rm(list=ls(all=t))
filename <- "Section_4" # !!!Update filename
functions_vers <- "functions_1.8.R" # !!!Update helper functions file
source (functions_vers)
Visually inspect variables in "dictionary.csv" and flag for risk, using the following flags:
# Direct PII: Respondent Names, Addresses, Identification Numbers, Phone Numbers
# Direct PII-team: Interviewer Names, other field team names
# Indirect PII-ordinal: Date of birth, Age, income, education, household composition.
# Indirect PII-categorical: Gender, education, ethnicity, nationality,
# occupation, employer, head of household, marital status
# GPS: Longitude, Latitude
# Small Location: Location (<100,000)
# Large Location (>100,000)
# Weight: weightVar
# Household ID: hhId,
# Open-ends: Review responses for any sensitive information, redact as necessary
# !!!No Direct PII
# !!!No Direct PII - team
# !!!No Small locations
# Focus on variables with a "Lowest Freq" in dictionary of 30 or less.
# Top code high income to the 99.5 percentile
percentile_99.5 <- floor(quantile(na.exclude(mydata$c_s4q37)[na.exclude(mydata$c_s4q37)!=-97], probs = c(0.995)))
mydata <- top_recode (variable="c_s4q37", break_point=percentile_99.5, missing=-97)
## [1] "Frequency table before encoding"
## c_s4q37. How much do you earn in a typical week (in cash or in kind and including the amo
## 0 2 3 4 5 6 8 10 12 14 15 20 25 28 30
## 752 2 3 1 13 1 1 29 1 2 15 66 8 1 24
## 35 36 40 42 45 50 54 55 57 60 63 65 70 75 80
## 4 1 26 1 3 91 1 1 1 18 1 1 13 4 16
## 90 100 105 110 111 112 116 120 125 130 137 140 145 150 160
## 6 119 1 4 1 1 1 20 3 5 1 17 1 77 8
## 170 175 180 190 200 210 220 225 230 235 240 245 250 260 270
## 2 1 8 3 94 3 2 1 1 1 2 2 53 2 1
## 280 287 300 310 320 338 340 350 360 368 380 400 408 420 425
## 3 1 90 2 3 1 5 34 3 1 3 57 1 3 2
## 440 450 466 467 480 490 500 510 513 520 525 550 560 575 600
## 2 20 1 1 1 3 75 2 1 1 2 4 2 1 49
## 620 625 630 650 682 700 720 750 758 780 800 840 850 875 900
## 1 5 1 3 1 41 2 17 1 2 15 8 2 3 20
## 945 950 1000 1020 1050 1060 1080 1100 1143 1150 1190 1200 1250 1320 1400
## 1 1 48 2 14 1 2 3 1 1 3 15 7 1 18
## 1408 1500 1510 1550 1572 1600 1680 1750 1770 1800 2000 2035 2100 2275 2280
## 1 26 1 1 1 3 1 7 1 8 9 1 3 1 1
## 2500 2700 2750 2800 3000 3100 3500 4000 4200 4500 4900 5000 5750 5800 6000
## 9 1 1 1 7 1 8 3 1 2 1 2 1 1 1
## 6300 6720 7000 7200 8000 10000 11000 11520 12000 12960 13600 15000 18000 19500 20000
## 1 1 2 1 3 2 1 1 1 1 1 1 1 1 1
## 22500 23400 24000 25200 28800 36400 54000 60000 108000 <NA>
## 1 1 1 2 1 1 1 1 1 35
## [1] "Frequency table after encoding"
## c_s4q37. How much do you earn in a typical week (in cash or in kind and including the amo
## 0 2 3 4 5 6 8
## 752 2 3 1 13 1 1
## 10 12 14 15 20 25 28
## 29 1 2 15 66 8 1
## 30 35 36 40 42 45 50
## 24 4 1 26 1 3 91
## 54 55 57 60 63 65 70
## 1 1 1 18 1 1 13
## 75 80 90 100 105 110 111
## 4 16 6 119 1 4 1
## 112 116 120 125 130 137 140
## 1 1 20 3 5 1 17
## 145 150 160 170 175 180 190
## 1 77 8 2 1 8 3
## 200 210 220 225 230 235 240
## 94 3 2 1 1 1 2
## 245 250 260 270 280 287 300
## 2 53 2 1 3 1 90
## 310 320 338 340 350 360 368
## 2 3 1 5 34 3 1
## 380 400 408 420 425 440 