DOL-ILAB SDC - Section_6
Pablo Diego-Rosell, PhD
2021-10-20
- 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_6" # !!!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.
pctile_99.5_eh_s6q71_1<- floor(quantile(na.exclude(mydata$eh_s6q71_1)[na.exclude(mydata$eh_s6q71_1)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q72_1<- floor(quantile(na.exclude(mydata$eh_s6q72_1)[na.exclude(mydata$eh_s6q72_1)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q76_1<- floor(quantile(na.exclude(mydata$eh_s6q76_1)[na.exclude(mydata$eh_s6q76_1)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q71_2<- floor(quantile(na.exclude(mydata$eh_s6q71_2)[na.exclude(mydata$eh_s6q71_2)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q72_2<- floor(quantile(na.exclude(mydata$eh_s6q72_2)[na.exclude(mydata$eh_s6q72_2)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q76_2<- floor(quantile(na.exclude(mydata$eh_s6q76_2)[na.exclude(mydata$eh_s6q76_2)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q71_3<- floor(quantile(na.exclude(mydata$eh_s6q71_3)[na.exclude(mydata$eh_s6q71_3)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q72_3<- floor(quantile(na.exclude(mydata$eh_s6q72_3)[na.exclude(mydata$eh_s6q72_3)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q76_3<- floor(quantile(na.exclude(mydata$eh_s6q76_3)[na.exclude(mydata$eh_s6q76_3)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q71_4<- floor(quantile(na.exclude(mydata$eh_s6q71_4)[na.exclude(mydata$eh_s6q71_4)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q72_4<- floor(quantile(na.exclude(mydata$eh_s6q72_4)[na.exclude(mydata$eh_s6q72_4)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q76_4<- floor(quantile(na.exclude(mydata$eh_s6q76_4)[na.exclude(mydata$eh_s6q76_4)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q71_5<- floor(quantile(na.exclude(mydata$eh_s6q71_5)[na.exclude(mydata$eh_s6q71_5)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q72_5<- floor(quantile(na.exclude(mydata$eh_s6q72_5)[na.exclude(mydata$eh_s6q72_5)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q76_5<- floor(quantile(na.exclude(mydata$eh_s6q76_5)[na.exclude(mydata$eh_s6q76_5)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q71_6<- floor(quantile(na.exclude(mydata$eh_s6q71_6)[na.exclude(mydata$eh_s6q71_6)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q72_6<- floor(quantile(na.exclude(mydata$eh_s6q72_6)[na.exclude(mydata$eh_s6q72_6)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q76_6<- floor(quantile(na.exclude(mydata$eh_s6q76_6)[na.exclude(mydata$eh_s6q76_6)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q71_7<- floor(quantile(na.exclude(mydata$eh_s6q71_7)[na.exclude(mydata$eh_s6q71_7)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q72_7<- floor(quantile(na.exclude(mydata$eh_s6q72_7)[na.exclude(mydata$eh_s6q72_7)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q76_7<- floor(quantile(na.exclude(mydata$eh_s6q76_7)[na.exclude(mydata$eh_s6q76_7)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q71_8<- floor(quantile(na.exclude(mydata$eh_s6q71_8)[na.exclude(mydata$eh_s6q71_8)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q72_8<- floor(quantile(na.exclude(mydata$eh_s6q72_8)[na.exclude(mydata$eh_s6q72_8)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q76_8<- floor(quantile(na.exclude(mydata$eh_s6q76_8)[na.exclude(mydata$eh_s6q76_8)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q71_9<- floor(quantile(na.exclude(mydata$eh_s6q71_9)[na.exclude(mydata$eh_s6q71_9)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q72_9<- floor(quantile(na.exclude(mydata$eh_s6q72_9)[na.exclude(mydata$eh_s6q72_9)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q76_9<- floor(quantile(na.exclude(mydata$eh_s6q76_9)[na.exclude(mydata$eh_s6q76_9)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q71_10<- floor(quantile(na.exclude(mydata$eh_s6q71_10)[na.exclude(mydata$eh_s6q71_10)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q72_10<- floor(quantile(na.exclude(mydata$eh_s6q72_10)[na.exclude(mydata$eh_s6q72_10)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q76_10<- floor(quantile(na.exclude(mydata$eh_s6q76_10)[na.exclude(mydata$eh_s6q76_10)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q71_11<- floor(quantile(na.exclude(mydata$eh_s6q71_11)[na.exclude(mydata$eh_s6q71_11)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q72_11<- floor(quantile(na.exclude(mydata$eh_s6q72_11)[na.exclude(mydata$eh_s6q72_11)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q76_11<- floor(quantile(na.exclude(mydata$eh_s6q76_11)[na.exclude(mydata$eh_s6q76_11)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q71_12<- floor(quantile(na.exclude(mydata$eh_s6q71_12)[na.exclude(mydata$eh_s6q71_12)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q72_12<- floor(quantile(na.exclude(mydata$eh_s6q72_12)[na.exclude(mydata$eh_s6q72_12)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q76_12<- floor(quantile(na.exclude(mydata$eh_s6q76_12)[na.exclude(mydata$eh_s6q76_12)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q71_13<- floor(quantile(na.exclude(mydata$eh_s6q71_13)[na.exclude(mydata$eh_s6q71_13)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q72_13<- floor(quantile(na.exclude(mydata$eh_s6q72_13)[na.exclude(mydata$eh_s6q72_13)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q76_13<- floor(quantile(na.exclude(mydata$eh_s6q76_13)[na.exclude(mydata$eh_s6q76_13)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q71_14<- floor(quantile(na.exclude(mydata$eh_s6q71_14)[na.exclude(mydata$eh_s6q71_14)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q72_14<- floor(quantile(na.exclude(mydata$eh_s6q72_14)[na.exclude(mydata$eh_s6q72_14)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q76_14<- floor(quantile(na.exclude(mydata$eh_s6q76_14)[na.exclude(mydata$eh_s6q76_14)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q71_15<- floor(quantile(na.exclude(mydata$eh_s6q71_15)[na.exclude(mydata$eh_s6q71_15)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q72_15<- floor(quantile(na.exclude(mydata$eh_s6q72_15)[na.exclude(mydata$eh_s6q72_15)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q76_15<- floor(quantile(na.exclude(mydata$eh_s6q76_15)[na.exclude(mydata$eh_s6q76_15)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q71_16<- floor(quantile(na.exclude(mydata$eh_s6q71_16)[na.exclude(mydata$eh_s6q71_16)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q72_16<- floor(quantile(na.exclude(mydata$eh_s6q72_16)[na.exclude(mydata$eh_s6q72_16)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q76_16<- floor(quantile(na.exclude(mydata$eh_s6q76_16)[na.exclude(mydata$eh_s6q76_16)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q71_17<- floor(quantile(na.exclude(mydata$eh_s6q71_17)[na.exclude(mydata$eh_s6q71_17)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q72_17<- floor(quantile(na.exclude(mydata$eh_s6q72_17)[na.exclude(mydata$eh_s6q72_17)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q76_17<- floor(quantile(na.exclude(mydata$eh_s6q76_17)[na.exclude(mydata$eh_s6q76_17)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q71_18<- floor(quantile(na.exclude(mydata$eh_s6q71_18)[na.exclude(mydata$eh_s6q71_18)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q72_18<- floor(quantile(na.exclude(mydata$eh_s6q72_18)[na.exclude(mydata$eh_s6q72_18)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q76_18<- floor(quantile(na.exclude(mydata$eh_s6q76_18)[na.exclude(mydata$eh_s6q76_18)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q71_19<- floor(quantile(na.exclude(mydata$eh_s6q71_19)[na.exclude(mydata$eh_s6q71_19)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q72_19<- floor(quantile(na.exclude(mydata$eh_s6q72_19)[na.exclude(mydata$eh_s6q72_19)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q76_19<- floor(quantile(na.exclude(mydata$eh_s6q76_19)[na.exclude(mydata$eh_s6q76_19)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q71_20<- floor(quantile(na.exclude(mydata$eh_s6q71_20)[na.exclude(mydata$eh_s6q71_20)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q72_20<- floor(quantile(na.exclude(mydata$eh_s6q72_20)[na.exclude(mydata$eh_s6q72_20)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q76_20<- floor(quantile(na.exclude(mydata$eh_s6q76_20)[na.exclude(mydata$eh_s6q76_20)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q71_21<- floor(quantile(na.exclude(mydata$eh_s6q71_21)[na.exclude(mydata$eh_s6q71_21)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q72_21<- floor(quantile(na.exclude(mydata$eh_s6q72_21)[na.exclude(mydata$eh_s6q72_21)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q76_21<- floor(quantile(na.exclude(mydata$eh_s6q76_21)[na.exclude(mydata$eh_s6q76_21)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q71_22<- floor(quantile(na.exclude(mydata$eh_s6q71_22)[na.exclude(mydata$eh_s6q71_22)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q72_22<- floor(quantile(na.exclude(mydata$eh_s6q72_22)[na.exclude(mydata$eh_s6q72_22)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q76_22<- floor(quantile(na.exclude(mydata$eh_s6q76_22)[na.exclude(mydata$eh_s6q76_22)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q71_23<- floor(quantile(na.exclude(mydata$eh_s6q71_23)[na.exclude(mydata$eh_s6q71_23)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q72_23<- floor(quantile(na.exclude(mydata$eh_s6q72_23)[na.exclude(mydata$eh_s6q72_23)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q76_23<- floor(quantile(na.exclude(mydata$eh_s6q76_23)[na.exclude(mydata$eh_s6q76_23)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q71_24<- floor(quantile(na.exclude(mydata$eh_s6q71_24)[na.exclude(mydata$eh_s6q71_24)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q72_24<- floor(quantile(na.exclude(mydata$eh_s6q72_24)[na.exclude(mydata$eh_s6q72_24)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q76_24<- floor(quantile(na.exclude(mydata$eh_s6q76_24)[na.exclude(mydata$eh_s6q76_24)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q71_25<- floor(quantile(na.exclude(mydata$eh_s6q71_25)[na.exclude(mydata$eh_s6q71_25)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q72_25<- floor(quantile(na.exclude(mydata$eh_s6q72_25)[na.exclude(mydata$eh_s6q72_25)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q76_25<- floor(quantile(na.exclude(mydata$eh_s6q76_25)[na.exclude(mydata$eh_s6q76_25)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q78<- floor(quantile(na.exclude(mydata$eh_s6q78)[na.exclude(mydata$eh_s6q78)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q79<- floor(quantile(na.exclude(mydata$eh_s6q79)[na.exclude(mydata$eh_s6q79)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q80<- floor(quantile(na.exclude(mydata$eh_s6q80)[na.exclude(mydata$eh_s6q80)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q82<- floor(quantile(na.exclude(mydata$eh_s6q82)[na.exclude(mydata$eh_s6q82)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q84<- floor(quantile(na.exclude(mydata$eh_s6q84)[na.exclude(mydata$eh_s6q84)!=-998], probs = c(0.995)))
pctile_99.5_eh_s6q85<- floor(quantile(na.exclude(mydata$eh_s6q85)[na.exclude(mydata$eh_s6q85)!=-998], probs = c(0.995)))
mydata <- top_recode (variable="eh_s6q71_1", break_point=pctile_99.5_eh_s6q71_1, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q71_1. How much gross income or revenue was earned over the last 12 months from this ac
## 0 50 100 160 200 220 240 250 265 280 300 350 400 450
## 7 1 2 1 2 1 1 1 1 2 6 1 6 1
## 500 600 680 700 750 800 1000 1050 1120 1150 1170 1200 1250 1300
## 7 10 1 2 4 1 6 2 2 2 1 9 1 3
## 1350 1385 1400 1440 1500 1550 1560 1600 1700 1740 1750 1800 1900 2000
## 1 1 4 1 4 1 1 3 2 1 1 7 2 13
## 2100 2160 2200 2250 2304 2340 2400 2500 2520 2600 2700 2720 2740 2800
## 5 1 1 5 1 1 16 4 1 2 5 1 1 5
## 2880 2900 2940 3000 3040 3100 3120 3150 3200 3300 3350 3400 3500 3600
## 1 1 1 13 1 1 1 1 5 2 1 1 9 17
## 3750 3800 3900 4000 4050 4100 4150 4200 4320 4350 4400 4500 4600 4680
## 1 1 3 10 1 1 1 9 1 1 3 8 1 1
## 4800 4900 5000 5100 5184 5280 5300 5400 5500 5600 5800 6000 6250 6300
## 18 2 21 3 1 1 1 5 2 6 1 22 1 3
## 6400 6500 6600 6720 6750 6900 7000 7140 7200 7350 7500 7560 7600 7650
## 5 1 2 2 2 1 4 1 13 2 3 1 1 2
## 7800 8000 8100 8200 8250 8400 8535 8600 8700 8750 8800 8900 9000 9100
## 4 20 3 1 1 11 1 1 1 2 3 1 22 1
## 9300 9360 9450 9500 9600 9940 10000 10080 10500 10695 10710 10800 11000 11020
## 2 1 1 3 25 1 23 2 2 1 1 9 2 1
## 11200 11340 11500 11520 11700 11760 11900 12000 12500 12600 12680 12790 12800 13000
## 5 1 1 3 2 1 1 40 4 4 1 1 5 2
## 13200 13300 13400 13500 13600 13608 13800 14000 14240 14350 14360 14400 14450 14496
## 3 1 1 2 2 1 1 7 1 1 1 40 1 1
## 14500 14600 14650 14700 15000 15050 15120 15200 15360 15400 15600 15750 15800 16000
## 1 2 1 3 6 1 1 1 1 1 1 1 1 15
## 16200 16330 16400 16660 16800 17010 17250 17280 17400 17500 17600 17820 18000 18400
## 1 1 1 1 11 1 1 1 2 3 2 1 24 1
## 18500 18750 19000 19200 19300 19500 19650 19800 20000 20160 20400 20800 21000 21120
## 1 1 2 18 1 2 1 1 13 5 4 1 10 1
## 21500 21600 21750 21760 21800 21900 22000 22400 22500 22570 22800 23000 23040 23100
## 1 5 1 1 1 2 5 1 2 1 2 3 2 1
## 23400 23500 23520 23700 23895 24000 24300 24400 24450 24500 24900 25000 25155 25200
## 1 1 2 1 1 45 1 1 1 1 1 6 1 9
## 25300 25600 25800 25900 26000 26200 26400 26410 26500 26600 26700 26880 27000 27200
## 2 1 1 1 2 1 3 1 1 1 1 3 5 1
## 27288 27600 27720 28000 28200 28800 28950 29000 29400 29800 29900 30000 30100 30240
## 1 1 1 9 1 32 1 2 1 1 1 14 1 2
## 30300 30400 30600 30800 31000 31500 31920 32000 32400 33000 33600 33660 34000 34200
## 1 1 1 1 2 5 1 4 4 3 20 1 1 1
## 34500 34560 34850 35000 35100 35200 35520 35640 35769 36000 36125 36244 36960 37000
## 1 2 1 4 1 1 1 1 1 45 1 1 1 2
## 37440 37500 37700 37800 38000 38160 38300 38400 38500 38640 38700 38800 39000 39200
## 1 2 1 1 2 1 1 20 1 1 1 1 1 1
## 39240 39360 39500 39700 40000 40200 40320 40500 40560 40800 41095 42000 43000 43200
## 1 1 1 1 15 1 2 1 1 3 1 19 1 15
## 44000 44100 44800 45000 45360 45600 46200 46500 46800 47000 47200 47250 47500 47520
## 2 1 2 10 1 3 2 3 2 1 1 1 1 1
## 47600 48000 49000 49500 49770 50000 50400 51000 51140 51840 52800 53000 53090 53300
## 1 39 2 1 1 2 11 1 1 3 5 1 1 1
## 54000 54400 54600 55000 55200 55500 56000 57000 57600 58032 58400 58600 58800 59360
## 17 1 3 1 3 1 8 1 36 1 1 1 1 1
## 59904 60000 60480 61200 61600 62160 62400 63000 63360 64000 64800 65000 65520 66000
## 1 38 1 2 4 1 8 5 1 3 4 1 1 5
## 66400 67200 68000 68040 68224 68400 69000 69217 69300 69440 69600 70000 70350 70560
## 1 33 2 1 1 1 3 1 1 1 1 7 1 1
## 70700 70800 71400 72000 72670 73000 73200 74000 75000 75600 75800 76000 76800 77000
## 1 1 1 40 1 1 1 1 3 4 1 2 4 2
## 77760 78000 78750 79200 79500 79526 79750 80000 80640 81000 81600 82080 82500 82800
## 1 2 1 5 1 1 1 5 1 2 2 1 1 1
## 83000 83424 83520 84000 84600 85000 85200 85248 85500 86400 86500 87900 88800 89000
## 2 1 1 21 1 1 1 1 1 13 1 1 2 1
## 89600 90000 90710 90720 91200 91250 92000 93600 94000 94080 94500 95424 95450 95760
## 1 12 1 1 1 1 2 3 1 2 1 1 1 1
## 96000 96400 96600 97100 97200 97920 98000 99000 99900 1e+05 100100 100300 100320 100800
## 33 1 1 1 1 1 2 1 1 3 1 1 1 32
## 101088 101360 101844 102000 102240 103200 103680 104400 105375 105600 105850 106560 106590 106800
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 108000 108480 108500 108680 109200 109440 109500 110000 110880 112000 112500 113400 114000 115200
## 8 1 1 1 1 2 2 2 2 2 1 1 1 29
## 116200 116640 117200 117600 118560 118800 120000 121000 121500 121800 122200 123300 124800 125000
## 1 1 1 4 3 2 16 1 2 1 1 1 1 1
## 125060 126000 127120 127160 127200 127534 128000 129000 129600 130000 132000 134000 134400 136000
## 1 4 1 1 1 1 2 1 9 3 6 1 10 1
## 136320 138000 139000 140000 140160 143600 144000 145600 146640 147456 148800 149760 150000 151200
## 1 1 1 3 1 2 37 1 1 1 1 1 2 5
## 152100 152640 152800 153600 155500 156000 157000 157260 157290 157500 158400 160000 168000 170000
## 1 1 1 1 1 1 1 1 1 2 2 1 21 1
## 172800 176400 177600 178000 178560 180000 182400 182500 184320 184800 187200 188000 191520 192000
## 5 1 1 1 1 9 1 1 1 1 2 1 1 9
## 194400 195800 196200 196500 197376 2e+05 201600 204000 207360 209600 210000 214600 215600 216000
## 2 1 1 1 1 2 3 1 1 1 2 1 1 8
## 218400 220800 222450 225600 230400 234080 235200 236000 240000 250880 252000 257600 258400 259200
## 1 1 1 1 3 1 2 1 2 1 6 1 1 1
## 260400 268800 272000 280560 285600 288000 288960 289980 293760 3e+05 302000 302400 308000 320000
## 1 4 1 1 2 6 1 1 1 1 1 3 1 1
## 324000 330000 336000 340800 355000 357600 360000 364000 369600 374400 381000 384000 396000 4e+05
## 2 1 7 1 1 1 7 1 1 1 1 4 1 1
## 401500 403200 420000 430000 432000 448000 468000 492000 5e+05 504000 518400 520800 528000 540000
## 1 3 1 1 1 1 1 1 2 5 1 1 1 1
## 571200 572000 576000 604800 648000 648960 662400 672000 720000 724800 892800 9e+05 960000 1008000
## 2 1 2 2 2 1 1 4 4 1 1 1 1 2
## 1142400 1152000 1440000 1800000 2184000 2520000 4032000 <NA>
## 1 1 2 1 1 1 1 77
## [1] "Frequency table after encoding"
## eh_s6q71_1. How much gross income or revenue was earned over the last 12 months from this ac
## 0 50 100 160 200 220 240
## 7 1 2 1 2 1 1
## 250 265 280 300 350 400 450
## 1 1 2 6 1 6 1
## 500 600 680 700 750 800 1000
## 7 10 1 2 4 1 6
## 1050 1120 1150 1170 1200 1250 1300
## 2 2 2 1 9 1 3
## 1350 1385 1400 1440 1500 1550 1560
## 1 1 4 1 4 1 1
## 1600 1700 1740 1750 1800 1900 2000
## 3 2 1 1 7 2 13
## 2100 2160 2200 2250 2304 2340 2400
## 5 1 1 5 1 1 16
## 2500 2520 2600 2700 2720 2740 2800
## 4 1 2 5 1 1 5
## 2880 2900 2940 3000 3040 3100 3120
## 1 1 1 13 1 1 1
## 3150 3200 3300 3350 3400 3500 3600
## 1 5 2 1 1 9 17
## 3750 3800 3900 4000 4050 4100 4150
## 1 1 3 10 1 1 1
## 4200 4320 4350 4400 4500 4600 4680
## 9 1 1 3 8 1 1
## 4800 4900 5000 5100 5184 5280 5300
## 18 2 21 3 1 1 1
## 5400 5500 5600 5800 6000 6250 6300
## 5 2 6 1 22 1 3
## 6400 6500 6600 6720 6750 6900 7000
## 5 1 2 2 2 1 4
## 7140 7200 7350 7500 7560 7600 7650
## 1 13 2 3 1 1 2
## 7800 8000 8100 8200 8250 8400 8535
## 4 20 3 1 1 11 1
## 8600 8700 8750 8800 8900 9000 9100
## 1 1 2 3 1 22 1
## 9300 9360 9450 9500 9600 9940 10000
## 2 1 1 3 25 1 23
## 10080 10500 10695 10710 10800 11000 11020
## 2 2 1 1 9 2 1
## 11200 11340 11500 11520 11700 11760 11900
## 5 1 1 3 2 1 1
## 12000 12500 12600 12680 12790 12800 13000
## 40 4 4 1 1 5 2
## 13200 13300 13400 13500 13600 13608 13800
## 3 1 1 2 2 1 1
## 14000 14240 14350 14360 14400 14450 14496
## 7 1 1 1 40 1 1
## 14500 14600 14650 14700 15000 15050 15120
## 1 2 1 3 6 1 1
## 15200 15360 15400 15600 15750 15800 16000
## 1 1 1 1 1 1 15
## 16200 16330 16400 16660 16800 17010 17250
## 1 1 1 1 11 1 1
## 17280 17400 17500 17600 17820 18000 18400
## 1 2 3 2 1 24 1
## 18500 18750 19000 19200 19300 19500 19650
## 1 1 2 18 1 2 1
## 19800 20000 20160 20400 20800 21000 21120
## 1 13 5 4 1 10 1
## 21500 21600 21750 21760 21800 21900 22000
## 1 5 1 1 1 2 5
## 22400 22500 22570 22800 23000 23040 23100
## 1 2 1 2 3 2 1
## 23400 23500 23520 23700 23895 24000 24300
## 1 1 2 1 1 45 1
## 24400 24450 24500 24900 25000 25155 25200
## 1 1 1 1 6 1 9
## 25300 25600 25800 25900 26000 26200 26400
## 2 1 1 1 2 1 3
## 26410 26500 26600 26700 26880 27000 27200
## 1 1 1 1 3 5 1
## 27288 27600 27720 28000 28200 28800 28950
## 1 1 1 9 1 32 1
## 29000 29400 29800 29900 30000 30100 30240
## 2 1 1 1 14 1 2
## 30300 30400 30600 30800 31000 31500 31920
## 1 1 1 1 2 5 1
## 32000 32400 33000 33600 33660 34000 34200
## 4 4 3 20 1 1 1
## 34500 34560 34850 35000 35100 35200 35520
## 1 2 1 4 1 1 1
## 35640 35769 36000 36125 36244 36960 37000
## 1 1 45 1 1 1 2
## 37440 37500 37700 37800 38000 38160 38300
## 1 2 1 1 2 1 1
## 38400 38500 38640 38700 38800 39000 39200
## 20 1 1 1 1 1 1
## 39240 39360 39500 39700 40000 40200 40320
## 1 1 1 1 15 1 2
## 40500 40560 40800 41095 42000 43000 43200
## 1 1 3 1 19 1 15
## 44000 44100 44800 45000 45360 45600 46200
## 2 1 2 10 1 3 2
## 46500 46800 47000 47200 47250 47500 47520
## 3 2 1 1 1 1 1
## 47600 48000 49000 49500 49770 50000 50400
## 1 39 2 1 1 2 11
## 51000 51140 51840 52800 53000 53090 53300
## 1 1 3 5 1 1 1
## 54000 54400 54600 55000 55200 55500 56000
## 17 1 3 1 3 1 8
## 57000 57600 58032 58400 58600 58800 59360
## 1 36 1 1 1 1 1
## 59904 60000 60480 61200 61600 62160 62400
## 1 38 1 2 4 1 8
## 63000 63360 64000 64800 65000 65520 66000
## 5 1 3 4 1 1 5
## 66400 67200 68000 68040 68224 68400 69000
## 1 33 2 1 1 1 3
## 69217 69300 69440 69600 70000 70350 70560
## 1 1 1 1 7 1 1
## 70700 70800 71400 72000 72670 73000 73200
## 1 1 1 40 1 1 1
## 74000 75000 75600 75800 76000 76800 77000
## 1 3 4 1 2 4 2
## 77760 78000 78750 79200 79500 79526 79750
## 1 2 1 5 1 1 1
## 80000 80640 81000 81600 82080 82500 82800
## 5 1 2 2 1 1 1
## 83000 83424 83520 84000 84600 85000 85200
## 2 1 1 21 1 1 1
## 85248 85500 86400 86500 87900 88800 89000
## 1 1 13 1 1 2 1
## 89600 90000 90710 90720 91200 91250 92000
## 1 12 1 1 1 1 2
## 93600 94000 94080 94500 95424 95450 95760
## 3 1 2 1 1 1 1
## 96000 96400 96600 97100 97200 97920 98000
## 33 1 1 1 1 1 2
## 99000 99900 1e+05 100100 100300 100320 100800
## 1 1 3 1 1 1 32
## 101088 101360 101844 102000 102240 103200 103680
## 1 1 1 1 1 1 1
## 104400 105375 105600 105850 106560 106590 106800
## 1 1 1 1 1 1 1
## 108000 108480 108500 108680 109200 109440 109500
## 8 1 1 1 1 2 2
## 110000 110880 112000 112500 113400 114000 115200
## 2 2 2 1 1 1 29
## 116200 116640 117200 117600 118560 118800 120000
## 1 1 1 4 3 2 16
## 121000 121500 121800 122200 123300 124800 125000
## 1 2 1 1 1 1 1
## 125060 126000 127120 127160 127200 127534 128000
## 1 4 1 1 1 1 2
## 129000 129600 130000 132000 134000 134400 136000
## 1 9 3 6 1 10 1
## 136320 138000 139000 140000 140160 143600 144000
## 1 1 1 3 1 2 37
## 145600 146640 147456 148800 149760 150000 151200
## 1 1 1 1 1 2 5
## 152100 152640 152800 153600 155500 156000 157000
## 1 1 1 1 1 1 1
## 157260 157290 157500 158400 160000 168000 170000
## 1 1 2 2 1 21 1
## 172800 176400 177600 178000 178560 180000 182400
## 5 1 1 1 1 9 1
## 182500 184320 184800 187200 188000 191520 192000
## 1 1 1 2 1 1 9
## 194400 195800 196200 196500 197376 2e+05 201600
## 2 1 1 1 1 2 3
## 204000 207360 209600 210000 214600 215600 216000
## 1 1 1 2 1 1 8
## 218400 220800 222450 225600 230400 234080 235200
## 1 1 1 1 3 1 2
## 236000 240000 250880 252000 257600 258400 259200
## 1 2 1 6 1 1 1
## 260400 268800 272000 280560 285600 288000 288960
## 1 4 1 1 2 6 1
## 289980 293760 3e+05 302000 302400 308000 320000
## 1 1 1 1 3 1 1
## 324000 330000 336000 340800 355000 357600 360000
## 2 1 7 1 1 1 7
## 364000 369600 374400 381000 384000 396000 4e+05
## 1 1 1 1 4 1 1
## 401500 403200 420000 430000 432000 448000 468000
## 1 3 1 1 1 1 1
## 492000 5e+05 504000 518400 520800 528000 540000
## 1 2 5 1 1 1 1
## 571200 572000 576000 604800 648000 648960 662400
## 2 1 2 2 2 1 1
## 672000 720000 724800 892800 899639 or more <NA>
## 4 4 1 1 12 77
mydata <- top_recode (variable="eh_s6q72_1", break_point=pctile_99.5_eh_s6q72_1, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q72_1. Some activities require expenses in order to do them. What are the total expense
