9500 10000 11000 11200 11500 12000 13000 15000 16000 20000 21000 74000 <NA>
## 1 4 4 2 1 1 3 1 1 1 1 2 1 1 2 1 1 1695
## [1] "Frequency table after encoding"
## eh_s9q55. Q388: Bicycle Bisekleta
## -998 0 1 21 30 50 100 120 150 200 250 300
## 1 3 1 1 1 2 4 1 1 9 2 22
## 400 500 600 700 750 800 900 1000 1008 1100 1200 1300
## 5 74 9 17 2 19 3 77 1 1 15 2
## 1400 1500 1600 1700 1800 1900 2000 2025 2200 2300 2400 2500
## 1 64 3 3 10 1 47 1 3 4 4 34
## 2600 2700 2800 3000 3200 3400 3500 3600 3700 3800 4000 4200
## 2 8 7 36 2 2 12 1 1 1 12 1
## 4500 4700 5000 5400 5500 5800 6000 6500 6700 7000 7500 8000
## 2 1 22 1 1 1 1 3 1 4 4 2
## 8500 9500 10000 11000 11200 11500 12000 13000 15000 16000 20000 or more <NA>
## 1 1 3 1 1 1 1 2 1 1 4 1695
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q56)[na.exclude(mydata$eh_s9q56)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q56", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q56. Q389: Tricycle Tricycle
## -998 1500 2000 3000 4000 4400 5000 6000 7000 8000 9000 10000 12000 14000 15000 20000 22000 23000 24000 25000 28000 30000 32000 35000
## 1 1 1 3 1 1 5 2 3 2 1 14 5 2 13 17 1 2 2 13 1 20 1 14
## 36000 40000 45000 48000 49000 49600 50000 55000 56340 59400 60000 63000 65000 68000 70000 72000 74000 75000 76800 80000 82380 82800 83808 84000
## 2 12 10 1 2 1 27 3 1 1 4 1 1 1 10 2 2 1 1 2 1 1 1 1
## 85000 86400 90000 98000 1e+05 110000 115000 120000 124000 130000 131000 132000 133200 147000 149000 150000 153000 160000 188000 195000 2e+05 <NA>
## 1 1 1 1 10 2 1 2 1 2 1 1 1 1 1 4 1 2 1 1 3 2034
## [1] "Frequency table after encoding"
## eh_s9q56. Q389: Tricycle Tricycle
## -998 1500 2000 3000 4000 4400 5000 6000 7000 8000 9000 10000
## 1 1 1 3 1 1 5 2 3 2 1 14
## 12000 14000 15000 20000 22000 23000 24000 25000 28000 30000 32000 35000
## 5 2 13 17 1 2 2 13 1 20 1 14
## 36000 40000 45000 48000 49000 49600 50000 55000 56340 59400 60000 63000
## 2 12 10 1 2 1 27 3 1 1 4 1
## 65000 68000 70000 72000 74000 75000 76800 80000 82380 82800 83808 84000
## 1 1 10 2 2 1 1 2 1 1 1 1
## 85000 86400 90000 98000 1e+05 110000 115000 120000 124000 130000 131000 132000
## 1 1 1 1 10 2 1 2 1 2 1 1
## 133200 147000 149000 150000 153000 160000 188000 195000 2e+05 or more <NA>
## 1 1 1 4 1 2 1 1 3 2034
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q57)[na.exclude(mydata$eh_s9q57)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q57", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q57. Q390: Motorbike Motor
## -999 -998 0 20 47 500 1000 1800 2000 2500 3000 3300 3500 4000 4500 4900 5000 6000 6500 7000 7500 8000 9000 10000
## 1 4 1 1 1 1 5 1 4 1 6 1 1 5 1 1 32 5 1 10 1 9 4 64
## 10200 10500 10800 11000 12000 12500 13000 14000 15000 15500 16000 17000 18000 20000 22000 23000 23100 24000 25000 27000 28000 30000 31000 32000
## 1 1 1 1 12 1 1 3 47 1 2 3 6 46 7 2 1 5 24 3 2 36 1 1
## 33000 35000 36000 36250 37000 38000 39000 39600 40000 42000 44000 45000 45600 46000 46800 47000 47800 48000 49000 50000 50400 51000 52000 54000
## 1 13 3 1 3 2 2 1 27 2 2 13 1 4 2 1 1 1 1 20 1 1 1 8
## 55000 57000 57600 58000 60000 60300 61200 62000 63000 63500 64000 64200 65000 66060 68000 68400 68580 69000 69600 70000 71000 72000 74000 74800
## 1 1 1 1 23 2 2 3 2 1 2 1 6 1 1 1 1 1 1 18 2 3 1 1
## 75000 75060 75600 76000 77000 77250 78000 79000 80000 80500 81000 82000 83000 85000 86400 89000 89244 89800 90000 90840 97000 97200 1e+05 102000
## 8 1 1 2 2 1 2 1 14 1 1 2 1 2 1 1 1 1 7 1 1 1 12 1
## 103536 104000 111492 112000 115000 116200 117000 117180 120000 132000 143000 146000 150000 180000 186000 2e+05 213348 220000 230000 287000 360000 <NA>
## 1 1 1 1 1 1 1 1 7 1 1 1 2 1 1 1 