450
## 3 57 1 3 2 2 20
## 466 467 480 490 500 510 513
## 1 1 1 3 75 2 1
## 520 525 550 560 575 600 620
## 1 2 4 2 1 49 1
## 625 630 650 682 700 720 750
## 5 1 3 1 41 2 17
## 758 780 800 840 850 875 900
## 1 2 15 8 2 3 20
## 945 950 1000 1020 1050 1060 1080
## 1 1 48 2 14 1 2
## 1100 1143 1150 1190 1200 1250 1320
## 3 1 1 3 15 7 1
## 1400 1408 1500 1510 1550 1572 1600
## 18 1 26 1 1 1 3
## 1680 1750 1770 1800 2000 2035 2100
## 1 7 1 8 9 1 3
## 2275 2280 2500 2700 2750 2800 3000
## 1 1 9 1 1 1 7
## 3100 3500 4000 4200 4500 4900 5000
## 1 8 3 1 2 1 2
## 5750 5800 6000 6300 6720 7000 7200
## 1 1 1 1 1 2 1
## 8000 10000 11000 11520 12000 12960 13600
## 3 2 1 1 1 1 1
## 15000 18000 18832 or more <NA>
## 1 1 12 35
# !!!Include relevant variables in list below (Indirect PII - Categorical, and Ordinal if not processed yet)
indirect_PII <- c("c_s4q1",
"c_s4q2",
"c_s4q3",
"c_s4q4",
"c_s4q5",
"c_s4q6",
"c_s4q7",
"c_s4q8",
"c_s4q9",
"c_s4q10",
"c_s4q11",
"c_s4q12",
"c_s4q13",
"c_s4q15",
"c_s4q16",
"c_s4q16extra",
"c_s4q17",
"c_s4q17extra",
"c_s4q18",
"c_s4q19",
"c_s4q20",
"c_s4q36")
capture_tables (indirect_PII)
# Recode those with very specific values.
break_activity <- c(1,2,3,4,5,6,7,8,9,10,11,12)
labels_activity <- c("Your family dwelling"=1,
"Family Field"=2,
"Employer House"=3,
"Other"=4,
"Other"=5,
"Other"=6,
"Shop, Market, Kiosk"=7,
"Street"=8,
"Other"=9,
"Other (Specify)"=10,
"Non-Family's Field"=11,
"Fishing area"=12)
mydata <- ordinal_recode (variable="c_s4q16", break_points=break_activity, missing=999999, value_labels=labels_activity)
## [1] "Frequency table before encoding"
## c_s4q16. How would you describe the worksite you've worked at most frequently over the la
## No Response Your family dwelling Family<U+0092>s Field Employer<U+0092>s House Formal Office
## 59 790 342 154 2
## Shed Factory Shop, Market, Kiosk Street Dumpsite
## 8 24 81 186 8
## Other (Specify) Non-Family's Field Fishing area
## 207 343 121
## recoded
## [1,2) [2,3) [3,4) [4,5) [5,6) [6,7) [7,8) [8,9) [9,10) [10,11) [11,12) [12,1e+06)
## -999 0 0 0 0 0 0 0 0 0 0 0 0
## 1 790 0 0 0 0 0 0 0 0 0 0 0
## 2 0 342 0 0 0 0 0 0 0 0 0 0
## 3 0 0 154 0 0 0 0 0 0 0 0 0
## 4 0 0 0 2 0 0 0 0 0 0 0 0
## 5 0 0 0 0 8 0 0 0 0 0 0 0
## 6 0 0 0 0 0 24 0 0 0 0 0 0
## 7 0 0 0 0 0 0 81 0 0 0 0 0
## 8 0 0 0 0 0 0 0 186 0 0 0 0
## 9 0 0 0 0 0 0 0 0 8 0 0 0
## 10 0 0 0 0 0 0 0 0 0 207 0 0
## 11 0 0 0 0 0 0 0 0 0 0 343 0
## 12 0 0 0 0 0 0 0 0 0 0 0 121
## [1] "Frequency table after encoding"
## c_s4q16. How would you describe the worksite you've worked at most frequently over the la
## Your family dwelling Family Field Employer House Other Shop, Market, Kiosk
## 790 342 154 42 81
## Street Other (Specify) Non-Family's Field Fishing area <NA>
## 186 207 343 121 59
## [1] "Inspect value labels and relabel as necessary"
## Your family dwelling Family Field Employer House Other Other
## 1 2 3 4 5
## Other Shop, Market, Kiosk Street Other Other (Specify)
## 6 7 8 9 10
## Non-Family's Field Fishing area
## 11 12
# Based on dictionary inspection, select variables for creating sdcMicro object
# See: https://sdcpractice.readthedocs.io/en/latest/anon_methods.html
# All variable names should correspond to the names in the data file
# selected categorical key variables: gender, occupation/education and age
# !!!Insufficient demographic data
# !!! Identify open-end variables here:
open_ends <- c("c_s4q1noresponse",