## -998 0 20 30 40 50 60 63 70 78 85 100 102 105
## 1 559 2 1 2 3 1 1 1 1 1 4 1 1
## 112 114 120 126 128 135 140 150 160 180 192 200 210 220
## 2 1 2 1 1 1 1 7 1 2 1 12 2 1
## 224 230 240 250 252 265 280 300 310 320 350 360 384 400
## 2 1 3 3 1 1 2 2 2 1 4 5 1 2
## 443 450 480 500 550 560 588 600 610 616 640 650 672 673
## 1 3 5 13 2 2 1 12 1 1 1 1 1 1
## 700 708 720 744 750 760 768 780 800 825 840 850 864 880
## 8 1 2 1 3 1 2 2 11 1 1 1 1 2
## 900 960 968 990 1000 1040 1050 1100 1120 1140 1150 1152 1168 1170
## 9 6 1 1 7 2 2 1 1 1 2 1 1 1
## 1190 1200 1220 1250 1260 1280 1296 1320 1338 1350 1400 1405 1440 1450
## 1 15 1 1 2 1 1 2 1 1 5 1 6 1
## 1468 1500 1512 1540 1575 1600 1620 1622 1674 1680 1700 1750 1760 1780
## 1 4 1 1 1 9 1 1 1 2 1 1 1 1
## 1800 1820 1845 1856 1870 1900 1920 1960 1970 1975 2000 2048 2080 2100
## 17 1 1 1 1 2 13 1 1 1 18 1 3 4
## 2130 2160 2280 2300 2360 2400 2500 2520 2580 2600 2625 2640 2700 2800
## 1 1 2 3 1 27 4 1 1 1 1 1 3 6
## 2880 2940 3000 3060 3100 3150 3169 3175 3200 3240 3280 3300 3360 3400
## 15 2 10 1 2 1 1 1 2 1 1 2 5 2
## 3480 3500 3600 3744 3780 3800 3840 3850 3872 3880 3900 3940 3960 3990
## 1 4 18 1 1 1 7 1 1 1 4 1 1 1
## 4000 4050 4060 4080 4160 4200 4320 4350 4400 4410 4422 4500 4550 4600
## 9 1 1 1 3 3 4 1 2 1 1 5 1 1
## 4608 4680 4800 4840 4994 5000 5040 5120 5150 5184 5250 5400 5460 5500
## 2 1 16 1 1 11 3 1 1 1 1 6 1 1
## 5540 5550 5600 5640 5700 5760 5800 6000 6013 6030 6100 6200 6240 6300
## 1 1 6 1 1 15 2 17 1 1 1 1 1 3
## 6340 6380 6400 6480 6490 6500 6540 6600 6720 6765 6800 6912 6920 7000
## 1 1 3 2 1 1 1 1 4 1 1 1 1 2
## 7176 7200 7236 7250 7300 7400 7500 7600 7650 7700 7740 7760 7920 8000
## 1 17 1 2 2 2 1 2 1 2 1 1 1 13
## 8100 8150 8170 8184 8200 8300 8320 8340 8400 8472 8640 8716 8832 8860
## 1 1 1 1 3 1 1 1 8 1 5 1 1 1
## 8880 8960 9000 9030 9100 9216 9250 9300 9360 9470 9570 9600 9680 9744
## 1 1 12 1 1 2 1 2 2 1 1 15 1 1
## 9792 9950 10000 10080 10104 10200 10260 10320 10350 10368 10440 10500 10530 10560
## 1 1 19 3 1 1 1 1 1 1 1 1 1 1
## 10600 10640 10800 10900 10980 11000 11100 11176 11200 11304 11328 11520 11567 11620
## 1 1 7 1 1 1 1 1 1 1 1 5 1 1
## 11650 11792 11800 11920 12000 12060 12096 12100 12160 12240 12349 12400 12480 12576
## 1 1 1 1 26 1 1 1 1 1 1 1 1 1
## 12600 12642 12720 12750 12768 12780 12800 12900 12960 13000 13068 13100 13196 13200
## 4 1 1 1 1 1 1 1 2 2 1 2 1 7
## 13440 13500 13600 13632 13700 14000 14170 14320 14400 14472 14600 14760 14800 14880
## 3 2 2 1 1 5 1 1 24 1 2 1 2 1
## 15000 15192 15200 15480 15600 15680 15840 15950 16000 16040 16058 16190 16200 16416
## 4 1 1 1 4 1 1 2 5 1 1 1 1 1
## 16430 16460 16500 16560 16620 16680 16800 16880 16900 17000 17200 17280 17820 17860
## 1 1 1 1 1 1 9 1 1 1 1 3 1 1
## 17900 18000 18100 18240 18420 18480 18688 18700 18710 18720 18900 19000 19008 19200
## 1 10 1 1 1 1 1 1 1 2 1 1 1 8
## 19220 19280 19320 19600 19680 19885 20000 20160 20200 20400 20520 20550 20800 20880
## 1 1 1 1 2 1 3 6 1 2 1 2 1 1
## 20970 21120 21216 21360 21410 21600 21840 22000 22200 22280 22320 22560 22680 22800
## 1 1 1 1 1 9 2 3 1 2 1 1 1 1
## 22820 23000 23040 23088 23400 23520 24000 24240 24300 24350 24480 24500 24768 25000
## 1 1 5 1 1 2 24 1 1 1 1 1 1 3
## 25200 25760 25920 26000 26100 26350 26400 26496 26800 26880 27000 27010 27200 27300
## 4 1 4 2 1 1 4 1 1 2 4 1 1 1
## 27360 27600 27800 28000 28080 28340 28560 28607 28700 28704 28710 28800 28896 29000
## 1 2 1 8 1 1 1 1 1 1 1 21 1 2
## 29296 29300 29376 29520 29664 29760 29800 30000 30240 30580 30785 30900 30910 30912
## 1 1 1 1 1 1 1 10 1 1 1 1 1 1
## 31000 31025 31500 31584 31680 31700 31800 31820 31932 32000 32070 32400 32610 32640
## 2 1 1 1 2 1 1 1 1 1 1 1 1 2
## 32700 32760 32800 32914 33000 33600 34200 34450 34470 34560 34720 34800 35000 35180
## 1 1 1 1 1 12 1 1 1 1 1 1 3 1
## 35280 35320 35600 35622 36000 36324 36800 36864 36960 37296 37300 37400 37800 37880
## 2 1 2 1 12 1 2 1 3 1 1 1 2 1
## 38000 38200 38304 38340 38400 38650 38800 38815 39810 40000 40320 40560 40600 40700
## 3 1 1 1 3 1 1 1 1 11 1 1 1 1
## 40800 40992 41000 41160 41600 41808 42000 42240 42900 42952 42972 43100 43200 43680
## 1 1 1 1 1 1 7 1 1 1 1 1 10 1
## 43720 44000 44364 44400 44700 45000 45190 45600 46000 46200 46424 46770 46800 47280
## 1 1 1 2 1 7 1 2 1 1 1 1 1 1
## 47880 48000 48960 49040 49240 49330 49440 49536 49680 49920 50000 50250 50400 50448
## 1 21 2 1 1 1 1 1 1 5 2 1 5 1
## 50640 50728 51000 51120 51800 51900 52000 52400 52416 52800 53646 53650 53670 53718
## 1 1 2 1 1 1 3 1 1 2 1 1 1 1
## 54000 55000 55120 55200 55400 55440 56000 56160 56340 56700 57024 57040 57408 57600
## 4 1 1 1 1 1 1 1 1 1 1 1 1 9
## 58500 59300 59927 60000 60400 60640 62050 62400 63000 63360 64000 64080 65000 65760
## 1 1 1 5 1 1 1 3 1 2 1 1 1 1
## 66000 67200 68000 68400 68768 69100 69215 69312 69400 69825 70000 72000 73440 73748
## 1 6 1 1 1 1 1 1 1 1 3 9 1 1
## 73920 74050 74880 76270 76800 77280 79145 79200 80000 80800 81000 81500 81600 83250
## 1 1 1 1 1 1 1 1 1 1 2 1 2 1
## 84000 86400 86560 87300 87500 87640 88000 88800 89610 90000 90480 90720 91200 91250
## 2 6 1 1 1 1 1 1 1 1 1 1 1 1
## 92831 93443 93600 94500 94750 96000 96500 96576 96800 97440 97920 98558 1e+05 100300
## 1 1 2 2 1 8 1 1 1 1 1 1 3 1
## 100800 101760 104070 104160 104640 105600 105840 107280 108000 108300 109440 109560 110400 113000
## 6 1 1 1 1 1 1 1 4 1 1 1 1 1
## 113400 114000 115700 116000 116024 117600 118080 118480 120000 120960 121800 122304 124800 126000
## 1 1 1 1 1 1 1 1 5 1 1 1 2 2
## 127680 129600 130000 130080 133280 135360 138052 138160 138240 140000 141120 142800 144000 146000
## 1 2 1 1 1 1 1 1 1 1 1 1 5 1
## 147000 148500 151200 152339 152800 153600 154560 155980 159500 160000 160320 160800 162000 162600
## 1 1 1 1 1 1 1 1 1 2 1 1 1 1
## 162768 168000 168480 172800 175500 176000 180364 180760 182400 183750 184000 192000 196000 201600
## 1 6 1 3 1 2 1 1 1 1 1 5 1 2
## 203616 206400 211200 215600 216000 224000 229920 230400 232500 233100 234000 240000 240960 242160
## 1 1 1 1 2 1 1 1 1 1 1 5 1 1
## 248500 256800 259200 268600 275584 280000 282000 282240 284000 288000 291600 297600 3e+05 316800
## 1 1 1 1 1 1 1 1 1 6 1 2 2 1
## 336000 345600 352800 367300 378000 381890 392000 403200 412800 416640 432000 437800 466200 470400
## 4 1 1 1 1 1 1 2 1 1 2 1 1 2
## 480000 480960 504000 510720 521136 524920 540000 541200 548640 550440 576000 698400 714170 731568
## 2 1 1 1 1 1 1 1 1 1 2 1 1 1
## 782000 1224000 1260000 1620000 1884816 2412000 3662400 <NA>
## 1 1 1 1 1 1 1 8
## [1] "Frequency table after encoding"
## eh_s6q72_1. Some activities require expenses in order to do them. What are the total expense
## -998 0 20 30 40 50 60
## 1 559 2 1 2 3 1
## 63 70 78 85 100 102 105
## 1 1 1 1 4 1 1
## 112 114 120 126 128 135 140
## 2 1 2 1 1 1 1
## 150 160 180 192 200 210 220
## 7 1 2 1 12 2 1
## 224 230 240 250 252 265 280
## 2 1 3 3 1 1 2
## 300 310 320 350 360 384 400
## 2 2 1 4 5 1 2
## 443 450 480 500 550 560 588
## 1 3 5 13 2 2 1
## 600 610 616 640 650 672 673
## 12 1 1 1 1 1 1
## 700 708 720 744 750 760 768
## 8 1 2 1 3 1 2
## 780 800 825 840 850 864 880
## 2 11 1 1 1 1 2
## 900 960 968 990 1000 1040 1050
## 9 6 1 1 7 2 2
## 1100 1120 1140 1150 1152 1168 1170
## 1 1 1 2 1 1 1
## 1190 1200 1220 1250 1260 1280 1296
## 1 15 1 1 2 1 1
## 1320 1338 1350 1400 1405 1440 1450
## 2 1 1 5 1 6 1
## 1468 1500 1512 1540 1575 1600 1620
## 1 4 1 1 1 9 1
## 1622 1674 1680 1700 1750 1760 1780
## 1 1 2 1 1 1 1
## 1800 1820 1845 1856 1870 1900 1920
## 17 1 1 1 1 2 13
## 1960 1970 1975 2000 2048 2080 2100
## 1 1 1 18 1 3 4
## 2130 2160 2280 2300 2360 2400 2500
## 1 1 2 3 1 27 4
## 2520 2580 2600 2625 2640 2700 2800
## 1 1 1 1 1 3 6
## 2880 2940 3000 3060 3100 3150 3169
## 15 2 10 1 2 1 1
## 3175 3200 3240 3280 3300 3360 3400
## 1 2 1 1 2 5 2
## 3480 3500 3600 3744 3780 3800 3840
## 1 4 18 1 1 1 7
## 3850 3872 3880 3900 3940 3960 3990
## 1 1 1 4 1 1 1
## 4000 4050 4060 4080 4160 4200 4320
## 9 1 1 1 3 3 4
## 4350 4400 4410 4422 4500 4550 4600
## 1 2 1 1 5 1 1
## 4608 4680 4800 4840 4994 5000 5040
## 2 1 16 1 1 11 3
## 5120 5150 5184 5250 5400 5460 5500
## 1 1 1 1 6 1 1
## 5540 5550 5600 5640 5700 5760 5800
## 1 1 6 1 1 15 2
## 6000 6013 6030 6100 6200 6240 6300
## 17 1 1 1 1 1 3
## 6340 6380 6400 6480 6490 6500 6540
## 1 1 3 2 1 1 1
## 6600 6720 6765 6800 6912 6920 7000
## 1 4 1 1 1 1 2
## 7176 7200 7236 7250 7300 7400 7500
## 1 17 1 2 2 2 1
## 7600 7650 7700 7740 7760 7920 8000
## 2 1 2 1 1 1 13
## 8100 8150 8170 8184 8200 8300 8320
## 1 1 1 1 3 1 1
## 8340 8400 8472 8640 8716 8832 8860
## 1 8 1 5 1 1 1
## 8880 8960 9000 9030 9100 9216 9250
## 1 1 12 1 1 2 1
## 9300 9360 9470 9570 9600 9680 9744
## 2 2 1 1 15 1 1
## 9792 9950 10000 10080 10104 10200 10260
## 1 1 19 3 1 1 1
## 10320 10350 10368 10440 10500 10530 10560
## 1 1 1 1 1 1 1
## 10600 10640 10800 10900 10980 11000 11100
## 1 1 7 1 1 1 1
## 11176 11200 11304 11328 11520 11567 11620
## 1 1 1 1 5 1 1
## 11650 11792 11800 11920 12000 12060 12096
## 1 1 1 1 26 1 1
## 12100 12160 12240 12349 12400 12480 12576
## 1 1 1 1 1 1 1
## 12600 12642 12720 12750 12768 12780 12800
## 4 1 1 1 1 1 1
## 12900 12960 13000 13068 13100 13196 13200
## 1 2 2 1 2 1 7
## 13440 13500 13600 13632 13700 14000 14170
## 3 2 2 1 1 5 1
## 14320 14400 14472 14600 14760 14800 14880
## 1 24 1 2 1 2 1
## 15000 15192 15200 15480 15600 15680 15840
## 4 1 1 1 4 1 1
## 15950 16000 16040 16058 16190 16200 16416
## 2 5 1 1 1 1 1
## 16430 16460 16500 16560 16620 16680 16800
## 1 1 1 1 1 1 9
## 16880 16900 17000 17200 17280 17820 17860
## 1 1 1 1 3 1 1
## 17900 18000 18100 18240 18420 18480 18688
## 1 10 1 1 1 1 1
## 18700 18710 18720 18900 19000 19008 19200
## 1 1 2 1 1 1 8
## 19220 19280 19320 19600 19680 19885 20000
## 1 1 1 1 2 1 3
## 20160 20200 20400 20520 20550 20800 20880
## 6 1 2 1 2 1 1
## 20970 21120 21216 21360 21410 21600 21840
## 1 1 1 1 1 9 2
## 22000 22200 22280 22320 22560 22680 22800
## 3 1 2 1 1 1 1
## 22820 23000 23040 23088 23400 23520 24000
## 1 1 5 1 1 2 24
## 24240 24300 24350 24480 24500 24768 25000
## 1 1 1 1 1 1 3
## 25200 25760 25920 26000 26100 26350 26400
## 4 1 4 2 1 1 4
## 26496 26800 26880 27000 27010 27200 27300
## 1 1 2 4 1 1 1
## 27360 27600 27800 28000 28080 28340 28560
## 1 2 1 8 1 1 1
## 28607 28700 28704 28710 28800 28896 29000
## 1 1 1 1 21 1 2
## 29296 29300 29376 29520 29664 29760 29800
## 1 1 1 1 1 1 1
## 30000 30240 30580 30785 30900 30910 30912
## 10 1 1 1 1 1 1
## 31000 31025 31500 31584 31680 31700 31800
## 2 1 1 1 2 1 1
## 31820 31932 32000 32070 32400 32610 32640
## 1 1 1 1 1 1 2
## 32700 32760 32800 32914 33000 33600 34200
## 1 1 1 1 1 12 1
## 34450 34470 34560 34720 34800 35000 35180
## 1 1 1 1 1 3 1
## 35280 35320 35600 35622 36000 36324 36800
## 2 1 2 1 12 1 2
## 36864 36960 37296 37300 37400 37800 37880
## 1 3 1 1 1 2 1
## 38000 38200 38304 38340 38400 38650 38800
## 3 1 1 1 3 1 1
## 38815 39810 40000 40320 40560 40600 40700
## 1 1 11 1 1 1 1
## 40800 40992 41000 41160 41600 41808 42000
## 1 1 1 1 1 1 7
## 42240 42900 42952 42972 43100 43200 43680
## 1 1 1 1 1 10 1
## 43720 44000 44364 44400 44700 45000 45190
## 1 1 1 2 1 7 1
## 45600 46000 46200 46424 46770 46800 47280
## 2 1 1 1 1 1 1
## 47880 48000 48960 49040 49240 49330 49440
## 1 21 2 1 1 1 1
## 49536 49680 49920 50000 50250 50400 50448
## 1 1 5 2 1 5 1
## 50640 50728 51000 51120 51800 51900 52000
## 1 1 2 1 1 1 3
## 52400 52416 52800 53646 53650 53670 53718
## 1 1 2 1 1 1 1
## 54000 55000 55120 55200 55400 55440 56000
## 4 1 1 1 1 1 1
## 56160 56340 56700 57024 57040 57408 57600
## 1 1 1 1 1 1 9
## 58500 59300 59927 60000 60400 60640 62050
## 1 1 1 5 1 1 1
## 62400 63000 63360 64000 64080 65000 65760
## 3 1 2 1 1 1 1
## 66000 67200 68000 68400 68768 69100 69215
## 1 6 1 1 1 1 1
## 69312 69400 69825 70000 72000 73440 73748
## 1 1 1 3 9 1 1
## 73920 74050 74880 76270 76800 77280 79145
## 1 1 1 1 1 1 1
## 79200 80000 80800 81000 81500 81600 83250
## 1 1 1 2 1 2 1
## 84000 86400 86560 87300 87500 87640 88000
## 2 6 1 1 1 1 1
## 88800 89610 90000 90480 90720 91200 91250
## 1 1 1 1 1 1 1
## 92831 93443 93600 94500 94750 96000 96500
## 1 1 2 2 1 8 1
## 96576 96800 97440 97920 98558 1e+05 100300
## 1 1 1 1 1 3 1
## 100800 101760 104070 104160 104640 105600 105840
## 6 1 1 1 1 1 1
## 107280 108000 108300 109440 109560 110400 113000
## 1 4 1 1 1 1 1
## 113400 114000 115700 116000 116024 117600 118080
## 1 1 1 1 1 1 1
## 118480 120000 120960 121800 122304 124800 126000
## 1 5 1 1 1 2 2
## 127680 129600 130000 130080 133280 135360 138052
## 1 2 1 1 1 1 1
## 138160 138240 140000 141120 142800 144000 146000
## 1 1 1 1 1 5 1
## 147000 148500 151200 152339 152800 153600 154560
## 1 1 1 1 1 1 1
## 155980 159500 160000 160320 160800 162000 162600
## 1 1 2 1 1 1 1
## 162768 168000 168480 172800 175500 176000 180364
## 1 6 1 3 1 2 1
## 180760 182400 183750 184000 192000 196000 201600
## 1 1 1 1 5 1 2
## 203616 206400 211200 215600 216000 224000 229920
## 1 1 1 1 2 1 1
## 230400 232500 233100 234000 240000 240960 242160
## 1 1 1 1 5 1 1
## 248500 256800 259200 268600 275584 280000 282000
## 1 1 1 1 1 1 1
## 282240 284000 288000 291600 297600 3e+05 316800
## 1 1 6 1 2 2 1
## 336000 345600 352800 367300 378000 381890 392000
## 4 1 1 1 1 1 1
## 403200 412800 416640 432000 437800 466200 470400
## 2 1 1 2 1 1 2
## 480000 480960 504000 510720 521136 524920 540000
## 2 1 1 1 1 1 1
## 541200 548640 550440 566031 or more <NA>
## 1 1 1 12 8
mydata <- top_recode (variable="eh_s6q76_1", break_point=pctile_99.5_eh_s6q76_1, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q76_1. If you were to buy those goods or services in a local market over the last 12 mo
## -998 5 10 15 17 18 20 25 27 30 31 35 40 50 60 65
## 3 2 1 3 1 2 9 4 1 5 1 2 7 13 4 1
## 72 75 80 90 95 96 100 104 110 120 140 141 144 150 160 175
## 2 1 7 1 1 1 19 1 1 8 3 1 1 19 1 1
## 180 199 200 207 210 217 240 250 276 280 300 310 350 360 384 400
## 2 1 17 1 3 1 6 5 1 2 22 1 5 2 1 18
## 420 450 480 500 540 560 576 600 624 640 650 700 720 750 768 800
## 2 3 1 30 1 1 1 10 1 2 3 3 6 2 1 6
## 830 840 860 864 900 960 981 1000 1020 1080 1100 1125 1150 1200 1250 1260
## 1 3 1 2 5 4 1 24 1 2 2 1 2 21 2 1
## 1280 1350 1400 1440 1500 1512 1536 1560 1575 1600 1680 1700 1736 1750 1800 1920
## 2 1 5 10 8 1 1 1 1 2 1 3 1 1 13 6
## 2000 2040 2100 2160 2200 2250 2304 2400 2460 2500 2560 2580 2592 2640 2700 2750
## 17 1 2 4 1 1 1 19 1 5 1 1 1 1 1 1
## 2800 2880 2940 3000 3130 3200 3220 3250 3360 3456 3500 3600 3700 3840 3920 3960
## 3 7 1 12 1 5 1 1 3 1 2 16 1 6 1 1
## 4000 4200 4320 4375 4400 4440 4500 4600 4800 5000 5040 5100 5160 5200 5250 5400
## 12 2 3 1 1 1 4 1 15 12 1 1 1 1 1 1
## 5600 5690 5700 5760 5880 6000 6100 6300 6440 6720 6800 7000 7200 7360 7420 7500
## 1 1 1 5 1 17 1 2 1 4 2 4 13 1 1 1
## 7588 7600 7616 7680 7700 7800 7975 8000 8064 8120 8400 8520 8640 9000 9600 9800
## 1 1 1 2 1 1 1 9 1 1 5 1 2 13 13 2
## 10000 10080 10500 10560 10800 10920 11100 11200 11250 11424 11500 11520 11760 12000 12240 12600
## 15 3 2 3 3 1 1 1 1 1 1 4 1 10 3 3
## 12800 12960 13000 13200 13440 13500 13600 13800 13824 14000 14400 14700 15000 15120 15360 15400
## 1 1 3 1 2 2 1 1 1 1 14 1 6 1 1 1
## 15500 15600 16000 16016 16800 17280 17640 17920 18000 18250 19000 19200 19320 19800 20000 20160
## 1 2 3 1 11 3 1 1 14 1 1 7 1 1 4 2
## 21000 21100 21600 22050 22400 22500 23040 23400 23520 24000 24500 25000 25200 25600 25800 26000
## 1 1 7 1 1 1 1 1 1 8 1 1 1 1 1 1
## 26880 27600 27860 28000 28224 28500 28800 28896 29328 29400 30000 30600 30800 31500 31680 31900
## 2 2 1 2 1 1 8 1 1 2 7 1 1 1 1 1
## 33600 34000 35280 36000 37800 38400 38880 39000 39240 40000 40320 42000 43200 43344 43680 43800
## 8 1 1 6 1 1 1 1 1 2 3 3 2 1 1 1
## 45000 46200 46800 48000 49000 50000 50400 50700 51840 52360 54000 56000 57600 60000 61600 67000
## 1 1 1 6 1 2 6 1 2 1 3 1 3 3 1 1
## 67200 78750 79200 80000 80640 81600 84000 86400 100800 107400 108000 120960 144000 160000 168650 180300
## 6 1 1 1 1 1 3 3 2 1 2 1 1 1 1 1
## <NA>
## 1279
## [1] "Frequency table after encoding"
## eh_s6q76_1. If you were to buy those goods or services in a local market over the last 12 mo
## -998 5 10 15 17 18 20
## 3 2 1 3 1 2 9
## 25 27 30 31 35 40 50
## 4 1 5 1 2 7 13
## 60 65 72 75 80 90 95
## 4 1 2 1 7 1 1
## 96 100 104 110 120 140 141
## 1 19 1 1 8 3 1
## 144 150 160 175 180 199 200
## 1 19 1 1 2 1 17
## 207 210 217 240 250 276 280
## 1 3 1 6 5 1 2
## 300 310 350 360 384 400 420
## 22 1 5 2 1 18 2
## 450 480 500 540 560 576 600
## 3 1 30 1 1 1 10
## 624 640 650 700 720 750 768
## 1 2 3 3 6 2 1
## 800 830 840 860 864 900 960
## 6 1 3 1 2 5 4
## 981 1000 1020 1080 1100 1125 1150
## 1 24 1 2 2 1 2
## 1200 1250 1260 1280 1350 1400 1440
## 21 2 1 2 1 5 10
## 1500 1512 1536 1560 1575 1600 1680
## 8 1 1 1 1 2 1
## 1700 1736 1750 1800 1920 2000 2040
## 3 1 1 13 6 17 1
## 2100 2160 2200 2250 2304 2400 2460
## 2 4 1 1 1 19 1
## 2500 2560 2580 2592 2640 2700 2750
## 5 1 1 1 1 1 1
## 2800 2880 2940 3000 3130 3200 3220
## 3 7 1 12 1 5 1
## 3250 3360 3456 3500 3600 3700 3840
## 1 3 1 2 16 1 6
## 3920 3960 4000 4200 4320 4375 4400
## 1 1 12 2 3 1 1
## 4440 4500 4600 4800 5000 5040 5100
## 1 4 1 15 12 1 1
## 5160 5200 5250 5400 5600 5690 5700
## 1 1 1 1 1 1 1
## 5760 5880 6000 6100 6300 6440 6720
## 5 1 17 1 2 1 4
## 6800 7000 7200 7360 7420 7500 7588
## 2 4 13 1 1 1 1
## 7600 7616 7680 7700 7800 7975 8000
## 1 1 2 1 1 1 9
## 8064 8120 8400 8520 8640 9000 9600
## 1 1 5 1 2 13 13
## 9800 10000 10080 10500 10560 10800 10920
## 2 15 3 2 3 3 1
## 11100 11200 11250 11424 11500 11520 11760
## 1 1 1 1 1 4 1
## 12000 12240 12600 12800 12960 13000 13200
## 10 3 3 1 1 3 1
## 13440 13500 13600 13800 13824 14000 14400
## 2 2 1 1 1 1 14
## 14700 15000 15120 15360 15400 15500 15600
## 1 6 1 1 1 1 2
## 16000 16016 16800 17280 17640 17920 18000
## 3 1 11 3 1 1 14
## 18250 19000 19200 19320 19800 20000 20160
## 1 1 7 1 1 4 2
## 21000 21100 21600 22050 22400 22500 23040
## 1 1 7 1 1 1 1
## 23400 23520 24000 24500 25000 25200 25600
## 1 1 8 1 1 1 1
## 25800 26000 26880 27600 27860 28000 28224
## 1 1 2 2 1 2 1
## 28500 28800 28896 29328 29400 30000 30600
## 1 8 1 1 2 7 1
## 30800 31500 31680 31900 33600 34000 35280
## 1 1 1 1 8 1 1
## 36000 37800 38400 38880 39000 39240 40000
## 6 1 1 1 1 1 2
## 40320 42000 43200 43344 43680 43800 45000
## 3 3 2 1 1 1 1
## 46200 46800 48000 49000 50000 50400 50700
## 1 1 6 1 2 6 1
## 51840 52360 54000 56000 57600 60000 61600
## 2 1 3 1 3 3 1
## 67000 67200 78750 79200 80000 80640 81600
## 1 6 1 1 1 1 1
## 84000 86400 100800 107400 108000 or more <NA>
## 3 3 2 1 7 1279
mydata <- top_recode (variable="eh_s6q71_2", break_point=pctile_99.5_eh_s6q71_2, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q71_2. How much gross income or revenue was earned over the last 12 months from this ac
## -998 0 1 45 60 75 80 100 120 125 130 140 150 200
## 1 12 1 1 1 1 1 4 1 1 1 1 1 19
## 240 250 255 270 280 300 350 360 375 400 420 450 460 500
## 1 5 1 1 1 16 2 3 1 11 1 3 2 14
## 520 540 600 640 665 690 700 740 750 800 825 840 900 912
## 1 4 10 2 1 1 3 1 3 10 1 1 17 1
## 960 970 1000 1050 1090 1095 1150 1200 1225 1250 1260 1300 1303 1320
## 1 1 20 1 1 1 1 19 1 1 2 3 1 1
## 1350 1400 1440 1500 1600 1650 1680 1700 1750 1800 1890 1900 1920 1936
## 1 10 3 17 8 1 1 1 2 15 1 1 1 1
## 2000 2040 2050 2100 2150 2160 2169 2175 2180 2200 2240 2250 2300 2304
## 25 1 1 5 1 1 1 1 1 5 1 1 1 1
## 2400 2450 2500 2550 2600 2650 2700 2750 2800 2880 2900 2960 3000 3024
## 26 1 4 1 4 1 5 1 9 3 1 1 31 1
## 3120 3150 3190 3200 3210 3250 3300 3375 3395 3400 3500 3600 3640 3700
## 1 3 1 7 1 1 2 1 1 2 3 20 1 1
## 3750 3780 3800 3832 3900 4000 4100 4200 4240 4250 4290 4300 4320 4360
## 1 1 2 1 1 15 1 13 2 1 1 1 2 1
## 4400 4440 4500 4620 4650 4800 4900 5000 5025 5100 5200 5250 5300 5320
## 5 1 13 1 1 25 1 10 1 2 2 2 1 1
## 5400 5472 5500 5580 5600 5670 5760 5850 6000 6300 6400 6480 6500 6580
## 8 1 4 1 10 1 2 2 33 8 7 2 1 1
## 6700 6720 6750 6825 6910 6960 7000 7100 7200 7250 7300 7336 7480 7500
## 2 2 1 1 1 1 15 1 25 1 1 1 1 10
## 7560 7600 7680 7688 7800 8000 8100 8200 8280 8400 8500 8640 8750 8960
## 1 6 1 1 5 23 1 1 1 12 1 1 1 1
## 9000 9080 9216 9300 9540 9560 9600 9800 10000 10080 10100 10200 10500 10545
## 18 1 1 1 1 1 35 2 17 2 1 1 6 1
## 10560 10600 10800 10920 11000 11200 11250 11400 11500 11520 11700 11760 11900 12000
## 1 1 9 1 2 3 1 2 3 2 1 1 3 51
## 12120 12150 12200 12360 12500 12600 12630 12756 12800 12900 13000 13200 13440 13500
## 1 1 2 1 4 4 1 1 2 1 1 6 1 4
## 13608 13800 14000 14112 14200 14300 14400 14450 14700 14820 15000 15305 15360 15400
## 1 1 7 1 1 2 34 1 1 1 10 1 1 1
## 15600 15840 15850 16000 16200 16800 17000 17170 17250 17400 17500 17600 18000 18240
## 4 1 1 7 2 16 3 1 1 1 1 1 25 2
## 18300 18400 18500 18600 18620 18900 19200 19600 19800 19950 20000 20400 20480 20800
## 1 1 2 2 1 1 18 1 1 1 19 1 1 1
## 21000 21200 21300 21330 21600 21700 21840 22000 22080 22200 22400 22500 22800 22950
## 8 1 1 1 11 1 1 5 1 1 4 1 2 1
## 23000 23040 23640 24000 24150 24500 24576 24960 25000 25200 25500 25550 25600 25800
## 4 1 1 48 1 1 1 2 7 3 1 1 5 2
## 25920 26000 26265 26400 26550 26880 27000 27360 27500 27600 28000 28400 28800 29880
## 1 2 1 2 1 3 6 2 1 4 5 1 26 1
## 30000 30100 30240 30600 31000 31160 31228 31500 31680 32000 32400 33000 33200 33400
## 18 1 1 1 1 1 1 2 2 6 4 3 1 1
## 33600 33670 33850 33960 34000 34440 34560 35000 35200 36000 36400 36700 36720 37000
## 13 1 1 1 1 1 2 3 2 31 1 1 1 1
## 37200 37500 37720 37800 38000 38400 39000 39200 39600 39825 40000 40320 40500 40800
## 1 1 1 1 3 11 1 2 2 1 7 1 1 4
## 41500 42000 42270 42720 43000 43200 43290 43470 44000 44500 44520 45000 45240 45500
## 1 13 1 1 1 14 1 1 3 1 1 3 1 1
## 46000 46650 46800 47000 47040 47520 47600 48000 48720 48900 49000 49400 50000 50400
## 1 1 1 1 1 1 1 31 1 1 1 1 4 9
## 51100 51400 51500 52000 52500 52615 52650 52800 53000 53400 53760 54000 54200 54720
## 1 1 1 1 1 1 1 3 1 1 1 5 1 1
## 54840 55000 55200 56000 56198 57600 58000 58410 58560 58800 59760 59792 60000 60120
## 1 3 1 7 1 18 1 1 1 1 1 1 26 1
## 60480 60522 61560 62200 62400 62784 63000 63700 64000 64800 65000 65520 65800 66000
## 2 1 1 1 1 1 2 1 3 4 1 1 1 3
## 67000 67200 68040 69000 69600 70000 70200 72000 73000 73920 74600 74880 75000 75600
## 1 9 1 2 1 5 2 27 1 1 1 1 1 4
## 76800 77000 77760 78000 78400 81000 81475 81900 82080 83000 84000 84900 85200 85500
## 4 2 1 1 1 1 1 1 1 1 17 1 1 1
## 86400 87120 89200 89600 90000 91000 91200 91488 92160 92400 93600 93840 96000 97200
## 19 1 1 1 4 1 3 1 2 2 3 1 17 2
## 98000 99360 1e+05 100100 100320 100800 102000 102400 104000 105000 105600 105710 108000 108360
## 3 1 5 2 1 13 3 1 1 1 2 1 4 1
## 108680 109000 109200 109440 109500 110000 110592 112200 112320 112500 113760 114000 115200 115440
## 1 1 1 3 1 1 1 1 1 1 1 1 18 1
## 117000 117600 118560 118800 120000 120400 120960 122000 122400 123200 124000 124800 129600 130000
## 1 1 2 1 11 1 1 2 1 1 1 3 3 2
## 132000 134400 135432 137900 138240 140400 144000 146640 150000 152640 153000 156000 158400 160000
## 4 9 1 1 1 1 27 1 1 1 1 1 1 3
## 162000 163200 167535 168000 172000 172800 180000 184000 185600 186000 187200 192000 198000 208800
## 3 1 1 10 1 1 6 1 1 1 1 2 1 1
## 211200 216000 218400 220000 224000 230000 235200 240000 252000 253000 256200 259200 260000 261700
## 1 7 2 1 1 1 1 4 3 1 1 1 1 1
## 268800 299000 302400 306600 328500 336000 342720 360000 363200 369600 4e+05 403200 406000 425400
## 3 1 2 1 1 1 1 3 1 1 1 1 1 1
## 432000 436800 477600 480000 480480 542400 561600 604800 720000 792000 804000 896000 989660 1166200
## 1 1 1 2 1 1 1 1 1 1 1 1 1 1
## 1200000 <NA>
## 1 224
## [1] "Frequency table after encoding"
## eh_s6q71_2. How much gross income or revenue was earned over the last 12 months from this ac
## -998 0 1 45 60 75 80
## 1 12 1 1 1 1 1
## 100 120 125 130 140 150 200
## 4 1 1 1 1 1 19
## 240 250 255 270 280 300 350
## 1 5 1 1 1 16 2
## 360 375 400 420 450 460 500
## 3 1 11 1 3 2 14
## 520 540 600 640 665 690 700
## 1 4 10 2 1 1 3
## 740 750 800 825 840 900 912
## 1 3 10 1 1 17 1
## 960 970 1000 1050 1090 1095 1150
## 1 1 20 1 1 1 1
## 1200 1225 1250 1260 1300 1303 1320
## 19 1 1 2 3 1 1
## 1350 1400 1440 1500 1600 1650 1680
## 1 10 3 17 8 1 1
## 1700 1750 1800 1890 1900 1920 1936
## 1 2 15 1 1 1 1
## 2000 2040 2050 2100 2150 2160 2169
## 25 1 1 5 1 1 1
## 2175 2180 2200 2240 2250 2300 2304
## 1 1 5 1 1 1 1
## 2400 2450 2500 2550 2600 2650 2700
## 26 1 4 1 4 1 5
## 2750 2800 2880 2900 2960 3000 3024
## 1 9 3 1 1 31 1
## 3120 3150 3190 3200 3210 3250 3300
## 1 3 1 7 1 1 2
## 3375 3395 3400 3500 3600 3640 3700
## 1 1 2 3 20 1 1
## 3750 3780 3800 3832 3900 4000 4100
## 1 1 2 1 1 15 1
## 4200 4240 4250 4290 4300 4320 4360
## 13 2 1 1 1 2 1
## 4400 4440 4500 4620 4650 4800 4900
## 5 1 13 1 1 25 1
## 5000 5025 5100 5200 5250 5300 5320
## 10 1 2 2 2 1 1
## 5400 5472 5500 5580 5600 5670 5760
## 8 1 4 1 10 1 2
## 5850 6000 6300 6400 6480 6500 6580
## 2 33 8 7 2 1 1
## 6700 6720 6750 6825 6910 6960 7000
## 2 2 1 1 1 1 15
## 7100 7200 7250 7300 7336 7480 7500
## 1 25 1 1 1 1 10
## 7560 7600 7680 7688 7800 8000 8100
## 1 6 1 1 5 23 1
## 8200 8280 8400 8500 8640 8750 8960
## 1 1 12 1 1 1 1
## 9000 9080 9216 9300 9540 9560 9600
## 18 1 1 1 1 1 35
## 9800 10000 10080 10100 10200 10500 10545
## 2 17 2 1 1 6 1
## 10560 10600 10800 10920 11000 11200 11250
## 1 1 9 1 2 3 1
## 11400 11500 11520 11700 11760 11900 12000
## 2 3 2 1 1 3 51
## 12120 12150 12200 12360 12500 12600 12630
## 1 1 2 1 4 4 1
## 12756 12800 12900 13000 13200 13440 13500
## 1 2 1 1 6 1 4
## 13608 13800 14000 14112 14200 14300 14400
## 1 1 7 1 1 2 34
## 14450 14700 14820 15000 15305 15360 15400
## 1 1 1 10 1 1 1
## 15600 15840 15850 16000 16200 16800 17000
## 4 1 1 7 2 16 3
## 17170 17250 17400 17500 17600 18000 18240
## 1 1 1 1 1 25 2
## 18300 18400 18500 18600 18620 18900 19200
## 1 1 2 2 1 1 18
## 19600 19800 19950 20000 20400 20480 20800
## 1 1 1 19 1 1 1
## 21000 21200 21300 21330 21600 21700 21840
## 8 1 1 1 11 1 1
## 22000 22080 22200 22400 22500 22800 22950
## 5 1 1 4 1 2 1
## 23000 23040 23640 24000 24150 24500 24576
## 4 1 1 48 1 1 1
## 24960 25000 25200 25500 25550 25600 25800
## 2 7 3 1 1 5 2
## 25920 26000 26265 26400 26550 26880 27000
## 1 2 1 2 1 3 6
## 27360 27500 27600 28000 28400 28800 29880
## 2 1 4 5 1 26 1
## 30000 30100 30240 30600 31000 31160 31228
## 18 1 1 1 1 1 1
## 31500 31680 32000 32400 33000 33200 33400
## 2 2 6 4 3 1 1
## 33600 33670 33850 33960 34000 34440 34560
## 13 1 1 1 1 1 2
## 35000 35200 36000 36400 36700 36720 37000
## 3 2 31 1 1 1 1
## 37200 37500 37720 37800 38000 38400 39000
## 1 1 1 1 3 11 1
## 39200 39600 39825 40000 40320 40500 40800
## 2 2 1 7 1 1 4
## 41500 42000 42270 42720 43000 43200 43290
## 1 13 1 1 1 14 1
## 43470 44000 44500 44520 45000 45240 45500
## 1 3 1 1 3 1 1
## 46000 46650 46800 47000 47040 47520 47600
## 1 1 1 1 1 1 1
## 48000 48720 48900 49000 49400 50000 50400
## 31 1 1 1 1 4 9
## 51100 51400 51500 52000 52500 52615 52650
## 1 1 1 1 1 1 1
## 52800 53000 53400 53760 54000 54200 54720
## 3 1 1 1 5 1 1
## 54840 55000 55200 56000 56198 57600 58000
## 1 3 1 7 1 18 1
## 58410 58560 58800 59760 59792 60000 60120
## 1 1 1 1 1 26 1
## 60480 60522 61560 62200 62400 62784 63000
## 2 1 1 1 1 1 2
## 63700 64000 64800 65000 65520 65800 66000
## 1 3 4 1 1 1 3
## 67000 67200 68040 69000 69600 70000 70200
## 1 9 1 2 1 5 2
## 72000 73000 73920 74600 74880 75000 75600
## 27 1 1 1 1 1 4
## 76800 77000 77760 78000 78400 81000 81475
## 4 2 1 1 1 1 1
## 81900 82080 83000 84000 84900 85200 85500
## 1 1 1 17 1 1 1
## 86400 87120 89200 89600 90000 91000 91200
## 19 1 1 1 4 1 3
## 91488 92160 92400 93600 93840 96000 97200
## 1 2 2 3 1 17 2
## 98000 99360 1e+05 100100 100320 100800 102000
## 3 1 5 2 1 13 3
## 102400 104000 105000 105600 105710 108000 108360
## 1 1 1 2 1 4 1
## 108680 109000 109200 109440 109500 110000 110592
## 1 1 1 3 1 1 1
## 112200 112320 112500 113760 114000 115200 115440
## 1 1 1 1 1 18 1
## 117000 117600 118560 118800 120000 120400 120960
## 1 1 2 1 11 1 1
## 122000 122400 123200 124000 124800 129600 130000
## 2 1 1 1 3 3 2
## 132000 134400 135432 137900 138240 140400 144000
## 4 9 1 1 1 1 27
## 146640 150000 152640 153000 156000 158400 160000
## 1 1 1 1 1 1 3
## 162000 163200 167535 168000 172000 172800 180000
## 3 1 1 10 1 1 6
## 184000 185600 186000 187200 192000 198000 208800
## 1 1 1 1 2 1 1
## 211200 216000 218400 220000 224000 230000 235200
## 1 7 2 1 1 1 1
## 240000 252000 253000 256200 259200 260000 261700
## 4 3 1 1 1 1 1
## 268800 299000 302400 306600 328500 336000 342720
## 3 1 2 1 1 1 1
## 360000 363200 369600 4e+05 403200 406000 425400
## 3 1 1 1 1 1 1
## 432000 436800 477600 480000 480331 or more <NA>
## 1 1 1 2 11 224
mydata <- top_recode (variable="eh_s6q72_2", break_point=pctile_99.5_eh_s6q72_2, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q72_2. Some activities require expenses in order to do them. What are the total expense