1 1 1 1 1 1636
## [1] "Frequency table after encoding"
## eh_s9q57. Q390: Motorbike Motor
## -999 -998 0 20 47 500 1000 1800 2000 2500 3000
## 1 4 1 1 1 1 5 1 4 1 6
## 3300 3500 4000 4500 4900 5000 6000 6500 7000 7500 8000
## 1 1 5 1 1 32 5 1 10 1 9
## 9000 10000 10200 10500 10800 11000 12000 12500 13000 14000 15000
## 4 64 1 1 1 1 12 1 1 3 47
## 15500 16000 17000 18000 20000 22000 23000 23100 24000 25000 27000
## 1 2 3 6 46 7 2 1 5 24 3
## 28000 30000 31000 32000 33000 35000 36000 36250 37000 38000 39000
## 2 36 1 1 1 13 3 1 3 2 2
## 39600 40000 42000 44000 45000 45600 46000 46800 47000 47800 48000
## 1 27 2 2 13 1 4 2 1 1 1
## 49000 50000 50400 51000 52000 54000 55000 57000 57600 58000 60000
## 1 20 1 1 1 8 1 1 1 1 23
## 60300 61200 62000 63000 63500 64000 64200 65000 66060 68000 68400
## 2 2 3 2 1 2 1 6 1 1 1
## 68580 69000 69600 70000 71000 72000 74000 74800 75000 75060 75600
## 1 1 1 18 2 3 1 1 8 1 1
## 76000 77000 77250 78000 79000 80000 80500 81000 82000 83000 85000
## 2 2 1 2 1 14 1 1 2 1 2
## 86400 89000 89244 89800 90000 90840 97000 97200 1e+05 102000 103536
## 1 1 1 1 7 1 1 1 12 1 1
## 104000 111492 112000 115000 116200 117000 117180 120000 132000 143000 146000
## 1 1 1 1 1 1 1 7 1 1 1
## 150000 180000 186000 2e+05 213348 218303 or more <NA>
## 2 1 1 1 1 4 1636
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q58)[na.exclude(mydata$eh_s9q58)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q58", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q58. Q391: Motorized Boat/Banca Bangkang de-makina/ bangka
## 0 46 500 1000 1500 2500 4000 4500 5000 5600 6000 7000 8000 9000 10000 12000 12008 13000 14000 15000 16000 17000 18000 20000
## 1 1 1 1 3 1 1 1 4 1 1 3 1 1 5 2 1 4 1 10 2 1 2 17
## 20500 22000 23000 25000 27000 29000 30000 35000 40000 45000 50000 55000 60000 64000 68000 70000 80000 90000 95000 160000 <NA>
## 1 1 2 9 1 1 8 2 5 6 11 1 1 1 1 2 1 2 1 1 2164
## [1] "Frequency table after encoding"
## eh_s9q58. Q391: Motorized Boat/Banca Bangkang de-makina/ bangka
## 0 46 500 1000 1500 2500 4000 4500 5000 5600 6000
## 1 1 1 1 3 1 1 1 4 1 1
## 7000 8000 9000 10000 12000 12008 13000 14000 15000 16000 17000
## 3 1 1 5 2 1 4 1 10 2 1
## 18000 20000 20500 22000 23000 25000 27000 29000 30000 35000 40000
## 2 17 1 1 2 9 1 1 8 2 5
## 45000 50000 55000 60000 64000 68000 70000 80000 90000 95000 120025 or more
## 6 11 1 1 1 1 2 1 2 1 1
## <NA>
## 2164
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q59)[na.exclude(mydata$eh_s9q59)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q59", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q59. Q392: Other Motorized Vehicle Iba pang sasakyang de-motor
## 2000 8000 12000 22000 39000 47000 55000 70000 90000 1e+05 120000 150000 2e+05 750000 <NA>
## 1 2 1 1 1 1 1 1 1 3 2 1 2 1 2269
## [1] "Frequency table after encoding"
## eh_s9q59. Q392: Other Motorized Vehicle Iba pang sasakyang de-motor
## 2000 8000 12000 22000 39000 47000 55000 70000 90000 1e+05 120000
## 1 2 1 1 1 1 1 1 1 3 2
## 150000 2e+05 700500 or more <NA>
## 1 2 1 2269
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q60)[na.exclude(mydata$eh_s9q60)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q60", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q60. Q393: Radio, Tape, or CD Player Radyo, Tape o CD Player