"c_s4q2noresponse",
"c_s4q3noresponse",
"c_s4q4noresponse",
"c_s4q5noresponse",
"c_s4q6noresponse",
"c_s4q7noresponse",
"c_s4q8noresponse",
"c_s4q9noresponse",
"c_s4q10noresponse",
"c_s4q11noresponse",
"c_s4q12noresponse",
"c_s4q13noresponse",
"c_s4q14noresponse",
"c_s4q15_other",
"c_s4q16_other",
"c_s4q16extranoresponse",
"c_s4q17noresponse",
"c_s4q17extranoresponse",
"c_s4q18noresponse",
"c_s4q19noresponse",
"c_s4q20noresponse",
"c_s4q27",
"c_s4q21noresponse",
"c_s4q27_other",
"c_s4q28noresponse",
"c_s4q35",
"c_s4q29noresponse",
"c_s4q36why",
"c_s4q37noresponse")
report_open (list_open_ends = open_ends)
# Review "verbatims.csv". Identify variables to be deleted or redacted and their row number
mydata$c_s4q14noresponse[99] <- "[language]"
mydata$c_s4q15_other[728] <- "She has no work since [date]"
mydata$c_s4q15_other[920] <- "They own [type of store] store"
mydata$c_s4q16_other[99] <- "Other"
mydata$c_s4q16_other[105] <- "Other"
mydata$c_s4q16_other[106] <- "Other"
mydata$c_s4q16_other[156] <- "Other"
mydata$c_s4q16_other[189] <- "Other"
mydata$c_s4q16_other[273] <- "Other"
mydata$c_s4q16_other[277] <- "Other"
mydata$c_s4q16_other[293] <- "Other"
mydata$c_s4q16_other[302] <- "Other"
mydata$c_s4q16_other[308] <- "Other"
mydata$c_s4q16_other[309] <- "Other"
mydata$c_s4q16_other[319] <- "Other"
mydata$c_s4q16_other[321] <- "Other"
mydata$c_s4q16_other[322] <- "Other"
mydata$c_s4q16_other[330] <- "Other"
mydata$c_s4q16_other[335] <- "Other"
mydata$c_s4q16_other[345] <- "Other"
mydata$c_s4q16_other[348] <- "Other"
mydata$c_s4q16_other[394] <- "Other"
mydata$c_s4q16_other[450] <- "Other"
mydata$c_s4q16_other[452] <- "Other"
mydata$c_s4q16_other[475] <- "Other"
mydata$c_s4q16_other[504] <- "Other"
mydata$c_s4q16_other[505] <- "Other"
mydata$c_s4q16_other[507] <- "Other"
mydata$c_s4q16_other[515] <- "Other"
mydata$c_s4q16_other[519] <- "Other"
mydata$c_s4q16_other[520] <- "Other"
mydata$c_s4q16_other[523] <- "Other"
mydata$c_s4q16_other[524] <- "Other"
mydata$c_s4q16_other[527] <- "Other"
mydata$c_s4q16_other[528] <- "Other"
mydata$c_s4q16_other[537] <- "Other"
mydata$c_s4q16_other[538] <- "Other"
mydata$c_s4q16_other[546] <- "Other"
mydata$c_s4q16_other[547] <- "Other"
mydata$c_s4q16_other[563] <- "Other"
mydata$c_s4q16_other[566] <- "Other"
mydata$c_s4q16_other[574] <- "Other"
mydata$c_s4q16_other[575] <- "Other"
mydata$c_s4q16_other[578] <- "Other"
mydata$c_s4q16_other[579] <- "Other"
mydata$c_s4q16_other[592] <- "Other"
mydata$c_s4q16_other[595] <- "Other"
mydata$c_s4q16_other[598] <- "Other"
mydata$c_s4q16_other[599] <- "Other"
mydata$c_s4q16_other[600] <- "Other"
mydata$c_s4q16_other[601] <- "Other"
mydata$c_s4q16_other[602] <- "Other"
mydata$c_s4q16_other[605] <- "Other"
mydata$c_s4q16_other[632] <- "Other"
mydata$c_s4q16_other[635] <- "Other"
mydata$c_s4q16_other[639] <- "Other"
mydata$c_s4q16_other[641] <- "Other"
mydata$c_s4q16_other[642] <- "Other"
mydata$c_s4q16_other[652] <- "Other"