## -998 0 1 10 20 25 30 32 36 40 50 60 80 88
## 5 714 2 1 3 1 1 1 1 3 4 5 5 1
## 90 100 114 115 120 124 125 132 135 136 140 144 150 170
## 1 15 1 2 6 2 1 1 1 1 3 1 10 2
## 174 175 180 200 210 216 225 240 250 272 280 288 290 300
## 1 1 2 17 1 1 1 7 8 1 3 1 1 15
## 320 330 335 336 338 350 360 375 400 420 429 450 455 480
## 1 1 1 1 1 4 2 2 8 1 1 5 1 10
## 484 500 504 515 516 520 540 542 550 556 560 576 600 624
## 1 12 1 2 1 1 2 1 1 1 1 1 18 1
## 638 650 688 700 720 740 770 800 816 840 880 890 896 900
## 1 1 1 7 7 1 1 12 1 2 1 1 1 5
## 924 930 956 960 980 990 1000 1030 1040 1050 1060 1070 1080 1100
## 1 2 1 3 2 1 16 1 1 3 1 1 1 1
## 1118 1120 1150 1152 1155 1200 1210 1220 1230 1240 1250 1260 1280 1300
## 1 5 1 2 1 23 1 1 1 1 2 3 2 2
## 1316 1330 1332 1350 1390 1400 1405 1440 1450 1470 1500 1536 1575 1592
## 1 1 1 2 1 6 1 9 1 1 11 2 1 1
## 1598 1600 1620 1640 1650 1680 1700 1728 1800 1820 1824 1848 1872 1890
## 1 5 1 1 2 3 3 2 10 1 1 1 1 1
## 1900 1920 1960 1990 2000 2030 2040 2100 2112 2150 2160 2168 2200 2260
## 1 9 1 1 13 1 2 10 1 1 3 1 4 1
## 2280 2300 2328 2400 2480 2500 2520 2550 2560 2580 2600 2604 2640 2688
## 1 1 1 20 2 8 2 1 1 1 3 1 2 1
## 2700 2720 2784 2800 2810 2880 2892 2900 2980 3000 3060 3072 3100 3150
## 6 1 1 5 1 10 1 2 1 19 1 1 1 2
## 3160 3200 3240 3360 3480 3500 3520 3600 3648 3675 3700 3740 3750 3760
## 1 4 1 7 1 3 1 15 1 1 1 1 1 1
## 3780 3800 3840 3852 3900 3999 4000 4032 4080 4125 4144 4200 4320 4376
## 1 4 5 1 1 1 18 1 3 1 1 7 4 1
## 4400 4455 4500 4540 4600 4700 4704 4800 4900 4944 5000 5040 5184 5200
## 2 1 4 1 3 2 1 25 2 1 10 3 1 1
## 5250 5280 5300 5376 5380 5400 5460 5500 5520 5566 5580 5600 5650 5700
## 1 2 1 1 2 6 1 1 1 1 1 2 1 1
## 5760 5800 5812 5880 5950 6000 6071 6100 6160 6200 6240 6250 6300 6360
## 7 1 1 2 1 18 1 1 1 1 2 1 3 1
## 6480 6500 6512 6600 6602 6630 6675 6720 6900 6960 6974 7000 7100 7200
## 1 1 1 2 1 1 1 3 1 1 1 4 1 24
## 7248 7280 7408 7440 7488 7500 7600 7620 7650 7675 7680 7740 7800 7840
## 1 1 1 1 1 1 1 1 1 1 1 1 2 1
## 7868 7880 7900 7920 7956 8000 8005 8170 8190 8200 8300 8400 8500 8544
## 1 1 2 1 1 11 1 1 1 1 1 6 2 1
## 8600 8640 8760 8800 8808 8832 9000 9100 9330 9360 9480 9504 9600 9720
## 1 4 1 2 1 1 4 1 1 1 1 1 20 1
## 9800 10000 10080 10100 10200 10410 10550 10700 10800 10820 10992 11000 11010 11200
## 2 8 9 2 2 1 1 1 6 1 1 1 1 4
## 11280 11400 11500 11520 11664 11700 11800 12000 12070 12120 12230 12400 12480 12500
## 1 2 1 4 1 1 1 19 1 1 1 1 1 1
## 12600 12800 12810 12960 13000 13440 13500 14000 14200 14225 14380 14400 14544 14640
## 2 2 1 1 1 2 3 1 2 1 1 23 1 1
## 14720 14900 14960 15000 15120 15400 15600 15696 15888 15950 16000 16140 16250 16360
## 2 1 1 13 1 1 3 1 1 1 6 1 1 1
## 16500 16560 16620 16795 16800 16860 16870 16920 17400 17520 17760 17808 18000 18480
## 1 2 1 1 4 1 1 1 1 2 1 1 6 2
## 18696 18945 19080 19200 19260 19320 19440 19488 19490 19680 19800 20000 20020 20160
## 1 1 1 7 1 1 1 1 1 2 2 7 1 4
## 20260 20290 20520 20640 20736 20900 21000 21120 21300 21440 21600 21616 21840 21900
## 1 1 1 1 1 1 2 1 2 1 5 1 2 1
## 22000 22152 22392 22500 22600 23000 23040 23200 23400 23632 23850 24000 24050 24120
## 3 1 1 2 1 1 2 1 1 1 1 20 1 1
## 24250 24480 24500 24660 24960 25000 25200 25600 25740 25920 26000 26300 26400 26448
## 1 1 1 1 2 1 3 2 1 1 1 1 2 1
## 26880 27000 27015 27200 27360 27380 27475 27600 27900 28000 28080 28088 28200 28350
## 2 1 1 1 3 1 1 1 1 7 1 1 1 1
## 28560 28800 29000 29376 30000 30056 30240 30960 31000 31200 31350 31680 32000 32040
## 1 17 1 1 7 1 1 2 1 4 1 2 2 1
## 32400 32500 33000 33500 33600 33650 33840 34112 34200 34848 35000 35040 36000 36720
## 1 1 2 1 14 1 1 1 1 1 1 1 10 1
## 37000 37060 37150 37200 37420 37440 37632 38000 38400 38520 38700 38900 39000 39200
## 1 1 1 1 1 1 2 1 5 1 1 1 1 1
## 39600 40000 40035 40560 40600 40800 41000 41300 41768 41856 42000 42840 42908 43000
## 1 4 1 1 1 1 2 1 1 1 2 1 1 1
## 43200 43290 43800 44000 44400 45000 45360 45600 45800 45900 46000 46080 47000 47040
## 7 1 1 1 1 2 1 1 1 1 1 1 1 1
## 47712 48000 48960 49800 49920 50000 50400 50600 51000 52000 52200 52800 53922 54000
## 1 10 2 1 2 2 2 1 1 1 1 3 1 2
## 55000 56520 57200 57600 57888 57920 59280 60000 61160 61200 61920 62000 62400 64800
## 1 1 1 6 1 1 1 5 1 2 1 1 2 2
## 64996 66000 67080 67200 67584 68960 70000 72000 72060 75600 76410 76800 78240 78720
## 1 1 1 4 1 1 4 9 1 1 1 1 1 1
## 79200 80000 84000 86400 89200 91500 93785 96000 98880 99000 99264 99600 1e+05 100800
## 1 2 3 3 1 1 1 4 1 2 1 1 1 2
## 102600 105000 106560 106848 108000 109100 118800 120000 122400 126000 128000 136320 140800 144000
## 1 1 1 1 2 1 1 3 1 1 1 1 1 4
## 145000 148500 161000 162600 163200 168000 172800 179200 180000 183600 188160 190800 2e+05 201600
## 1 1 1 1 1 2 1 1 2 1 1 1 1 1
## 204000 208000 210300 216000 217160 227150 240000 241920 242400 244800 244950 252000 253440 258480
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 259200 268800 291200 294960 300950 312000 386400 405600 432000 460800 477480 518400 681760 705600
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 720000 1097600 <NA>
## 2 1 52
## [1] "Frequency table after encoding"
## eh_s6q72_2. Some activities require expenses in order to do them. What are the total expense
## -998 0 1 10 20 25 30
## 5 714 2 1 3 1 1
## 32 36 40 50 60 80 88
## 1 1 3 4 5 5 1
## 90 100 114 115 120 124 125
## 1 15 1 2 6 2 1
## 132 135 136 140 144 150 170
## 1 1 1 3 1 10 2
## 174 175 180 200 210 216 225
## 1 1 2 17 1 1 1
## 240 250 272 280 288 290 300
## 7 8 1 3 1 1 15
## 320 330 335 336 338 350 360
## 1 1 1 1 1 4 2
## 375 400 420 429 450 455 480
## 2 8 1 1 5 1 10
## 484 500 504 515 516 520 540
## 1 12 1 2 1 1 2
## 542 550 556 560 576 600 624
## 1 1 1 1 1 18 1
## 638 650 688 700 720 740 770
## 1 1 1 7 7 1 1
## 800 816 840 880 890 896 900
## 12 1 2 1 1 1 5
## 924 930 956 960 980 990 1000
## 1 2 1 3 2 1 16
## 1030 1040 1050 1060 1070 1080 1100
## 1 1 3 1 1 1 1
## 1118 1120 1150 1152 1155 1200 1210
## 1 5 1 2 1 23 1
## 1220 1230 1240 1250 1260 1280 1300
## 1 1 1 2 3 2 2
## 1316 1330 1332 1350 1390 1400 1405
## 1 1 1 2 1 6 1
## 1440 1450 1470 1500 1536 1575 1592
## 9 1 1 11 2 1 1
## 1598 1600 1620 1640 1650 1680 1700
## 1 5 1 1 2 3 3
## 1728 1800 1820 1824 1848 1872 1890
## 2 10 1 1 1 1 1
## 1900 1920 1960 1990 2000 2030 2040
## 1 9 1 1 13 1 2
## 2100 2112 2150 2160 2168 2200 2260
## 10 1 1 3 1 4 1
## 2280 2300 2328 2400 2480 2500 2520
## 1 1 1 20 2 8 2
## 2550 2560 2580 2600 2604 2640 2688
## 1 1 1 3 1 2 1
## 2700 2720 2784 2800 2810 2880 2892
## 6 1 1 5 1 10 1
## 2900 2980 3000 3060 3072 3100 3150
## 2 1 19 1 1 1 2
## 3160 3200 3240 3360 3480 3500 3520
## 1 4 1 7 1 3 1
## 3600 3648 3675 3700 3740 3750 3760
## 15 1 1 1 1 1 1
## 3780 3800 3840 3852 3900 3999 4000
## 1 4 5 1 1 1 18
## 4032 4080 4125 4144 4200 4320 4376
## 1 3 1 1 7 4 1
## 4400 4455 4500 4540 4600 4700 4704
## 2 1 4 1 3 2 1
## 4800 4900 4944 5000 5040 5184 5200
## 25 2 1 10 3 1 1
## 5250 5280 5300 5376 5380 5400 5460
## 1 2 1 1 2 6 1
## 5500 5520 5566 5580 5600 5650 5700
## 1 1 1 1 2 1 1
## 5760 5800 5812 5880 5950 6000 6071
## 7 1 1 2 1 18 1
## 6100 6160 6200 6240 6250 6300 6360
## 1 1 1 2 1 3 1
## 6480 6500 6512 6600 6602 6630 6675
## 1 1 1 2 1 1 1
## 6720 6900 6960 6974 7000 7100 7200
## 3 1 1 1 4 1 24
## 7248 7280 7408 7440 7488 7500 7600
## 1 1 1 1 1 1 1
## 7620 7650 7675 7680 7740 7800 7840
## 1 1 1 1 1 2 1
## 7868 7880 7900 7920 7956 8000 8005
## 1 1 2 1 1 11 1
## 8170 8190 8200 8300 8400 8500 8544
## 1 1 1 1 6 2 1
## 8600 8640 8760 8800 8808 8832 9000
## 1 4 1 2 1 1 4
## 9100 9330 9360 9480 9504 9600 9720
## 1 1 1 1 1 20 1
## 9800 10000 10080 10100 10200 10410 10550
## 2 8 9 2 2 1 1
## 10700 10800 10820 10992 11000 11010 11200
## 1 6 1 1 1 1 4
## 11280 11400 11500 11520 11664 11700 11800
## 1 2 1 4 1 1 1
## 12000 12070 12120 12230 12400 12480 12500
## 19 1 1 1 1 1 1
## 12600 12800 12810 12960 13000 13440 13500
## 2 2 1 1 1 2 3
## 14000 14200 14225 14380 14400 14544 14640
## 1 2 1 1 23 1 1
## 14720 14900 14960 15000 15120 15400 15600
## 2 1 1 13 1 1 3
## 15696 15888 15950 16000 16140 16250 16360
## 1 1 1 6 1 1 1
## 16500 16560 16620 16795 16800 16860 16870
## 1 2 1 1 4 1 1
## 16920 17400 17520 17760 17808 18000 18480
## 1 1 2 1 1 6 2
## 18696 18945 19080 19200 19260 19320 19440
## 1 1 1 7 1 1 1
## 19488 19490 19680 19800 20000 20020 20160
## 1 1 2 2 7 1 4
## 20260 20290 20520 20640 20736 20900 21000
## 1 1 1 1 1 1 2
## 21120 21300 21440 21600 21616 21840 21900
## 1 2 1 5 1 2 1
## 22000 22152 22392 22500 22600 23000 23040
## 3 1 1 2 1 1 2
## 23200 23400 23632 23850 24000 24050 24120
## 1 1 1 1 20 1 1
## 24250 24480 24500 24660 24960 25000 25200
## 1 1 1 1 2 1 3
## 25600 25740 25920 26000 26300 26400 26448
## 2 1 1 1 1 2 1
## 26880 27000 27015 27200 27360 27380 27475
## 2 1 1 1 3 1 1
## 27600 27900 28000 28080 28088 28200 28350
## 1 1 7 1 1 1 1
## 28560 28800 29000 29376 30000 30056 30240
## 1 17 1 1 7 1 1
## 30960 31000 31200 31350 31680 32000 32040
## 2 1 4 1 2 2 1
## 32400 32500 33000 33500 33600 33650 33840
## 1 1 2 1 14 1 1
## 34112 34200 34848 35000 35040 36000 36720
## 1 1 1 1 1 10 1
## 37000 37060 37150 37200 37420 37440 37632
## 1 1 1 1 1 1 2
## 38000 38400 38520 38700 38900 39000 39200
## 1 5 1 1 1 1 1
## 39600 40000 40035 40560 40600 40800 41000
## 1 4 1 1 1 1 2
## 41300 41768 41856 42000 42840 42908 43000
## 1 1 1 2 1 1 1
## 43200 43290 43800 44000 44400 45000 45360
## 7 1 1 1 1 2 1
## 45600 45800 45900 46000 46080 47000 47040
## 1 1 1 1 1 1 1
## 47712 48000 48960 49800 49920 50000 50400
## 1 10 2 1 2 2 2
## 50600 51000 52000 52200 52800 53922 54000
## 1 1 1 1 3 1 2
## 55000 56520 57200 57600 57888 57920 59280
## 1 1 1 6 1 1 1
## 60000 61160 61200 61920 62000 62400 64800
## 5 1 2 1 1 2 2
## 64996 66000 67080 67200 67584 68960 70000
## 1 1 1 4 1 1 4
## 72000 72060 75600 76410 76800 78240 78720
## 9 1 1 1 1 1 1
## 79200 80000 84000 86400 89200 91500 93785
## 1 2 3 3 1 1 1
## 96000 98880 99000 99264 99600 1e+05 100800
## 4 1 2 1 1 1 2
## 102600 105000 106560 106848 108000 109100 118800
## 1 1 1 1 2 1 1
## 120000 122400 126000 128000 136320 140800 144000
## 3 1 1 1 1 1 4
## 145000 148500 161000 162600 163200 168000 172800
## 1 1 1 1 1 2 1
## 179200 180000 183600 188160 190800 2e+05 201600
## 1 2 1 1 1 1 1
## 204000 208000 210300 216000 217160 227150 240000
## 1 1 1 1 1 1 1
## 241920 242400 244800 244950 252000 253440 258480
## 1 1 1 1 1 1 1
## 259200 268800 291200 294960 300950 310342 or more <NA>
## 1 1 1 1 1 12 52
mydata <- top_recode (variable="eh_s6q76_2", break_point=pctile_99.5_eh_s6q76_2, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q76_2. If you were to buy those goods or services in a local market over the last 12 mo
## -998 1 5 7 10 12 15 20 21 25 30 35 40 45 50 60
## 1 1 1 1 6 1 1 12 1 1 6 5 1 2 11 6
## 70 80 90 100 110 120 125 140 150 170 178 180 199 200 210 220
## 3 8 2 22 1 4 3 1 15 1 1 6 1 30 1 3
## 240 250 260 280 300 340 350 360 375 379 380 400 420 440 450 456
## 7 7 1 1 20 1 6 2 1 1 1 12 3 1 6 1
## 480 500 576 600 605 650 672 680 700 720 740 750 800 816 880 900
## 6 40 2 13 1 1 2 1 6 12 1 2 9 1 1 6
## 920 950 960 1000 1080 1100 1120 1150 1162 1200 1280 1300 1350 1360 1400 1440
## 1 1 3 13 1 1 2 1 1 15 1 1 1 1 4 5
## 1500 1600 1680 1700 1800 1900 1920 1953 2000 2016 2040 2100 2160 2200 2250 2400
## 11 4 2 1 10 1 4 1 18 1 1 1 4 1 2 10
## 2436 2500 2560 2592 2640 2700 2745 2800 2880 3000 3004 3024 3150 3200 3290 3360
## 1 2 1 1 1 1 1 6 2 17 1 1 1 3 1 2
## 3375 3410 3456 3500 3560 3600 3660 3750 3780 3800 3840 3960 4000 4200 4320 4359
## 1 1 1 4 1 14 1 1 1 1 1 1 9 3 5 1
## 4500 4620 4800 4950 4992 5000 5040 5120 5400 5500 5600 5625 5760 6000 6300 6400
## 2 1 8 1 1 9 1 1 2 1 1 1 3 19 2 2
## 6480 6500 6600 6720 6974 7000 7120 7200 7280 7500 7680 8000 8400 8640 9000 9600
## 1 2 2 2 1 3 1 14 1 3 1 5 1 3 3 12
## 10000 10080 10560 10752 10800 11000 11040 11200 11900 12000 12320 13000 13440 14000 14400 15000
## 9 4 1 1 1 1 1 1 2 9 1 1 3 3 10 3
## 15360 15400 16000 16200 16800 17000 17500 18000 18360 19200 19584 20000 20160 21600 22000 22500
## 1 1 2 1 4 1 2 6 1 2 1 5 2 2 1 1
## 22560 22800 23040 24000 24250 24500 25200 25600 26544 27000 28000 28224 28300 28800 29400 30000
## 1 1 1 5 1 1 1 1 1 2 2 1 1 3 1 1
## 30240 30260 31680 32000 33000 33360 33600 38000 40000 40320 42000 43200 44800 48000 50000 50400
## 2 1 1 1 1 1 4 1 1 1 1 1 2 4 1 4
## 52800 61600 63000 65600 67200 72000 73500 91250 95000 96000 108000 120960 153600 <NA>
## 1 2 1 1 2 1 1 1 1 1 1 1 1 1482
## [1] "Frequency table after encoding"
## eh_s6q76_2. If you were to buy those goods or services in a local market over the last 12 mo
## -998 1 5 7 10 12 15 20
## 1 1 1 1 6 1 1 12
## 21 25 30 35 40 45 50 60
## 1 1 6 5 1 2 11 6
## 70 80 90 100 110 120 125 140
## 3 8 2 22 1 4 3 1
## 150 170 178 180 199 200 210 220
## 15 1 1 6 1 30 1 3
## 240 250 260 280 300 340 350 360
## 7 7 1 1 20 1 6 2
## 375 379 380 400 420 440 450 456
## 1 1 1 12 3 1 6 1
## 480 500 576 600 605 650 672 680
## 6 40 2 13 1 1 2 1
## 700 720 740 750 800 816 880 900
## 6 12 1 2 9 1 1 6
## 920 950 960 1000 1080 1100 1120 1150
## 1 1 3 13 1 1 2 1
## 1162 1200 1280 1300 1350 1360 1400 1440
## 1 15 1 1 1 1 4 5
## 1500 1600 1680 1700 1800 1900 1920 1953
## 11 4 2 1 10 1 4 1
## 2000 2016 2040 2100 2160 2200 2250 2400
## 18 1 1 1 4 1 2 10
## 2436 2500 2560 2592 2640 2700 2745 2800
## 1 2 1 1 1 1 1 6
## 2880 3000 3004 3024 3150 3200 3290 3360
## 2 17 1 1 1 3 1 2
## 3375 3410 3456 3500 3560 3600 3660 3750
## 1 1 1 4 1 14 1 1
## 3780 3800 3840 3960 4000 4200 4320 4359
## 1 1 1 1 9 3 5 1
## 4500 4620 4800 4950 4992 5000 5040 5120
## 2 1 8 1 1 9 1 1
## 5400 5500 5600 5625 5760 6000 6300 6400
## 2 1 1 1 3 19 2 2
## 6480 6500 6600 6720 6974 7000 7120 7200
## 1 2 2 2 1 3 1 14
## 7280 7500 7680 8000 8400 8640 9000 9600
## 1 3 1 5 1 3 3 12
## 10000 10080 10560 10752 10800 11000 11040 11200
## 9 4 1 1 1 1 1 1
## 11900 12000 12320 13000 13440 14000 14400 15000
## 2 9 1 1 3 3 10 3
## 15360 15400 16000 16200 16800 17000 17500 18000
## 1 1 2 1 4 1 2 6
## 18360 19200 19584 20000 20160 21600 22000 22500
## 1 2 1 5 2 2 1 1
## 22560 22800 23040 24000 24250 24500 25200 25600
## 1 1 1 5 1 1 1 1
## 26544 27000 28000 28224 28300 28800 29400 30000
## 1 2 2 1 1 3 1 1
## 30240 30260 31680 32000 33000 33360 33600 38000
## 2 1 1 1 1 1 4 1
## 40000 40320 42000 43200 44800 48000 50000 50400
## 1 1 1 1 2 4 1 4
## 52800 61600 63000 65600 67200 72000 73500 91250
## 1 2 1 1 2 1 1 1
## 94925 or more <NA>
## 5 1482
mydata <- top_recode (variable="eh_s6q71_3", break_point=pctile_99.5_eh_s6q71_3, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q71_3. How much gross income or revenue was earned over the last 12 months from this ac
## -998 0 10 20 40 50 60 75 90 100 110 120 145 150 155 180
## 4 5 1 1 1 1 1 1 1 4 1 1 1 8 1 1
## 190 200 230 235 240 250 270 280 285 300 320 330 350 360 380 400
## 1 18 1 1 1 3 1 2 1 13 2 1 6 3 1 19
## 405 420 440 450 480 500 540 550 555 560 600 630 640 650 700 720
## 1 1 1 7 1 20 1 1 1 1 27 1 1 1 7 2
## 750 800 850 885 900 950 960 1000 1050 1100 1120 1140 1160 1200 1250 1280
## 7 18 1 1 10 1 1 29 1 1 1 1 1 25 2 1
## 1290 1300 1400 1500 1512 1540 1560 1600 1620 1650 1700 1750 1800 1920 1950 2000
## 1 2 14 25 1 1 1 16 1 1 2 2 14 1 2 34
## 2100 2160 2200 2250 2300 2350 2400 2450 2500 2560 2573 2600 2625 2640 2650 2652
## 7 2 6 1 3 1 22 2 7 1 1 1 1 1 1 1
## 2700 2800 2850 2880 2982 3000 3100 3150 3200 3240 3300 3360 3400 3500 3600 3750
## 8 7 1 3 1 27 1 2 7 1 1 1 3 6 22 3
## 3800 3840 3900 4000 4200 4230 4400 4480 4500 4550 4600 4692 4700 4750 4760 4800
## 1 1 1 17 10 1 2 1 9 1 1 1 2 1 1 17
## 4900 5000 5040 5068 5100 5250 5400 5500 5520 5600 5624 5670 5760 6000 6150 6200
## 6 22 1 1 1 2 5 4 1 7 1 1 1 34 1 2
## 6300 6400 6438 6500 6690 6750 6760 6800 6900 6930 7000 7200 7400 7440 7500 7600
## 7 4 1 2 1 1 1 1 1 1 8 21 2 1 4 1
## 7800 7872 7910 8000 8100 8300 8320 8400 8488 8500 8640 8686 8700 8800 8964 9000
## 2 1 1 20 1 1 1 12 1 2 1 1 1 1 1 18
## 9100 9200 9250 9450 9500 9600 10000 10200 10240 10300 10320 10500 10800 11000 11200 11250
## 1 1 1 2 2 22 24 3 1 1 1 5 8 1 2 2
## 11500 11700 11900 12000 12138 12204 12250 12500 12600 13000 13100 13200 13440 13500 13550 13600
## 1 1 2 39 1 1 2 4 2 2 1 3 3 5 1 1
## 13680 13775 13800 13920 14000 14400 14700 14880 15000 15150 15300 15400 15500 15900 16000 16128
## 1 1 2 1 12 19 1 1 12 1 1 1 1 1 8 1
## 16200 16240 16320 16500 16770 16800 17200 17280 17400 17500 17600 17640 17680 17800 18000 18100
## 5 1 1 1 1 13 1 2 1 1 1 1 1 1 26 1
## 18192 18240 18400 18500 19000 19130 19200 19440 19500 19600 19800 20000 20160 20400 20600 20700
## 1 2 1 1 1 1 10 1 1 1 3 11 2 1 1 1
## 21000 21120 21600 21780 21880 22000 22050 22080 22250 22400 22500 22700 22800 23000 23100 23400
## 5 1 11 1 1 3 1 1 1 5 3 1 1 2 1 1
## 24000 24360 24475 25000 25200 25500 25600 25800 26000 26250 26400 26640 27000 27200 27300 27360
## 29 1 1 2 7 1 1 1 4 1 3 1 6 2 1 6
## 27648 28000 28350 28500 28800 28880 29000 29400 29700 29940 30000 30240 30400 30720 31000 31200
## 1 11 1 1 21 1 1 2 1 1 14 2 1 1 1 1
## 31300 32000 32400 32600 33600 34000 34400 35000 35250 36000 36960 37000 37800 38400 38500 38880
## 1 4 4 1 9 2 1 4 1 21 1 1 2 5 1 1
## 39200 39600 39800 40000 40320 40500 40572 40800 40992 42000 42720 43200 43800 44400 44600 44800
## 3 3 1 4 1 1 1 1 1 8 2 16 1 1 1 1
## 45000 46000 46400 46800 47520 48000 48720 48800 49200 49600 49840 49920 50400 50800 51200 51840
## 1 1 1 2 1 22 1 1 1 1 1 1 9 1 1 1
## 52000 52500 52800 54000 54720 54760 56000 56160 57600 58000 60000 60400 60720 61152 61600 62000
## 1 1 2 3 1 1 2 1 8 1 14 1 1 1 2 2
## 62400 62790 63000 63700 64080 64800 66000 66500 66600 67200 67500 67600 69160 69504 69700 70000
## 1 1 1 1 1 2 4 1 1 6 1 1 1 1 1 1
## 70400 71000 72000 73440 73920 75000 75600 76000 76800 77400 77600 77760 78000 78400 79040 79200
## 1 1 26 1 1 2 1 3 4 2 1 2 1 1 1 1
## 80000 80340 80400 80640 81000 81600 82488 82500 83300 83520 84000 84400 85008 85440 86000 86400
## 3 1 1 2 1 1 1 1 1 1 9 1 1 2 1 12
## 86800 87000 87360 88000 89280 89600 90000 90480 92160 92400 93312 93600 94080 94500 96000 97920
## 1 1 2 1 1 1 4 1 2 1 1 2 1 1 13 1
## 98000 1e+05 100100 100800 101640 103680 104000 105600 105840 106176 107088 108000 108480 109200 109440 110000
## 1 2 1 16 1 2 1 2 1 1 1 6 1 1 6 1
## 110400 110640 114240 114800 115200 117000 117600 119520 120000 123840 126000 127000 127200 128000 129600 130000
## 1 1 1 1 4 2 1 1 11 1 1 1 1 1 2 1
## 132000 133440 134400 140000 142560 144000 144480 146250 148000 149760 151000 151200 152000 155200 158400 161840
## 2 1 2 2 1 10 1 1 1 1 1 1 1 1 1 1
## 164800 168000 172800 175000 176400 180000 182500 187200 192000 194000 200200 201600 210240 212000 216000 220000
## 1 5 1 1 1 2 1 1 4 1 1 1 1 1 3 1
## 228000 231695 235200 238000 240000 252000 261000 270000 272000 280800 288000 3e+05 302400 312000 336000 355200
## 1 1 1 1 1 1 1 1 1 1 1 2 1 1 3 1
## 378000 432000 470400 518400 912000 960000 972000 <NA>
## 1 2 1 1 1 1 1 487
## [1] "Frequency table after encoding"
## eh_s6q71_3. How much gross income or revenue was earned over the last 12 months from this ac
## -998 0 10 20 40 50 60
## 4 5 1 1 1 1 1
## 75 90 100 110 120 145 150
## 1 1 4 1 1 1 8
## 155 180 190 200 230 235 240
## 1 1 1 18 1 1 1
## 250 270 280 285 300 320 330
## 3 1 2 1 13 2 1
## 350 360 380 400 405 420 440
## 6 3 1 19 1 1 1
## 450 480 500 540 550 555 560
## 7 1 20 1 1 1 1
## 600 630 640 650 700 720 750
## 27 1 1 1 7 2 7
## 800 850 885 900 950 960 1000
## 18 1 1 10 1 1 29
## 1050 1100 1120 1140 1160 1200 1250
## 1 1 1 1 1 25 2
## 1280 1290 1300 1400 1500 1512 1540
## 1 1 2 14 25 1 1
## 1560 1600 1620 1650 1700 1750 1800
## 1 16 1 1 2 2 14
## 1920 1950 2000 2100 2160 2200 2250
## 1 2 34 7 2 6 1
## 2300 2350 2400 2450 2500 2560 2573
## 3 1 22 2 7 1 1
## 2600 2625 2640 2650 2652 2700 2800
## 1 1 1 1 1 8 7
## 2850 2880 2982 3000 3100 3150 3200
## 1 3 1 27 1 2 7
## 3240 3300 3360 3400 3500 3600 3750
## 1 1 1 3 6 22 3
## 3800 3840 3900 4000 4200 4230 4400
## 1 1 1 17 10 1 2
## 4480 4500 4550 4600 4692 4700 4750
## 1 9 1 1 1 2 1
## 4760 4800 4900 5000 5040 5068 5100
## 1 17 6 22 1 1 1
## 5250 5400 5500 5520 5600 5624 5670
## 2 5 4 1 7 1 1
## 5760 6000 6150 6200 6300 6400 6438
## 1 34 1 2 7 4 1
## 6500 6690 6750 6760 6800 6900 6930
## 2 1 1 1 1 1 1
## 7000 7200 7400 7440 7500 7600 7800
## 8 21 2 1 4 1 2
## 7872 7910 8000 8100 8300 8320 8400
## 1 1 20 1 1 1 12
## 8488 8500 8640 8686 8700 8800 8964
## 1 2 1 1 1 1 1
## 9000 9100 9200 9250 9450 9500 9600
## 18 1 1 1 2 2 22
## 10000 10200 10240 10300 10320 10500 10800
## 24 3 1 1 1 5 8
## 11000 11200 11250 11500 11700 11900 12000
## 1 2 2 1 1 2 39
## 12138 12204 12250 12500 12600 13000 13100
## 1 1 2 4 2 2 1
## 13200 13440 13500 13550 13600 13680 13775
## 3 3 5 1 1 1 1
## 13800 13920 14000 14400 14700 14880 15000
## 2 1 12 19 1 1 12
## 15150 15300 15400 15500 15900 16000 16128
## 1 1 1 1 1 8 1
## 16200 16240 16320 16500 16770 16800 17200
## 5 1 1 1 1 13 1
## 17280 17400 17500 17600 17640 17680 17800
## 2 1 1 1 1 1 1
## 18000 18100 18192 18240 18400 18500 19000
## 26 1 1 2 1 1 1
## 19130 19200 19440 19500 19600 19800 20000
## 1 10 1 1 1 3 11
## 20160 20400 20600 20700 21000 21120 21600
## 2 1 1 1 5 1 11
## 21780 21880 22000 22050 22080 22250 22400
## 1 1 3 1 1 1 5
## 22500 22700 22800 23000 23100 23400 24000
## 3 1 1 2 1 1 29
## 24360 24475 25000 25200 25500 25600 25800
## 1 1 2 7 1 1 1
## 26000 26250 26400 26640 27000 27200 27300
## 4 1 3 1 6 2 1
## 27360 27648 28000 28350 28500 28800 28880
## 6 1 11 1 1 21 1
## 29000 29400 29700 29940 30000 30240 30400
## 1 2 1 1 14 2 1
## 30720 31000 31200 31300 32000 32400 32600
## 1 1 1 1 4 4 1
## 33600 34000 34400 35000 35250 36000 36960
## 9 2 1 4 1 21 1
## 37000 37800 38400 38500 38880 39200 39600
## 1 2 5 1 1 3 3
## 39800 40000 40320 40500 40572 40800 40992
## 1 4 1 1 1 1 1
## 42000 42720 43200 43800 44400 44600 44800
## 8 2 16 1 1 1 1
## 45000 46000 46400 46800 47520 48000 48720
## 1 1 1 2 1 22 1
## 48800 49200 49600 49840 49920 50400 50800
## 1 1 1 1 1 9 1
## 51200 51840 52000 52500 52800 54000 54720
## 1 1 1 1 2 3 1
## 54760 56000 56160 57600 58000 60000 60400
## 1 2 1 8 1 14 1
## 60720 61152 61600 62000 62400 62790 63000
## 1 1 2 2 1 1 1
## 63700 64080 64800 66000 66500 66600 67200
## 1 1 2 4 1 1 6
## 67500 67600 69160 69504 69700 70000 70400
## 1 1 1 1 1 1 1
## 71000 72000 73440 73920 75000 75600 76000
## 1 26 1 1 2 1 3
## 76800 77400 77600 77760 78000 78400 79040
## 4 2 1 2 1 1 1
## 79200 80000 80340 80400 80640 81000 81600
## 1 3 1 1 2 1 1
## 82488 82500 83300 83520 84000 84400 85008
## 1 1 1 1 9 1 1
## 85440 86000 86400 86800 87000 87360 88000
## 2 1 12 1 1 2 1
## 89280 89600 90000 90480 92160 92400 93312
## 1 1 4 1 2 1 1
## 93600 94080 94500 96000 97920 98000 1e+05
## 2 1 1 13 1 1 2
## 100100 100800 101640 103680 104000 105600 105840
## 1 16 1 2 1 2 1
## 106176 107088 108000 108480 109200 109440 110000
## 1 1 6 1 1 6 1
## 110400 110640 114240 114800 115200 117000 117600
## 1 1 1 1 4 2 1
## 119520 120000 123840 126000 127000 127200 128000
## 1 11 1 1 1 1 1
## 129600 130000 132000 133440 134400 140000 142560
## 2 1 2 1 2 2 1
## 144000 144480 146250 148000 149760 151000 151200
## 10 1 1 1 1 1 1
## 152000 155200 158400 161840 164800 168000 172800
## 1 1 1 1 1 5 1
## 175000 176400 180000 182500 187200 192000 194000
## 1 1 2 1 1 4 1
## 200200 201600 210240 212000 216000 220000 228000
## 1 1 1 1 3 1 1
## 231695 235200 238000 240000 252000 261000 270000
## 1 1 1 1 1 1 1
## 272000 280800 288000 3e+05 302400 312000 336000
## 1 1 1 2 1 1 3
## 336383 or more <NA>
## 9 487
mydata <- top_recode (variable="eh_s6q72_3", break_point=pctile_99.5_eh_s6q72_3, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q72_3. Some activities require expenses in order to do them. What are the total expense
## -998 0 7 8 10 15 20 30 35 40 45 46 50 59 60 70
## 6 789 1 1 2 1 6 2 1 2 1 1 6 1 3 1
## 71 72 80 90 97 98 100 105 114 120 128 135 140 144 150 160
## 1 1 7 1 1 1 17 1 1 9 1 1 2 1 13 1
## 165 168 180 182 186 188 190 192 195 200 210 232 236 240 250 260
## 1 1 5 1 1 1 1 1 1 8 2 1 1 7 8 2
## 264 267 275 276 280 285 300 305 320 336 350 360 378 380 384 396
## 1 1 1 1 1 1 14 1 1 1 6 5 1 1 1 1
## 400 410 420 430 448 450 456 480 485 495 500 516 520 540 550 560
## 12 1 2 1 1 5 1 6 1 1 28 1 1 3 2 1
## 600 608 640 650 664 700 702 704 705 720 729 736 740 744 750 767
## 18 1 1 1 1 7 1 1 1 5 1 1 1 1 3 1
## 770 800 831 843 850 880 900 920 930 950 960 970 990 1000 1008 1020
## 1 8 1 1 1 1 6 2 2 1 9 1 1 16 1 1
## 1040 1050 1080 1100 1120 1130 1150 1152 1160 1188 1200 1204 1212 1250 1260 1296
## 3 1 3 3 2 1 1 4 2 1 21 1 1 1 2 1
## 1300 1320 1330 1332 1350 1392 1400 1440 1460 1500 1520 1536 1550 1560 1575 1600
## 2 1 1 1 2 1 4 10 1 9 1 1 1 1 1 15
## 1620 1640 1650 1680 1700 1706 1728 1736 1760 1800 1815 1850 1920 2000 2040 2080
## 1 1 1 3 2 1 3 1 1 10 1 1 9 13 1 1
## 2100 2160 2163 2240 2250 2280 2300 2325 2400 2405 2450 2500 2550 2600 2715 2730
## 4 3 1 1 3 1 1 1 22 1 1 6 2 1 1 1
## 2784 2800 2850 2880 2934 2940 3000 3021 3060 3072 3144 3150 3200 3225 3240 3300
## 1 10 1 10 1 1 20 1 2 3 1 1 1 1 1 2
## 3360 3375 3400 3480 3500 3600 3648 3700 3750 3800 3840 3855 3930 3960 4000 4080
## 3 1 2 1 2 7 1 1 1 1 1 1 1 3 11 1
## 4100 4130 4160 4180 4200 4256 4280 4320 4450 4480 4500 4550 4600 4608 4800 4992
## 1 1 1 1 8 1 1 5 1 2 5 1 1 1 14 1
## 5000 5040 5190 5200 5250 5272 5280 5400 5496 5568 5600 5635 5700 5724 5745 5760
## 13 3 1 1 1 1 2 6 1 2 2 1 1 1 1 6
## 5790 5800 5834 5904 6000 6100 6144 6200 6240 6250 6300 6325 6400 6432 6500 6558
## 1 2 1 1 14 1 1 1 2 1 4 1 3 1 2 1
## 6600 6645 6669 6720 6800 6900 6930 7000 7200 7220 7310 7340 7488 7500 7670 7680
## 1 1 1 4 1 1 1 8 18 1 1 1 1 3 1 4
## 7800 7884 7920 7930 8000 8016 8064 8100 8280 8400 8496 8640 8650 8680 8800 8960
## 1 1 1 1 6 1 1 2 1 5 1 3 1 1 3 2
## 9000 9006 9180 9240 9300 9360 9456 9520 9600 9688 9700 9800 9984 10000 10080 10100
## 3 1 1 1 1 2 1 1 11 1 1 1 1 13 3 1
## 10400 10560 10680 10800 11000 11088 11160 11200 11368 11400 11500 11520 11600 11700 11880 11904
## 1 1 2 8 2 1 1 3 1 1 2 2 2 1 1 1
## 12000 12100 12300 12400 12480 12500 12504 12564 12600 12800 12960 13140 13200 13440 13500 13600
## 14 1 1 1 1 1 1 1 3 1 1 1 2 3 1 1
## 13860 14000 14160 14200 14400 14460 14520 14560 14562 14784 15000 15100 15170 15300 15400 15600
## 1 2 1 1 19 1 1 1 1 1 4 1 1 1 1 2
## 15660 15695 15720 15900 16000 16080 16128 16200 16224 16240 16384 16800 17000 17200 17280 17472
## 1 1 1 2 2 1 1 2 1 1 1 9 1 1 2 1
## 18000 18090 18150 18153 18300 18360 18600 18720 18750 18900 18950 19000 19008 19200 19400 19650
## 7 1 1 1 1 1 1 2 1 1 1 1 1 6 1 1
## 19680 19800 19940 20000 20100 20160 20320 20400 20720 20736 21000 21450 21600 21840 21888 22326
## 1 1 1 4 1 5 1 2 1 1 1 1 4 1 1 1
## 23000 23040 23200 23206 23400 23500 23550 23760 23904 24000 24500 24960 25000 25200 25440 25480
## 1 2 1 1 2 1 1 1 1 26 1 1 3 4 1 1
## 25620 25920 26000 26400 26580 26880 27000 27360 27500 27508 27840 28000 28100 28200 28524 28600
## 1 2 1 1 1 1 2 1 1 1 1 1 1 1 1 1
## 28800 29200 29520 30000 30240 30400 30600 30880 30888 31080 31200 32256 32400 32560 33000 33320
## 8 1 1 2 1 1 1 1 1 1 3 1 1 1 1 1
## 33500 33600 34200 34560 35520 36000 36720 37200 37440 38400 38880 38976 39600 39610 39720 39840
## 1 6 1 4 1 12 1 1 1 2 1 1 1 1 1 1
## 40000 40200 41280 42000 42180 42720 42900 43200 45720 48000 49000 49400 49800 49920 50000 50400
## 2 1 1 2 1 1 1 4 1 9 1 1 1 1 1 5
## 51300 51600 52000 52112 53200 54000 55440 56000 56784 57600 58000 60000 60200 60500 60980 61432
## 1 1 1 1 1 1 1 3 1 5 1 5 1 1 1 1
## 63000 63600 67200 67680 68700 69612 70560 70900 72000 72480 72500 72840 74000 74400 76800 79920
## 2 1 2 1 1 1 1 1 9 1 1 1 1 1 1 1
## 80000 81600 86550 87360 88608 88672 89880 96000 1e+05 100800 103200 108020 110000 120000 123840 124800
## 3 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1
## 126000 126880 129600 132000 136080 138000 144000 160000 180000 188300 190800 196000 2e+05 201600 226980 240000
## 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1
## 246800 278400 288000 336000 357840 432000 864000 9e+05 <NA>
## 1 1 2 1 1 1 1 1 174
## [1] "Frequency table after encoding"
## eh_s6q72_3. Some activities require expenses in order