## -998 0 20 50 75 100 120 150 180 200 208 250 270 280 300 320 350 360 390 399 400 450 480 499 500 550 570 600
## 6 6 2 8 1 42 2 23 4 32 1 27 1 3 79 3 18 1 1 1 25 15 2 1 131 2 1 22
## 650 700 750 800 850 899 900 1000 1005 1050 1100 1200 1300 1400 1500 1600 1700 1800 2000 2100 2200 2300 2400 2500 2700 2800 3000 3200
## 1 26 1 22 1 1 5 118 1 1 11 21 4 2 80 5 7 8 40 3 3 1 4 28 3 8 28 1
## 3300 3400 3500 3700 3800 4000 4200 4300 4450 4500 4800 5000 5300 5600 6000 7000 7500 8000 9000 10000 10800 12000 14000 15000 20000 24000 29000 <NA>
## 2 1 10 1 1 13 2 1 1 7 1 22 1 1 5 4 1 3 1 11 1 2 1 1 1 1 1 1297
## [1] "Frequency table after encoding"
## eh_s9q60. Q393: Radio, Tape, or CD Player Radyo, Tape o CD Player
## -998 0 20 50 75 100 120 150 180 200 208 250
## 6 6 2 8 1 42 2 23 4 32 1 27
## 270 280 300 320 350 360 390 399 400 450 480 499
## 1 3 79 3 18 1 1 1 25 15 2 1
## 500 550 570 600 650 700 750 800 850 899 900 1000
## 131 2 1 22 1 26 1 22 1 1 5 118
## 1005 1050 1100 1200 1300 1400 1500 1600 1700 1800 2000 2100
## 1 1 11 21 4 2 80 5 7 8 40 3
## 2200 2300 2400 2500 2700 2800 3000 3200 3300 3400 3500 3700
## 3 1 4 28 3 8 28 1 2 1 10 1
## 3800 4000 4200 4300 4450 4500 4800 5000 5300 5600 6000 7000
## 1 13 2 1 1 7 1 22 1 1 5 4
## 7500 8000 9000 10000 10800 12000 12099 or more <NA>
## 1 3 1 11 1 2 5 1297
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q61)[na.exclude(mydata$eh_s9q61)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q61", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q61. Q394: Beds Mga kama
## -998 0 5 10 20 50 75 80 100 120 150 160 200 250 300 350 360 400 450 500 600 650 700 750 800 900 950 1000
## 11 14 1 1 3 12 3 1 36 1 23 1 93 11 99 5 1 39 1 180 59 2 19 1 24 13 1 131
## 1050 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2400 2500 2600 2800 3000 3200 3300 3500 3600 4000 4500 5000 5200 5300 6000
## 1 1 17 5 5 74 2 1 6 1 67 2 1 3 21 1 1 51 1 1 13 2 18 5 24 1 1 9
## 6500 7000 7500 8000 9000 10000 10003 10300 10500 10600 11000 12000 13500 15000 20000 23000 24000 25000 25070 27000 32000 40000 <NA>
## 4 6 1 7 2 11 1 1 1 1 1 2 1 5 1 1 1 1 1 1 1 1 1115
## [1] "Frequency table after encoding"
## eh_s9q61. Q394: Beds Mga kama
## -998 0 5 10 20 50 75 80 100 120 150 160
## 11 14 1 1 3 12 3 1 36 1 23 1
## 200 250 300 350 360 400 450 500 600 650 700 750
## 93 11 99 5 1 39 1 180 59 2 19 1
## 800 900 950 1000 1050 1100 1200 1300 1400 1500 1600 1700
## 24 13 1 131 1 1 17 5 5 74 2 1
## 1800 1900 2000 2100 2200 2400 2500 2600 2800 3000 3200 3300
## 6 1 67 2 1 3 21 1 1 51 1 1
## 3500 3600 4000 4500 5000 5200 5300 6000 6500 7000 7500 8000
## 13 2 18 5 24 1 1 9 4 6 1 7
## 9000 10000 10003 10300 10500 10600 11000 12000 13500 15000 20000 23000
## 2 11 1 1 1 1 1 2 1 5 1 1
## 23140 or more <NA>
## 6 1115
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q62)[na.exclude(mydata$eh_s9q62)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q62", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q62. Q395: Mattresses Mga kutson ng kama
## -998 0 10 20 50 100 125 150 200 250 300 350 380 400 450 480 500 600 700 750 800 850 900 950 1000 1100 1200 1300
## 9 19 1 1 15 38 1 11 49 6 46 4 1 17 2 1 112 21 12 2 27 1 5 1 156 5 26 10
## 1350 1400 1500 1600 1700 1800 1900 2000 2002 2100 2200 2250 2300 2400 2500 2600 2700 2800 2900 3000 3050 3200 3300 3302 3400 3500 3600 3700
## 1 6 77 14 5 19 4 98 1 1 2 1 3 10 40 6 5 4 1 76 1 9 4 1 2 25 2 1
## 3800 4000 4050 4350 4400 4500 4570 4600 4700 4800 4900 5000 5100 5300 5400 5500 5600 5800 6000 6300 6400 7000 7100 7200 7300 7500 7600 8000
## 5 29 1 1 2 14 1 2 1 7 2 41 2 1 5 4 3 3 29 2 2 7 1 2 1 2 1 8
## 8500 9000 9500 9700 10000 10800 11000 11600 12000 12500 12900 13000 13500 14700 15000 81000 <NA>
## 3 5 2 1 8 1 3 1 2 1 1 1 1 1 2 1 1057
## [1] "Frequency table after encoding"
## eh_s9q62. Q395: Mattresses Mga kutson ng kama
## -998 0 10 20 50 100 125 150 200 250 300 350
## 9 19 1 1 15 38 1 11 49 6 46 4
## 380 400 450 480 500 600 700 750 800 850 900 950
## 1 17 2 1 112 21 12 2 27 1 5 1
## 1000 1100 1200 1300 1350 1400 1500 1600 1700 1800 1900 2000
## 156 5 26 10 1 6 77 14 5 19 4 98
## 2002 2100 2200 2250 2300 2400 2500 2600 2700 2800 2900 3000
## 1 1 2 1 3 10 40 6 5 4 1 76
## 3050 3200 3300 3302 3400 3500 3600 3700 3800 4000 4050 4350
## 1 9 4 1 2 25 2 1 5 29 1 1
## 4400 4500 4570 4600 4700 4800 4900 5000 5100 5300 5400 5500
## 2 14 1 2 1 7 2 41 2 1 5 4
## 5600 5800 6000 6300 6400 7000 7100 7200 7300 7500 7600 8000
## 3 3 29 2 2 7 1 2 1 2 1 8
## 8500 9000 9500 9700 10000 10800 11000 11600 12000 12500 12839 or more <NA>
## 3 5 2 1 8 1 3 1 2 1 7 1057
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q63)[na.exclude(mydata$eh_s9q63)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q63", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q63. Q396: Solar Panel Solar Panel
## 0 100 150 200 240 250 300 360 400 500 700 1000 1400 1500 2000 2500 2600 3000 3500 4400 8500 9000 15500 <NA>
## 1 1 2 5 1 1 5 1 1 3 3 1 1 5 3 1 1 4 1 1 1 1 1 2243
## [1] "Frequency table after encoding"
## eh_s9q63. Q396: Solar Panel Solar Panel
## 0 100 150 200 240 250 300 360 400 500 700 1000
## 1 1 2 5 1 1 5 1 1 3 3 1
## 1400 1500 2000 2500 2600 3000 3500 4400 8500 9000 14070 or more <NA>
## 1 5 3 1 1 4 1 1 1 1 1 2243
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q64)[na.exclude(mydata$eh_s9q64)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q64", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q64. Q397: Generator Generator
## 5000 6000 10000 11000 12000 15000 18000 23000 <NA>
## 2 3 2 1 1 1 1 1 2276
## [1] "Frequency table after encoding"
## eh_s9q64. Q397: Generator Generator
## 5000 6000 10000 11000 12000 15000 18000 22725 or more <NA>
## 2 3 2 1 1 1 1 1 2276
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q65)[na.exclude(mydata$eh_s9q65)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q65", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q65. Q398: Television TV
## -998 0 2 20 50 100 150 200 300 330 400 450 500 600 700 800 850 900 1000 1100 1200 1300 1400 1500 1550 1600 1650 1700
## 6 5 2 1 1 10 3 14 15 1 3 1 111 7 19 16 1 1 188 2 45 7 8 207 1 21 1 23
## 1750 1800 1900 2000 2100 2200 2300 2400 2500 2600 2700 2800 2900 3000 3100 3200 3250 3300 3400 3500 3700 3800 4000 4100 4300 4500 4700 4800
## 1 47 9 187 6 27 14 10 105 6 7 11 2 135 2 7 1 2 1 40 3 3 62 3 1 14 1 1
## 4900 5000 5200 5300 5400 5500 5700 6000 6500 6700 7000 7200 7500 7700 7800 8000 8500 8700 8800 8900 9000 9500 9600 9800 9999 10000 11000 12000
## 2 96 1 2 1 6 1 33 6 1 26 2 2 1 1 25 2 1 1 1 3 2 1 1 1 33 8 20
## 12500 12600 12960 13000 13500 13800 14000 15000 16000 16500 18000 19000 19500 20000 21000 22000 23500 24000 25000 27000 28000 30000 32000 33000 35000 36000 <NA>
## 1 1 1 12 2 1 6 15 5 2 5 3 2 7 3 1 2 2 5 1 2 1 2 1 1 2 494
## [1] "Frequency table after encoding"
## eh_s9q65. Q398: Television TV
## -998 0 2 20 50 100 150 200 300 330 400 450
## 6 5 2 1 1 10 3 14 15 1 3 1
## 500 600 700 800 850 900 1000 1100 1200 1300 1400 1500
## 111 7 19 16 1 1 188 2 45 7 8 207
## 1550 1600 1650 1700 1750 1800 1900 2000 2100 2200 2300 2400
## 1 21 1 23 1 47 9 187 6 27 14 10
## 2500 2600 2700 2800 2900 3000 3100 3200 3250 3300 3400 3500
## 105 6 7 11 2 135 2 7 1 2 1 40
## 3700 3800 4000 4100 4300 4500 4700 4800 4900 5000 5200 5300
## 3 3 62 3 1 14 1 1 2 96 1 2
## 5400 5500 5700 6000 6500 6700 7000 7200 7500 7700 7800 8000
## 1 6 1 33 6 1 26 2 2 1 1 25
## 8500 8700 8800 8900 9000 9500 9600 9800 9999 10000 11000 12000
## 2 1 1 1 3 2 1 1 1 33 8 20
## 12500 12600 12960 13000 13500 13800 14000 15000 16000 16500 18000 19000
## 1 1 1 12 2 1 6 15 5 2 5 3
## 19500 20000 21000 22000 23500 24000 25000 27000 27035 or more <NA>
## 2 7 3 1 2 2 5 1 9 494
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q66)[na.exclude(mydata$eh_s9q66)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q66", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q66. Q399: VCR/DVD VCR/DVD