to do them. What are the total expense
## -998 0 7 8 10 15 20
## 6 789 1 1 2 1 6
## 30 35 40 45 46 50 59
## 2 1 2 1 1 6 1
## 60 70 71 72 80 90 97
## 3 1 1 1 7 1 1
## 98 100 105 114 120 128 135
## 1 17 1 1 9 1 1
## 140 144 150 160 165 168 180
## 2 1 13 1 1 1 5
## 182 186 188 190 192 195 200
## 1 1 1 1 1 1 8
## 210 232 236 240 250 260 264
## 2 1 1 7 8 2 1
## 267 275 276 280 285 300 305
## 1 1 1 1 1 14 1
## 320 336 350 360 378 380 384
## 1 1 6 5 1 1 1
## 396 400 410 420 430 448 450
## 1 12 1 2 1 1 5
## 456 480 485 495 500 516 520
## 1 6 1 1 28 1 1
## 540 550 560 600 608 640 650
## 3 2 1 18 1 1 1
## 664 700 702 704 705 720 729
## 1 7 1 1 1 5 1
## 736 740 744 750 767 770 800
## 1 1 1 3 1 1 8
## 831 843 850 880 900 920 930
## 1 1 1 1 6 2 2
## 950 960 970 990 1000 1008 1020
## 1 9 1 1 16 1 1
## 1040 1050 1080 1100 1120 1130 1150
## 3 1 3 3 2 1 1
## 1152 1160 1188 1200 1204 1212 1250
## 4 2 1 21 1 1 1
## 1260 1296 1300 1320 1330 1332 1350
## 2 1 2 1 1 1 2
## 1392 1400 1440 1460 1500 1520 1536
## 1 4 10 1 9 1 1
## 1550 1560 1575 1600 1620 1640 1650
## 1 1 1 15 1 1 1
## 1680 1700 1706 1728 1736 1760 1800
## 3 2 1 3 1 1 10
## 1815 1850 1920 2000 2040 2080 2100
## 1 1 9 13 1 1 4
## 2160 2163 2240 2250 2280 2300 2325
## 3 1 1 3 1 1 1
## 2400 2405 2450 2500 2550 2600 2715
## 22 1 1 6 2 1 1
## 2730 2784 2800 2850 2880 2934 2940
## 1 1 10 1 10 1 1
## 3000 3021 3060 3072 3144 3150 3200
## 20 1 2 3 1 1 1
## 3225 3240 3300 3360 3375 3400 3480
## 1 1 2 3 1 2 1
## 3500 3600 3648 3700 3750 3800 3840
## 2 7 1 1 1 1 1
## 3855 3930 3960 4000 4080 4100 4130
## 1 1 3 11 1 1 1
## 4160 4180 4200 4256 4280 4320 4450
## 1 1 8 1 1 5 1
## 4480 4500 4550 4600 4608 4800 4992
## 2 5 1 1 1 14 1
## 5000 5040 5190 5200 5250 5272 5280
## 13 3 1 1 1 1 2
## 5400 5496 5568 5600 5635 5700 5724
## 6 1 2 2 1 1 1
## 5745 5760 5790 5800 5834 5904 6000
## 1 6 1 2 1 1 14
## 6100 6144 6200 6240 6250 6300 6325
## 1 1 1 2 1 4 1
## 6400 6432 6500 6558 6600 6645 6669
## 3 1 2 1 1 1 1
## 6720 6800 6900 6930 7000 7200 7220
## 4 1 1 1 8 18 1
## 7310 7340 7488 7500 7670 7680 7800
## 1 1 1 3 1 4 1
## 7884 7920 7930 8000 8016 8064 8100
## 1 1 1 6 1 1 2
## 8280 8400 8496 8640 8650 8680 8800
## 1 5 1 3 1 1 3
## 8960 9000 9006 9180 9240 9300 9360
## 2 3 1 1 1 1 2
## 9456 9520 9600 9688 9700 9800 9984
## 1 1 11 1 1 1 1
## 10000 10080 10100 10400 10560 10680 10800
## 13 3 1 1 1 2 8
## 11000 11088 11160 11200 11368 11400 11500
## 2 1 1 3 1 1 2
## 11520 11600 11700 11880 11904 12000 12100
## 2 2 1 1 1 14 1
## 12300 12400 12480 12500 12504 12564 12600
## 1 1 1 1 1 1 3
## 12800 12960 13140 13200 13440 13500 13600
## 1 1 1 2 3 1 1
## 13860 14000 14160 14200 14400 14460 14520
## 1 2 1 1 19 1 1
## 14560 14562 14784 15000 15100 15170 15300
## 1 1 1 4 1 1 1
## 15400 15600 15660 15695 15720 15900 16000
## 1 2 1 1 1 2 2
## 16080 16128 16200 16224 16240 16384 16800
## 1 1 2 1 1 1 9
## 17000 17200 17280 17472 18000 18090 18150
## 1 1 2 1 7 1 1
## 18153 18300 18360 18600 18720 18750 18900
## 1 1 1 1 2 1 1
## 18950 19000 19008 19200 19400 19650 19680
## 1 1 1 6 1 1 1
## 19800 19940 20000 20100 20160 20320 20400
## 1 1 4 1 5 1 2
## 20720 20736 21000 21450 21600 21840 21888
## 1 1 1 1 4 1 1
## 22326 23000 23040 23200 23206 23400 23500
## 1 1 2 1 1 2 1
## 23550 23760 23904 24000 24500 24960 25000
## 1 1 1 26 1 1 3
## 25200 25440 25480 25620 25920 26000 26400
## 4 1 1 1 2 1 1
## 26580 26880 27000 27360 27500 27508 27840
## 1 1 2 1 1 1 1
## 28000 28100 28200 28524 28600 28800 29200
## 1 1 1 1 1 8 1
## 29520 30000 30240 30400 30600 30880 30888
## 1 2 1 1 1 1 1
## 31080 31200 32256 32400 32560 33000 33320
## 1 3 1 1 1 1 1
## 33500 33600 34200 34560 35520 36000 36720
## 1 6 1 4 1 12 1
## 37200 37440 38400 38880 38976 39600 39610
## 1 1 2 1 1 1 1
## 39720 39840 40000 40200 41280 42000 42180
## 1 1 2 1 1 2 1
## 42720 42900 43200 45720 48000 49000 49400
## 1 1 4 1 9 1 1
## 49800 49920 50000 50400 51300 51600 52000
## 1 1 1 5 1 1 1
## 52112 53200 54000 55440 56000 56784 57600
## 1 1 1 1 3 1 5
## 58000 60000 60200 60500 60980 61432 63000
## 1 5 1 1 1 1 2
## 63600 67200 67680 68700 69612 70560 70900
## 1 2 1 1 1 1 1
## 72000 72480 72500 72840 74000 74400 76800
## 9 1 1 1 1 1 1
## 79920 80000 81600 86550 87360 88608 88672
## 1 3 1 1 1 1 1
## 89880 96000 1e+05 100800 103200 108020 110000
## 1 1 1 1 1 1 1
## 120000 123840 124800 126000 126880 129600 132000
## 2 1 1 1 1 1 1
## 136080 138000 144000 160000 180000 188300 190800
## 1 1 1 1 2 1 1
## 196000 2e+05 201600 213401 or more <NA>
## 1 1 1 11 174
mydata <- top_recode (variable="eh_s6q76_3", break_point=pctile_99.5_eh_s6q76_3, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q76_3. If you were to buy those goods or services in a local market over the last 12 mo
## -998 5 9 10 12 15 20 25 30 40 42 45 50 55 60 70
## 1 3 1 5 1 3 5 2 11 6 1 1 15 1 9 1
## 80 84 90 99 100 120 125 130 140 150 160 170 180 185 190 200
## 4 1 1 1 24 7 1 1 2 16 3 1 2 1 2 23
## 210 216 225 235 236 237 240 250 255 260 270 280 300 302 304 320
## 2 1 1 1 1 1 6 4 1 1 1 3 31 1 1 1
## 335 350 360 400 420 450 480 500 510 518 535 540 576 600 640 650
## 1 10 4 11 4 4 7 33 1 1 1 2 1 12 1 2
## 672 700 720 750 770 800 864 875 900 950 955 960 1000 1050 1071 1080
## 1 4 7 2 1 6 1 1 8 1 1 6 19 4 1 2
## 1152 1200 1250 1290 1300 1320 1350 1400 1440 1500 1560 1600 1652 1680 1700 1728
## 1 20 1 1 1 2 2 1 5 14 1 5 1 4 1 1
## 1800 1890 1920 1950 1980 2000 2100 2150 2160 2200 2240 2250 2320 2400 2450 2475
## 8 1 3 1 1 23 3 1 1 2 2 1 1 17 1 1
## 2500 2700 2760 2800 2880 3000 3120 3220 3360 3500 3600 3780 3840 3900 3960 4000
## 5 1 1 5 5 16 1 1 4 3 13 1 6 1 1 4
## 4100 4200 4320 4360 4500 4600 4800 4860 5000 5040 5400 5600 5760 5920 5950 6000
## 1 3 4 1 4 1 8 1 12 1 2 1 2 1 1 9
## 6333 6400 6600 6720 6912 7000 7200 7300 7500 7680 8000 8400 8500 8640 9000 9408
## 1 1 1 4 1 3 9 1 1 1 4 1 1 3 5 1
## 9500 9600 10000 10080 10300 10800 10910 11000 11400 11760 12000 13000 13300 13440 14000 14400
## 1 4 5 1 1 2 1 1 1 2 5 1 1 2 3 11
## 14500 15000 15400 15912 16200 16800 17280 17400 18000 18250 18354 19200 20000 21000 21600 21840
## 1 3 1 1 1 5 2 1 6 1 1 3 2 1 2 1
## 22000 22400 22500 23200 23250 23280 24000 25000 25500 27300 28000 28224 28800 30000 33600 34000
## 1 1 1 1 1 1 2 1 1 1 4 1 3 5 3 1
## 34560 36000 38000 38200 40000 40320 50400 55000 60000 72000 168000 170000 <NA>
## 1 2 1 1 1 1 2 1 1 1 1 1 1513
## [1] "Frequency table after encoding"
## eh_s6q76_3. If you were to buy those goods or services in a local market over the last 12 mo
## -998 5 9 10 12 15 20 25
## 1 3 1 5 1 3 5 2
## 30 40 42 45 50 55 60 70
## 11 6 1 1 15 1 9 1
## 80 84 90 99 100 120 125 130
## 4 1 1 1 24 7 1 1
## 140 150 160 170 180 185 190 200
## 2 16 3 1 2 1 2 23
## 210 216 225 235 236 237 240 250
## 2 1 1 1 1 1 6 4
## 255 260 270 280 300 302 304 320
## 1 1 1 3 31 1 1 1
## 335 350 360 400 420 450 480 500
## 1 10 4 11 4 4 7 33
## 510 518 535 540 576 600 640 650
## 1 1 1 2 1 12 1 2
## 672 700 720 750 770 800 864 875
## 1 4 7 2 1 6 1 1
## 900 950 955 960 1000 1050 1071 1080
## 8 1 1 6 19 4 1 2
## 1152 1200 1250 1290 1300 1320 1350 1400
## 1 20 1 1 1 2 2 1
## 1440 1500 1560 1600 1652 1680 1700 1728
## 5 14 1 5 1 4 1 1
## 1800 1890 1920 1950 1980 2000 2100 2150
## 8 1 3 1 1 23 3 1
## 2160 2200 2240 2250 2320 2400 2450 2475
## 1 2 2 1 1 17 1 1
## 2500 2700 2760 2800 2880 3000 3120 3220
## 5 1 1 5 5 16 1 1
## 3360 3500 3600 3780 3840 3900 3960 4000
## 4 3 13 1 6 1 1 4
## 4100 4200 4320 4360 4500 4600 4800 4860
## 1 3 4 1 4 1 8 1
## 5000 5040 5400 5600 5760 5920 5950 6000
## 12 1 2 1 2 1 1 9
## 6333 6400 6600 6720 6912 7000 7200 7300
## 1 1 1 4 1 3 9 1
## 7500 7680 8000 8400 8500 8640 9000 9408
## 1 1 4 1 1 3 5 1
## 9500 9600 10000 10080 10300 10800 10910 11000
## 1 4 5 1 1 2 1 1
## 11400 11760 12000 13000 13300 13440 14000 14400
## 1 2 5 1 1 2 3 11
## 14500 15000 15400 15912 16200 16800 17280 17400
## 1 3 1 1 1 5 2 1
## 18000 18250 18354 19200 20000 21000 21600 21840
## 6 1 1 3 2 1 2 1
## 22000 22400 22500 23200 23250 23280 24000 25000
## 1 1 1 1 1 1 2 1
## 25500 27300 28000 28224 28800 30000 33600 34000
## 1 1 4 1 3 5 3 1
## 34560 36000 38000 38200 40000 40320 50400 55000
## 1 2 1 1 1 1 2 1
## 55674 or more <NA>
## 4 1513
mydata <- top_recode (variable="eh_s6q71_4", break_point=pctile_99.5_eh_s6q71_4, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q71_4. How much gross income or revenue was earned over the last 12 months from this ac
## -998 0 15 30 40 50 70 80 90 100 150 180 200 210
## 3 14 1 1 2 3 2 2 1 7 2 1 17 1
## 225 238 240 250 260 295 300 350 360 371 390 400 448 450
## 1 1 2 2 1 1 13 1 4 1 1 10 1 3
## 480 498 500 510 520 581 600 630 690 700 720 750 780 800
## 3 1 15 1 1 1 26 1 1 6 3 9 1 17
## 850 875 900 912 960 1000 1050 1100 1110 1125 1160 1170 1200 1250
## 1 1 12 1 2 25 2 3 1 1 1 1 32 1
## 1300 1320 1333 1350 1400 1440 1500 1590 1597 1600 1700 1728 1750 1800
## 5 1 1 1 9 1 22 1 1 11 1 1 4 17
## 1845 1920 1950 1980 2000 2100 2160 2200 2250 2400 2450 2500 2650 2700
## 1 1 1 1 26 6 2 1 2 24 1 6 2 4
## 2760 2800 3000 3100 3120 3150 3200 3300 3328 3360 3400 3420 3500 3600
## 1 6 32 1 2 1 5 1 1 3 2 1 2 14
## 3650 3800 3840 4000 4125 4150 4200 4225 4320 4375 4500 4560 4600 4700
## 1 2 1 15 1 1 14 1 1 1 9 1 1 1
## 4750 4800 4950 5000 5040 5100 5200 5250 5400 5600 5700 5900 5950 6000
## 2 14 2 18 2 1 1 2 6 9 2 1 1 33
## 6048 6050 6160 6200 6250 6300 6460 6500 6600 6750 6800 6864 6960 7000
## 1 1 1 1 1 3 1 4 2 1 2 1 1 9
## 7200 7280 7500 7600 7700 7800 8000 8100 8160 8400 8500 9000 9012 9100
## 16 1 4 1 1 2 21 2 1 8 1 17 1 1
## 9120 9145 9200 9400 9500 9600 9800 9880 10000 10080 10500 10800 10920 11000
## 1 1 1 1 1 20 1 1 12 1 4 9 1 3
## 11100 11200 11316 11500 11760 11800 12000 12100 12288 12300 12480 12500 12600 12750
## 1 4 1 1 1 1 30 1 1 1 1 2 5 1
## 12800 13000 13200 13440 13500 13520 13600 13920 14000 14350 14400 14500 14700 15000
## 1 2 1 1 1 1 1 1 7 1 16 1 1 8
## 15240 15600 15900 16000 16200 16240 16500 16800 17000 17600 18000 18240 18400 18720
## 1 1 1 7 2 1 2 13 4 2 13 2 1 1
## 18800 19200 19600 19700 19800 20000 20160 20400 20880 21000 21400 21560 21600 21840
## 1 10 1 1 1 9 1 1 1 3 1 1 9 1
## 22000 22400 22500 23000 23040 24000 24948 25000 25200 25500 25900 26000 26300 27000
## 4 1 2 1 2 12 1 4 8 2 1 3 1 4
## 27200 27600 28000 28800 29400 29500 29640 30000 30500 30800 31332 31500 31600 32000
## 1 1 2 16 1 1 1 9 1 1 1 2 1 1
## 32400 33000 33500 33600 34000 34200 35000 35600 36000 36720 37128 37440 37800 38400
## 1 4 2 11 2 1 1 1 16 1 1 1 1 4
## 38880 39000 39200 39600 39960 40000 40150 40320 40905 41400 42000 42500 43150 43200
## 1 1 3 2 1 5 1 2 1 1 10 1 1 5
## 44000 44800 45000 46200 47340 48000 49000 49200 49680 50000 50400 50544 51200 51840
## 1 1 2 2 1 12 2 1 1 4 7 1 1 1
## 52000 52800 54000 54600 54720 56000 56920 56960 57000 57600 58800 59000 60000 61440
## 1 1 5 1 1 2 1 1 1 5 1 1 6 2
## 62400 62600 63000 63200 64560 66000 67200 70000 70400 70464 72000 72960 73728 75000
## 2 1 3 1 1 1 5 1 1 1 11 1 1 1
## 75600 77000 78000 78400 81000 81120 82080 82200 84000 84960 86400 86480 87800 89040
## 1 2 3 2 2 1 1 1 6 1 10 1 1 1
## 89280 89600 90000 91200 92400 95040 95550 96000 97200 97920 98000 99360 1e+05 100800
## 1 1 3 2 2 1 1 11 1 1 2 1 2 14
## 103200 103680 104000 106000 106260 106560 107800 108000 108500 108620 109440 112320 114504 115200
## 1 1 1 1 1 1 1 5 1 1 3 2 1 4
## 117600 118800 120000 122400 126000 127680 128856 129600 132000 140000 141120 141504 144000 146000
## 1 1 6 1 1 1 1 4 2 1 1 1 4 1
## 147456 156000 164000 168000 172800 174720 180000 191080 192000 216000 228000 240000 252000 260000
## 1 3 1 3 1 1 2 1 2 1 1 1 1 1
## 264000 268800 302400 312800 316800 336000 384000 6e+05 672000 768000 1526100 <NA>
## 2 1 1 1 1 2 1 1 1 1 1 877
## [1] "Frequency table after encoding"
## eh_s6q71_4. How much gross income or revenue was earned over the last 12 months from this ac
## -998 0 15 30 40 50 70
## 3 14 1 1 2 3 2
## 80 90 100 150 180 200 210
## 2 1 7 2 1 17 1
## 225 238 240 250 260 295 300
## 1 1 2 2 1 1 13
## 350 360 371 390 400 448 450
## 1 4 1 1 10 1 3
## 480 498 500 510 520 581 600
## 3 1 15 1 1 1 26
## 630 690 700 720 750 780 800
## 1 1 6 3 9 1 17
## 850 875 900 912 960 1000 1050
## 1 1 12 1 2 25 2
## 1100 1110 1125 1160 1170 1200 1250
## 3 1 1 1 1 32 1
## 1300 1320 1333 1350 1400 1440 1500
## 5 1 1 1 9 1 22
## 1590 1597 1600 1700 1728 1750 1800
## 1 1 11 1 1 4 17
## 1845 1920 1950 1980 2000 2100 2160
## 1 1 1 1 26 6 2
## 2200 2250 2400 2450 2500 2650 2700
## 1 2 24 1 6 2 4
## 2760 2800 3000 3100 3120 3150 3200
## 1 6 32 1 2 1 5
## 3300 3328 3360 3400 3420 3500 3600
## 1 1 3 2 1 2 14
## 3650 3800 3840 4000 4125 4150 4200
## 1 2 1 15 1 1 14
## 4225 4320 4375 4500 4560 4600 4700
## 1 1 1 9 1 1 1
## 4750 4800 4950 5000 5040 5100 5200
## 2 14 2 18 2 1 1
## 5250 5400 5600 5700 5900 5950 6000
## 2 6 9 2 1 1 33
## 6048 6050 6160 6200 6250 6300 6460
## 1 1 1 1 1 3 1
## 6500 6600 6750 6800 6864 6960 7000
## 4 2 1 2 1 1 9
## 7200 7280 7500 7600 7700 7800 8000
## 16 1 4 1 1 2 21
## 8100 8160 8400 8500 9000 9012 9100
## 2 1 8 1 17 1 1
## 9120 9145 9200 9400 9500 9600 9800
## 1 1 1 1 1 20 1
## 9880 10000 10080 10500 10800 10920 11000
## 1 12 1 4 9 1 3
## 11100 11200 11316 11500 11760 11800 12000
## 1 4 1 1 1 1 30
## 12100 12288 12300 12480 12500 12600 12750
## 1 1 1 1 2 5 1
## 12800 13000 13200 13440 13500 13520 13600
## 1 2 1 1 1 1 1
## 13920 14000 14350 14400 14500 14700 15000
## 1 7 1 16 1 1 8
## 15240 15600 15900 16000 16200 16240 16500
## 1 1 1 7 2 1 2
## 16800 17000 17600 18000 18240 18400 18720
## 13 4 2 13 2 1 1
## 18800 19200 19600 19700 19800 20000 20160
## 1 10 1 1 1 9 1
## 20400 20880 21000 21400 21560 21600 21840
## 1 1 3 1 1 9 1
## 22000 22400 22500 23000 23040 24000 24948
## 4 1 2 1 2 12 1
## 25000 25200 25500 25900 26000 26300 27000
## 4 8 2 1 3 1 4
## 27200 27600 28000 28800 29400 29500 29640
## 1 1 2 16 1 1 1
## 30000 30500 30800 31332 31500 31600 32000
## 9 1 1 1 2 1 1
## 32400 33000 33500 33600 34000 34200 35000
## 1 4 2 11 2 1 1
## 35600 36000 36720 37128 37440 37800 38400
## 1 16 1 1 1 1 4
## 38880 39000 39200 39600 39960 40000 40150
## 1 1 3 2 1 5 1
## 40320 40905 41400 42000 42500 43150 43200
## 2 1 1 10 1 1 5
## 44000 44800 45000 46200 47340 48000 49000
## 1 1 2 2 1 12 2
## 49200 49680 50000 50400 50544 51200 51840
## 1 1 4 7 1 1 1
## 52000 52800 54000 54600 54720 56000 56920
## 1 1 5 1 1 2 1
## 56960 57000 57600 58800 59000 60000 61440
## 1 1 5 1 1 6 2
## 62400 62600 63000 63200 64560 66000 67200
## 2 1 3 1 1 1 5
## 70000 70400 70464 72000 72960 73728 75000
## 1 1 1 11 1 1 1
## 75600 77000 78000 78400 81000 81120 82080
## 1 2 3 2 2 1 1
## 82200 84000 84960 86400 86480 87800 89040
## 1 6 1 10 1 1 1
## 89280 89600 90000 91200 92400 95040 95550
## 1 1 3 2 2 1 1
## 96000 97200 97920 98000 99360 1e+05 100800
## 11 1 1 2 1 2 14
## 103200 103680 104000 106000 106260 106560 107800
## 1 1 1 1 1 1 1
## 108000 108500 108620 109440 112320 114504 115200
## 5 1 1 3 2 1 4
## 117600 118800 120000 122400 126000 127680 128856
## 1 1 6 1 1 1 1
## 129600 132000 140000 141120 141504 144000 146000
## 4 2 1 1 1 4 1
## 147456 156000 164000 168000 172800 174720 180000
## 1 3 1 3 1 1 2
## 191080 192000 216000 228000 240000 252000 260000
## 1 2 1 1 1 1 1
## 264000 268800 302400 312800 316659 or more <NA>
## 2 1 1 1 8 877
mydata <- top_recode (variable="eh_s6q72_4", break_point=pctile_99.5_eh_s6q72_4, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q72_4. Some activities require expenses in order to do them. What are the total expense
## -998 0 1 10 12 15 20 30 32 37 40 44 45 48
## 3 807 1 3 1 2 1 6 1 1 3 1 3 1
## 50 54 55 60 72 80 84 90 92 96 100 113 114 120
## 7 1 2 5 2 2 1 3 1 1 10 1 1 6
## 130 135 140 144 150 160 162 168 175 180 186 200 206 208
## 1 1 3 1 7 7 1 1 1 5 1 15 1 1
## 210 216 218 220 224 238 240 250 252 256 258 288 290 300
## 1 2 1 2 1 1 6 4 2 1 1 1 1 17
## 312 333 336 350 360 400 405 410 420 440 448 450 465 468
## 1 1 1 2 6 10 1 1 1 1 1 4 1 1
## 476 480 498 500 510 512 536 540 550 560 600 620 650 661
## 1 9 1 19 1 2 1 3 2 2 15 1 2 1
## 670 700 720 728 780 800 810 816 820 832 840 870 875 888
## 2 4 4 1 2 9 2 1 1 1 1 2 1 1
## 900 920 921 930 935 950 960 962 980 984 990 1000 1032 1040
## 8 1 1 1 1 1 7 1 1 1 1 20 1 1
## 1050 1056 1060 1070 1080 1095 1100 1104 1114 1120 1200 1210 1250 1260
## 7 1 1 2 1 1 1 2 1 3 25 1 2 2
## 1320 1350 1360 1373 1380 1392 1400 1440 1500 1520 1524 1536 1555 1584
## 1 2 1 1 2 1 4 9 7 1 1 4 1 1
## 1600 1620 1650 1680 1700 1728 1750 1760 1800 1804 1825 1850 1890 1916
## 1 1 1 6 1 2 1 1 7 1 1 1 1 1
## 1920 1975 2000 2040 2100 2112 2149 2160 2200 2226 2232 2250 2332 2345
## 6 1 10 1 2 1 1 2 1 1 1 2 1 1
## 2376 2400 2448 2496 2500 2520 2549 2560 2600 2640 2688 2700 2720 2750
## 1 14 1 1 2 2 1 1 3 3 3 1 1 1
## 2760 2800 2804 2870 2880 2920 2976 3000 3072 3090 3100 3120 3150 3200
## 1 4 1 1 5 1 1 16 2 1 4 2 1 1
## 3226 3280 3328 3360 3380 3400 3500 3570 3600 3604 3640 3750 3800 3840
## 1 1 1 3 1 1 2 1 10 1 1 2 1 3
## 3870 3872 3900 3960 4000 4050 4140 4160 4200 4320 4350 4360 4400 4435
## 1 1 1 1 13 1 1 1 4 2 2 1 2 1
## 4480 4560 4608 4660 4725 4760 4800 4850 4872 4912 4920 5000 5040 5150
## 1 1 1 1 1 1 17 1 1 1 1 9 1 1
## 5200 5280 5300 5350 5400 5450 5496 5500 5544 5600 5620 5660 5745 5760
## 3 1 1 1 2 1 1 1 1 4 1 1 1 3
## 5850 6000 6060 6216 6270 6350 6370 6600 6700 6720 6800 6816 6900 6912
## 1 15 2 1 1 1 1 4 1 2 2 1 1 1
## 6930 7000 7056 7080 7100 7200 7359 7400 7560 7600 7680 7920 8000 8100
## 1 6 1 1 1 12 1 1 2 1 1 2 4 2
## 8400 8500 8550 8640 8800 8900 9000 9360 9450 9516 9600 9800 10000 10080
## 3 1 1 2 1 1 4 1 1 1 10 2 8 4
## 10120 10230 10320 10400 10490 10630 10640 10800 10920 11400 11420 11480 11700 11760
## 1 1 1 1 1 1 1 2 1 2 1 1 1 2
## 12000 12020 12100 12200 12320 12480 12600 12620 12864 12900 12960 13000 13140 13400
## 15 1 1 1 1 1 1 1 1 1 2 1 1 1
## 13500 13560 13800 14000 14400 14600 14850 15000 15050 15120 15600 15840 15900 16000
## 1 1 2 4 10 1 1 7 1 1 3 1 1 3
## 16010 16200 16400 16750 16800 17136 17192 17280 17800 18000 18150 18480 18800 18900
## 1 1 2 1 8 1 1 5 1 8 1 1 1 1
## 18980 19000 19200 20000 20200 20460 20890 21400 21592 21595 21600 22340 22400 22776
## 1 1 4 2 1 1 1 1 1 1 3 1 1 1
## 22800 23400 24000 24288 24480 24960 25000 25200 25900 27000 27700 27872 28000 28800
## 1 1 14 1 1 2 1 1 1 2 1 1 4 6
## 29440 29800 29880 30000 30464 30528 30800 31200 32000 32400 32440 32730 33408 33600
## 1 1 1 4 1 1 1 3 3 1 1 1 1 7
## 34280 34320 34560 35000 35580 36000 36800 36960 37200 38000 38500 38800 38880 39200
## 1 1 1 1 1 5 2 1 2 1 1 1 1 1
## 39600 40000 40320 40560 41650 42000 42120 43200 43220 43500 44600 44880 46800 48000
## 1 1 2 1 1 3 1 6 1 1 1 1 2 5
## 50400 51600 54000 56000 57600 60000 60480 66000 66500 66600 68200 70416 72000 79800
## 1 1 2 2 2 3 1 2 1 1 1 1 4 1
## 80000 84000 84600 90000 90800 94080 94800 96000 97200 114000 120000 134400 144000 148800
## 2 2 1 2 1 1 1 2 1 1 1 1 2 1
## 153600 160000 168000 192000 248160 252000 288000 302400 511488 570000 1094100 <NA>
## 1 1 1 1 1 1 1 1 1 1 1 404
## [1] "Frequency table after encoding"
## eh_s6q72_4. Some activities require expenses in order to do them. What are the total expense
## -998 0 1 10 12 15 20
## 3 807 1 3 1 2 1
## 30 32 37 40 44 45 48
## 6 1 1 3 1 3 1
## 50 54 55 60 72 80 84
## 7 1 2 5 2 2 1
## 90 92 96 100 113 114 120
## 3 1 1 10 1 1 6
## 130 135 140 144 150 160 162
## 1 1 3 1 7 7 1
## 168 175 180 186 200 206 208
## 1 1 5 1 15 1 1
## 210 216 218 220 224 238 240
## 1 2 1 2 1 1 6
## 250 252 256 258 288 290 300
## 4 2 1 1 1 1 17
## 312 333 336 350 360 400 405
## 1 1 1 2 6 10 1
## 410 420 440 448 450 465 468
## 1 1 1 1 4 1 1
## 476 480 498 500 510 512 536
## 1 9 1 19 1 2 1
## 540 550 560 600 620 650 661
## 3 2 2 15 1 2 1
## 670 700 720 728 780 800 810
## 2 4 4 1 2 9 2
## 816 820 832 840 870 875 888
## 1 1 1 1 2 1 1
## 900 920 921 930 935 950 960
## 8 1 1 1 1 1 7
## 962 980 984 990 1000 1032 1040
## 1 1 1 1 20 1 1
## 1050 1056 1060 1070 1080 1095 1100
## 7 1 1 2 1 1 1
## 1104 1114 1120 1200 1210 1250 1260
## 2 1 3 25 1 2 2
## 1320 1350 1360 1373 1380 1392 1400
## 1 2 1 1 2 1 4
## 1440 1500 1520 1524 1536 1555 1584
## 9 7 1 1 4 1 1
## 1600 1620 1650 1680 1700 1728 1750
## 1 1 1 6 1 2 1
## 1760 1800 1804 1825 1850 1890 1916
## 1 7 1 1 1 1 1
## 1920 1975 2000 2040 2100 2112 2149
## 6 1 10 1 2 1 1
## 2160 2200 2226 2232 2250 2332 2345
## 2 1 1 1 2 1 1
## 2376 2400 2448 2496 2500 2520 2549
## 1 14 1 1 2 2 1
## 2560 2600 2640 2688 2700 2720 2750
## 1 3 3 3 1 1 1
## 2760 2800 2804 2870 2880 2920 2976
## 1 4 1 1 5 1 1
## 3000 3072 3090 3100 3120 3150 3200
## 16 2 1 4 2 1 1
## 3226 3280 3328 3360 3380 3400 3500
## 1 1 1 3 1 1 2
## 3570 3600 3604 3640 3750 3800 3840
## 1 10 1 1 2 1 3
## 3870 3872 3900 3960 4000 4050 4140
## 1 1 1 1 13 1 1
## 4160 4200 4320 4350 4360 4400 4435
## 1 4 2 2 1 2 1
## 4480 4560 4608 4660 4725 4760 4800
## 1 1 1 1 1 1 17
## 4850 4872 4912 4920 5000 5040 5150
## 1 1 1 1 9 1 1
## 5200 5280 5300 5350 5400 5450 5496
## 3 1 1 1 2 1 1
## 5500 5544 5600 5620 5660 5745 5760
## 1 1 4 1 1 1 3
## 5850 6000 6060 6216 6270 6350 6370
## 1 15 2 1 1 1 1
## 6600 6700 6720 6800 6816 6900 6912
## 4 1 2 2 1 1 1
## 6930 7000 7056 7080 7100 7200 7359
## 1 6 1 1 1 12 1
## 7400 7560 7600 7680 7920 8000 8100
## 1 2 1 1 2 4 2
## 8400 8500 8550 8640 8800 8900 9000
## 3 1 1 2 1 1 4
## 9360 9450 9516 9600 9800 10000 10080
## 1 1 1 10 2 8 4
## 10120 10230 10320 10400 10490 10630 10640
## 1 1 1 1 1 1 1
## 10800 10920 11400 11420 11480 11700 11760
## 2 1 2 1 1 1 2
## 12000 12020 12100 12200 12320 12480 12600
## 15 1 1 1 1 1 1
## 12620 12864 12900 12960 13000 13140 13400
## 1 1 1 2 1 1 1
## 13500 13560 13800 14000 14400 14600 14850
## 1 1 2 4 10 1 1
## 15000 15050 15120 15600 15840 15900 16000
## 7 1 1 3 1 1 3
## 16010 16200 16400 16750 16800 17136 17192
## 1 1 2 1 8 1 1
## 17280 17800 18000 18150 18480 18800 18900
## 5 1 8 1 1 1 1
## 18980 19000 19200 20000 20200 20460 20890
## 1 1 4 2 1 1 1
## 21400 21592 21595 21600 22340 22400 22776
## 1 1 1 3 1 1 1
## 22800 23400 24000 24288 24480 24960 25000
## 1 1 14 1 1 2 1
## 25200 25900 27000 27700 27872 28000 28800
## 1 1 2 1 1 4 6
## 29440 29800 29880 30000 30464 30528 30800
## 1 1 1 4 1 1 1
## 31200 32000 32400 32440 32730 33408 33600
## 3 3 1 1 1 1 7
## 34280 34320 34560 35000 35580 36000 36800
## 1 1 1 1 1 5 2
## 36960 37200 38000 38500 38800 38880 39200
## 1 2 1 1 1 1 1
## 39600 40000 40320 40560 41650 42000 42120
## 1 1 2 1 1 3 1
## 43200 43220 43500 44600 44880 46800 48000
## 6 1 1 1 1 2 5
## 50400 51600 54000 56000 57600 60000 60480
## 1 1 2 2 2 3 1
## 66000 66500 66600 68200 70416 72000 79800
## 2 1 1 1 1 4 1
## 80000 84000 84600 90000 90800 94080 94800
## 2 2 1 2 1 1 1
## 96000 97200 114000 120000 134400 144000 148800
## 2 1 1 1 1 2 1
## 153600 157439 or more <NA>
## 1 10 404
mydata <- top_recode (variable="eh_s6q76_4", break_point=pctile_99.5_eh_s6q76_4, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q76_4. If you were to buy those goods or services in a local market over the last 12 mo
## -998 2 5 6 10 15 18 20 30 35 40 48 50 60 70 80
## 6 1 2 1 3 2 1 9 3 2 6 1 13 4 5 3
## 90 99 100 120 125 126 129 130 140 147 150 160 190 192 200 210
## 1 1 25 9 1 1 1 1 2 1 12 6 2 1 24 1
## 225 240 250 252 255 260 265 280 288 290 295 300 310 320 340 350
## 3 4 6 1 1 1 1 1 2 1 1 28 1 4 1 3
## 360 370 390 400 420 432 450 480 490 500 501 510 520 525 540 546
## 8 1 1 7 6 2 10 6 1 33 1 1 1 1 2 1
## 560 576 589 600 630 640 650 660 680 700 720 750 760 770 780 800
## 1 1 1 10 1 2 1 1 2 5 3 10 1 2 1 10
## 808 825 840 864 900 950 960 1000 1020 1025 1050 1058 1080 1100 1120 1170
## 1 1 3 1 10 1 3 24 1 1 1 1 1 1 1 1
## 1200 1250 1275 1280 1300 1320 1400 1440 1500 1520 1536 1600 1700 1760 1800 1825
## 21 2 2 1 1 1 3 4 14 1 1 2 1 1 9 1
## 1920 1980 2000 2020 2100 2230 2250 2350 2352 2400 2496 2500 2772 2775 2800 2880
## 2 1 16 1 2 1 5 1 1 15 1 3 1 1 2 4
## 2950 3000 3300 3328 3350 3360 3384 3480 3500 3600 3840 3900 4000 4200 4320 4400
## 1 25 2 1 1 2 1 1 3 13 2 1 7 2 4 1
## 4480 4500 4800 5000 5040 5400 5600 5640 5760 5880 6000 6600 6720 6750 7000 7120
## 1 1 15 7 3 3 2 1 3 1 12 2 7 1 3 1
## 7200 7500 7680 8100 8400 8640 8800 8900 9000 9240 9600 9900 10000 10080 10200 10800
## 5 2 1 2 1 3 1 1 1 1 6 1 6 1 1 3
## 11040 11520 12000 12150 12500 12768 13000 14280 14400 14500 15000 15120 16000 16500 16800 17800
## 1 2 7 1 1 1 1 1 9 1 2 1 2 2 3 1
## 18000 19200 20000 20160 20520 21600 22160 22800 22815 23760 24000 25000 25200 28800 30000 31680
## 4 4 2 1 1 1 1 1 1 1 3 1 1 1 1 1
## 33600 35000 36000 38400 40000 50000 51840 54000 57600 58464 60000 64800 66000 70560 72000 73500
## 3 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1
## 319200 <NA>
## 1 1523
## [1] "Frequency table after encoding"
## eh_s6q76_4. If you were to buy those goods or services in a local market over the last 12 mo
## -998 2 5 6 10 15 18 20
## 6 1 2 1 3 2 1 9
## 30 35 40 48 50 60 70 80
## 3 2 6 1 13 4 5 3
## 90 99 100 120 125 126 129 130
## 1 1 25 9 1 1 1 1
## 140 147 150 160 190 192 200 210
## 2 1 12 6 2 1 24 1
## 225 240 250 252 255 260 265 280
## 3 4 6 1 1 1 1 1
## 288 290 295 300 310 320 340 350
## 2 1 1 28 1 4 1 3
## 360 370 390 400 420 432 450 480
## 8 1 1 7 6 2 10 6
## 490 500 501 510 520 525 540 546
## 1 33 1 1 1 1 2 1
## 560 576 589 600 630 640 650 660
## 1 1 1 10 1 2 1 1
## 680 700 720 750 760 770 780 800
## 2 5 3 10 1 2 1 10
## 808 825 840 864 900 950 960 1000
## 1 1 3 1 10 1 3 24
## 1020 1025 1050 1058 1080 1100 1120 1170
## 1 1 1 1 1 1 1 1
## 1200 1250 1275 1280 1300 1320 1400 1440
## 21 2 2 1 1 1 3 4
## 1500 1520 1536 1600 1700 1760 1800 1825
## 14 1 1 2 1 1 9 1
## 1920 1980 2000 2020 2100 2230 2250 2350
## 2 1 16 1 2 1 5 1
## 2352 2400 2496 2500 2772 2775 2800 2880
## 1 15 1 3 1 1 2 4
## 2950 3000 3300 3328 3350 3360 3384 3480
## 1 25 2 1 1 2 1 1
## 3500 3600 3840 3900 4000 4200 4320 4400
## 3 13 2 1 7 2 4 1
## 4480 4500 4800 5000 5040 5400 5600 5640
## 1 1 15 7 3 3 2 1
## 5760 5880 6000 6600 6720 6750 7000 7120
## 3 1 12 2 7 1 3 1
## 7200 7500 7680 8100 8400 8640 8800 8900
## 5 2 1 2 1 3 1 1
## 9000 9240 9600 9900 10000 10080 10200 10800
## 1 1 6 1 6 1 1 3
## 11040 11520 12000 12150 12500 12768 13000 14280
## 1 2 7 1 1 1 1 1
## 14400 14500 15000 15120 16000 16500 16800 17800
## 9 1 2 1 2 2 3 1
## 18000 19200 20000 20160 20520 21600 22160 22800
## 4 4 2 1 1 1 1 1
## 22815 23760 24000 25000 25200 28800 30000 31680
## 1 1 3 1 1 1 1 1
## 33600 35000 36000 38400 40000 50000 51840 54000
## 3 1 2 1 1 1 1 1
## 57600 58464 60000 64800 66000 66957 or more <NA>
## 1 1 1 1 1 4 1523
mydata <- top_recode (variable="eh_s6q71_5", break_point=pctile_99.5_eh_s6q71_5, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q71_5. How much gross income or revenue was earned over the last 12 months from this ac