## -998 0 30 100 150 200 250 300 400 500 600 650 700 800 900 1000 1100 1150 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100
## 5 2 1 5 2 9 2 15 5 75 2 1 20 19 5 99 5 1 54 12 5 137 13 9 19 4 39 3
## 2200 2300 2400 2500 2600 2800 3000 3200 3400 3500 3800 4000 4500 5000 5500 5700 6000 7000 8000 10000 11000 12000 14000 15000 18500 26000 <NA>
## 4 1 1 28 1 3 25 1 1 4 1 5 2 8 1 1 4 1 4 4 1 1 1 2 1 1 1613
## [1] "Frequency table after encoding"
## eh_s9q66. Q399: VCR/DVD VCR/DVD
## -998 0 30 100 150 200 250 300 400 500 600 650
## 5 2 1 5 2 9 2 15 5 75 2 1
## 700 800 900 1000 1100 1150 1200 1300 1400 1500 1600 1700
## 20 19 5 99 5 1 54 12 5 137 13 9
## 1800 1900 2000 2100 2200 2300 2400 2500 2600 2800 3000 3200
## 19 4 39 3 4 1 1 28 1 3 25 1
## 3400 3500 3800 4000 4500 5000 5500 5700 6000 7000 8000 10000
## 1 4 1 5 2 8 1 1 4 1 4 4
## 11000 12000 14000 14629 or more <NA>
## 1 1 1 4 1613
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q67)[na.exclude(mydata$eh_s9q67)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q67", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q67. Q400: Computer Computer
## -998 0 1000 2000 2800 3000 3500 5000 6000 7000 8000 8500 9500 10000 11000 12000 13000 14000 15000 16000 16500 17000 18000 20000 21000 22000 24000 25000
## 3 1 1 2 1 5 2 8 1 3 3 1 1 11 2 4 1 2 6 1 2 2 2 3 1 1 2 2
## 30000 42000 45000 59000 60000 70000 <NA>
## 1 1 1 1 1 1 2208
## [1] "Frequency table after encoding"
## eh_s9q67. Q400: Computer Computer
## -998 0 1000 2000 2800 3000 3500 5000 6000 7000 8000 8500
## 3 1 1 2 1 5 2 8 1 3 3 1
## 9500 10000 11000 12000 13000 14000 15000 16000 16500 17000 18000 20000
## 1 11 2 4 1 2 6 1 2 2 2 3
## 21000 22000 24000 25000 30000 42000 45000 59000 60000 66050 or more <NA>
## 1 1 2 2 1 1 1 1 1 1 2208
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q68)[na.exclude(mydata$eh_s9q68)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q68", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q68. Q402: Wheelbarrow Wheelbarrow
## 200 300 450 500 600 1000 2000 2500 3000 4000 4500 5000 6000 7000 8000 15000 <NA>
## 1 1 1 1 1 3 1 2 1 1 1 1 2 3 1 1 2266
## [1] "Frequency table after encoding"
## eh_s9q68. Q402: Wheelbarrow Wheelbarrow
## 200 300 450 500 600 1000 2000 2500 3000 4000 4500 5000
## 1 1 1 1 1 3 1 2 1 1 1 1
## 6000 7000 8000 14264 or more <NA>
## 2 3 1 1 2266
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q69)[na.exclude(mydata$eh_s9q69)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q69", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q69. Q403: Cart Kariton
## 0 100 150 200 250 300 350 500 600 1000 1200 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 7000 8000 9000 10000 12000 13000 15000
## 1 4 2 3 1 7 1 3 2 4 1 5 8 3 7 3 6 3 15 1 4 2 2 1 2 1 1 1
## 25000 <NA>
## 1 2193
## [1] "Frequency table after encoding"
## eh_s9q69. Q403: Cart Kariton
## 0 100 150 200 250 300 350 500 600 1000 1200 1500
## 1 4 2 3 1 7 1 3 2 4 1 5
## 2000 2500 3000 3500 4000 4500 5000 5500 6000 7000 8000 9000
## 8 3 7 3 6 3 15 1 4 2 2 1
## 10000 12000 13000 15000 20300 or more <NA>
## 2 1 1 1 1 2193
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q70)[na.exclude(mydata$eh_s9q70)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q70", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q70. Q404: Kerosene or propane stove Kerosene o propane stove
## -998 0 5 10 45 50 55 60 100 150 200 250 280 300 350 400 450 500 520 525 550 565 600 650 700 750 800 850 900 1000 1100 1200 1300
## 12 6 1 1 1 3 1 1 10 7 19 7 1 18 5 9 3 69 1 1 4 1 24 4 25 4 29 3 12 77 3 36 6
## 1400 1500 1600 1700 1760 1800 1900 2000 2050 2100 2200 2250 2300 2400 2450 2500 2600 2700 2800 2900 3000 3100 3200 3400 3500 3600 3700 3800 3900 3950 4000 4200 4500
## 7 75 5 5 1 7 1 47 1 5 6 2 6 1 1 25 4 4 8 1 26 1 4 1 14 2 3 1 1 1 8 1 3
## 5000 5500 6000 6500 7000 7800 8000 <NA>
## 8 1 1 1 1 1 1 1592
## [1] "Frequency table after encoding"
## eh_s9q70. Q404: Kerosene or propane stove Kerosene o propane stove
## -998 0 5 10 45 50 55 60 100 150 200 250
## 12 6 1 1 1 3 1 1 10 7 19 7
## 280 300 350 400 450 500 520 525 550 565 600 650
## 1 18 5 9 3 69 1 1 4 1 24 4
## 700 750 800 850 900 1000 1100 1200 1300 1400 1500 1600
## 25 4 29 3 12 77 3 36 6 7 75 5
## 1700 1760 1800 1900 2000 2050 2100 2200 2250 2300 2400 2450
## 5 1 7 1 47 1 5 6 2 6 1 1
## 2500 2600 2700 2800 2900 3000 3100 3200 3400 3500 3600 3700
## 25 4 4 8 1 26 1 4 1 14 2 3
## 3800 3900 3950 4000 4200 4500 5000 5500 6000 6262 or more <NA>
## 1 1 1 8 1 3 8 1 1 4 1592
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q71)[na.exclude(mydata$eh_s9q71)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q71", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q71. Q405: Stove with Oven/Gas Range Stove na may oven/gas range
## -998 0 30 65 100 150 200 300 390 400 450 500 530 550 555 600 625 650 680 700 730 750 790 800 850 900 1000 1100
## 4 3 1 1 2 1 2 4 1 1 4 29 2 1 1 6 1 1 1 10 1 2 1 12 1 4 40 2
## 1200 1280 1300 1350 1400 1500 1600 1700 1800 1860 2000 2100 2200 2250 2300 2400 2450 2500 2600 2700 2800 2900 3000 3200 3400 3500 3600 3900
## 16 1 2 1 1 41 5 4 6 1 42 4 5 1 4 1 1 13 3 2 5 3 40 2 1 11 2 2
## 4000 4100 5000 5500 5880 6000 7000 8500 10000 11000 15000 19000 19150 20000 <NA>
## 13 1 7 1 1 1 2 1 1 1 1 1 1 1 1896
## [1] "Frequency table after encoding"
## eh_s9q71. Q405: Stove with Oven/Gas Range Stove na may oven/gas range
## -998 0 30 65 100 150 200 300 390 400 450 500
## 4 3 1 1 2 1 2 4 1 1 4 29
## 530 550 555 600 625 650 680 700 730 750 790 800
## 2 1 1 6 1 1 1 10 1 2 1 12
## 850 900 1000 1100 1200 1280 1300 1350 1400 1500 1600 1700
## 1 4 40 2 16 1 2 1 1 41 5 4
## 1800 1860 2000 2100 2200 2250 2300 2400 2450 2500 2600 2700
## 6 1 42 4 5 1 4 1 1 13 3 2
## 2800 2900 3000 3200 3400 3500 3600 3900 4000 4100 5000 5500
## 5 3 40 2 1 11 2 2 13 1 7 1
## 5880 6000 7000 8500 10000 11000 15000 19000 19006 or more <NA>
## 1 1 2 1 1 1 1 1 2 1896
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q72)[na.exclude(mydata$eh_s9q72)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q72", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q72. Q406: Refrigerator Refrigerator
## -999 -998 12 150 200 250 300 500 700 800 900 1000 1200 1500 2000 2200 2500 2800 3000 3500 4000 4500 5000 5500 6000 6100 6500 6800
## 1 4 1 1 1 1 3 12 2 2 1 23 1 9 27 1 5 1 29 3 13 3 43 2 12 1 2 1
## 7000 7500 8000 8500 9000 9300 9800 10000 10300 10500 11000 11500 11800 12000 12500 12800 12900 13000 14000 14500 14700 15000 16000 17000 17900 18000 19000 20000
## 25 1 16 2 9 1 1 46 1 3 7 1 1 27 1 1 1 5 14 2 1 15 7 4 1 12 2 5
## 22000 24000 25000 30000 34000 36000 37000 <NA>
## 2 2 1 2 1 1 1 1861
## [1] "Frequency table after encoding"
## eh_s9q72. Q406: Refrigerator Refrigerator
## -999 -998 12 150 200 250 300 500 700 800 900 1000
## 1 4 1 1 1 1 3 12 2 2 1 23
## 1200 1500 2000 2200 2500 2800 3000 3500 4000 4500 5000 5500
## 1 9 27 1 5 1 29 3 13 3 43 2
## 6000 6100 6500 6800 7000 7500 8000 8500 9000 9300 9800 10000
## 12 1 2 1 25 1 16 2 9 1 1 46
## 10300 10500 11000 11500 11800 12000 12500 12800 12900 13000 14000 14500
## 1 3 7 1 1 27 1 1 1 5 14 2
## 14700 15000 16000 17000 17900 18000 19000 20000 22000 24000 25000 30000
## 1 15 7 4 1 12 2 5 2 2 1 2
## 33480 or more <NA>
## 3 1861
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q73)[na.exclude(mydata$eh_s9q73)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q73", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q73. Q407: Clothes Washing Machine Washing Machine