## -999 0 15 20 50 70 75 80 90 100 105 120 140 142 150 155
## 1 3 1 1 4 1 1 1 1 9 1 1 2 1 3 1
## 200 220 240 250 260 280 300 350 360 384 390 400 450 480 490 500
## 16 1 3 4 1 2 14 5 3 1 2 15 2 1 1 17
## 600 640 650 700 720 740 750 780 800 840 850 900 960 1000 1050 1080
## 19 1 2 11 2 1 6 1 14 2 1 7 3 14 4 1
## 1100 1140 1195 1200 1260 1300 1333 1350 1400 1425 1430 1440 1500 1550 1590 1600
## 1 1 1 14 1 5 1 1 4 1 1 1 13 1 1 4
## 1680 1700 1750 1800 1850 1950 2000 2040 2080 2090 2100 2150 2160 2200 2250 2352
## 1 3 1 11 2 1 23 2 1 1 3 1 1 3 3 1
## 2370 2400 2450 2500 2550 2700 2800 2850 2880 3000 3150 3200 3300 3400 3500 3600
## 1 16 1 5 2 5 6 1 2 20 1 2 1 1 8 17
## 3750 3900 3960 4000 4160 4190 4200 4250 4300 4450 4500 4590 4700 4800 4900 4950
## 2 1 1 14 1 1 6 2 1 1 7 1 2 8 1 1
## 5000 5040 5050 5100 5200 5300 5340 5400 5450 5500 5600 5760 5976 6000 6200 6250
## 7 1 1 2 1 1 1 6 1 2 5 1 1 21 3 1
## 6300 6400 6500 6600 6700 6840 6900 7000 7200 7350 7400 7480 7500 7650 7680 7800
## 1 3 2 3 1 1 1 5 12 1 1 1 1 1 1 2
## 8000 8035 8100 8150 8400 8460 8700 8800 9000 9100 9125 9180 9230 9400 9500 9600
## 9 1 1 1 2 1 1 2 9 3 1 1 1 1 1 6
## 9750 10000 10100 10400 10500 10800 11000 11200 11470 11500 11520 11640 12000 12040 12480 12500
## 2 10 1 1 2 3 1 5 1 1 2 1 21 1 1 3
## 12600 13000 13010 13200 13290 13440 13700 14000 14400 15000 15500 15600 16000 16500 16800 17000
## 1 2 1 1 1 2 1 5 17 7 2 1 7 2 8 1
## 17280 17500 18000 18240 18400 19200 19500 19600 19950 20000 20160 20400 20500 20520 20800 21000
## 1 1 16 1 1 7 2 1 1 9 1 2 1 1 1 2
## 21100 21600 22000 22400 22500 23000 23400 23600 24000 24500 24576 24900 25000 25200 25920 26000
## 1 8 1 1 1 1 1 1 15 1 1 1 4 7 1 1
## 27000 27216 27300 28000 28800 29520 29600 29640 30000 30500 31500 32000 33600 34000 34650 34710
## 3 1 1 3 7 1 1 1 11 1 2 1 6 1 1 1
## 35000 36000 36400 37000 38000 38400 39200 40000 40320 40500 41760 42000 42240 43200 44440 44800
## 1 5 2 1 1 1 1 3 2 1 1 8 1 7 1 1
## 45000 45240 45600 46080 46500 46800 48000 49000 49200 49400 49680 50000 50400 51000 52000 52800
## 4 1 1 1 1 1 10 1 1 1 1 2 4 1 1 1
## 54000 55000 56000 56600 57600 57720 58800 60000 60480 61920 62400 63000 63840 64000 64800 65934
## 3 3 2 1 4 1 3 6 1 1 2 1 1 2 1 1
## 67200 69504 70000 72000 72288 73000 73500 75900 77000 81000 84000 84320 86400 86640 88800 89400
## 6 1 2 10 1 1 1 1 1 1 2 1 5 1 1 1
## 89700 90000 90480 91000 91200 92400 95040 96000 97920 98000 1e+05 100800 101280 102000 104440 105600
## 1 5 1 1 1 1 1 4 2 1 2 6 1 1 1 1
## 108000 108700 109440 112000 112320 115000 115200 117600 118560 120000 123200 124800 126000 127680 129600 130000
## 4 1 1 1 1 1 3 1 1 4 1 1 1 1 1 1
## 132800 134400 135000 140000 144000 147456 148800 162000 169400 192000 204000 235200 252000 256400 360000 1e+06
## 1 2 1 1 2 1 2 1 1 2 1 1 1 1 1 1
## <NA>
## 1235
## [1] "Frequency table after encoding"
## eh_s6q71_5. How much gross income or revenue was earned over the last 12 months from this ac
## -999 0 15 20 50 70 75
## 1 3 1 1 4 1 1
## 80 90 100 105 120 140 142
## 1 1 9 1 1 2 1
## 150 155 200 220 240 250 260
## 3 1 16 1 3 4 1
## 280 300 350 360 384 390 400
## 2 14 5 3 1 2 15
## 450 480 490 500 600 640 650
## 2 1 1 17 19 1 2
## 700 720 740 750 780 800 840
## 11 2 1 6 1 14 2
## 850 900 960 1000 1050 1080 1100
## 1 7 3 14 4 1 1
## 1140 1195 1200 1260 1300 1333 1350
## 1 1 14 1 5 1 1
## 1400 1425 1430 1440 1500 1550 1590
## 4 1 1 1 13 1 1
## 1600 1680 1700 1750 1800 1850 1950
## 4 1 3 1 11 2 1
## 2000 2040 2080 2090 2100 2150 2160
## 23 2 1 1 3 1 1
## 2200 2250 2352 2370 2400 2450 2500
## 3 3 1 1 16 1 5
## 2550 2700 2800 2850 2880 3000 3150
## 2 5 6 1 2 20 1
## 3200 3300 3400 3500 3600 3750 3900
## 2 1 1 8 17 2 1
## 3960 4000 4160 4190 4200 4250 4300
## 1 14 1 1 6 2 1
## 4450 4500 4590 4700 4800 4900 4950
## 1 7 1 2 8 1 1
## 5000 5040 5050 5100 5200 5300 5340
## 7 1 1 2 1 1 1
## 5400 5450 5500 5600 5760 5976 6000
## 6 1 2 5 1 1 21
## 6200 6250 6300 6400 6500 6600 6700
## 3 1 1 3 2 3 1
## 6840 6900 7000 7200 7350 7400 7480
## 1 1 5 12 1 1 1
## 7500 7650 7680 7800 8000 8035 8100
## 1 1 1 2 9 1 1
## 8150 8400 8460 8700 8800 9000 9100
## 1 2 1 1 2 9 3
## 9125 9180 9230 9400 9500 9600 9750
## 1 1 1 1 1 6 2
## 10000 10100 10400 10500 10800 11000 11200
## 10 1 1 2 3 1 5
## 11470 11500 11520 11640 12000 12040 12480
## 1 1 2 1 21 1 1
## 12500 12600 13000 13010 13200 13290 13440
## 3 1 2 1 1 1 2
## 13700 14000 14400 15000 15500 15600 16000
## 1 5 17 7 2 1 7
## 16500 16800 17000 17280 17500 18000 18240
## 2 8 1 1 1 16 1
## 18400 19200 19500 19600 19950 20000 20160
## 1 7 2 1 1 9 1
## 20400 20500 20520 20800 21000 21100 21600
## 2 1 1 1 2 1 8
## 22000 22400 22500 23000 23400 23600 24000
## 1 1 1 1 1 1 15
## 24500 24576 24900 25000 25200 25920 26000
## 1 1 1 4 7 1 1
## 27000 27216 27300 28000 28800 29520 29600
## 3 1 1 3 7 1 1
## 29640 30000 30500 31500 32000 33600 34000
## 1 11 1 2 1 6 1
## 34650 34710 35000 36000 36400 37000 38000
## 1 1 1 5 2 1 1
## 38400 39200 40000 40320 40500 41760 42000
## 1 1 3 2 1 1 8
## 42240 43200 44440 44800 45000 45240 45600
## 1 7 1 1 4 1 1
## 46080 46500 46800 48000 49000 49200 49400
## 1 1 1 10 1 1 1
## 49680 50000 50400 51000 52000 52800 54000
## 1 2 4 1 1 1 3
## 55000 56000 56600 57600 57720 58800 60000
## 3 2 1 4 1 3 6
## 60480 61920 62400 63000 63840 64000 64800
## 1 1 2 1 1 2 1
## 65934 67200 69504 70000 72000 72288 73000
## 1 6 1 2 10 1 1
## 73500 75900 77000 81000 84000 84320 86400
## 1 1 1 1 2 1 5
## 86640 88800 89400 89700 90000 90480 91000
## 1 1 1 1 5 1 1
## 91200 92400 95040 96000 97920 98000 1e+05
## 1 1 1 4 2 1 2
## 100800 101280 102000 104440 105600 108000 108700
## 6 1 1 1 1 4 1
## 109440 112000 112320 115000 115200 117600 118560
## 1 1 1 1 3 1 1
## 120000 123200 124800 126000 127680 129600 130000
## 4 1 1 1 1 1 1
## 132800 134400 135000 140000 144000 147456 148800
## 1 2 1 1 2 1 2
## 162000 169400 192000 200880 or more <NA>
## 1 1 2 6 1235
mydata <- top_recode (variable="eh_s6q72_5", break_point=pctile_99.5_eh_s6q72_5, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q72_5. Some activities require expenses in order to do them. What are the total expense
## -999 -998 0 6 10 16 17 20 25 30 32 35 40 45 50 53
## 1 2 684 1 3 1 1 2 1 3 1 4 6 1 9 1
## 56 60 70 72 75 76 80 84 90 96 100 105 112 114 115 120
## 1 3 1 1 1 1 2 1 2 1 17 1 1 2 2 5
## 132 140 144 145 150 154 160 180 186 200 204 210 216 235 240 245
## 1 4 1 1 14 1 3 3 1 17 1 1 1 1 6 1
## 250 256 260 265 270 280 300 310 320 333 350 360 380 384 385 400
## 7 1 1 1 1 5 14 1 2 1 2 5 2 1 1 5
## 408 414 416 420 428 450 470 480 500 502 540 550 560 576 600 640
## 1 1 1 1 1 4 2 6 15 1 2 1 1 1 7 2
## 650 670 672 700 710 720 750 768 800 840 850 864 880 882 896 900
## 1 1 1 8 1 3 3 4 5 1 2 1 1 1 1 5
## 950 958 960 980 1000 1008 1050 1060 1080 1095 1100 1160 1166 1188 1200 1230
## 1 1 9 1 16 1 2 1 2 1 1 1 1 1 13 1
## 1300 1320 1334 1344 1350 1380 1392 1400 1440 1465 1500 1536 1560 1600 1620 1680
## 3 2 1 2 1 1 1 1 8 1 11 2 1 2 1 3
## 1728 1800 1830 1890 1910 1920 1960 2000 2010 2016 2025 2100 2160 2200 2300 2304
## 2 7 1 1 1 5 1 16 1 1 1 1 1 1 1 3
## 2375 2376 2400 2460 2500 2520 2550 2672 2688 2690 2700 2772 2784 2800 2880 2940
## 1 1 13 1 2 3 1 1 3 1 2 1 1 3 6 2
## 3000 3024 3168 3200 3255 3264 3360 3480 3500 3600 3744 3750 3780 3800 3840 4000
## 9 1 2 1 1 1 4 2 4 7 2 1 1 2 3 9
## 4032 4320 4400 4464 4500 4608 4614 4658 4740 4800 4900 5000 5060 5120 5200 5250
## 1 3 4 1 1 1 1 1 1 6 1 9 1 1 2 1
## 5280 5300 5400 5425 5500 5520 5600 5728 5760 5880 6000 6125 6200 6240 6300 6320
## 3 1 4 1 2 1 1 1 7 1 10 1 2 1 1 1
## 6350 6480 6500 6600 6630 6720 6845 6912 7000 7200 7400 7500 7680 8000 8040 8320
## 1 1 1 1 1 1 1 1 1 14 1 1 1 6 1 1
## 8340 8400 8550 8640 8750 8800 9000 9168 9580 9600 9900 10000 10080 10150 10173 10260
## 1 7 1 2 1 1 4 1 1 6 1 1 1 1 1 1
## 10400 10500 10800 10850 10900 10940 10960 11000 11160 11200 11424 11500 11520 11700 11800 11904
## 1 1 4 1 1 1 1 2 1 1 1 1 1 1 1 1
## 12000 12162 12200 12240 12400 12432 12500 12720 12930 12960 13050 13440 13530 13632 13680 14000
## 7 1 1 1 1 1 2 1 1 1 1 1 1 1 1 2
## 14024 14080 14100 14400 14560 14900 15000 15120 15270 16000 16200 16400 16560 16800 17280 17400
## 1 1 1 14 1 1 4 1 1 3 1 1 1 4 2 1
## 17520 17704 18000 18184 18480 18720 18816 19200 19656 19680 19800 20000 20160 20400 20460 20568
## 1 1 2 1 1 1 1 4 1 3 1 3 2 1 1 1
## 20640 21000 21560 21600 21910 22000 22360 22400 22800 23040 23360 23400 23640 23760 24000 24288
## 1 1 1 2 1 1 1 2 1 1 1 1 1 1 12 1
## 24500 24560 24768 25200 25500 26000 26400 26840 26880 27000 28800 29000 29100 30000 30600 31104
## 1 1 1 1 1 1 2 1 1 1 9 2 1 1 1 1
## 31200 31680 31920 32400 32500 33000 33600 34200 35000 36000 36840 37000 38060 38500 39240 39600
## 1 1 1 1 1 1 5 1 1 4 1 1 1 1 1 1
## 40000 40320 40992 42000 44400 45000 46080 48000 48960 49500 49560 50500 52290 52800 54000 54600
## 1 1 1 2 1 1 1 2 1 1 1 1 1 1 1 1
## 56000 56640 57600 58240 60000 60800 65230 67200 69000 69800 72000 72576 75000 80000 84000 90000
## 1 1 2 1 1 1 1 3 1 1 2 1 1 1 1 1
## 100800 104400 110000 123200 126000 128000 152000 223200 294000 360000 <NA>
## 2 1 1 1 1 1 1 1 1 1 730
## [1] "Frequency table after encoding"
## eh_s6q72_5. Some activities require expenses in order to do them. What are the total expense
## -999 -998 0 6 10 16 17
## 1 2 684 1 3 1 1
## 20 25 30 32 35 40 45
## 2 1 3 1 4 6 1
## 50 53 56 60 70 72 75
## 9 1 1 3 1 1 1
## 76 80 84 90 96 100 105
## 1 2 1 2 1 17 1
## 112 114 115 120 132 140 144
## 1 2 2 5 1 4 1
## 145 150 154 160 180 186 200
## 1 14 1 3 3 1 17
## 204 210 216 235 240 245 250
## 1 1 1 1 6 1 7
## 256 260 265 270 280 300 310
## 1 1 1 1 5 14 1
## 320 333 350 360 380 384 385
## 2 1 2 5 2 1 1
## 400 408 414 416 420 428 450
## 5 1 1 1 1 1 4
## 470 480 500 502 540 550 560
## 2 6 15 1 2 1 1
## 576 600 640 650 670 672 700
## 1 7 2 1 1 1 8
## 710 720 750 768 800 840 850
## 1 3 3 4 5 1 2
## 864 880 882 896 900 950 958
## 1 1 1 1 5 1 1
## 960 980 1000 1008 1050 1060 1080
## 9 1 16 1 2 1 2
## 1095 1100 1160 1166 1188 1200 1230
## 1 1 1 1 1 13 1
## 1300 1320 1334 1344 1350 1380 1392
## 3 2 1 2 1 1 1
## 1400 1440 1465 1500 1536 1560 1600
## 1 8 1 11 2 1 2
## 1620 1680 1728 1800 1830 1890 1910
## 1 3 2 7 1 1 1
## 1920 1960 2000 2010 2016 2025 2100
## 5 1 16 1 1 1 1
## 2160 2200 2300 2304 2375 2376 2400
## 1 1 1 3 1 1 13
## 2460 2500 2520 2550 2672 2688 2690
## 1 2 3 1 1 3 1
## 2700 2772 2784 2800 2880 2940 3000
## 2 1 1 3 6 2 9
## 3024 3168 3200 3255 3264 3360 3480
## 1 2 1 1 1 4 2
## 3500 3600 3744 3750 3780 3800 3840
## 4 7 2 1 1 2 3
## 4000 4032 4320 4400 4464 4500 4608
## 9 1 3 4 1 1 1
## 4614 4658 4740 4800 4900 5000 5060
## 1 1 1 6 1 9 1
## 5120 5200 5250 5280 5300 5400 5425
## 1 2 1 3 1 4 1
## 5500 5520 5600 5728 5760 5880 6000
## 2 1 1 1 7 1 10
## 6125 6200 6240 6300 6320 6350 6480
## 1 2 1 1 1 1 1
## 6500 6600 6630 6720 6845 6912 7000
## 1 1 1 1 1 1 1
## 7200 7400 7500 7680 8000 8040 8320
## 14 1 1 1 6 1 1
## 8340 8400 8550 8640 8750 8800 9000
## 1 7 1 2 1 1 4
## 9168 9580 9600 9900 10000 10080 10150
## 1 1 6 1 1 1 1
## 10173 10260 10400 10500 10800 10850 10900
## 1 1 1 1 4 1 1
## 10940 10960 11000 11160 11200 11424 11500
## 1 1 2 1 1 1 1
## 11520 11700 11800 11904 12000 12162 12200
## 1 1 1 1 7 1 1
## 12240 12400 12432 12500 12720 12930 12960
## 1 1 1 2 1 1 1
## 13050 13440 13530 13632 13680 14000 14024
## 1 1 1 1 1 2 1
## 14080 14100 14400 14560 14900 15000 15120
## 1 1 14 1 1 4 1
## 15270 16000 16200 16400 16560 16800 17280
## 1 3 1 1 1 4 2
## 17400 17520 17704 18000 18184 18480 18720
## 1 1 1 2 1 1 1
## 18816 19200 19656 19680 19800 20000 20160
## 1 4 1 3 1 3 2
## 20400 20460 20568 20640 21000 21560 21600
## 1 1 1 1 1 1 2
## 21910 22000 22360 22400 22800 23040 23360
## 1 1 1 2 1 1 1
## 23400 23640 23760 24000 24288 24500 24560
## 1 1 1 12 1 1 1
## 24768 25200 25500 26000 26400 26840 26880
## 1 1 1 1 2 1 1
## 27000 28800 29000 29100 30000 30600 31104
## 1 9 2 1 1 1 1
## 31200 31680 31920 32400 32500 33000 33600
## 1 1 1 1 1 1 5
## 34200 35000 36000 36840 37000 38060 38500
## 1 1 4 1 1 1 1
## 39240 39600 40000 40320 40992 42000 44400
## 1 1 1 1 1 2 1
## 45000 46080 48000 48960 49500 49560 50500
## 1 1 2 1 1 1 1
## 52290 52800 54000 54600 56000 56640 57600
## 1 1 1 1 1 1 2
## 58240 60000 60800 65230 67200 69000 69800
## 1 1 1 1 3 1 1
## 72000 72576 75000 80000 84000 90000 100800
## 2 1 1 1 1 1 2
## 104400 105659 or more <NA>
## 1 8 730
mydata <- top_recode (variable="eh_s6q76_5", break_point=pctile_99.5_eh_s6q76_5, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q76_5. If you were to buy those goods or services in a local market over the last 12 mo
## -998 3 5 7 9 10 15 20 25 30 35 40 50 60 70 75 80 89 90
## 5 1 2 1 3 5 2 4 2 1 1 5 9 7 1 1 3 1 2
## 100 105 120 130 140 150 155 160 175 180 192 200 210 216 220 225 231 240 250
## 18 1 7 1 3 11 1 5 1 4 1 22 3 1 2 1 1 3 6
## 260 270 280 290 300 315 320 340 350 360 399 400 420 450 460 480 490 500 504
## 2 1 2 1 19 1 1 2 3 5 1 7 1 7 2 6 1 30 1
## 510 540 550 560 576 600 650 655 690 700 720 750 768 780 800 840 850 852 880
## 1 3 2 1 1 23 1 1 1 6 5 8 1 1 10 2 1 1 1
## 900 950 960 1000 1008 1050 1080 1100 1120 1200 1230 1250 1280 1300 1320 1350 1400 1440 1490
## 8 1 5 22 1 1 1 3 2 19 1 2 1 1 4 1 2 4 1
## 1500 1560 1600 1700 1750 1760 1800 1848 1900 1920 1950 1980 2000 2100 2160 2200 2250 2300 2400
## 15 1 3 1 1 1 9 1 1 1 1 1 19 3 1 1 1 1 15
## 2500 2600 2750 2800 2880 2940 3000 3120 3150 3200 3360 3540 3600 3650 3750 3800 3840 3920 4000
## 2 1 1 2 5 1 14 1 1 1 5 1 13 1 1 1 6 1 10
## 4200 4500 4680 4800 4860 5000 5250 5400 5600 5625 5700 5760 6000 6480 6500 6720 6900 7000 7200
## 6 1 1 15 1 4 1 6 2 1 1 3 11 1 1 3 1 1 8
## 7300 7420 7500 7680 7800 8000 8400 8500 8800 9000 9600 9750 10000 10080 10800 11200 11500 11520 12000
## 1 1 2 2 1 1 6 2 1 1 11 1 4 1 2 1 1 1 7
## 12500 12720 12800 14000 14400 15000 16100 16800 18000 18360 18648 19200 21000 22500 23625 24000 26680 28000 28800
## 1 1 1 2 4 4 1 2 1 1 1 1 1 1 1 8 1 1 1
## 30000 36000 42850 54600 61440 66000 67200 72000 <NA>
## 1 3 1 1 1 1 1 1 1593
## [1] "Frequency table after encoding"
## eh_s6q76_5. If you were to buy those goods or services in a local market over the last 12 mo
## -998 3 5 7 9 10 15 20
## 5 1 2 1 3 5 2 4
## 25 30 35 40 50 60 70 75
## 2 1 1 5 9 7 1 1
## 80 89 90 100 105 120 130 140
## 3 1 2 18 1 7 1 3
## 150 155 160 175 180 192 200 210
## 11 1 5 1 4 1 22 3
## 216 220 225 231 240 250 260 270
## 1 2 1 1 3 6 2 1
## 280 290 300 315 320 340 350 360
## 2 1 19 1 1 2 3 5
## 399 400 420 450 460 480 490 500
## 1 7 1 7 2 6 1 30
## 504 510 540 550 560 576 600 650
## 1 1 3 2 1 1 23 1
## 655 690 700 720 750 768 780 800
## 1 1 6 5 8 1 1 10
## 840 850 852 880 900 950 960 1000
## 2 1 1 1 8 1 5 22
## 1008 1050 1080 1100 1120 1200 1230 1250
## 1 1 1 3 2 19 1 2
## 1280 1300 1320 1350 1400 1440 1490 1500
## 1 1 4 1 2 4 1 15
## 1560 1600 1700 1750 1760 1800 1848 1900
## 1 3 1 1 1 9 1 1
## 1920 1950 1980 2000 2100 2160 2200 2250
## 1 1 1 19 3 1 1 1
## 2300 2400 2500 2600 2750 2800 2880 2940
## 1 15 2 1 1 2 5 1
## 3000 3120 3150 3200 3360 3540 3600 3650
## 14 1 1 1 5 1 13 1
## 3750 3800 3840 3920 4000 4200 4500 4680
## 1 1 6 1 10 6 1 1
## 4800 4860 5000 5250 5400 5600 5625 5700
## 15 1 4 1 6 2 1 1
## 5760 6000 6480 6500 6720 6900 7000 7200
## 3 11 1 1 3 1 1 8
## 7300 7420 7500 7680 7800 8000 8400 8500
## 1 1 2 2 1 1 6 2
## 8800 9000 9600 9750 10000 10080 10800 11200
## 1 1 11 1 4 1 2 1
## 11500 11520 12000 12500 12720 12800 14000 14400
## 1 1 7 1 1 1 2 4
## 15000 16100 16800 18000 18360 18648 19200 21000
## 4 1 2 1 1 1 1 1
## 22500 23625 24000 26680 28000 28800 30000 36000
## 1 1 8 1 1 1 1 3
## 42850 54600 58396 or more <NA>
## 1 1 4 1593
mydata <- top_recode (variable="eh_s6q71_6", break_point=pctile_99.5_eh_s6q71_6, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q71_6. How much gross income or revenue was earned over the last 12 months from this ac
## -998 0 15 20 25 50 56 70 75 100 140 145 150 160 168 170
## 3 3 1 1 1 3 1 1 2 6 1 1 6 1 1 1
## 200 210 225 240 250 278 280 300 350 360 375 400 410 420 450 460
## 12 1 1 3 3 1 1 16 5 3 1 10 1 2 3 1
## 500 530 600 645 680 700 720 750 780 800 840 900 925 960 1000 1050
## 11 1 14 1 1 7 3 3 1 13 2 8 1 2 14 1
## 1063 1100 1200 1250 1300 1333 1350 1400 1430 1450 1500 1536 1560 1568 1600 1650
## 1 2 12 8 2 1 1 4 1 1 8 1 1 1 10 1
## 1659 1680 1750 1800 1900 1920 2000 2008 2050 2100 2160 2250 2352 2400 2500 2600
## 1 1 1 7 1 1 11 1 1 7 1 2 1 10 4 1
## 2650 2700 2730 2800 2900 3000 3060 3200 3300 3400 3500 3600 3624 3709 3750 4000
## 1 1 1 5 2 14 1 2 1 2 4 8 1 1 1 11
## 4200 4350 4400 4500 4800 4900 5000 5100 5200 5250 5300 5500 5520 5600 5980 6000
## 3 2 1 3 13 1 12 1 1 1 2 2 1 3 1 20
## 6250 6300 6400 7000 7200 7350 7480 7500 7800 8000 8400 8640 9000 9100 9200 9600
## 1 2 3 5 15 1 1 1 1 4 6 1 9 1 1 8
## 9750 9800 9900 10000 10080 10300 10500 10800 11000 11200 11250 11520 11700 12000 12200 12500
## 1 1 1 5 1 1 1 4 3 2 1 1 1 19 1 1
## 12600 13500 14000 14300 14400 14700 15000 15700 16000 16200 16800 17000 17280 17400 17500 18000
## 1 1 6 1 9 1 3 1 4 1 4 2 1 1 1 9
## 18200 18240 18250 18750 19000 19200 19600 19980 20000 20800 20900 21000 21600 22400 23400 24000
## 1 1 1 1 2 3 1 1 5 1 1 3 3 1 2 9
## 24200 25000 25200 26400 26800 27500 27600 28000 28800 29050 29400 30000 30600 31300 31428 31500
## 1 1 1 1 1 1 1 1 8 1 1 7 1 1 1 1
## 31600 32400 33000 33600 33930 34560 34650 35000 36000 36190 38000 38400 39520 39600 40000 42000
## 1 1 1 3 1 2 1 1 11 1 3 2 1 2 2 8
## 42400 43200 44352 44800 47040 48000 48800 49000 49500 50000 51840 54000 54600 56000 57600 60000
## 1 3 1 1 1 3 1 1 1 3 1 3 1 2 2 3
## 61200 62400 66600 67200 67500 70000 72000 72800 75000 79000 80000 83520 84000 86400 88000 90000
## 1 1 1 3 1 1 4 1 1 1 2 1 2 3 1 1
## 92000 93000 93600 96000 98400 98700 100800 101000 103000 115200 120000 127600 132480 134000 136000 139200
## 1 1 1 1 1 1 9 1 1 2 2 1 1 1 1 1
## 144000 156000 168000 187200 201600 216000 230400 235200 240000 286000 360000 420000 <NA>
## 6 1 2 1 1 1 1 1 2 1 2 1 1524
## [1] "Frequency table after encoding"
## eh_s6q71_6. How much gross income or revenue was earned over the last 12 months from this ac
## -998 0 15 20 25 50 56
## 3 3 1 1 1 3 1
## 70 75 100 140 145 150 160
## 1 2 6 1 1 6 1
## 168 170 200 210 225 240 250
## 1 1 12 1 1 3 3
## 278 280 300 350 360 375 400
## 1 1 16 5 3 1 10
## 410 420 450 460 500 530 600
## 1 2 3 1 11 1 14
## 645 680 700 720 750 780 800
## 1 1 7 3 3 1 13
## 840 900 925 960 1000 1050 1063
## 2 8 1 2 14 1 1
## 1100 1200 1250 1300 1333 1350 1400
## 2 12 8 2 1 1 4
## 1430 1450 1500 1536 1560 1568 1600
## 1 1 8 1 1 1 10
## 1650 1659 1680 1750 1800 1900 1920
## 1 1 1 1 7 1 1
## 2000 2008 2050 2100 2160 2250 2352
## 11 1 1 7 1 2 1
## 2400 2500 2600 2650 2700 2730 2800
## 10 4 1 1 1 1 5
## 2900 3000 3060 3200 3300 3400 3500
## 2 14 1 2 1 2 4
## 3600 3624 3709 3750 4000 4200 4350
## 8 1 1 1 11 3 2
## 4400 4500 4800 4900 5000 5100 5200
## 1 3 13 1 12 1 1
## 5250 5300 5500 5520 5600 5980 6000
## 1 2 2 1 3 1 20
## 6250 6300 6400 7000 7200 7350 7480
## 1 2 3 5 15 1 1
## 7500 7800 8000 8400 8640 9000 9100
## 1 1 4 6 1 9 1
## 9200 9600 9750 9800 9900 10000 10080
## 1 8 1 1 1 5 1
## 10300 10500 10800 11000 11200 11250 11520
## 1 1 4 3 2 1 1
## 11700 12000 12200 12500 12600 13500 14000
## 1 19 1 1 1 1 6
## 14300 14400 14700 15000 15700 16000 16200
## 1 9 1 3 1 4 1
## 16800 17000 17280 17400 17500 18000 18200
## 4 2 1 1 1 9 1
## 18240 18250 18750 19000 19200 19600 19980
## 1 1 1 2 3 1 1
## 20000 20800 20900 21000 21600 22400 23400
## 5 1 1 3 3 1 2
## 24000 24200 25000 25200 26400 26800 27500
## 9 1 1 1 1 1 1
## 27600 28000 28800 29050 29400 30000 30600
## 1 1 8 1 1 7 1
## 31300 31428 31500 31600 32400 33000 33600
## 1 1 1 1 1 1 3
## 33930 34560 34650 35000 36000 36190 38000
## 1 2 1 1 11 1 3
## 38400 39520 39600 40000 42000 42400 43200
## 2 1 2 2 8 1 3
## 44352 44800 47040 48000 48800 49000 49500
## 1 1 1 3 1 1 1
## 50000 51840 54000 54600 56000 57600 60000
## 3 1 3 1 2 2 3
## 61200 62400 66600 67200 67500 70000 72000
## 1 1 1 3 1 1 4
## 72800 75000 79000 80000 83520 84000 86400
## 1 1 1 2 1 2 3
## 88000 90000 92000 93000 93600 96000 98400
## 1 1 1 1 1 1 1
## 98700 100800 101000 103000 115200 120000 127600
## 1 9 1 1 2 2 1
## 132480 134000 136000 139200 144000 156000 168000
## 1 1 1 1 6 1 2
## 187200 201600 216000 230400 235200 240000 249200 or more
## 1 1 1 1 1 2 4
## <NA>
## 1524
mydata <- top_recode (variable="eh_s6q72_6", break_point=pctile_99.5_eh_s6q72_6, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q72_6. Some activities require expenses in order to do them. What are the total expense
## -998 0 1 10 12 15 20 25 35 36 40 45 50 54 55 58
## 3 628 1 1 1 2 3 3 2 2 4 3 6 1 1 1
## 60 70 80 88 90 96 100 105 114 120 135 140 144 145 150 162
## 10 2 4 1 3 1 8 1 1 8 1 3 1 1 5 1
## 180 184 190 200 220 230 235 240 244 250 270 280 288 300 320 333
## 5 1 1 14 1 1 1 4 1 3 3 1 2 9 1 1
## 340 350 352 360 375 384 400 408 414 420 450 480 500 510 540 560
## 1 1 1 2 1 1 3 1 1 3 3 6 10 1 1 2
## 600 630 640 650 660 680 690 700 720 757 800 840 853 880 896 900
## 8 1 2 2 2 1 1 4 5 1 1 4 1 1 1 2
## 960 1000 1040 1050 1078 1080 1095 1152 1188 1200 1250 1300 1344 1345 1380 1400
## 1 14 2 1 1 1 1 1 1 8 3 3 1 1 1 5
## 1440 1500 1512 1570 1600 1680 1700 1800 1820 1890 1920 1960 1968 2000 2016 2100
## 9 6 1 1 3 1 1 10 1 1 2 1 1 9 1 1
## 2112 2160 2200 2208 2240 2250 2310 2400 2500 2640 2688 2800 2880 3000 3100 3120
## 1 1 2 1 1 1 1 9 3 1 3 3 5 13 1 1
## 3168 3200 3250 3300 3330 3360 3400 3450 3500 3552 3600 3636 3640 3700 3900 4000
## 1 2 1 1 1 4 1 1 1 1 6 1 1 1 1 6
## 4032 4155 4200 4290 4320 4500 4600 4676 4752 4800 4965 5000 5040 5160 5180 5200
## 1 1 5 1 2 2 1 1 1 8 1 6 2 1 1 1
## 5240 5340 5400 5550 5560 5600 5640 5760 6000 6400 6480 6500 6528 6720 6900 7000
## 1 1 1 1 1 2 1 1 4 1 2 1 1 1 1 2
## 7200 7400 7500 7680 8000 8150 8160 8300 8400 8640 8700 8940 9000 9320 9600 9625
## 8 1 2 1 2 1 1 1 5 1 1 1 3 1 5 1
## 10000 10080 10250 10320 10590 10600 10800 11400 11520 11880 11900 12000 12004 12300 12500 12600
## 4 1 1 1 1 1 3 1 1 1 1 6 1 1 1 1
## 12610 12785 12800 13300 13440 13600 13860 14000 14119 14160 14400 14740 14976 15000 15510 15600
## 1 1 1 1 1 1 1 1 1 1 5 1 1 2 1 1
## 15840 16520 16800 17280 17880 17888 18000 18924 19000 19200 19524 19650 20000 21440 21600 22000
## 1 1 1 3 1 1 1 1 1 4 1 1 1 1 2 1
## 22050 22100 22350 22400 23000 23380 24000 25200 25800 25920 28000 28800 30000 30792 30870 31120
## 1 1 1 1 1 1 5 1 1 1 1 4 2 1 1 1
## 31200 33600 34500 36000 37090 38400 39240 40650 42000 43200 43680 44200 45000 48000 50400 54720
## 2 2 1 2 1 1 1 1 1 1 1 1 1 2 1 1
## 57600 60000 67200 73440 74400 76000 100800 105000 132000 145920 168000 180000 288000 9e+05 <NA>
## 4 3 2 1 1 1 1 1 1 1 2 2 1 1 1053
## [1] "Frequency table after encoding"
## eh_s6q72_6. Some activities require expenses in order to do them. What are the total expense
## -998 0 1 10 12 15 20
## 3 628 1 1 1 2 3
## 25 35 36 40 45 50 54
## 3 2 2 4 3 6 1
## 55 58 60 70 80 88 90
## 1 1 10 2 4 1 3
## 96 100 105 114 120 135 140
## 1 8 1 1 8 1 3
## 144 145 150 162 180 184 190
## 1 1 5 1 5 1 1
## 200 220 230 235 240 244 250
## 14 1 1 1 4 1 3
## 270 280 288 300 320 333 340
## 3 1 2 9 1 1 1
## 350 352 360 375 384 400 408
## 1 1 2 1 1 3 1
## 414 420 450 480 500 510 540
## 1 3 3 6 10 1 1
## 560 600 630 640 650 660 680
## 2 8 1 2 2 2 1
## 690 700 720 757 800 840 853
## 1 4 5 1 1 4 1
## 880 896 900 960 1000 1040 1050
## 1 1 2 1 14 2 1
## 1078 1080 1095 1152 1188 1200 1250
## 1 1 1 1 1 8 3
## 1300 1344 1345 1380 1400 1440 1500
## 3 1 1 1 5 9 6
## 1512 1570 1600 1680 1700 1800 1820
## 1 1 3 1 1 10 1
## 1890 1920 1960 1968 2000 2016 2100
## 1 2 1 1 9 1 1
## 2112 2160 2200 2208 2240 2250 2310
## 1 1 2 1 1 1 1
## 2400 2500 2640 2688 2800 2880 3000
## 9 3 1 3 3 5 13
## 3100 3120 3168 3200 3250 3300 3330
## 1 1 1 2 1 1 1
## 3360 3400 3450 3500 3552 3600 3636
## 4 1 1 1 1 6 1
## 3640 3700 3900 4000 4032 4155 4200
## 1 1 1 6 1 1 5
## 4290 4320 4500 4600 4676 4752 4800
## 1 2 2 1 1 1 8
## 4965 5000 5040 5160 5180 5200 5240
## 1 6 2 1 1 1 1
## 5340 5400 5550 5560 5600 5640 5760
## 1 1 1 1 2 1 1
## 6000 6400 6480 6500 6528 6720 6900
## 4 1 2 1 1 1 1
## 7000 7200 7400 7500 7680 8000 8150
## 2 8 1 2 1 2 1
## 8160 8300 8400 8640 8700 8940 9000
## 1 1 5 1 1 1 3
## 9320 9600 9625 10000 10080 10250 10320
## 1 5 1 4 1 1 1
## 10590 10600 10800 11400 11520 11880 11900
## 1 1 3 1 1 1 1
## 12000 12004 12300 12500 12600 12610 12785
## 6 1 1 1 1 1 1
## 12800 13300 13440 13600 13860 14000 14119
## 1 1 1 1 1 1 1
## 14160 14400 14740 14976 15000 15510 15600
## 1 5 1 1 2 1 1
## 15840 16520 16800 17280 17880 17888 18000
## 1 1 1 3 1 1 1
## 18924 19000 19200 19524 19650 20000 21440
## 1 1 4 1 1 1 1
## 21600 22000 22050 22100 22350 22400 23000
## 2 1 1 1 1 1 1
## 23380 24000 25200 25800 25920 28000 28800
## 1 5 1 1 1 1 4
## 30000 30792 30870 31120 31200 33600 34500
## 2 1 1 1 2 2 1
## 36000 37090 38400 39240 40650 42000 43200
## 2 1 1 1 1 1 1
## 43680 44200 45000 48000 50400 54720 57600
## 1 1 1 2 1 1 4
## 60000 67200 73440 74400 76000 100800 105000
## 3 2 1 1 1 1 1
## 132000 143762 or more <NA>
## 1 7 1053
mydata <- top_recode (variable="eh_s6q76_6", break_point=pctile_99.5_eh_s6q76_6, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q76_6. If you were to buy those goods or services in a local market over the last 12 mo
## -998 2 4 5 10 20 28 30 35 40 45 50 60 70 80 90
## 6 1 1 1 3 6 1 1 3 4 2 6 4 3 1 1
## 100 120 130 140 145 150 160 170 200 210 220 240 245 250 260 300
## 12 6 1 1 1 9 4 1 14 1 1 7 1 6 1 16
## 320 360 376 400 420 450 475 480 490 500 520 540 550 560 600 630
## 1 4 1 11 1 5 1 5 1 23 2 2 1 2 12 1
## 636 640 650 680 700 720 724 729 750 760 780 800 850 900 950 960
## 1 1 1 2 4 5 1 1 1 1 1 5 1 9 1 5
## 1000 1040 1050 1072 1080 1095 1110 1200 1250 1300 1344 1400 1430 1440 1500 1560
## 23 1 1 1 1 1 1 14 2 2 1 2 1 5 12 1
## 1575 1600 1680 1700 1750 1760 1800 1920 2000 2040 2075 2160 2190 2200 2250 2400
## 1 5 1 1 2 1 12 2 16 1 1 1 1 1 1 19
## 2500 2520 2560 2750 2800 2880 3000 3125 3136 3360 3500 3600 3650 3840 3860 4000
## 1 1 1 1 1 7 8 1 1 4 4 12 1 1 1 3
## 4200 4320 4500 4690 4800 5000 5040 5100 5320 5400 5760 5800 6000 6240 6480 6720
## 2 2 3 1 13 7 1 2 1 1 4 1 12 2 1 6
## 6960 7000 7200 7800 8030 8400 8640 8736 9000 9180 9600 9900 10000 10080 10800 11520
## 1 3 7 2 1 3 1 1 2 1 9 1 2 2 5 1
## 12000 12500 14300 14400 15000 15600 16000 16800 18000 19800 20000 21000 21600 22320 22800 24000
## 9 1 1 8 1 1 1 2 4 1 1 1 2 1 1 3
## 25000 25200 28800 31200 33600 48000 51600 90000 180000 <NA>
## 1 2 1 1 3 1 1 1 1 1715
## [1] "Frequency table after encoding"
## eh_s6q76_6. If you were to buy those goods or services in a local market over the last 12 mo
## -998 2 4 5 10 20 28 30
## 6 1 1 1 3 6 1 1
## 35 40 45 50 60 70 80 90
## 3 4 2 6 4 3 1 1
## 100 120 130 140 145 150 160 170
## 12 6 1 1 1 9 4 1
## 200 210 220 240 245 250 260 300
## 14 1 1 7 1 6 1 16
## 320 360 376 400 420 450 475 480
## 1 4 1 11 1 5 1 5
## 490 500 520 540 550 560 600 630
## 1 23 2 2 1 2 12 1
## 636 640 650 680 700 720 724 729
## 1 1 1 2 4 5 1 1
## 750 760 780 800 850 900 950 960
## 1 1 1 5 1 9 1 5
## 1000 1040 1050 1072 1080 1095 1110 1200
## 23 1 1 1 1 1 1 14
## 1250 1300 1344 1400 1430 1440 1500 1560
## 2 2 1 2 1 5 12 1
## 1575 1600 1680 1700 1750 1760 1800 1920
## 1 5 1 1 2 1 12 2
## 2000 2040 2075 2160 2190 2200 2250 2400
## 16 1 1 1 1 1 1 19
## 2500 2520 2560 2750 2800 2880 3000 3125
## 1 1 1 1 1 7 8 1
## 3136 3360 3500 3600 3650 3840 3860 4000
## 1 4 4 12 1 1 1 3
## 4200 4320 4500 4690 4800 5000 5040 5100
## 2 2 3 1 13 7 1 2
## 5320 5400 5760 5800 6000 6240 6480 6720
## 1 1 4 1 12 2 1 6
## 6960 7000 7200 7800 8030 8400 8640 8736
## 1 3 7 2 1 3 1 1
## 9000 9180 9600 9900 10000 10080 10800 11520
## 2 1 9 1 2 2 5 1
## 12000 12500 14300 14400 15000 15600 16000 16800
## 9 1 1 8 1 1 1 2
## 18000 19800 20000 21000 21600 22320 22800 24000
## 4 1 1 1 2 1 1 3
## 25000 25200 28800 31200 33600 48000 48611 or more <NA>
## 1 2 1 1 3 1 3 1715
mydata <- top_recode (variable="eh_s6q71_7", break_point=pctile_99.5_eh_s6q71_7, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q71_7. How much gross income or revenue was earned over the last 12 months from this ac
## 0 8 10 13 20 40 50 59 100 120 150 160 180 200 240 250
## 2 1 1 1 2 1 1 1 7 1 4 1 2 9 1 1
## 300 333 350 400 480 500 525 540 600 700 705 720 730 750 770 780
## 8 1 1 11 2 7 1 1 9 3 1 1 1 2 1 1
## 790 800 840 900 1000 1080 1120 1200 1250 1260 1300 1333 1350 1360 1400 1500
## 1 5 1 4 12 1 1 16 1 1 1 1 2 1 7 10
## 1520 1536 1550 1600 1750 1800 1920 2000 2020 2050 2100 2200 2400 2428 2500 2560
## 1 1 1 8 3 5 1 14 1 1 1 1 8 1 2 2
## 2600 2625 2640 2700 2800 2900 2970 3000 3100 3120 3200 3300 3400 3500 3600 3700
## 1 1 1 1 4 1 1 11 1 1 5 3 1 5 8 1
## 3750 4000 4200 4320 4500 4600 4700 4750 4800 4900 5000 5200 5250 5400 5500 5600
## 1 7 1 1 2 1 1 1 9 1 6 1 3 1 1 1
## 5760 6000 6090 6250 6300 6350 6400 6720 6960 7000 7200 7260 7600 7700 7800 8000
## 1 14 1 1 2 1 1 1 1 3 9 1 1 1 3 4
## 8350 8400 8500 9000 9600 9720 10000 10500 10800 11200 12000 12500 13000 13440 13500 14400
## 1 1 1 3 3 1 8 1 6 1 11 1 2 1 1 7
## 15000 16000 16800 17600 18000 19200 20000 21000 21600 22400 22500 23800 24000 24600 25000 25200
## 4 1 3 1 5 4 3 1 1 1 1 1 9 1 1 2
## 26000 26400 27000 28000 28800 30000 30800 33600 34200 36000 37500 37600 38400 39600 40000 42000
## 1 1 2 2 2 2 1 3 2 3 1 1 1 1 2 7
## 43200 43680 44000 48000 49200 50400 51000 51600 52800 55000 57600 60000 63000 66000 67200 69600
## 1 1 1 2 1 3 1 1 1 1 1 2 1 1 1 1
## 71050 72000 73000 73440 75600 79200 81000 84000 85800 86400 94500 98000 100800 105600 109440 115200
## 1 4 1 2 1 1 1 2 1 2 1 1 1 1 1 1
## 118560 120000 139200 140000 144000 184800 189000 192000 196000 210000 228000 3e+05 336000 388800 420000 504000
## 1 3 1 1 1 1 1 2 1 1 1 1 1 1 1 1
## <NA>
## 1775
## [1] "Frequency table after encoding"
## eh_s6q71_7. How much gross income or revenue was earned over the last 12 months from this ac
## 0 8 10 13 20 40 50
## 2 1 1 1 2 1 1
## 59 100 120 150 160 180 200
## 1 7 1 4 1 2 9
## 240 250 300 333 350 400 480
## 1 1 8 1 1 11 2
## 500 525 540 600 700 705 720
## 7 1 1 9 3 1 1
## 730 750 770 780 790 800 840
## 1 2 1 1 1 5 1
## 900 1000 1080 1120 1200 1250 1260
## 4 12 1 1 16 1 1
## 1300 1333 1350 1360 1400 1500 1520
## 1 1 2 1 7 10 1
## 1536 1550 1600 1750 1800 1920 2000
## 1 1 8 3 5 1 14
## 2020 2050 2100 2200 2400 2428 2500
## 1 1 1 1 8 1 2
## 2560 2600 2625 2640 2700 2800 2900
## 2 1 1 1 1 4 1
## 2970 3000 3100 3120 3200 3300 3400
## 1 11 1 1 5 3 1
## 3500 3600 3700 3750 4000 4200 4320
## 5 8 1 1 7 1 1
## 4500 4600 4700 4750 4800 4900 5000
## 2 1 1 1 9 1 6
## 5200 5250 5400 5500 5600 5760 6000
## 1 3 1 1 1 1 14
## 6090 6250 6300 6350 6400 6720 6960
## 1 1 2 1 1 1 1
## 7000 7200 7260 7600 7700 7800 8000
## 3 9 1 1 1 3 4
## 8350 8400 8500 9000 9600 9720 10000
## 1 1 1 3 3 1 8
## 10500 10800 11200 12000 12500 13000 13440
## 1 6 1 11 1 2 1
## 13500 14400 15000 16000 16800 17600 18000
## 1 7 4 1 3 1 5
## 19200 20000 21000 21600 22400 22500 23800
## 4 3 1 1 1 1 1
## 24000 24600 25000 25200 26000 26400 27000
## 9 1 1 2 1 1 2
## 28000 28800 30000 30800 33600 34200 36000
## 2 2 2 1 3 2 3
## 37500 37600 38400 39600 40000 42000 43200
## 1 1 1 1 2 7 1
## 43680 44000 48000 49200 50400 51000 51600
## 1 1 2 1 3 1 1
## 52800 55000 57600 60000 63000 66000 67200
## 1 1 1 2 1 1 1
## 69600 71050 72000 73000 73440 75600 79200
## 1 1 4 1 2 1 1
## 81000 84000 85800 86400 94500 98000 100800
## 1 2 1 2 1 1 1
## 105600 109440 115200 118560 120000 139200 140000
## 1 1 1 1 3 1 1
## 144000 184800 189000 192000 196000 210000 228000
## 1 1 1 2 1 1 1
## 3e+05 336000 359231 or more <NA>
## 1 1 3 1775
mydata <- top_recode (variable="eh_s6q72_7", break_point=pctile_99.5_eh_s6q72_7, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q72_7. Some activities require expenses in order to do them. What are the total expense
## -998 0 1 10 15 18 20 22 25 45 50 60 70 80 90 96
## 1 501 1 3 1 1 2 1 2 1 6 3 1 1 1 2
## 100 105 120 130 140 144 150 180 190 200 220 240 250 256 260 300
## 18 1 4 3 3 1 7 2 1 9 1 5 1 1 1 8
## 320 350 360 380 390 400 420 456 480 500 520 525 552 570 576 600
## 3 8 1 1 1 7 3 1 3 12 1 1 1 1 1 9
## 672 700 720 800 900 930 960 1000 1050 1125 1130 1200 1220 1290 1296 1300
## 1 3 2 4 2 1 3 8 2 1 1 10 1 1 1 2
## 1360 1400 1440 1500 1512 1536 1560 1600 1680 1792 1800 1920 2000 2100 2112 2160
## 1 2 7 4 1 1 1 2 2 1 2 3 10 3 1 1
## 2307 2400 2445 2484 2500 2592 2600 2688 2940 3000 3200 3312 3360 3400 3430 3456
## 1 12 1 1 1 1 2 1 1 3 4 1 1 1 1 1
## 3500 3600 3840 3980 4000 4144 4200 4320 4368 4440 4500 4752 4800 4900 5000 5040
## 1 7 4 1 3 1 1 2 1 1 2 1 7 1 2 1
## 5184 5376 5400 5600 6000 6120 6640 6950 7000 7200 7560 7840 8400 8448 8500 8550
## 1 1 3 2 9 1 1 1 3 4 1 1 2 1 1 1
## 8640 9000 9600 9650 9704 10000 10080 10452 10500 10760 10800 11200 11500 11520 12000 12500
## 1 1 3 1 1 8 1 1 1 1 2 1 1 2 4 1
## 12800 13500 13680 13800 14000 14200 14400 15072 15840 16000 16880 18000 18720 20000 20160 21000
## 1 1 1 1 2 1 3 1 1 2 1 5 1 2 1 1
## 21600 21672 23400 24000 24960 25200 26280 28200 28800 29900 30128 30600 30670 33600 35000 35300
## 2 1 1 1 1 1 1 1 2 1 1 1 1 4 1 1
## 35950 36800 38165 38400 39600 40000 40800 44856 46080 48000 50000 54720 60000 63810 66960 80000
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 90720 95000 96000 128670 160000 240000 288000 <NA>
## 1 1 1 1 1 1 1 1354
## [1] "Frequency table after encoding"
## eh_s6q72_7. Some activities require expenses in order to do them. What are the total expense
## -998 0 1 10 15 18 20 22
## 1 501 1 3 1 1 2 1
## 25 45 50 60 70 80 90 96
## 2 1 6 3 1 1 1 2
## 100 105 120 130 140 144 150 180
## 18 1 4 3 3 1 7 2
## 190 200 220 240 250 256 260 300
## 1 9 1 5 1 1 1 8
## 320 350 360 380 390 400 420 456
## 3 8 1 1 1 7 3 1
## 480 500 520 525 552 570 576 600
## 3 12 1 1 1 1 1 9
## 672 700 720 800 900 930 960 1000
## 1 3 2 4 2 1 3 8
## 1050 1125 1130 1200 1220 1290 1296 1300
## 2 1 1 10 1 1 1 2
## 1360 1400 1440 1500 1512 1536 1560 1600
## 1 2 7 4 1 1 1 2
## 1680 1792 1800 1920 2000 2100 2112 2160
## 2 1 2 3 10 3 1 1
## 2307 2400 2445 2484 2500 2592 2600 2688
## 1 12 1 1 1 1 2 1
## 2940 3000 3200 3312 3360 3400 3430 3456
## 1 3 4 1 1 1 1 1
## 3500 3600 3840 3980 4000 4144 4200 4320
## 1 7 4 1 3 1 1 2
## 4368 4440 4500 4752 4800 4900 5000 5040
## 1 1 2 1 7 1 2 1
## 5184 5376 5400 5600 6000 6120 6640 6950
## 1 1 3 2 9 1 1 1
## 7000 7200 7560 7840 8400 8448 8500 8550
## 3 4 1 1 2 1 1 1
## 8640 9000 9600 9650 9704 10000 10080 10452
## 1 1 3 1 1 8 1 1
## 10500 10760 10800 11200 11500 11520 12000 12500
## 1 1 2 1 1 2 4 1
## 12800 13500 13680 13800 14000 14200 14400 15072
## 1 1 1 1 2 1 3 1
## 15840 16000 16880 18000 18720 20000 20160 21000
## 1 2 1 5 1 2 1 1
## 21600 21672 23400 24000 24960 25200 26280 28200
## 2 1 1 1 1 1 1 1
## 28800 29900 30128 30600 30670 33600 35000 35300
## 2 1 1 1 1 4 1 1
## 35950 36800 38165 38400 39600 40000 40800 44856
## 1 1 1 1 1 1 1 1
## 46080 48000 50000 54720 60000 63810 66960 80000
## 1 1 1 1 1 1 1 1
## 90720 95000 95340 or more <NA>
## 1 1 5 1354
mydata <- top_recode (variable="eh_s6q76_7", break_point=pctile_99.5_eh_s6q76_7, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q76_7. If you were to buy those goods or services in a local market over the last 12 mo
## -998 1 5 10 15 20 30 32 40 50 60 70 75 80 100 120
## 8 1 1 1 3 5 4 1 1 7 5 5 2 3 15 4
## 125 140 150 160 180 200 210 220 240 250 260 300 320 350 360 390
## 2 1 6 2 1 18 1 1 5 4 2 21 2 1 2 2
## 400 420 450 470 480 500 540 560 600 610 640 650 690 700 720 750
## 3 2 6 1 2 24 1 1 15 1 1 2 1 5 3 8
## 780 800 840 900 960 1000 1008 1080 1200 1250 1300 1344 1350 1380 1400 1430
## 1 9 1 5 2 26 1 1 24 1 2 1 1 1 2 1
## 1440 1500 1600 1680 1800 1920 2000 2016 2160 2250 2400 2500 2520 2640 2700 2850
## 7 13 1 2 9 5 10 1 1 1 7 2 1 1 1 1
## 2880 3000 3200 3240 3360 3500 3600 3650 3840 4000 4200 4320 4500 4800 5000 5040
## 3 8 1 1 2 2 14 1 1 8 1 1 2 9 3 1
## 5400 5600 5625 5760 6000 6384 6528 6600 6720 6900 7060 7200 7500 7680 8000 8064
## 1 2 1 1 9 1 1 1 3 1 1 7 1 1 1 1
## 8400 9000 9240 9600 10000 10080 10320 10800 11388 11520 11760 12000 13680 14400 14750 15000
## 4 2 1 5 3 1 1 6 1 1 1 6 1 4 1 1
## 16000 16800 17500 18000 19200 25200 28000 30000 30240 33600 36000 45000 57000 70000 115320 <NA>
## 2 3 1 4 2 1 2 1 1 1 2 1 1 1 1 1779
## [1] "Frequency table after encoding"
## eh_s6q76_7. If you were to buy those goods or services in a local market over the last 12 mo
## -998 1 5 10 15 20 30 32
## 8 1 1 1 3 5 4 1
## 40 50 60 70 75 80 100 120
## 1 7 5 5 2 3 15 4
## 125 140 150 160 180 200 210 220
## 2 1 6 2 1 18 1 1
## 240 250 260 300 320 350 360 390
## 5 4 2 21 2 1 2 2
## 400 420 450 470 480 500 540 560
## 3 2 6 1 2 24 1 1
## 600 610 640 650 690 700 720 750
## 15 1 1 2 1 5 3 8
## 780 800 840 900 960 1000 1008 1080
## 1 9 1 5 2 26 1 1
## 1200 1250 1300 1344 1350 1380 1400 1430
## 24 1 2 1 1 1 2 1
## 1440 1500 1600 1680 1800 1920 2000 2016
## 7 13 1 2 9 5 10 1
## 2160 2250 2400 2500 2520 2640 2700 2850
## 1 1 7 2 1 1 1 1
## 2880 3000 3200 3240 3360 3500 3600 3650
## 3 8 1 1 2 2 14 1
## 3840 4000 4200 4320 4500 4800 5000 5040
## 1 8 1 1 2 9 3 1
## 5400 5600 5625 5760 6000 6384 6528 6600
## 1 2 1 1 9 1 1 1
## 6720 6900 7060 7200 7500 7680 8000 8064
## 3 1 1 7 1 1 1 1
## 8400 9000 9240 9600 10000 10080 10320 10800
## 4 2 1 5 3 1 1 6
## 11388 11520 11760 12000 13680 14400 14750 15000
## 1 1 1 6 1 4 1 1
## 16000 16800 17500 18000 19200 25200 28000 30000
## 2 3 1 4 2 1 2 1
## 30240 33600 36000 45000 51000 or more <NA>
## 1 1 2 1 3 1779
mydata <- top_recode (variable="eh_s6q71_8", break_point=pctile_99.5_eh_s6q71_8, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q71_8. How much gross income or revenue was earned over the last 12 months from this ac
## -998 0 1 5 6 20 40 50 60 75 100 120 140 150 200 220
## 1 2 1 1 1 2 1 1 2 1 2 1 1 4 7 1
## 240 250 255 300 320 350 360 400 420 450 480 500 550 600 650 700
## 2 3 1 8 1 2 1 5 1 3 2 4 1 11 1 3
## 720 750 780 800 880 900 912 950 1000 1050 1063 1100 1125 1200 1230 1250
## 3 1 1 6 1 2 1 2 9 3 1 1 1 7 1 1
## 1260 1300 1333 1400 1500 1540 1600 1700 1800 1950 2000 2100 2150 2220 2250 2400
## 1 2 1 2 10 1 1 3 3 1 5 2 1 1 1 6
## 2500 2560 2600 2700 2800 2940 3000 3120 3200 3480 3500 3600 3840 4000 4200 4300
## 2 1 1 1 2 1 13 1 1 1 4 5 2 10 2 1
## 4375 4500 4600 4760 4800 5000 5300 5600 5750 5760 6000 6060 6300 6600 6720 7000
## 1 1 1 1 5 3 2 4 1 1 4 1 1 1 1 1
## 7200 7300 7500 7560 8000 8100 8400 8800 8880 9600 10000 10080 10100 10800 11000 11520
## 2 1 5 1 3 1 3 1 1 3 3 1 1 1 1 1
## 12000 12400 13000 13600 14000 14400 14700 15000 15360 16000 16800 18000 19000 19200 19600 20000
## 10 1 1 3 1 2 1 8 1 3 1 4 2 4 1 4
## 21000 21600 22000 24000 24970 25200 27000 28000 28800 30000 30240 30380 33600 34000 37000 38160
## 4 1 1 3 1 2 1 3 5 2 1 1 1 1 1 1
## 38400 39144 40000 42000 43200 45000 46200 48000 51300 52000 52920 55440 60000 61440 63000 64000
## 1 1 1 1 1 1 1 2 1 1 1 1 4 1 1 1
## 72000 73400 84000 86400 89600 92400 96000 98800 1e+05 102528 105600 109440 130000 144000 168000 188000
## 3 1 1 1 1 2 1 1 1 1 1 2 2 1 2 1
## 201600 217200 234000 <NA>
## 1 1 1 1901
## [1] "Frequency table after encoding"
## eh_s6q71_8. How much gross income or revenue was earned over the last 12 months from this ac
## -998 0 1 5 6 20 40
## 1 2 1 1 1 2 1
## 50 60 75 100 120 140 150
## 1 2 1 2 1 1 4
## 200 220 240 250 255 300 320
## 7 1 2 3 1 8 1
## 350 360 400 420 450 480 500
## 2 1 5 1 3 2 4
## 550 600 650 700 720 750 780
## 1 11 1 3 3 1 1
## 800 880 900 912 950 1000 1050
## 6 1 2 1 2 9 3
## 1063 1100 1125 1200 1230 1250 1260
## 1 1 1 7 1 1 1
## 1300 1333 1400 1500 1540 1600 1700
## 2 1 2 10 1 1 3
## 1800 1950 2000 2100 2150 2220 2250
## 3 1 5 2 1 1 1
## 2400 2500 2560 2600 2700 2800 2940
## 6 2 1 1 1 2 1
## 3000 3120 3200 3480 3500 3600 3840
## 13 1 1 1 4 5 2
## 4000 4200 4300 4375 4500 4600 4760
## 10 2 1 1 1 1 1
## 4800 5000 5300 5600 5750 5760 6000
## 5 3 2 4 1 1 4
## 6060 6300 6600 6720 7000 7200 7300
## 1 1 1 1 1 2 1
## 7500 7560 8000 8100 8400 8800 8880
## 5 1 3 1 3 1 1
## 9600 10000 10080 10100 10800 11000 11520
## 3 3 1 1 1 1 1
## 12000 12400 13000 13600 14000 14400 14700
## 10 1 1 3 1 2 1
## 15000 15360 16000 16800 18000 19000 19200
## 8 1 3 1 4 2 4
## 19600 20000 21000 21600 22000 24000 24970
## 1 4 4 1 1 3 1
## 25200 27000 28000 28800 30000 30240 30380
## 2 1 3 5 2 1 1
## 33600 34000 37000 38160 38400 39144 40000
## 1 1 1 1 1 1 1
## 42000 43200 45000 46200 48000 51300 52000
## 1 1 1 1 2 1 1
## 52920 55440 60000 61440 63000 64000 72000
## 1 1 4 1 1 1 3
## 73400 84000 86400 89600 92400 96000 98800
## 1 1 1 1 2 1 1
## 1e+05 102528 105600 109440 130000 144000 168000
## 1 1 1 2 2 1 2
## 188000 201600 202769 or more <NA>
## 1 1 2 1901
mydata <- top_recode (variable="eh_s6q72_8", break_point=pctile_99.5_eh_s6q72_8, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q72_8. Some activities require expenses in order to do them. What are the total expense
## -998 0 1 6 10 15 20 24 25 30 45 50 59 60 66 70
## 2 350 1 1 3 1 2 1 2 2 1 1 1 3 1 3
## 80 90 100 105 120 140 144 150 165 180 185 200 210 250 269 288
## 4 1 16 1 3 1 1 5 1 2 1 5 1 1 1 1
## 300 350 360 368 371 390 400 415 432 450 480 500 528 540 560 600
## 9 2 3 1 1 1 4 1 1 2 4 4 1 2 3 4
## 612 700 720 740 750 800 840 858 900 930 1000 1024 1036 1090 1100 1200
## 1 1 2 1 3 3 5 1 3 1 7 1 1 1 1 8
## 1250 1300 1388 1400 1440 1488 1500 1600 1640 1721 1800 1920 2000 2055 2100 2150
## 1 1 1 3 1 1 3 3 1 1 5 3 8 1 1 1
## 2160 2200 2240 2270 2400 2425 2650 2880 2900 2940 3000 3200 3240 3330 3360 3400
## 1 1 1 1 7 1 1 1 2 1 4 2 1 1 1 1
## 3450 3500 3600 3750 4000 4200 4320 4464 4752 4800 5000 5400 5536 5600 5760 6000
## 1 1 5 1 1 1 2 1 1 4 1 1 1 2 2 1
## 6720 6960 7000 7200 7500 7680 8000 8568 8640 8750 9600 10000 10080 10500 10530 10780
## 1 1 1 4 1 1 3 1 1 1 3 2 2 2 1 1
## 11000 11200 11520 12000 12400 14400 15000 15440 16388 17280 18720 19050 20000 20800 21500 24000
## 1 1 1 5 1 5 3 1 1 1 1 1 2 1 1 2
## 24720 26300 28200 28440 28800 30000 30800 32000 32400 32600 33000 33730 34944 36000 36360 40800
## 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1
## 43250 48000 52540 54000 56000 57600 60000 116000 180000 <NA>
## 1 1 1 2 1 1 1 1 1 1616
## [1] "Frequency table after encoding"
## eh_s6q72_8. Some activities require expenses in order to do them. What are the total expense
## -998 0 1 6 10 15 20 24
## 2 350 1 1 3 1 2 1
## 25 30 45 50 59 60 66 70
## 2 2 1 1 1 3 1 3
## 80 90 100 105 120 140 144 150
## 4 1 16 1 3 1 1 5
## 165 180 185 200 210 250 269 288
## 1 2 1 5 1 1 1 1
## 300 350 360 368 371 390 400 415
## 9 2 3 1 1 1 4 1
## 432 450 480 500 528 540 560 600
## 1 2 4 4 1 2 3 4
## 612 700 720 740 750 800 840 858
## 1 1 2 1 3 3 5 1
## 900 930 1000 1024 1036 1090 1100 1200
## 3 1 7 1 1 1 1 8
## 1250 1300 1388 1400 1440 1488 1500 1600
## 1 1 1 3 1 1 3 3
## 1640 1721 1800 1920 2000 2055 2100 2150
## 1 1 5 3 8 1 1 1
## 2160 2200 2240 2270 2400 2425 2650 2880
## 1 1 1 1 7 1 1 1
## 2900 2940 3000 3200 3240 3330 3360 3400
## 2 1 4 2 1 1 1 1
## 3450 3500 3600 3750 4000 4200 4320 4464
## 1 1 5 1 1 1 2 1
## 4752 4800 5000 5400 5536 5600 5760 6000
## 1 4 1 1 1 2 2 1
## 6720 6960 7000 7200 7500 7680 8000 8568
## 1 1 1 4 1 1 3 1
## 8640 8750 9600 10000 10080 10500 10530 10780
## 1 1 3 2 2 2 1 1
## 11000 11200 11520 12000 12400 14400 15000 15440
## 1 1 1 5 1 5 3 1
## 16388 17280 18720 19050 20000 20800 21500 24000
## 1 1 1 1 2 1 1 2
## 24720 26300 28200 28440 28800 30000 30800 32000
## 1 1 1 1 1 2 1 1
## 32400 32600 33000 33730 34944 36000 36360 40800
## 1 1 1 1 1 1 1 1
## 43250 48000 52540 54000 56000 57047 or more <NA>
## 1 1 1 2 1 4 1616
mydata <- top_recode (variable="eh_s6q76_8", break_point=pctile_99.5_eh_s6q76_8, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q76_8. If you were to buy those goods or services in a local market over the last 12 mo
## -998 4 10 20 25 30 39 40 50 60 70 80 100 110 120 150
## 6 1 3 2 1 3 1 2 7 4 1 1 9 1 2 3
## 160 175 200 220 240 250 300 360 380 400 420 450 480 500 550 560
## 1 1 14 1 3 4 11 2 1 11 2 5 4 17 1 1
## 564 600 610 700 720 750 800 900 950 960 990 1000 1018 1050 1080 1100
## 1 14 1 5 2 2 3 4 1 1 1 13 1 1 1 1
## 1200 1280 1300 1400 1440 1500 1590 1600 1660 1700 1750 1800 1920 2000 2100 2220
## 11 3 1 1 3 7 1 2 1 1 1 4 2 4 1 1
## 2250 2400 2500 2520 2550 2553 2640 2700 2800 2860 2880 2940 3000 3360 3500 3600
## 1 8 2 1 1 1 1 1 1 1 1 1 7 1 1 11
## 3650 3800 3840 3860 4000 4200 4500 4680 4800 4875 5000 5040 5400 5750 5760 6000
## 1 1 1 1 2 1 1 1 6 1 5 2 1 1 3 4
## 6720 7000 7200 7500 8280 8781 9000 9600 10080 10800 10950 11115 11520 11760 12000 12600
## 1 2 5 1 1 1 2 3 2 2 1 1 1 1 5 1
## 13200 13500 14400 14490 16800 18000 19200 27000 33600 36000 48000 50400 67200 72000 108000 <NA>
## 1 1 5 1 1 3 1 1 1 2 2 1 1 1 2 1940
## [1] "Frequency table after encoding"
## eh_s6q76_8. If you were to buy those goods or services in a local market over the last 12 mo
## -998 4 10 20 25 30 39 40
## 6 1 3 2 1 3 1 2
## 50 60 70 80 100 110 120 150
## 7 4 1 1 9 1 2 3
## 160 175 200 220 240 250 300 360
## 1 1 14 1 3 4 11 2
## 380 400 420 450 480 500 550 560
## 1 11 2 5 4 17 1 1
## 564 600 610 700 720 750 800 900
## 1 14 1 5 2 2 3 4
## 950 960 990 1000 1018 1050 1080 1100
## 1 1 1 13 1 1 1 1
## 1200 1280 1300 1400 1440 1500 1590 1600
## 11 3 1 1 3 7 1 2
## 1660 1700 1750 1800 1920 2000 2100 2220
## 1 1 1 4 2 4 1 1
## 2250 2400 2500 2520 2550 2553 2640 2700
## 1 8 2 1 1 1 1 1
## 2800 2860 2880 2940 3000 3360 3500 3600
## 1 1 1 1 7 1 1 11
## 3650 3800 3840 3860 4000 4200 4500 4680
## 1 1 1 1 2 1 1 1
## 4800 4875 5000 5040 5400 5750 5760 6000
## 6 1 5 2 1 1 3 4
## 6720 7000 7200 7500 8280 8781 9000 9600
## 1 2 5 1 1 1 2 3
## 10080 10800 10950 11115 11520 11760 12000 12600
## 2 2 1 1 1 1 5 1
## 13200 13500 14400 14490 16800 18000 19200 27000
## 1 1 5 1 1 3 1 1
## 33600 36000 48000 50400 67200 72000 82620 or more <NA>
## 1 2 2 1 1 1 2 1940
mydata <- top_recode (variable="eh_s6q71_9", break_point=pctile_99.5_eh_s6q71_9, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q71_9. How much gross income or revenue was earned over the last 12 months from this ac
## 25 40 50 56 60 100 130 150 160 184 200 210 220 280 300 400
## 1 1 1 1 1 6 1 1 1 1 7 1 1 1 6 2
## 450 500 600 660 700 720 750 800 810 840 900 950 1000 1050 1063 1080
## 1 9 10 1 2 1 1 1 1 1 3 1 5 1 1 1
## 1140 1200 1300 1333 1400 1500 1600 1700 1750 1800 2000 2100 2120 2200 2400 2640
## 1 7 1 1 2 4 3 1 2 5 3 3 1 1 6 1
## 2750 2880 2920 3000 3120 3150 3200 3312 3500 3600 4000 4500 4800 5000 5250 5400
## 1 1 1 1 1 1 1 1 1 3 2 1 2 3 1 1
## 5500 5600 6000 6720 7000 7200 7500 7600 8000 9000 9500 9600 9800 10000 10200 10260
## 1 1 6 3 2 3 1 1 1 3 2 5 2 4 1 1
## 10500 11000 11200 12000 12600 12700 13700 14000 15000 16000 16800 18000 18500 19200 20000 20400
## 2 1 3 7 1 1 1 2 2 1 1 3 1 1 2 1
## 20700 21600 24000 24300 25200 27000 27360 28800 29000 30000 30500 32000 33600 34486 35000 36000
## 1 3 4 1 1 2 1 2 1 3 1 1 1 1 1 3
## 37500 39200 40000 44000 50400 51600 52800 54000 54528 57600 64800 72000 86400 96000 97920 1e+05
## 1 1 1 1 1 1 1 1 1 2 1 1 1 2 1 1
## 100800 120000 120864 120960 129600 168000 228000 240000 268800 <NA>
## 1 1 1 1 1 1 1 1 1 2030
## [1] "Frequency table after encoding"
## eh_s6q71_9. How much gross income or revenue was earned over the last 12 months from this ac
## 25 40 50 56 60 100 130
## 1 1 1 1 1 6 1
## 150 160 184 200 210 220 280
## 1 1 1 7 1 1 1
## 300 400 450 500 600 660 700
## 6 2 1 9 10 1 2
## 720 750 800 810 840 900 950
## 1 1 1 1 1 3 1
## 1000 1050 1063 1080 1140 1200 1300
## 5 1 1 1 1 7 1
## 1333 1400 1500 1600 1700 1750 1800
## 1 2 4 3 1 2 5
## 2000 2100 2120 2200 2400 2640 2750
## 3 3 1 1 6 1 1
## 2880 2920 3000 3120 3150 3200 3312
## 1 1 1 1 1 1 1
## 3500 3600 4000 4500 4800 5000 5250
## 1 3 2 1 2 3 1
## 5400 5500 5600 6000 6720 7000 7200
## 1 1 1 6 3 2 3
## 7500 7600 8000 9000 9500 9600 9800
## 1 1 1 3 2 5 2
## 10000 10200 10260 10500 11000 11200 12000
## 4 1 1 2 1 3 7
## 12600 12700 13700 14000 15000 16000 16800
## 1 1 1 2 2 1 1
## 18000 18500 19200 20000 20400 20700 21600
## 3 1 1 2 1 1 3
## 24000 24300 25200 27000 27360 28800 29000
## 4 1 1 2 1 2 1
## 30000 30500 32000 33600 34486 35000 36000
## 3 1 1 1 1 1 3
## 37500 39200 40000 44000 50400 51600 52800
## 1 1 1 1 1 1 1
## 54000 54528 57600 64800 72000 86400 96000
## 1 1 2 1 1 1 2
## 97920 1e+05 100800 120000 120864 120960 129600
## 1 1 1 1 1 1 1
## 168000 228000 236580 or more <NA>
## 1 1 2 2030
mydata <- top_recode (variable="eh_s6q72_9", break_point=pctile_99.5_eh_s6q72_9, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q72_9. Some activities require expenses in order to do them. What are the total expense
## 0 8 10 12 15 20 25 30 36 45 48 50 60 65 72 80
## 284 1 2 1 1 1 1 1 1 1 1 5 2 1 1 2
## 85 90 100 120 150 160 200 240 250 256 270 280 285 300 320 350
## 1 2 9 1 3 1 4 1 1 1 1 1 1 4 1 2
## 360 372 400 420 480 500 520 540 560 576 600 640 680 720 740 768
## 1 1 3 2 2 3 1 3 1 1 1 1 1 1 1 1
## 800 840 858 960 972 1000 1080 1120 1200 1250 1320 1400 1440 1488 1500 1536
## 2 1 1 1 1 5 1 1 1 1 1 1 1 1 2 1
## 1700 1920 2000 2100 2160 2300 2400 2500 2800 3000 3360 3372 3620 4000 4200 4320
## 1 1 2 2 2 1 3 1 1 1 1 1 1 1 2 1
## 4500 4800 5000 5040 5096 5304 5500 5550 5760 6000 6550 7000 7680 8000 8280 8400
## 1 2 1 2 1 1 1 1 1 2 1 3 1 1 1 2
## 8500 9600 9980 10000 10080 10240 10640 10800 12000 12600 13300 13486 13500 14300 14400 14500
## 2 2 1 5 1 1 1 4 2 1 1 1 1 1 2 1
## 15000 16700 16800 17304 18000 21600 24000 24960 25920 28800 30000 30736 32400 42110 48000 57600
## 1 1 2 1 3 1 4 1 1 2 1 1 1 1 1 1
## 104880 241920 <NA>
## 1 1 1806
## [1] "Frequency table after encoding"
## eh_s6q72_9. Some activities require expenses in order to do them. What are the total expense
## 0 8 10 12 15 20 25 30
## 284 1 2 1 1 1 1 1
## 36 45 48 50 60 65 72 80
## 1 1 1 5 2 1 1 2
## 85 90 100 120 150 160 200 240
## 1 2 9 1 3 1 4 1
## 250 256 270 280 285 300 320 350
## 1 1 1 1 1 4 1 2
## 360 372 400 420 480 500 520 540
## 1 1 3 2 2 3 1 3
## 560 576 600 640 680 720 740 768
## 1 1 1 1 1 1 1 1
## 800 840 858 960 972 1000 1080 1120
## 2 1 1 1 1 5 1 1
## 1200 1250 1320 1400 1440 1488 1500 1536
## 1 1 1 1 1 1 2 1
## 1700 1920 2000 2100 2160 2300 2400 2500
## 1 1 2 2 2 1 3 1
## 2800 3000 3360 3372 3620 4000 4200 4320
## 1 1 1 1 1 1 2 1
## 4500 4800 5000 5040 5096 5304 5500 5550
## 1 2 1 2 1 1 1 1
## 5760 6000 6550 7000 7680 8000 8280 8400
## 1 2 1 3 1 1 1 2
## 8500 9600 9980 10000 10080 10240 10640 10800
## 2 2 1 5 1 1 1 4
## 12000 12600 13300 13486 13500 14300 14400 14500
## 2 1 1 1 1 1 2 1
## 15000 16700 16800 17304 18000 21600 24000 24960
## 1 1 2 1 3 1 4 1
## 25920 28800 30000 30736 32400 42110 48000 53711 or more
## 1 2 1 1 1 1 1 3
## <NA>
## 1806
mydata <- top_recode (variable="eh_s6q76_9", break_point=pctile_99.5_eh_s6q76_9, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q76_9. If you were to buy those goods or services in a local market over the last 12 mo
## -998 28 30 35 50 80 90 100 111 120 140 150 165 175 180 200
## 3 2 4 2 3 2 1 5 1 1 1 4 1 1 1 10
## 210 225 240 250 260 271 280 288 300 350 360 400 450 480 500 520
## 1 1 1 2 1 1 1 1 11 3 1 1 2 1 8 1
## 540 600 640 650 700 720 750 800 850 900 960 1000 1200 1300 1350 1400
## 2 5 1 3 1 3 3 2 1 5 1 10 5 1 1 1
## 1440 1500 1530 1565 1680 1800 1870 1920 2000 2160 2200 2300 2400 2450 2500 2800
## 3 4 1 1 1 2 1 3 7 2 1 1 10 1 1 1
## 2880 2920 3000 3200 3300 3500 3600 3720 3840 4200 4345 4800 5000 5400 5600 5700
## 2 1 7 1 1 1 4 1 1 1 1 7 4 1 1 1
## 5760 6000 6240 6300 6480 6720 7200 7300 8000 8064 9600 10000 10080 10500 10800 11000
## 1 6 1 1 1 2 3 1 1 1 1 3 1 1 2 1
## 12000 13440 14400 15000 16500 16800 17600 18000 19200 24000 28800 33600 36000 38416 40320 50400
## 2 2 3 1 1 3 1 1 1 3 1 2 1 1 1 1
## 58800 91400 126000 216000 <NA>
## 1 1 1 1 2033
## [1] "Frequency table after encoding"
## eh_s6q76_9. If you were to buy those goods or services in a local market over the last 12 mo
## -998 28 30 35 50 80 90
## 3 2 4 2 3 2 1
## 100 111 120 140 150 165 175
## 5 1 1 1 4 1 1
## 180 200 210 225 240 250 260
## 1 10 1 1 1 2 1
## 271 280 288 300 350 360 400
## 1 1 1 11 3 1 1
## 450 480 500 520 540 600 640
## 2 1 8 1 2 5 1
## 650 700 720 750 800 850 900
## 3 1 3 3 2 1 5
## 960 1000 1200 1300 1350 1400 1440
## 1 10 5 1 1 1 3
## 1500 1530 1565 1680 1800 1870 1920
## 4 1 1 1 2 1 3
## 2000 2160 2200 2300 2400 2450 2500
## 7 2 1 1 10 1 1
## 2800 2880 2920 3000 3200 3300 3500
## 1 2 1 7 1 1 1
## 3600 3720 3840 4200 4345 4800 5000
## 4 1 1 1 1 7 4
## 5400 5600 5700 5760 6000 6240 6300
## 1 1 1 1 6 1 1
## 6480 6720 7200 7300 8000 8064 9600
## 1 2 3 1 1 1 1
## 10000 10080 10500 10800 11000 12000 13440
## 3 1 1 2 1 2 2
## 14400 15000 16500 16800 17600 18000 19200
## 3 1 1 3 1 1 1
## 24000 28800 33600 36000 38416 40320 50400
## 3 1 2 1 1 1 1
## 58800 91400 117177 or more <NA>
## 1 1 2 2033
mydata <- top_recode (variable="eh_s6q71_10", break_point=pctile_99.5_eh_s6q71_10, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q71_10. How much gross income or revenue was earned over the last 12 months from this ac