## -998 0 50 100 150 200 250 300 400 500 550 600 700 750 800 900 1000 1200 1500 1800 1900 2000 2015 2100 2200 2300 2400 2500
## 3 2 1 2 1 3 2 4 1 25 1 3 4 2 6 1 49 2 40 3 1 68 1 1 4 6 6 48
## 2600 2700 2750 2800 2900 3000 3100 3200 3300 3400 3500 3600 3650 3700 3800 3900 4000 4100 4200 4300 4500 4600 4700 4800 4900 5000 5100 5125
## 8 12 1 20 5 73 3 11 3 1 38 2 1 2 8 1 27 1 3 2 19 2 1 1 2 50 2 1
## 5200 5500 5700 5800 6000 6100 6300 6500 7000 7499 7500 7700 8000 8500 8700 9000 9800 10000 11000 12000 12500 13000 15000 20000 <NA>
## 1 13 1 1 15 1 1 4 18 1 7 1 12 1 2 6 1 6 1 4 1 4 1 1 1594
## [1] "Frequency table after encoding"
## eh_s9q73. Q407: Clothes Washing Machine Washing Machine
## -998 0 50 100 150 200 250 300 400 500 550 600
## 3 2 1 2 1 3 2 4 1 25 1 3
## 700 750 800 900 1000 1200 1500 1800 1900 2000 2015 2100
## 4 2 6 1 49 2 40 3 1 68 1 1
## 2200 2300 2400 2500 2600 2700 2750 2800 2900 3000 3100 3200
## 4 6 6 48 8 12 1 20 5 73 3 11
## 3300 3400 3500 3600 3650 3700 3800 3900 4000 4100 4200 4300
## 3 1 38 2 1 2 8 1 27 1 3 2
## 4500 4600 4700 4800 4900 5000 5100 5125 5200 5500 5700 5800
## 19 2 1 1 2 50 2 1 1 13 1 1
## 6000 6100 6300 6500 7000 7499 7500 7700 8000 8500 8700 9000
## 15 1 1 4 18 1 7 1 12 1 2 6
## 9800 10000 11000 12000 12500 13000 or more <NA>
## 1 6 1 4 1 6 1594
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q74)[na.exclude(mydata$eh_s9q74)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q74", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q74. Q408: Air Conditioner Air Con
## 0 1000 4000 5000 5300 6500 8000 9000 10000 <NA>
## 1 1 2 1 1 1 2 1 2 2276
## [1] "Frequency table after encoding"
## eh_s9q74. Q408: Air Conditioner Air Con
## 0 1000 4000 5000 5300 6500 8000 9000 10000 or more <NA>
## 1 1 2 1 1 1 2 1 2 2276
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q75)[na.exclude(mydata$eh_s9q75)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q75", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q75. Q409: Electric Fan Electric Fan
## -998 0 20 50 60 70 80 90 95 100 110 120 125 130 150 160 180 200 220 230 240 248 250 260 300 350 380 400
## 8 12 1 19 1 1 1 1 1 37 1 2 1 1 36 2 4 70 1 1 2 1 21 1 104 15 1 48
## 425 450 500 540 549 550 560 580 599 600 630 650 680 699 700 750 780 800 850 900 920 925 950 980 999 1000 1020 1025
## 1 6 224 1 1 6 1 2 2 115 1 11 1 1 160 19 1 74 4 36 1 1 3 1 1 146 1 1
## 1050 1100 1150 1175 1198 1200 1250 1300 1350 1355 1390 1400 1450 1500 1550 1580 1600 1650 1700 1750 1800 1900 1950 2000 2040 2050 2100 2150
## 1 21 2 2 1 80 1 22 2 1 1 27 1 104 1 1 16 1 10 2 28 6 3 58 1 1 8 1
## 2200 2239 2250 2300 2340 2400 2500 2700 2800 2870 2900 3000 3100 3150 3200 3300 3500 3600 3700 3800 3900 4000 4200 4500 5000 6000 6800 7000
## 10 1 2 3 1 10 12 3 6 1 1 32 1 1 3 2 10 3 3 1 1 5 2 3 4 3 1 1
## 7600 8000 12000 15003 <NA>
## 1 1 1 1 539
## [1] "Frequency table after encoding"
## eh_s9q75. Q409: Electric Fan Electric Fan
## -998 0 20 50 60 70 80 90 95 100 110 120
## 8 12 1 19 1 1 1 1 1 37 1 2
## 125 130 150 160 180 200 220 230 240 248 250 260
## 1 1 36 2 4 70 1 1 2 1 21 1
## 300 350 380 400 425 450 500 540 549 550 560 580
## 104 15 1 48 1 6 224 1 1 6 1 2
## 599 600 630 650 680 699 700 750 780 800 850 900