## 0 10 40 48 80 100 110 150 175 200 250 270 280 299 300 350
## 1 1 1 1 1 1 1 1 1 4 2 1 1 1 3 2
## 400 450 475 480 500 588 600 700 800 900 910 950 1000 1200 1300 1333
## 5 2 1 2 2 1 3 2 6 5 1 1 5 4 1 1
## 1500 1560 1600 1650 1800 2000 2100 2250 2400 2500 2600 2640 2880 3000 3150 3200
## 3 1 3 1 2 1 1 2 3 1 1 1 1 5 1 2
## 3360 3450 3500 3600 4000 4440 4500 4800 5000 5040 6000 6500 7000 7200 8000 9400
## 1 1 1 2 1 1 1 2 2 1 4 1 1 2 2 1
## 9600 10000 10080 10400 11000 12000 12600 13650 14000 14400 14750 15000 16500 16800 18000 19200
## 1 3 1 1 1 2 1 1 1 1 1 2 1 1 1 2
## 20000 20800 21000 22000 24900 32000 33000 35000 36864 40500 43200 46082 49000 56700 66000 69600
## 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1
## 70000 70200 72000 100800 112800 118560 120000 134400 168000 180000 <NA>
## 2 1 2 1 1 1 1 1 1 1 2120
## [1] "Frequency table after encoding"
## eh_s6q71_10. How much gross income or revenue was earned over the last 12 months from this ac
## 0 10 40 48 80 100 110
## 1 1 1 1 1 1 1
## 150 175 200 250 270 280 299
## 1 1 4 2 1 1 1
## 300 350 400 450 475 480 500
## 3 2 5 2 1 2 2
## 588 600 700 800 900 910 950
## 1 3 2 6 5 1 1
## 1000 1200 1300 1333 1500 1560 1600
## 5 4 1 1 3 1 3
## 1650 1800 2000 2100 2250 2400 2500
## 1 2 1 1 2 3 1
## 2600 2640 2880 3000 3150 3200 3360
## 1 1 1 5 1 2 1
## 3450 3500 3600 4000 4440 4500 4800
## 1 1 2 1 1 1 2
## 5000 5040 6000 6500 7000 7200 8000
## 2 1 4 1 1 2 2
## 9400 9600 10000 10080 10400 11000 12000
## 1 1 3 1 1 1 2
## 12600 13650 14000 14400 14750 15000 16500
## 1 1 1 1 1 2 1
## 16800 18000 19200 20000 20800 21000 22000
## 1 1 2 1 1 1 1
## 24900 32000 33000 35000 36864 40500 43200
## 1 2 1 1 1 1 1
## 46082 49000 56700 66000 69600 70000 70200
## 1 1 1 1 1 2 1
## 72000 100800 112800 118560 120000 134400 168000
## 2 1 1 1 1 1 1
## 169979 or more <NA>
## 1 2120
mydata <- top_recode (variable="eh_s6q72_10", break_point=pctile_99.5_eh_s6q72_10, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q72_10. Some activities require expenses in order to do them. What are the total expense
## 0 5 10 20 25 30 35 40 48 50 60 70 80 100 104 120
## 209 1 2 1 1 1 1 1 1 3 1 1 1 6 1 3
## 129 150 160 180 200 210 240 270 280 300 330 340 396 400 420 450
## 1 1 1 1 2 1 1 1 1 4 1 1 1 1 1 1
## 500 540 600 720 750 800 840 900 1000 1025 1110 1152 1250 1440 1488 1500
## 2 1 3 2 1 3 2 1 4 1 1 1 1 1 1 2
## 1600 1680 1690 1800 1920 2000 2160 2200 2400 2448 2500 3000 3360 3600 3680 4000
## 1 1 1 2 1 1 1 2 1 1 2 1 1 1 1 1
## 4032 5000 5260 5760 5900 6000 6200 6300 6960 7200 7600 9600 10000 10340 11520 15000
## 1 3 1 1 1 1 1 2 1 1 1 2 1 1 1 1
## 15630 16000 18300 20736 22700 24000 25500 28600 28800 29200 31200 42000 56000 84000 96000 120000
## 1 1 1 1 1 1 1 1 2 1 1 1 1 3 2 1
## <NA>
## 1948
## [1] "Frequency table after encoding"
## eh_s6q72_10. Some activities require expenses in order to do them. What are the total expense
## 0 5 10 20 25 30 35 40
## 209 1 2 1 1 1 1 1
## 48 50 60 70 80 100 104 120
## 1 3 1 1 1 6 1 3
## 129 150 160 180 200 210 240 270
## 1 1 1 1 2 1 1 1
## 280 300 330 340 396 400 420 450
## 1 4 1 1 1 1 1 1
## 500 540 600 720 750 800 840 900
## 2 1 3 2 1 3 2 1
## 1000 1025 1110 1152 1250 1440 1488 1500
## 4 1 1 1 1 1 1 2
## 1600 1680 1690 1800 1920 2000 2160 2200
## 1 1 1 2 1 1 1 2
## 2400 2448 2500 3000 3360 3600 3680 4000
## 1 1 2 1 1 1 1 1
## 4032 5000 5260 5760 5900 6000 6200 6300
## 1 3 1 1 1 1 1 2
## 6960 7200 7600 9600 10000 10340 11520 15000
## 1 1 1 2 1 1 1 1
## 15630 16000 18300 20736 22700 24000 25500 28600
## 1 1 1 1 1 1 1 1
## 28800 29200 31200 42000 56000 84000 96000 or more <NA>
## 2 1 1 1 1 3 3 1948
mydata <- top_recode (variable="eh_s6q76_10", break_point=pctile_99.5_eh_s6q76_10, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q76_10. If you were to buy those goods or services in a local market over the last 12 mo
## -998 10 15 20 50 55 60 70 80 85 90 100 120 135 140 145
## 3 1 1 4 2 1 3 3 2 1 2 4 1 1 2 1
## 150 165 180 200 205 206 240 300 350 360 400 450 460 480 500 520
## 2 1 1 6 1 1 2 5 1 1 3 1 1 2 13 1
## 550 600 640 650 700 720 800 900 930 960 1000 1050 1120 1200 1440 1500
## 1 2 2 1 1 1 2 2 1 2 8 1 1 3 2 3
## 1600 1620 1800 1900 2000 2100 2250 2400 2500 2520 2880 3000 3240 3360 3600 3650
## 3 1 2 1 3 2 1 7 2 1 1 3 1 2 6 1
## 3750 3840 4200 4800 5000 5400 5760 6000 6720 7200 7500 8000 8400 9000 9400 9600
## 1 1 1 4 2 1 1 6 2 1 1 3 1 3 1 4
## 10000 10080 10500 14000 14400 14900 16800 28000 28800 29970 30000 33600 36000 48000 72000 108000
## 1 1 1 1 3 1 1 1 1 1 1 1 2 1 2 1
## <NA>
## 2091
## [1] "Frequency table after encoding"
## eh_s6q76_10. If you were to buy those goods or services in a local market over the last 12 mo
## -998 10 15 20 50 55 60 70
## 3 1 1 4 2 1 3 3
## 80 85 90 100 120 135 140 145
## 2 1 2 4 1 1 2 1
## 150 165 180 200 205 206 240 300
## 2 1 1 6 1 1 2 5
## 350 360 400 450 460 480 500 520
## 1 1 3 1 1 2 13 1
## 550 600 640 650 700 720 800 900
## 1 2 2 1 1 1 2 2
## 930 960 1000 1050 1120 1200 1440 1500
## 1 2 8 1 1 3 2 3
## 1600 1620 1800 1900 2000 2100 2250 2400
## 3 1 2 1 3 2 1 7
## 2500 2520 2880 3000 3240 3360 3600 3650
## 2 1 1 3 1 2 6 1
## 3750 3840 4200 4800 5000 5400 5760 6000
## 1 1 1 4 2 1 1 6
## 6720 7200 7500 8000 8400 9000 9400 9600
## 2 1 1 3 1 3 1 4
## 10000 10080 10500 14000 14400 14900 16800 28000
## 1 1 1 1 3 1 1 1
## 28800 29970 30000 33600 36000 48000 72000 73259 or more
## 1 1 1 1 2 1 2 1
## <NA>
## 2091
mydata <- top_recode (variable="eh_s6q71_11", break_point=pctile_99.5_eh_s6q71_11, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q71_11. How much gross income or revenue was earned over the last 12 months from this ac
## 0 20 50 60 100 200 300 340 370 400 450 500 600 700 750 800
## 2 2 1 2 2 2 6 1 1 5 1 1 6 1 1 2
## 1000 1170 1250 1333 1350 1500 1600 1650 1800 1850 2000 2100 2200 2220 2250 2400
## 7 1 1 1 1 2 2 1 3 1 4 1 1 1 1 3
## 2500 2650 2825 3000 3200 3450 3500 4000 4220 4560 4800 5000 5300 5600 5700 6000
## 1 1 1 1 1 1 1 1 1 1 2 2 1 1 1 2
## 6250 6400 7000 7200 7500 9600 9750 10500 10800 11000 12000 14400 15528 16000 16800 19200
## 1 1 1 2 1 2 1 1 2 1 2 1 1 1 1 2
## 20000 20160 22000 27360 28000 29880 30000 31200 36000 43200 48000 96000 100800 128400 156000 168000
## 2 1 1 1 1 1 2 1 2 1 1 1 2 1 1 1
## 336000 385000 <NA>
## 1 1 2159
## [1] "Frequency table after encoding"
## eh_s6q71_11. How much gross income or revenue was earned over the last 12 months from this ac
## 0 20 50 60 100 200 300
## 2 2 1 2 2 2 6
## 340 370 400 450 500 600 700
## 1 1 5 1 1 6 1
## 750 800 1000 1170 1250 1333 1350
## 1 2 7 1 1 1 1
## 1500 1600 1650 1800 1850 2000 2100
## 2 2 1 3 1 4 1
## 2200 2220 2250 2400 2500 2650 2825
## 1 1 1 3 1 1 1
## 3000 3200 3450 3500 4000 4220 4560
## 1 1 1 1 1 1 1
## 4800 5000 5300 5600 5700 6000 6250
## 2 2 1 1 1 2 1
## 6400 7000 7200 7500 9600 9750 10500
## 1 1 2 1 2 1 1
## 10800 11000 12000 14400 15528 16000 16800
## 2 1 2 1 1 1 1
## 19200 20000 20160 22000 27360 28000 29880
## 2 2 1 1 1 1 1
## 30000 31200 36000 43200 48000 96000 100800
## 2 1 2 1 1 1 2
## 128400 156000 168000 336000 353640 or more <NA>
## 1 1 1 1 1 2159
mydata <- top_recode (variable="eh_s6q72_11", break_point=pctile_99.5_eh_s6q72_11, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q72_11. Some activities require expenses in order to do them. What are the total expense
## 0 2 5 17 20 50 60 70 100 120 122 150 200 210 225 270
## 131 1 1 1 1 1 1 1 5 1 1 3 2 1 1 1
## 280 300 330 400 450 540 600 780 800 825 840 1000 1040 1100 1140 1200
## 1 3 1 1 1 1 2 1 1 1 1 1 1 1 1 2
## 1440 1824 1840 1920 2000 2100 2400 2500 3000 3525 3600 3650 3700 3960 4000 4100
## 2 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1
## 4320 4550 4800 5000 6150 8000 8330 8400 9000 10000 10340 10728 10800 12500 13890 14400
## 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 14800 15000 16800 17280 23520 23600 24000 36845 77280 144000 230000 <NA>
## 1 1 1 1 1 1 1 1 1 1 1 2068
## [1] "Frequency table after encoding"
## eh_s6q72_11. Some activities require expenses in order to do them. What are the total expense
## 0 2 5 17 20 50 60
## 131 1 1 1 1 1 1
## 70 100 120 122 150 200 210
## 1 5 1 1 3 2 1
## 225 270 280 300 330 400 450
## 1 1 1 3 1 1 1
## 540 600 780 800 825 840 1000
## 1 2 1 1 1 1 1
## 1040 1100 1140 1200 1440 1824 1840
## 1 1 1 2 2 1 1
## 1920 2000 2100 2400 2500 3000 3525
## 2 2 1 1 1 1 1
## 3600 3650 3700 3960 4000 4100 4320
## 1 1 1 1 1 1 2
## 4550 4800 5000 6150 8000 8330 8400
## 1 1 1 1 1 1 1
## 9000 10000 10340 10728 10800 12500 13890
## 1 1 1 1 1 1 1
## 14400 14800 15000 16800 17280 23520 23600
## 1 1 1 1 1 1 1
## 24000 36845 77280 137661 or more <NA>
## 1 1 1 2 2068
mydata <- top_recode (variable="eh_s6q76_11", break_point=pctile_99.5_eh_s6q76_11, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q76_11. If you were to buy those goods or services in a local market over the last 12 mo
## -998 15 20 30 50 75 80 90 100 120 150 180 200 210 240 300 350 450 480
## 1 1 3 1 2 1 1 1 1 1 2 1 5 1 3 6 1 1 1
## 500 520 550 600 720 750 780 800 1000 1200 1300 1320 1440 1500 1520 1920 2000 2250 2400
## 4 1 1 1 2 1 1 1 2 4 1 2 1 3 1 1 2 1 5
## 2850 2880 3000 3030 3150 3600 3650 4200 4320 4800 5000 5400 5600 6000 7800 8000 9000 9600 10000
## 1 1 5 1 1 5 1 1 1 1 4 1 1 1 1 1 2 2 2
## 10800 12000 12600 16800 20160 20800 21600 23520 24000 28000 36000 38400 43200 72000 <NA>
## 1 4 1 1 1 1 1 1 1 1 1 1 1 1 2169
## [1] "Frequency table after encoding"
## eh_s6q76_11. If you were to buy those goods or services in a local market over the last 12 mo
## -998 15 20 30 50 75 80 90
## 1 1 3 1 2 1 1 1
## 100 120 150 180 200 210 240 300
## 1 1 2 1 5 1 3 6
## 350 450 480 500 520 550 600 720
## 1 1 1 4 1 1 1 2
## 750 780 800 1000 1200 1300 1320 1440
## 1 1 1 2 4 1 2 1
## 1500 1520 1920 2000 2250 2400 2850 2880
## 3 1 1 2 1 5 1 1
## 3000 3030 3150 3600 3650 4200 4320 4800
## 5 1 1 5 1 1 1 1
## 5000 5400 5600 6000 7800 8000 9000 9600
## 4 1 1 1 1 1 2 2
## 10000 10800 12000 12600 16800 20160 20800 21600
## 2 1 4 1 1 1 1 1
## 23520 24000 28000 36000 38400 43200 55152 or more <NA>
## 1 1 1 1 1 1 1 2169
mydata <- top_recode (variable="eh_s6q71_12", break_point=pctile_99.5_eh_s6q71_12, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q71_12. How much gross income or revenue was earned over the last 12 months from this ac
## 100 120 150 200 250 300 400 450 500 600 800 950 1200 1250 1333 1500
## 1 2 2 1 2 2 3 2 5 3 1 1 3 1 1 2
## 2000 2400 2700 2750 3000 3240 3500 3900 4000 4400 4800 5000 5200 5400 6000 7000
## 2 2 2 1 1 1 2 1 2 1 1 2 1 1 4 1
## 7680 8000 8400 9000 9600 9800 10800 12600 14400 16000 16900 18000 18500 19200 21600 26040
## 1 2 2 1 2 1 1 1 3 1 1 1 1 1 1 1
## 30000 31000 36000 36671 38400 45360 54000 60000 80000 86400 95200 1e+05 100800 134400 <NA>
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2196
## [1] "Frequency table after encoding"
## eh_s6q71_12. How much gross income or revenue was earned over the last 12 months from this ac
## 100 120 150 200 250 300 400
## 1 2 2 1 2 2 3
## 450 500 600 800 950 1200 1250
## 2 5 3 1 1 3 1
## 1333 1500 2000 2400 2700 2750 3000
## 1 2 2 2 2 1 1
## 3240 3500 3900 4000 4400 4800 5000
## 1 2 1 2 1 1 2
## 5200 5400 6000 7000 7680 8000 8400
## 1 1 4 1 1 2 2
## 9000 9600 9800 10800 12600 14400 16000
## 1 2 1 1 1 3 1
## 16900 18000 18500 19200 21600 26040 30000
## 1 1 1 1 1 1 1
## 31000 36000 36671 38400 45360 54000 60000
## 1 1 1 1 1 1 1
## 80000 86400 95200 1e+05 100800 119112 or more <NA>
## 1 1 1 1 1 1 2196
mydata <- top_recode (variable="eh_s6q72_12", break_point=pctile_99.5_eh_s6q72_12, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q72_12. Some activities require expenses in order to do them. What are the total expense
## 0 24 30 55 80 100 150 200 210 252 350 400 600 670 800 1000 1044 1200 1260
## 86 1 1 1 1 6 2 1 1 1 1 1 1 1 3 2 1 1 2
## 1300 1440 1500 1520 1536 2400 2750 2800 3000 3440 3840 4000 4320 4580 4800 5040 5760 6700 8671
## 1 2 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1
## 10800 14000 16000 18000 24000 25000 25200 30000 43200 49000 50400 <NA>
## 1 1 1 1 2 1 1 1 1 1 1 2141
## [1] "Frequency table after encoding"
## eh_s6q72_12. Some activities require expenses in order to do them. What are the total expense
## 0 24 30 55 80 100 150 200
## 86 1 1 1 1 6 2 1
## 210 252 350 400 600 670 800 1000
## 1 1 1 1 1 1 3 2
## 1044 1200 1260 1300 1440 1500 1520 1536
## 1 1 2 1 2 1 1 1
## 2400 2750 2800 3000 3440 3840 4000 4320
## 1 1 1 2 1 1 1 1
## 4580 4800 5040 5760 6700 8671 10800 14000
## 1 1 1 1 1 1 1 1
## 16000 18000 24000 25000 25200 30000 43200 49000
## 1 1 2 1 1 1 1 1
## 49378 or more <NA>
## 1 2141
mydata <- top_recode (variable="eh_s6q76_12", break_point=pctile_99.5_eh_s6q76_12, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q76_12. If you were to buy those goods or services in a local market over the last 12 mo
## -998 20 30 40 46 50 100 200 300 360 480 490 500 600 720 800
## 1 1 1 1 1 2 2 3 2 1 1 1 4 3 1 2
## 1000 1200 1300 1400 1500 1680 1750 1800 2000 2400 2500 2620 2880 3500 3600 4000
## 4 2 1 1 5 1 1 2 2 5 1 1 1 1 3 1
## 4320 5760 6000 6720 7200 8300 9600 10800 14400 16800 18000 28800 33600 54000 108000 <NA>
## 1 1 2 1 1 1 1 2 2 1 1 1 1 1 1 2211
## [1] "Frequency table after encoding"
## eh_s6q76_12. If you were to buy those goods or services in a local market over the last 12 mo
## -998 20 30 40 46 50 100 200
## 1 1 1 1 1 2 2 3
## 300 360 480 490 500 600 720 800
## 2 1 1 1 4 3 1 2
## 1000 1200 1300 1400 1500 1680 1750 1800
## 4 2 1 1 5 1 1 2
## 2000 2400 2500 2620 2880 3500 3600 4000
## 2 5 1 1 1 1 3 1
## 4320 5760 6000 6720 7200 8300 9600 10800
## 1 1 2 1 1 1 1 2
## 14400 16800 18000 28800 33600 54000 87750 or more <NA>
## 2 1 1 1 1 1 1 2211
mydata <- top_recode (variable="eh_s6q71_13", break_point=pctile_99.5_eh_s6q71_13, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q71_13. How much gross income or revenue was earned over the last 12 months from this ac
## 20 60 100 120 160 200 300 400 450 460 800 900 1200 1250 1400 1450 1500 1680 2000
## 1 1 1 1 1 1 2 2 1 1 1 3 3 1 1 1 1 1 1
## 2100 2400 2500 2800 3000 3235 3240 3600 3850 4000 4320 4600 4800 6000 6450 7200 8000 10000 10500
## 1 1 1 1 3 1 1 2 1 2 1 1 2 3 1 1 1 1 1
## 11700 12000 12750 14400 20200 25000 28158 30000 33000 36400 41740 56000 <NA>
## 1 1 1 1 1 1 1 1 1 1 1 1 2225
## [1] "Frequency table after encoding"
## eh_s6q71_13. How much gross income or revenue was earned over the last 12 months from this ac
## 20 60 100 120 160 200 300 400
## 1 1 1 1 1 1 2 2
## 450 460 800 900 1200 1250 1400 1450
## 1 1 1 3 3 1 1 1
## 1500 1680 2000 2100 2400 2500 2800 3000
## 1 1 1 1 1 1 1 3
## 3235 3240 3600 3850 4000 4320 4600 4800
## 1 1 2 1 2 1 1 2
## 6000 6450 7200 8000 10000 10500 11700 12000
## 3 1 1 1 1 1 1 1
## 12750 14400 20200 25000 28158 30000 33000 36400
## 1 1 1 1 1 1 1 1
## 41740 51579 or more <NA>
## 1 1 2225
mydata <- top_recode (variable="eh_s6q72_13", break_point=pctile_99.5_eh_s6q72_13, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q72_13. Some activities require expenses in order to do them. What are the total expense
## 0 20 30 35 48 75 100 150 160 200 228 270 300 350 360 500 600 750 800
## 69 1 1 1 1 1 1 3 1 1 1 1 1 1 1 2 1 1 1
## 920 955 960 1000 1200 1600 2280 2880 3000 3300 6500 6720 7200 8400 9360 13000 35000 <NA>
## 1 1 1 1 1 1 1 1 3 1 1 1 1 1 1 1 1 2179
## [1] "Frequency table after encoding"
## eh_s6q72_13. Some activities require expenses in order to do them. What are the total expense
## 0 20 30 35 48 75 100 150
## 69 1 1 1 1 1 1 3
## 160 200 228 270 300 350 360 500
## 1 1 1 1 1 1 1 2
## 600 750 800 920 955 960 1000 1200
## 1 1 1 1 1 1 1 1
## 1600 2280 2880 3000 3300 6500 6720 7200
## 1 1 1 3 1 1 1 1
## 8400 9360 13000 23119 or more <NA>
## 1 1 1 1 2179
mydata <- top_recode (variable="eh_s6q76_13", break_point=pctile_99.5_eh_s6q76_13, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q76_13. If you were to buy those goods or services in a local market over the last 12 mo
## 40 50 72 80 100 160 240 360 450 500 550 560 600 960 1000 1200 1500 1800 2100
## 2 1 1 1 5 1 1 1 1 2 1 1 1 1 2 3 1 3 1
## 2400 2640 3235 3360 3600 3840 4000 4200 4800 7200 7800 9000 9600 10000 12080 18000 20160 24000 25200
## 3 1 1 1 1 1 1 1 2 2 1 1 1 1 1 2 1 1 1
## 33600 67200 <NA>
## 1 1 2232
## [1] "Frequency table after encoding"
## eh_s6q76_13. If you were to buy those goods or services in a local market over the last 12 mo
## 40 50 72 80 100 160 240 360
## 2 1 1 1 5 1 1 1
## 450 500 550 560 600 960 1000 1200
## 1 2 1 1 1 1 2 3
## 1500 1800 2100 2400 2640 3235 3360 3600
## 1 3 1 3 1 1 1 1
## 3840 4000 4200 4800 7200 7800 9000 9600
## 1 1 1 2 2 1 1 1
## 10000 12080 18000 20160 24000 25200 33600 57960 or more
## 1 1 2 1 1 1 1 1
## <NA>
## 2232
mydata <- top_recode (variable="eh_s6q71_14", break_point=pctile_99.5_eh_s6q71_14, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q71_14. How much gross income or revenue was earned over the last 12 months from this ac
## 100 150 200 400 600 700 920 950 960 1000 1200 2000 2250 2400 2800 3000
## 1 1 2 1 3 3 1 1 1 1 1 2 1 1 1 2
## 3600 3750 4500 7000 9000 10000 10600 10920 12750 14400 18000 25000 30000 72000 75088 87000
## 1 1 1 2 1 3 1 1 1 2 1 1 1 1 1 1
## 100800 108680 <NA>
## 1 1 2243
## [1] "Frequency table after encoding"
## eh_s6q71_14. How much gross income or revenue was earned over the last 12 months from this ac
## 100 150 200 400 600 700 920
## 1 1 2 1 3 3 1
## 950 960 1000 1200 2000 2250 2400
## 1 1 1 1 2 1 1
## 2800 3000 3600 3750 4500 7000 9000
## 1 2 1 1 1 2 1
## 10000 10600 10920 12750 14400 18000 25000
## 3 1 1 1 2 1 1
## 30000 72000 75088 87000 100800 106946 or more <NA>
## 1 1 1 1 1 1 2243
mydata <- top_recode (variable="eh_s6q72_14", break_point=pctile_99.5_eh_s6q72_14, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q72_14. Some activities require expenses in order to do them. What are the total expense
## 0 5 48 100 105 160 200 300 350 400 450 500 700 900 955 1000 1200 1400 3360
## 47 1 2 2 1 1 1 1 1 1 1 3 1 1 1 1 1 1 1
## 3430 3600 4000 5500 6720 7500 9000 24960 33220 <NA>
## 1 1 1 1 1 1 2 1 1 2209
## [1] "Frequency table after encoding"
## eh_s6q72_14. Some activities require expenses in order to do them. What are the total expense
## 0 5 48 100 105 160 200 300
## 47 1 2 2 1 1 1 1
## 350 400 450 500 700 900 955 1000
## 1 1 1 3 1 1 1 1
## 1200 1400 3360 3430 3600 4000 5500 6720
## 1 1 1 1 1 1 1 1
## 7500 9000 24960 29998 or more <NA>
## 1 2 1 1 2209
mydata <- top_recode (variable="eh_s6q76_14", break_point=pctile_99.5_eh_s6q76_14, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q76_14. If you were to buy those goods or services in a local market over the last 12 mo
## 40 50 80 100 200 250 295 300 400 450 500 750 800 840 900 1000 1135 1200 1500
## 1 2 1 1 1 2 1 2 1 1 2 1 2 2 1 2 1 2 1
## 2400 2700 3000 3400 3750 4320 4800 7000 7200 10800 14400 16800 18000 24000 48000 <NA>
## 1 1 1 1 1 1 2 1 1 1 1 1 1 2 1 2244
## [1] "Frequency table after encoding"
## eh_s6q76_14. If you were to buy those goods or services in a local market over the last 12 mo
## 40 50 80 100 200 250 295 300
## 1 2 1 1 1 2 1 2
## 400 450 500 750 800 840 900 1000
## 1 1 2 1 2 2 1 2
## 1135 1200 1500 2400 2700 3000 3400 3750
## 1 2 1 1 1 1 1 1
## 4320 4800 7000 7200 10800 14400 16800 18000
## 1 2 1 1 1 1 1 1
## 24000 42839 or more <NA>
## 2 1 2244
mydata <- top_recode (variable="eh_s6q71_15", break_point=pctile_99.5_eh_s6q71_15, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q71_15. How much gross income or revenue was earned over the last 12 months from this ac
## 100 300 400 450 500 750 1000 1200 1500 1600 1800 2000 2100 2500 3000 3235
## 1 2 1 1 2 1 2 1 2 1 1 2 1 1 1 1
## 3500 3600 3750 4341 5000 5100 8800 9750 10000 11000 12000 13440 14400 18000 27000 27300
## 1 1 1 1 1 1 1 1 1 2 1 1 2 1 1 1
## 70000 112632 <NA>
## 1 1 2247
## [1] "Frequency table after encoding"
## eh_s6q71_15. How much gross income or revenue was earned over the last 12 months from this ac
## 100 300 400 450 500 750 1000
## 1 2 1 1 2 1 2
## 1200 1500 1600 1800 2000 2100 2500
## 1 2 1 1 2 1 1
## 3000 3235 3500 3600 3750 4341 5000
## 1 1 1 1 1 1 1
## 5100 8800 9750 10000 11000 12000 13440
## 1 1 1 1 2 1 1
## 14400 18000 27000 27300 70000 104105 or more <NA>
## 2 1 1 1 1 1 2247
mydata <- top_recode (variable="eh_s6q72_15", break_point=pctile_99.5_eh_s6q72_15, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q72_15. Some activities require expenses in order to do them. What are the total expense
## 0 40 48 50 70 100 124 200 210 332 350 480 500 600 768 825 1200 1250 2000
## 32 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1
## 2224 3200 4800 4950 9700 12600 37440 <NA>
## 1 1 1 1 1 1 1 2230
## [1] "Frequency table after encoding"
## eh_s6q72_15. Some activities require expenses in order to do them. What are the total expense
## 0 40 48 50 70 100 124 200
## 32 1 1 1 1 1 1 1
## 210 332 350 480 500 600 768 825
## 1 1 1 1 1 1 1 1
## 1200 1250 2000 2224 3200 4800 4950 9700
## 2 1 1 1 1 1 1 1
## 12600 30360 or more <NA>
## 1 1 2230
mydata <- top_recode (variable="eh_s6q76_15", break_point=pctile_99.5_eh_s6q76_15, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q76_15. If you were to buy those goods or services in a local market over the last 12 mo
## 80 100 150 160 500 1000 2000 2885 3600 3750 4000 4800 5400 6500 8400 9000 12480 24000 <NA>
## 1 2 1 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 2266
## [1] "Frequency table after encoding"
## eh_s6q76_15. If you were to buy those goods or services in a local market over the last 12 mo
## 80 100 150 160 500 1000 2000 2885
## 1 2 1 2 2 2 1 1
## 3600 3750 4000 4800 5400 6500 8400 9000
## 1 1 1 1 1 1 1 1
## 12480 22790 or more <NA>
## 1 1 2266
mydata <- top_recode (variable="eh_s6q71_16", break_point=pctile_99.5_eh_s6q71_16, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q71_16. How much gross income or revenue was earned over the last 12 months from this ac
## 60 70 150 160 300 400 600 700 750 800 900 1200 1460 1500 2000 2150 3600 3780 4000
## 1 1 1 1 1 1 1 1 1 1 1 2 1 1 2 1 1 1 1
## 6800 8600 10000 10380 12000 24000 30000 93860 <NA>
## 1 1 3 1 1 1 1 1 2257
## [1] "Frequency table after encoding"
## eh_s6q71_16. How much gross income or revenue was earned over the last 12 months from this ac
## 60 70 150 160 300 400 600 700
## 1 1 1 1 1 1 1 1
## 750 800 900 1200 1460 1500 2000 2150
## 1 1 1 2 1 1 2 1
## 3600 3780 4000 6800 8600 10000 10380 12000
## 1 1 1 1 1 3 1 1
## 24000 30000 84281 or more <NA>
## 1 1 1 2257
mydata <- top_recode (variable="eh_s6q72_16", break_point=pctile_99.5_eh_s6q72_16, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q72_16. Some activities require expenses in order to do them. What are the total expense
## 0 48 100 300 500 600 1200 2000 2304 3600 10000 16200 31200 <NA>
## 31 1 1 1 1 1 1 2 2 1 1 1 1 2243
## [1] "Frequency table after encoding"
## eh_s6q72_16. Some activities require expenses in order to do them. What are the total expense
## 0 48 100 300 500 600 1200 2000
## 31 1 1 1 1 1 1 2
## 2304 3600 10000 16200 27900 or more <NA>
## 2 1 1 1 1 2243
mydata <- top_recode (variable="eh_s6q76_16", break_point=pctile_99.5_eh_s6q76_16, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q76_16. If you were to buy those goods or services in a local market over the last 12 mo
## 15 100 330 400 480 600 1200 1400 1440 1800 5400 5895 8000 10000 28800 <NA>
## 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2272
## [1] "Frequency table after encoding"
## eh_s6q76_16. If you were to buy those goods or services in a local market over the last 12 mo
## 15 100 330 400 480 600 1200 1400
## 1 2 1 1 1 1 1 1
## 1440 1800 5400 5895 8000 10000 27390 or more <NA>
## 1 1 1 1 1 1 1 2272
mydata <- top_recode (variable="eh_s6q71_17", break_point=pctile_99.5_eh_s6q71_17, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q71_17. How much gross income or revenue was earned over the last 12 months from this ac
## -998 80 200 480 500 600 900 1200 1950 2000 2400 2800 3000 3500 5700 9000
## 1 1 1 1 1 1 1 2 1 1 1 1 2 1 1 1
## 9400 9600 19200 23000 30500 187200 <NA>
## 1 1 1 1 1 1 2264
## [1] "Frequency table after encoding"
## eh_s6q71_17. How much gross income or revenue was earned over the last 12 months from this ac
## -998 80 200 480 500 600 900
## 1 1 1 1 1 1 1
## 1200 1950 2000 2400 2800 3000 3500
## 2 1 1 1 1 2 1
## 5700 9000 9400 9600 19200 23000 30500
## 1 1 1 1 1 1 1
## 169963 or more <NA>
## 1 2264
mydata <- top_recode (variable="eh_s6q72_17", break_point=pctile_99.5_eh_s6q72_17, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q72_17. Some activities require expenses in order to do them. What are the total expense
## 0 15 48 140 150 180 2700 3000 4000 5000 15900 63600 <NA>
## 22 1 1 1 1 1 1 1 1 1 1 1 2255
## [1] "Frequency table after encoding"
## eh_s6q72_17. Some activities require expenses in order to do them. What are the total expense
## 0 15 48 140 150 180 2700 3000
## 22 1 1 1 1 1 1 1
## 4000 5000 15900 55968 or more <NA>
## 1 1 1 1 2255
mydata <- top_recode (variable="eh_s6q76_17", break_point=pctile_99.5_eh_s6q76_17, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q76_17. If you were to buy those goods or services in a local market over the last 12 mo
## 80 100 200 360 500 1600 1800 2676 4800 6000 7200 11856 20160 <NA>
## 1 1 1 2 1 1 1 1 1 1 2 1 1 2273
## [1] "Frequency table after encoding"
## eh_s6q76_17. If you were to buy those goods or services in a local market over the last 12 mo
## 80 100 200 360 500 1600 1800 2676
## 1 1 1 2 1 1 1 1
## 4800 6000 7200 11856 19578 or more <NA>
## 1 1 2 1 1 2273
mydata <- top_recode (variable="eh_s6q71_18", break_point=pctile_99.5_eh_s6q71_18, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q71_18. How much gross income or revenue was earned over the last 12 months from this ac
## 60 80 100 200 600 800 1200 1400 5000 8000 18200 27800 60000 <NA>
## 1 1 1 1 1 1 3 1 1 1 1 1 1 2273
## [1] "Frequency table after encoding"
## eh_s6q71_18. How much gross income or revenue was earned over the last 12 months from this ac
## 60 80 100 200 600 800 1200 1400
## 1 1 1 1 1 1 3 1
## 5000 8000 18200 27800 57745 or more <NA>
## 1 1 1 1 1 2273
mydata <- top_recode (variable="eh_s6q72_18", break_point=pctile_99.5_eh_s6q72_18, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q72_18. Some activities require expenses in order to do them. What are the total expense
## -998 0 70 150 384 2500 3600 10000 10300 <NA>
## 1 13 1 1 1 1 1 1 1 2267
## [1] "Frequency table after encoding"
## eh_s6q72_18. Some activities require expenses in order to do them. What are the total expense
## -998 0 70 150 384 2500 3600 10000
## 1 13 1 1 1 1 1 1
## 10271 or more <NA>
## 1 2267
mydata <- top_recode (variable="eh_s6q76_18", break_point=pctile_99.5_eh_s6q76_18, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q76_18. If you were to buy those goods or services in a local market over the last 12 mo
## 325 360 2400 7200 14000 24000 <NA>
## 1 1 1 1 1 1 2282
## [1] "Frequency table after encoding"
## eh_s6q76_18. If you were to buy those goods or services in a local market over the last 12 mo
## 325 360 2400 7200 14000 23749 or more <NA>
## 1 1 1 1 1 1 2282
mydata <- top_recode (variable="eh_s6q71_19", break_point=pctile_99.5_eh_s6q71_19, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q71_19. How much gross income or revenue was earned over the last 12 months from this ac