## 2 115 1 11 1 1 160 19 1 74 4 36
## 920 925 950 980 999 1000 1020 1025 1050 1100 1150 1175
## 1 1 3 1 1 146 1 1 1 21 2 2
## 1198 1200 1250 1300 1350 1355 1390 1400 1450 1500 1550 1580
## 1 80 1 22 2 1 1 27 1 104 1 1
## 1600 1650 1700 1750 1800 1900 1950 2000 2040 2050 2100 2150
## 16 1 10 2 28 6 3 58 1 1 8 1
## 2200 2239 2250 2300 2340 2400 2500 2700 2800 2870 2900 3000
## 10 1 2 3 1 10 12 3 6 1 1 32
## 3100 3150 3200 3300 3500 3600 3700 3800 3900 4000 4200 4500
## 1 1 3 2 10 3 3 1 1 5 2 3
## 5000 5259 or more <NA>
## 4 9 539
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q76)[na.exclude(mydata$eh_s9q76)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q76", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q76. Q411: Pedicab Pedicab
## 0 300 400 500 1000 1200 1500 2000 2500 3000 3200 4000 4500 5000 5400 6000 7000 8000 9000 9500 10000 12000 12600 13000 14000 18000 21000 25200
## 1 1 1 3 8 1 4 5 3 12 1 3 2 17 1 1 6 4 1 1 12 2 1 1 1 1 1 1
## 30000 <NA>
## 1 2191
## [1] "Frequency table after encoding"
## eh_s9q76. Q411: Pedicab Pedicab
## 0 300 400 500 1000 1200 1500 2000 2500 3000 3200 4000
## 1 1 1 3 8 1 4 5 3 12 1 3
## 4500 5000 5400 6000 7000 8000 9000 9500 10000 12000 12600 13000
## 2 17 1 1 6 4 1 1 12 2 1 1
## 14000 18000 21000 25200 27695 or more <NA>
## 1 1 1 1 1 2191
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s9q77)[na.exclude(mydata$eh_s9q77)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s9q77", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s9q77. Q412: Rice Stocks [Un-milled dry rice] Palay
## 0 30 100 200 300 450 500 540 600 700 752 800 850 855 900 1000 1100 1200 1300 1350 1400 1500 1564 1600 1800 1850 1908 2000
## 1 1 1 1 1 1 3 1 3 7 1 9 4 1 4 10 2 1 1 1 3 10 1 4 3 1 1 12
## 2058 2070 2100 2200 2400 2500 2600 2660 2700 2958 3000 3200 3300 3400 3500 3600 3750 3825 3900 4000 4200 4230 4320 4400 4500 4700 4800 5000
## 1 1 3 1 1 1 1 1 4 1 16 2 3 2 3 1 1 1 1 8 3 1 2 2 3 1 5 13
## 5400 5600 5750 6000 6120 6300 6480 6960 7000 7200 7350 7400 7452 7500 7560 7680 8000 8100 8400 9000 9200 9600 9800 9960 10000 10080 10170 10500
## 1 2 1 9 1 1 1 1 7 5 1 1 1 1 1 1 7 1 2 6 1 3 1 1 4 1 1 1
## 11000 11730 12000 13000 13200 13500 14200 14250 14260 14300 15000 16000 18000 20000 21000 22496 22932 23760 24000 25000 36000 90000 <NA>
## 1 1 6 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2023
## [1] "Frequency table after encoding"
## eh_s9q77. Q412: Rice Stocks [Un-milled dry rice] Palay
## 0 30 100 200 300 450 500 540 600 700 752 800
## 1 1 1 1 1 1 3 1 3 7 1 9
## 850 855 900 1000 1100 1200 1300 1350 1400 1500 1564 1600
## 4 1 4 10 2 1 1 1 3 10 1 4
## 1800 1850 1908 2000 2058 2070 2100 2200 2400 2500 2600 2660
## 3 1 1 12 1 1 3 1 1 1 1 1
## 2700 2958 3000 3200 3300 3400 3500 3600 3750 3825 3900 4000
## 4 1 16 2 3 2 3 1 1 1 1 8
## 4200 4230 4320 4400 4500 4700 4800 5000 5400 5600 5750 6000
## 3 1 2 2 3 1 5 13 1 2 1 9
## 6120 6300 6480 6960 7000 7200 7350 7400 7452 7500 7560 7680
## 1 1 1 1 7 5 1 1 1 1 1 1
## 8000 8100 8400 9000 9200 9600 9800 9960 10000 10080 10170 10500
## 7 1 2 6 1 3 1 1 4 1 1 1
## 11000 11730 12000 13000 13200 13500 14200 14250 14260 14300 15000 16000
## 1 1 6 1 2 2 1 1 1 1 1 1
## 18000 20000 21000 22496 22932 23760 24000 25000 32480 or more <NA>
## 1 1 1 1 1 1 1 1 2 2023
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("eh_s9q1",
"eh_s9q4",
"eh_s9q5",
"eh_s9q6")
capture_tables (indirect_PII)Matching and crosstabulations: Run automated PII check
# !!!Insufficient demographic dataOpen-ends: review responses for any sensitive information, redact as necessary
# !!!No Open-endsGPS 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)