## 100 240 300 400 570 600 1000 1200 3000 4800 13500 14400 98800 <NA>
## 1 1 1 1 1 1 1 1 1 1 1 1 1 2275
## [1] "Frequency table after encoding"
## eh_s6q71_19. How much gross income or revenue was earned over the last 12 months from this ac
## 100 240 300 400 570 600 1000 1200
## 1 1 1 1 1 1 1 1
## 3000 4800 13500 14400 93735 or more <NA>
## 1 1 1 1 1 2275
mydata <- top_recode (variable="eh_s6q72_19", break_point=pctile_99.5_eh_s6q72_19, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q72_19. Some activities require expenses in order to do them. What are the total expense
## 0 150 2700 6000 14400 20800 <NA>
## 11 1 1 1 1 1 2272
## [1] "Frequency table after encoding"
## eh_s6q72_19. Some activities require expenses in order to do them. What are the total expense
## 0 150 2700 6000 14400 20320 or more <NA>
## 11 1 1 1 1 1 2272
mydata <- top_recode (variable="eh_s6q76_19", break_point=pctile_99.5_eh_s6q76_19, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q76_19. If you were to buy those goods or services in a local market over the last 12 mo
## 570 3360 24000 <NA>
## 1 1 1 2285
## [1] "Frequency table after encoding"
## eh_s6q76_19. If you were to buy those goods or services in a local market over the last 12 mo
## 570 3360 23793 or more <NA>
## 1 1 1 2285
mydata <- top_recode (variable="eh_s6q71_20", break_point=pctile_99.5_eh_s6q71_20, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q71_20. How much gross income or revenue was earned over the last 12 months from this ac
## 200 300 400 600 1440 2000 3600 4800 18000 <NA>
## 1 2 1 1 1 1 1 1 1 2278
## [1] "Frequency table after encoding"
## eh_s6q71_20. How much gross income or revenue was earned over the last 12 months from this ac
## 200 300 400 600 1440 2000 3600 4800
## 1 2 1 1 1 1 1 1
## 17406 or more <NA>
## 1 2278
mydata <- top_recode (variable="eh_s6q72_20", break_point=pctile_99.5_eh_s6q72_20, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q72_20. Some activities require expenses in order to do them. What are the total expense
## 0 10 48 500 600 750 1200 5000 <NA>
## 7 1 1 1 1 1 1 1 2274
## [1] "Frequency table after encoding"
## eh_s6q72_20. Some activities require expenses in order to do them. What are the total expense
## 0 10 48 500 600 750 1200 4753 or more <NA>
## 7 1 1 1 1 1 1 1 2274
mydata <- top_recode (variable="eh_s6q76_20", break_point=pctile_99.5_eh_s6q76_20, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q76_20. If you were to buy those goods or services in a local market over the last 12 mo
## 95 500 2700 4560 4800 15000 21600 <NA>
## 1 1 2 1 1 1 1 2280
## [1] "Frequency table after encoding"
## eh_s6q76_20. If you were to buy those goods or services in a local market over the last 12 mo
## 95 500 2700 4560 4800 15000 21369 or more <NA>
## 1 1 2 1 1 1 1 2280
mydata <- top_recode (variable="eh_s6q71_21", break_point=pctile_99.5_eh_s6q71_21, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q71_21. How much gross income or revenue was earned over the last 12 months from this ac
## 150 800 820 1500 1940 2650 13440 144000 <NA>
## 1 1 1 1 1 1 1 1 2280
## [1] "Frequency table after encoding"
## eh_s6q71_21. How much gross income or revenue was earned over the last 12 months from this ac
## 150 800 820 1500 1940 2650 13440
## 1 1 1 1 1 1 1
## 139430 or more <NA>
## 1 2280
mydata <- top_recode (variable="eh_s6q72_21", break_point=pctile_99.5_eh_s6q72_21, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q72_21. Some activities require expenses in order to do them. What are the total expense
## 0 300 500 4800 7200 <NA>
## 7 2 1 1 1 2276
## [1] "Frequency table after encoding"
## eh_s6q72_21. Some activities require expenses in order to do them. What are the total expense
## 0 300 500 4800 7068 or more <NA>
## 7 2 1 1 1 2276
mydata <- top_recode (variable="eh_s6q76_21", break_point=pctile_99.5_eh_s6q76_21, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q76_21. If you were to buy those goods or services in a local market over the last 12 mo
## 100 200 300 350 900 1200 1400 1500 <NA>
## 1 1 1 1 1 1 1 1 2280
## [1] "Frequency table after encoding"
## eh_s6q76_21. If you were to buy those goods or services in a local market over the last 12 mo
## 100 200 300 350 900 1200 1400 1496 or more <NA>
## 1 1 1 1 1 1 1 1 2280
mydata <- top_recode (variable="eh_s6q71_22", break_point=pctile_99.5_eh_s6q71_22, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q71_22. How much gross income or revenue was earned over the last 12 months from this ac
## 120 150 10000 16000 648000 <NA>
## 1 1 1 1 1 2283
## [1] "Frequency table after encoding"
## eh_s6q71_22. How much gross income or revenue was earned over the last 12 months from this ac
## 120 150 10000 16000 635360 or more <NA>
## 1 1 1 1 1 2283
mydata <- top_recode (variable="eh_s6q72_22", break_point=pctile_99.5_eh_s6q72_22, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q72_22. Some activities require expenses in order to do them. What are the total expense
## 0 20 3200 540000 <NA>
## 5 1 1 1 2280
## [1] "Frequency table after encoding"
## eh_s6q72_22. Some activities require expenses in order to do them. What are the total expense
## 0 20 3200 521211 or more <NA>
## 5 1 1 1 2280
mydata <- top_recode (variable="eh_s6q76_22", break_point=pctile_99.5_eh_s6q76_22, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q76_22. If you were to buy those goods or services in a local market over the last 12 mo
## 300 750 10800 <NA>
## 2 1 1 2284
## [1] "Frequency table after encoding"
## eh_s6q76_22. If you were to buy those goods or services in a local market over the last 12 mo
## 300 750 10649 or more <NA>
## 2 1 1 2284
mydata <- top_recode (variable="eh_s6q71_23", break_point=pctile_99.5_eh_s6q71_23, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q71_23. How much gross income or revenue was earned over the last 12 months from this ac
## 120 9600 24000 59280 <NA>
## 1 1 1 1 2284
## [1] "Frequency table after encoding"
## eh_s6q71_23. How much gross income or revenue was earned over the last 12 months from this ac
## 120 9600 24000 58750 or more <NA>
## 1 1 1 1 2284
mydata <- top_recode (variable="eh_s6q72_23", break_point=pctile_99.5_eh_s6q72_23, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q72_23. Some activities require expenses in order to do them. What are the total expense
## 0 20 18720 <NA>
## 3 1 1 2283
## [1] "Frequency table after encoding"
## eh_s6q72_23. Some activities require expenses in order to do them. What are the total expense
## 0 20 18346 or more <NA>
## 3 1 1 2283
#mydata <- top_recode (variable="eh_s6q76_23", break_point=pctile_99.5_eh_s6q76_23, missing=-998) #Only one response
mydata <- top_recode (variable="eh_s6q71_24", break_point=pctile_99.5_eh_s6q71_24, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q71_24. How much gross income or revenue was earned over the last 12 months from this ac
## 150 3000 8000 <NA>
## 1 1 1 2285
## [1] "Frequency table after encoding"
## eh_s6q71_24. How much gross income or revenue was earned over the last 12 months from this ac
## 150 3000 7950 or more <NA>
## 1 1 1 2285
mydata <- top_recode (variable="eh_s6q72_24", break_point=pctile_99.5_eh_s6q72_24, missing=-998) ## [1] "Frequency table before encoding"
## eh_s6q72_24. Some activities require expenses in order to do them. What are the total expense
## 0 41600 <NA>
## 2 1 2285
## [1] "Frequency table after encoding"
## eh_s6q72_24. Some activities require expenses in order to do them. What are the total expense
## 0 41184 or more <NA>
## 2 1 2285
#mydata <- top_recode (variable="eh_s6q76_24", break_point=pctile_99.5_eh_s6q76_24, missing=-998) #Only one response
mydata <- top_recode (variable="eh_s6q71_25", break_point=pctile_99.5_eh_s6q71_25, missing=-998) ## [1] "Frequency table before encoding"
## eh_s6q71_25. How much gross income or revenue was earned over the last 12 months from this ac
## 270 1000 <NA>
## 1 1 2286
## [1] "Frequency table after encoding"
## eh_s6q71_25. How much gross income or revenue was earned over the last 12 months from this ac
## 270 996 or more <NA>
## 1 1 2286
#mydata <- top_recode (variable="eh_s6q72_25", break_point=pctile_99.5_eh_s6q72_25, missing=-998) #Only one response
mydata <- top_recode (variable="eh_s6q76_25", break_point=pctile_99.5_eh_s6q76_25, missing=-998) ## [1] "Frequency table before encoding"
## eh_s6q76_25. If you were to buy those goods or services in a local market over the last 12 mo
## 90 19200 <NA>
## 1 1 2286
## [1] "Frequency table after encoding"
## eh_s6q76_25. If you were to buy those goods or services in a local market over the last 12 mo
## 90 19104 or more <NA>
## 1 1 2286
mydata <- top_recode (variable="eh_s6q78", break_point=pctile_99.5_eh_s6q78, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q78. Q299: In the past 12 months, how much did you or other members of your household
## -998 0 5 10 15 20 25 30 40 50 60 100 120 150 200 300
## 3 169 2 4 1 1 2 1 3 6 3 10 2 3 9 2
## 350 400 420 460 480 500 600 650 700 720 750 770 800 900 960 1000
## 1 2 1 1 3 8 2 1 4 2 1 1 1 3 1 5
## 1200 1300 1500 1600 1700 1750 1800 1920 2000 2100 2150 2300 2400 2600 2800 3000
## 5 1 11 1 1 2 7 2 4 2 1 1 4 1 1 12
## 3500 3560 3600 3650 3800 3840 3920 4000 4200 4500 4800 5000 5400 6000 7000 7200
## 10 1 2 1 1 1 1 2 5 3 1 3 1 6 2 1
## 7400 7800 8000 9050 10000 10300 11000 12000 13000 13825 14240 16800 17050 18000 20000 20300
## 1 1 1 1 2 1 1 2 1 1 1 2 1 2 1 1
## 21000 24000 30000 50000 60000 81000 84000 130000 <NA>
## 1 1 1 1 1 1 1 1 1905
## [1] "Frequency table after encoding"
## eh_s6q78. Q299: In the past 12 months, how much did you or other members of your household
## -998 0 5 10 15 20 25 30
## 3 169 2 4 1 1 2 1
## 40 50 60 100 120 150 200 300
## 3 6 3 10 2 3 9 2
## 350 400 420 460 480 500 600 650
## 1 2 1 1 3 8 2 1
## 700 720 750 770 800 900 960 1000
## 4 2 1 1 1 3 1 5
## 1200 1300 1500 1600 1700 1750 1800 1920
## 5 1 11 1 1 2 7 2
## 2000 2100 2150 2300 2400 2600 2800 3000
## 4 2 1 1 4 1 1 12
## 3500 3560 3600 3650 3800 3840 3920 4000
## 10 1 2 1 1 1 1 2
## 4200 4500 4800 5000 5400 6000 7000 7200
## 5 3 1 3 1 6 2 1
## 7400 7800 8000 9050 10000 10300 11000 12000
## 1 1 1 1 2 1 1 2
## 13000 13825 14240 16800 17050 18000 20000 20300
## 1 1 1 2 1 2 1 1
## 21000 24000 30000 50000 60000 81000 81315 or more <NA>
## 1 1 1 1 1 1 2 1905
mydata <- top_recode (variable="eh_s6q79", break_point=pctile_99.5_eh_s6q79, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q79. Q300: In the past 12 months, what was the total value of any monetary or non-mon
## -999 -998 0 5 10 15 20 30 40 43 50 60 80 84 90 100
## 2 6 1182 1 3 1 3 2 1 1 23 6 2 1 2 83
## 120 130 150 160 185 190 200 210 215 220 230 240 250 260 290 300
## 2 2 23 1 1 1 80 1 1 2 1 4 12 1 1 85
## 320 350 360 400 405 410 450 460 500 530 550 600 630 635 650 660
## 1 6 1 31 1 1 7 1 174 1 2 21 2 1 3 1
## 700 750 800 850 900 950 960 990 1000 1050 1080 1100 1165 1200 1300 1400
## 20 2 13 2 5 1 3 1 120 2 1 4 1 13 5 4
## 1500 1510 1600 1700 1800 1950 2000 2100 2200 2250 2300 2400 2500 2600 2680 2700
## 33 1 1 3 4 1 50 3 4 1 1 4 16 1 1 1
## 2800 3000 3070 3100 3110 3150 3200 3300 3400 3420 3500 3600 3850 3950 4000 4100
## 3 35 1 2 1 1 1 2 1 1 6 2 1 1 13 1
## 4400 4500 5000 5100 5500 5610 5630 6000 6500 6900 7000 7200 7500 8000 8500 9500
## 1 1 18 1 1 1 1 8 3 1 5 1 3 6 1 1
## 9600 9800 10000 10200 12000 14700 15000 16000 16500 17000 18000 20000 20200 23000 24000 25000
## 2 1 11 1 5 1 3 1 1 1 3 1 1 1 1 1
## 30000 35000 36000 40000 42000 48000 50000 60000 67200 72000 1e+05 144000
## 2 1 2 1 2 1 1 1 1 1 1 1
## [1] "Frequency table after encoding"
## eh_s6q79. Q300: In the past 12 months, what was the total value of any monetary or non-mon
## -999 -998 0 5 10 15 20 30
## 2 6 1182 1 3 1 3 2
## 40 43 50 60 80 84 90 100
## 1 1 23 6 2 1 2 83
## 120 130 150 160 185 190 200 210
## 2 2 23 1 1 1 80 1
## 215 220 230 240 250 260 290 300
## 1 2 1 4 12 1 1 85
## 320 350 360 400 405 410 450 460
## 1 6 1 31 1 1 7 1
## 500 530 550 600 630 635 650 660
## 174 1 2 21 2 1 3 1
## 700 750 800 850 900 950 960 990
## 20 2 13 2 5 1 3 1
## 1000 1050 1080 1100 1165 1200 1300 1400
## 120 2 1 4 1 13 5 4
## 1500 1510 1600 1700 1800 1950 2000 2100
## 33 1 1 3 4 1 50 3
## 2200 2250 2300 2400 2500 2600 2680 2700
## 4 1 1 4 16 1 1 1
## 2800 3000 3070 3100 3110 3150 3200 3300
## 3 35 1 2 1 1 1 2
## 3400 3420 3500 3600 3850 3950 4000 4100
## 1 1 6 2 1 1 13 1
## 4400 4500 5000 5100 5500 5610 5630 6000
## 1 1 18 1 1 1 1 8
## 6500 6900 7000 7200 7500 8000 8500 9500
## 3 1 5 1 3 6 1 1
## 9600 9800 10000 10200 12000 14700 15000 16000
## 2 1 11 1 5 1 3 1
## 16500 17000 18000 20000 20200 23000 24000 25000
## 1 1 3 1 1 1 1 1
## 30000 35000 35594 or more
## 2 1 12
mydata <- top_recode (variable="eh_s6q80", break_point=pctile_99.5_eh_s6q80, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q80. Q301: In the past 12 months, what was the total value of any monetary or non-mon
## -999 -998 0 20 30 40 43 50 70 90 100 105 110 120 130 140
## 1 9 710 1 3 1 1 8 1 1 31 1 1 2 3 1
## 150 160 180 200 220 240 245 250 299 300 330 340 350 380 400 420
## 14 2 2 45 2 1 1 10 1 59 1 1 7 2 25 2
## 430 435 450 465 490 500 507 545 550 580 600 609 622 630 650 700
## 1 1 7 1 1 126 1 1 3 1 32 1 1 1 3 21
## 719 720 750 780 800 830 840 850 880 900 922 980 1000 1050 1100 1150
## 1 1 1 2 9 1 1 1 1 10 1 1 142 1 7 2
## 1200 1250 1282 1300 1350 1400 1440 1450 1455 1500 1550 1600 1650 1699 1700 1750
## 16 1 1 6 1 5 1 3 1 64 2 12 1 1 4 3
## 1800 1825 1840 1850 1900 1950 2000 2060 2180 2200 2210 2250 2300 2380 2399 2400
## 8 1 1 2 7 1 80 1 1 8 1 2 3 1 1 5
## 2428 2450 2500 2550 2600 2650 2700 2800 2900 3000 3100 3150 3200 3300 3400 3500
## 1 2 24 1 7 1 2 4 3 62 6 1 4 3 2 17
## 3600 3700 3750 3800 3900 4000 4200 4300 4400 4500 4600 4800 5000 5100 5150 5200
## 2 2 3 2 1 26 1 2 1 4 2 5 65 2 1 4
## 5470 5500 5550 5600 5800 5900 6000 6100 6120 6150 6180 6300 6400 6500 6650 6700
## 1 6 1 2 1 1 25 2 1 1 1 1 3 3 1 1
## 6800 7000 7100 7200 7250 7350 7400 7500 7760 8000 8150 8300 8400 8700 8750 8800
## 1 15 1 2 1 1 2 7 1 12 1 1 3 1 1 1
## 9000 9500 10000 10250 10500 10700 10800 11300 11500 12000 12080 12100 12200 12500 12700 13000
## 6 2 27 1 2 1 1 1 1 22 1 1 1 3 1 1
## 13240 13500 14000 14400 15000 15230 15500 16000 16500 16600 17000 17500 17902 18000 18300 19200
## 1 1 5 3 8 1 1 3 1 1 2 1 1 15 2 1
## 20000 20095 20160 20400 20900 21000 21950 22000 23000 23600 24000 24400 24500 25000 26700 28000
## 13 1 1 1 1 1 1 1 1 1 18 1 1 4 1 1
## 28800 29700 30000 31000 32000 32300 35000 35500 35950 36000 36600 37000 37500 38400 38450 40000
## 1 1 4 1 1 1 1 1 1 17 1 1 1 1 1 4
## 42000 42600 44000 44700 45000 45500 46900 48000 49500 49800 50000 51000 52000 52400 54000 54880
## 3 1 2 1 1 1 1 14 1 1 1 1 1 1 3 1
## 56000 57600 59000 60000 61218 61500 62000 62300 63700 64000 65000 66000 70000 72000 73000 75000
## 2 1 1 9 1 1 1 1 1 1 1 1 1 4 1 1
## 76900 78000 78660 81400 84000 85500 88000 90000 90500 96000 97800 1e+05 108000 108970 110000 110500
## 1 2 1 1 4 1 1 2 1 8 1 4 2 1 1 1
## 112600 115000 115400 117000 120000 120250 121000 125000 126000 130000 132000 135400 140000 144000 146000 158400
## 1 1 1 1 9 1 1 1 1 2 1 1 1 5 1 1
## 163500 168000 180000 204000 216000 217150 240000 3e+05 336000 360000 432000 841900
## 1 1 2 1 2 1 2 2 1 1 1 1
## [1] "Frequency table after encoding"
## eh_s6q80. Q301: In the past 12 months, what was the total value of any monetary or non-mon
## -999 -998 0 20 30 40 43
## 1 9 710 1 3 1 1
## 50 70 90 100 105 110 120
## 8 1 1 31 1 1 2
## 130 140 150 160 180 200 220
## 3 1 14 2 2 45 2
## 240 245 250 299 300 330 340
## 1 1 10 1 59 1 1
## 350 380 400 420 430 435 450
## 7 2 25 2 1 1 7
## 465 490 500 507 545 550 580
## 1 1 126 1 1 3 1
## 600 609 622 630 650 700 719
## 32 1 1 1 3 21 1
## 720 750 780 800 830 840 850
## 1 1 2 9 1 1 1
## 880 900 922 980 1000 1050 1100
## 1 10 1 1 142 1 7
## 1150 1200 1250 1282 1300 1350 1400
## 2 16 1 1 6 1 5
## 1440 1450 1455 1500 1550 1600 1650
## 1 3 1 64 2 12 1
## 1699 1700 1750 1800 1825 1840 1850
## 1 4 3 8 1 1 2
## 1900 1950 2000 2060 2180 2200 2210
## 7 1 80 1 1 8 1
## 2250 2300 2380 2399 2400 2428 2450
## 2 3 1 1 5 1 2
## 2500 2550 2600 2650 2700 2800 2900
## 24 1 7 1 2 4 3
## 3000 3100 3150 3200 3300 3400 3500
## 62 6 1 4 3 2 17
## 3600 3700 3750 3800 3900 4000 4200
## 2 2 3 2 1 26 1
## 4300 4400 4500 4600 4800 5000 5100
## 2 1 4 2 5 65 2
## 5150 5200 5470 5500 5550 5600 5800
## 1 4 1 6 1 2 1
## 5900 6000 6100 6120 6150 6180 6300
## 1 25 2 1 1 1 1
## 6400 6500 6650 6700 6800 7000 7100
## 3 3 1 1 1 15 1
## 7200 7250 7350 7400 7500 7760 8000
## 2 1 1 2 7 1 12
## 8150 8300 8400 8700 8750 8800 9000
## 1 1 3 1 1 1 6
## 9500 10000 10250 10500 10700 10800 11300
## 2 27 1 2 1 1 1
## 11500 12000 12080 12100 12200 12500 12700
## 1 22 1 1 1 3 1
## 13000 13240 13500 14000 14400 15000 15230
## 1 1 1 5 3 8 1
## 15500 16000 16500 16600 17000 17500 17902
## 1 3 1 1 2 1 1
## 18000 18300 19200 20000 20095 20160 20400
## 15 2 1 13 1 1 1
## 20900 21000 21950 22000 23000 23600 24000
## 1 1 1 1 1 1 18
## 24400 24500 25000 26700 28000 28800 29700
## 1 1 4 1 1 1 1
## 30000 31000 32000 32300 35000 35500 35950
## 4 1 1 1 1 1 1
## 36000 36600 37000 37500 38400 38450 40000
## 17 1 1 1 1 1 4
## 42000 42600 44000 44700 45000 45500 46900
## 3 1 2 1 1 1 1
## 48000 49500 49800 50000 51000 52000 52400
## 14 1 1 1 1 1 1
## 54000 54880 56000 57600 59000 60000 61218
## 3 1 2 1 1 9 1
## 61500 62000 62300 63700 64000 65000 66000
## 1 1 1 1 1 1 1
## 70000 72000 73000 75000 76900 78000 78660
## 1 4 1 1 1 2 1
## 81400 84000 85500 88000 90000 90500 96000
## 1 4 1 1 2 1 8
## 97800 1e+05 108000 108970 110000 110500 112600
## 1 4 2 1 1 1 1
## 115000 115400 117000 120000 120250 121000 125000
## 1 1 1 9 1 1 1
## 126000 130000 132000 135400 140000 144000 146000
## 1 2 1 1 1 5 1
## 158400 163500 168000 180000 194640 or more
## 1 1 1 2 12
mydata <- top_recode (variable="eh_s6q82", break_point=pctile_99.5_eh_s6q82, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q82. In the past 12 months, what is the total monetary value of everything you receiv
## 0 250 3000 3500 4000 8000 24000 30000 35000 40000 50000 60000 80000 110000 150000 250000
## 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1
## 350000 <NA>
## 1 2269
## [1] "Frequency table after encoding"
## eh_s6q82. In the past 12 months, what is the total monetary value of everything you receiv
## 0 250 3000 3500 4000 8000 24000
## 2 1 1 1 1 1 1
## 30000 35000 40000 50000 60000 80000 110000
## 1 1 1 1 1 1 1
## 150000 250000 341000 or more <NA>
## 2 1 1 2269
mydata <- top_recode (variable="eh_s6q84", break_point=pctile_99.5_eh_s6q84, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q84. In the past 12 months, what is the total monetary value of everything you receiv
## 0 400 900 1000 1500 2000 2500 4000 5000 6250 10000 10920 11732 23400 25000 <NA>
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2273
## [1] "Frequency table after encoding"
## eh_s6q84. In the past 12 months, what is the total monetary value of everything you receiv
## 0 400 900 1000 1500 2000 2500 4000
## 1 1 1 1 1 1 1 1
## 5000 6250 10000 10920 11732 23400 24888 or more <NA>
## 1 1 1 1 1 1 1 2273
mydata <- top_recode (variable="eh_s6q85", break_point=pctile_99.5_eh_s6q85, missing=-998)## [1] "Frequency table before encoding"
## eh_s6q85. In the past 12 months, how much did you or other members of your household spend
## -999 -998 0 1 20 25 30 32 50 100 102 130 150 200 220 237
## 11 9 2141 1 2 1 1 1 2 3 1 2 1 5 1 1
## 240 250 260 300 350 450 500 501 600 670 700 720 800 972 980 1000
## 2 1 1 2 1 1 2 1 1 1 2 1 1 1 1 12
## 1186 1200 1300 1500 1900 2000 2160 2200 2400 2700 3000 3250 3500 3600 3893 4000
## 1 4 2 4 1 7 1 1 2 1 1 1 2 1 1 3
## 4500 4700 5000 5700 6000 7000 8000 9000 9300 10000 11000 13000 15000 17000 20000 22500
## 1 1 3 1 4 1 1 1 1 4 1 1 1 1 1 1
## 26550 30000 32000 32560 33280 36800 39825 44440 45000 50000 55000 60000 70000 120750 146000 180000
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 250000
## 1
## [1] "Frequency table after encoding"
## eh_s6q85. In the past 12 months, how much did you or other members of your household spend
## -999 -998 0 1 20 25 30 32
## 11 9 2141 1 2 1 1 1
## 50 100 102 130 150 200 220 237
## 2 3 1 2 1 5 1 1
## 240 250 260 300 350 450 500 501
## 2 1 1 2 1 1 2 1
## 600 670 700 720 800 972 980 1000
## 1 1 2 1 1 1 1 12
## 1186 1200 1300 1500 1900 2000 2160 2200
## 1 4 2 4 1 7 1 1
## 2400 2700 3000 3250 3500 3600 3893 4000
## 2 1 1 1 2 1 1 3
## 4500 4700 5000 5700 6000 7000 8000 9000
## 1 1 3 1 4 1 1 1
## 9300 10000 11000 13000 15000 17000 20000 22500
## 1 4 1 1 1 1 1 1
## 26550 30000 32000 32560 33280 35427 or more
## 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("eh_s6q3_1",
"eh_s6q5_1",
"eh_s6q6_1",
"eh_s6q5_2",
"eh_s6q6_2",
"eh_s6q5_3",
"eh_s6q6_3",
"eh_s6q5_4",
"eh_s6q6_4",
"eh_s6q5_5",
"eh_s6q6_5",
"eh_s6q5_6",
"eh_s6q6_6",
"eh_s6q5_7",
"eh_s6q6_7",
"eh_s6q5_8",
"eh_s6q6_8",
"eh_s6q5_9",
"eh_s6q6_9",
"eh_s6q5_10",
"eh_s6q6_10",
"eh_s6q5_11",
"eh_s6q6_11",
"eh_s6q5_12",
"eh_s6q6_12",
"eh_s6q5_13",
"eh_s6q6_13",
"eh_s6q5_14",
"eh_s6q6_14",
"eh_s6q5_15",
"eh_s6q6_15",
"eh_s6q5_16",
"eh_s6q6_16",
"eh_s6q5_17",
"eh_s6q6_17",
"eh_s6q5_18",
"eh_s6q6_18",
"eh_s6q5_19",
"eh_s6q6_19",
"eh_s6q5_20",
"eh_s6q6_20",
"eh_s6q5_21",
"eh_s6q6_21",
"eh_s6q5_22",
"eh_s6q6_22",
"eh_s6q5_23",
"eh_s6q6_23",
"eh_s6q5_24",
"eh_s6q6_24",
"eh_s6q5_25",
"eh_s6q6_25",
"eh_s6q9_1",
"eh_s6q11_1",
"eh_s6q12_1",
"eh_s6q11_2",
"eh_s6q12_2",
"eh_s6q11_3",
"eh_s6q12_3",
"eh_s6q11_4",
"eh_s6q12_4",
"eh_s6q11_5",
"eh_s6q12_5",
"eh_s6q11_6",
"eh_s6q12_6",
"eh_s6q11_7",
"eh_s6q12_7",
"eh_s6q15_1",
"eh_s6q17_1",
"eh_s6q18_1",
"eh_s6q17_2",
"eh_s6q18_2",
"eh_s6q17_3",
"eh_s6q18_3",
"eh_s6q17_4",
"eh_s6q18_4",
"eh_s6q17_5",
"eh_s6q18_5",
"eh_s6q17_6",
"eh_s6q18_6",
"eh_s6q17_7",
"eh_s6q18_7",
"eh_s6q17_8",
"eh_s6q18_8",
"eh_s6q21_1",
"eh_s6q23_1",
"eh_s6q23_2",
"eh_s6q23_3",
"eh_s6q23_4",
"eh_s6q23_5",
"eh_s6q23_6",
"eh_s6q23_7",
"eh_s6q23_8",
"eh_s6q27_1",
"eh_s6q29_1",
"eh_s6q29_2",
"eh_s6q29_3",
"eh_s6q29_4",
"eh_s6q29_5",
"eh_s6q29_6",
"eh_s6q29_7",
"eh_s6q29_8",
"eh_s6q33_1",
"eh_s6q35_1",
"eh_s6q35_2",
"eh_s6q35_3",
"eh_s6q35_4",
"eh_s6q35_5",
"eh_s6q39_1",
"eh_s6q41_1",
"eh_s6q41_2",
"eh_s6q41_3",
"eh_s6q41_4",
"eh_s6q41_5",
"eh_s6q41_6",
"eh_s6q45_1",
"eh_s6q47_1",
"eh_s6q47_2",
"eh_s6q47_3",
"eh_s6q47_4",
"eh_s6q47_5",
"eh_s6q47_6",
"eh_s6q47_7",
"eh_s6q50_1",
"eh_s6q55_1",
"eh_s6q56_1",
"eh_s6q69_1",
"eh_s6q75_1",
"eh_s6q56_2",
"eh_s6q69_2",
"eh_s6q75_2",
"eh_s6q56_3",
"eh_s6q69_3",
"eh_s6q75_3",
"eh_s6q56_4",
"eh_s6q69_4",
"eh_s6q75_4",
"eh_s6q56_5",
"eh_s6q69_5",
"eh_s6q75_5",
"eh_s6q56_6",
"eh_s6q69_6",
"eh_s6q75_6",
"eh_s6q56_7",
"eh_s6q69_7",
"eh_s6q75_7",
"eh_s6q56_8",
"eh_s6q69_8",
"eh_s6q75_8",
"eh_s6q56_9",
"eh_s6q69_9",
"eh_s6q75_9",
"eh_s6q56_10",
"eh_s6q69_10",
"eh_s6q75_10",
"eh_s6q56_11",
"eh_s6q69_11",
"eh_s6q75_11",
"eh_s6q56_12",
"eh_s6q69_12",
"eh_s6q75_12",
"eh_s6q56_13",
"eh_s6q69_13",
"eh_s6q75_13",
"eh_s6q56_14",
"eh_s6q69_14",
"eh_s6q75_14",
"eh_s6q56_15",
"eh_s6q69_15",
"eh_s6q75_15",
"eh_s6q56_16",
"eh_s6q69_16",
"eh_s6q75_16",
"eh_s6q56_17",
"eh_s6q69_17",
"eh_s6q75_17",
"eh_s6q56_18",
"eh_s6q69_18",
"eh_s6q75_18",
"eh_s6q56_19",
"eh_s6q69_19",
"eh_s6q75_19",
"eh_s6q56_20",
"eh_s6q69_20",
"eh_s6q75_20",
"eh_s6q56_21",
"eh_s6q69_21",
"eh_s6q75_21",
"eh_s6q56_22",
"eh_s6q69_22",
"eh_s6q75_22",
"eh_s6q56_23",
"eh_s6q69_23",
"eh_s6q75_23",
"eh_s6q56_24",
"eh_s6q69_24",
"eh_s6q75_24",
"eh_s6q56_25",
"eh_s6q69_25",
"eh_s6q75_25")
capture_tables (indirect_PII)
remove_vars <- c("eh_s6q3_1","eh_s6q9_1","eh_s6q15_1","eh_s6q21_1","eh_s6q27_1","eh_s6q33_1","eh_s6q39_1",
"eh_s6q45_1")
mydata <- mydata[!names(mydata) %in% remove_vars]Matching and crosstabulations: Run automated PII check
# !!! Insufficient demographic dataOpen-ends: review responses for any sensitive information, redact as necessary
# !!! Identify open-end variables here:
open_ends <- c("eh_s6q2_1",
"eh_s6q4_1",
"eh_s6q2_2",
"eh_s6q4_2",
"eh_s6q2_3",
"eh_s6q4_3",
"eh_s6q2_4",
"eh_s6q4_4",
"eh_s6q2_5",
"eh_s6q4_5",
"eh_s6q2_6",
"eh_s6q4_6",
"eh_s6q2_7",
"eh_s6q4_7",
"eh_s6q2_8",
"eh_s6q4_8",
"eh_s6q2_9",
"eh_s6q4_9",
"eh_s6q2_10",
"eh_s6q4_10",
"eh_s6q2_11",
"eh_s6q4_11",
"eh_s6q2_12",
"eh_s6q4_12",
"eh_s6q2_13",
"eh_s6q4_13",
"eh_s6q2_14",
"eh_s6q4_14",
"eh_s6q2_15",
"eh_s6q4_15",
"eh_s6q2_16",
"eh_s6q4_16",
"eh_s6q2_17",
"eh_s6q4_17",
"eh_s6q2_18",
"eh_s6q4_18",
"eh_s6q2_19",
"eh_s6q4_19",
"eh_s6q2_20",
"eh_s6q4_20",
"eh_s6q2_21",
"eh_s6q4_21",
"eh_s6q2_22",
"eh_s6q4_22",
"eh_s6q2_23",
"eh_s6q4_23",
"eh_s6q2_24",
"eh_s6q4_24",
"eh_s6q2_25",
"eh_s6q4_25",
"eh_s6q8_1",
"eh_s6q10_1",
"eh_s6q8_2",
"eh_s6q10_2",
"eh_s6q8_3",
"eh_s6q10_3",
"eh_s6q8_4",
"eh_s6q10_4",
"eh_s6q8_5",
"eh_s6q10_5",
"eh_s6q8_6",
"eh_s6q10_6",
"eh_s6q8_7",
"eh_s6q10_7",
"eh_s6q14_1",
"eh_s6q16_1",
"eh_s6q14_2",
"eh_s6q16_2",
"eh_s6q14_3",
"eh_s6q16_3",
"eh_s6q14_4",
"eh_s6q16_4",
"eh_s6q14_5",
"eh_s6q16_5",
"eh_s6q14_6",
"eh_s6q16_6",
"eh_s6q14_7",
"eh_s6q16_7",
"eh_s6q14_8",
"eh_s6q16_8",
"eh_s6q20_1",
"eh_s6q22_1",
"eh_s6q20_2",
"eh_s6q22_2",
"eh_s6q20_3",
"eh_s6q22_3",
"eh_s6q20_4",
"eh_s6q22_4",
"eh_s6q20_5",
"eh_s6q22_5",
"eh_s6q20_6",
"eh_s6q22_6",
"eh_s6q20_7",
"eh_s6q22_7",
"eh_s6q20_8",
"eh_s6q22_8",
"eh_s6q26_1",
"eh_s6q28_1",
"eh_s6q26_2",
"eh_s6q28_2",
"eh_s6q26_3",
"eh_s6q28_3",
"eh_s6q26_4",
"eh_s6q28_4",
"eh_s6q26_5",
"eh_s6q28_5",
"eh_s6q26_6",
"eh_s6q28_6",
"eh_s6q26_7",
"eh_s6q28_7",
"eh_s6q26_8",
"eh_s6q28_8",
"eh_s6q32_1",
"eh_s6q34_1",
"eh_s6q32_2",
"eh_s6q34_2",
"eh_s6q32_3",
"eh_s6q34_3",
"eh_s6q32_4",
"eh_s6q34_4",
"eh_s6q32_5",
"eh_s6q34_5",
"eh_s6q38_1",
"eh_s6q40_1",
"eh_s6q38_2",
"eh_s6q40_2",
"eh_s6q38_3",
"eh_s6q40_3",
"eh_s6q38_4",
"eh_s6q40_4",
"eh_s6q38_5",
"eh_s6q40_5",
"eh_s6q38_6",
"eh_s6q40_6",
"eh_s6q44_1",
"eh_s6q46_1",
"eh_s6q44_2",
"eh_s6q46_2",
"eh_s6q44_3",
"eh_s6q46_3",
"eh_s6q44_4",
"eh_s6q46_4",
"eh_s6q44_5",
"eh_s6q46_5",
"eh_s6q44_6",
"eh_s6q46_6",
"eh_s6q44_7",
"eh_s6q46_7",
"eh_s6q51_1",
"eh_s6q51_2",
"eh_s6q51_3",
"eh_s6q51_4",
"eh_s6q51_5",
"eh_s6q51_6",
"eh_s6q51_7",
"eh_s6q51_8",
"eh_s6q51_9",
"eh_s6q51_10",
"eh_s6q51_11",
"eh_s6q51_12",
"eh_s6q51_13",
"eh_s6q51_14",
"eh_s6q51_15",
"eh_s6q51_16",
"eh_s6q51_17",
"eh_s6q51_18",
"eh_s6q51_19",
"eh_s6q51_20",
"eh_s6q51_21",
"eh_s6q51_22",
"eh_s6q51_23",
"eh_s6q51_24",
"eh_s6q51_25")
report_open (list_open_ends = open_ends)
# Review "verbatims.csv". Identify variables to be deleted or redacted and their row number
mydata <- mydata[!names(mydata) %in% open_ends]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)