mydata$c_s4q16_other[669] <- "Other"
mydata$c_s4q16_other[675] <- "Other"
mydata$c_s4q16_other[678] <- "Other"
mydata$c_s4q16_other[681] <- "Other"
mydata$c_s4q16_other[692] <- "Other"
mydata$c_s4q16_other[725] <- "Other"
mydata$c_s4q16_other[726] <- "Other"
mydata$c_s4q16_other[728] <- "Other"
mydata$c_s4q16_other[730] <- "Other"
mydata$c_s4q16_other[738] <- "Other"
mydata$c_s4q16_other[754] <- "Other"
mydata$c_s4q16_other[760] <- "Other"
mydata$c_s4q16_other[761] <- "Other"
mydata$c_s4q16_other[769] <- "Other"
mydata$c_s4q16_other[772] <- "Other"
mydata$c_s4q16_other[788] <- "Other"
mydata$c_s4q16_other[791] <- "Other"
mydata$c_s4q16_other[795] <- "Other"
mydata$c_s4q16_other[798] <- "Other"
mydata$c_s4q16_other[800] <- "Other"
mydata$c_s4q16_other[802] <- "Other"
mydata$c_s4q16_other[804] <- "Other"
mydata$c_s4q16_other[805] <- "Other"
mydata$c_s4q16_other[806] <- "Other"
mydata$c_s4q16_other[813] <- "Other"
mydata$c_s4q16_other[815] <- "Other"
mydata$c_s4q16_other[821] <- "Other"
mydata$c_s4q16_other[831] <- "Other"
mydata$c_s4q16_other[832] <- "Other"
mydata$c_s4q16_other[837] <- "Other"
mydata$c_s4q16_other[838] <- "Other"
mydata$c_s4q16_other[840] <- "Other"
mydata$c_s4q16_other[854] <- "Other"
mydata$c_s4q16_other[861] <- "Other"
mydata$c_s4q16_other[865] <- "Other"
mydata$c_s4q16_other[867] <- "Other"
mydata$c_s4q16_other[868] <- "Other"
mydata$c_s4q16_other[877] <- "Other"
mydata$c_s4q16_other[878] <- "Other"
mydata$c_s4q16_other[879] <- "Other"
mydata$c_s4q16_other[889] <- "Other"
mydata$c_s4q16_other[925] <- "Other"
mydata$c_s4q16_other[933] <- "Other"
mydata$c_s4q16_other[936] <- "Other"
mydata$c_s4q16_other[939] <- "Other"
mydata$c_s4q16_other[942] <- "Other"
mydata$c_s4q16_other[943] <- "Other"
mydata$c_s4q16_other[963] <- "Other"
mydata$c_s4q16_other[968] <- "Other"
mydata$c_s4q16_other[969] <- "Other"
mydata$c_s4q16_other[972] <- "Other"
mydata$c_s4q16_other[973] <- "Other"
mydata$c_s4q16_other[982] <- "Other"
mydata$c_s4q16_other[984] <- "Other"
mydata$c_s4q16_other[1004] <- "Other"
mydata$c_s4q16_other[1006] <- "Other"
mydata$c_s4q16_other[1008] <- "Other"
mydata$c_s4q16_other[1041] <- "Other"
mydata$c_s4q16_other[1065] <- "Other"
mydata$c_s4q16_other[1066] <- "Other"
mydata$c_s4q16_other[1075] <- "Other"
mydata$c_s4q16_other[1084] <- "Other"
mydata$c_s4q16_other[1091] <- "Other"
mydata$c_s4q16_other[1094] <- "Other"
mydata$c_s4q16_other[1097] <- "Other"
mydata$c_s4q16_other[1098] <- "Other"
mydata$c_s4q16_other[1100] <- "Other"
mydata$c_s4q16_other[1106] <- "Other"
mydata$c_s4q16_other[1128] <- "Other"
mydata$c_s4q16_other[1134] <- "Other"
mydata$c_s4q16_other[1150] <- "Other"
mydata$c_s4q16_other[1151] <- "Other"
mydata$c_s4q16_other[1172] <- "Other"
mydata$c_s4q16_other[1189] <- "Other"
mydata$c_s4q16_other[1201] <- "Other"
mydata$c_s4q16_other[1214] <- "Other"
mydata$c_s4q16_other[1216] <- "Other"
mydata$c_s4q16_other[1227] <- "Other"
mydata$c_s4q16_other[1228] <- "Other"
mydata$c_s4q16_other[1237] <- "Other"
mydata$c_s4q16_other[1239] <- "Other"
mydata$c_s4q16_other[1245] <- "Other"
mydata$c_s4q16_other[1246] <- "Other"
mydata$c_s4q16_other[1271] <- "Other"
mydata$c_s4q16_other[1274] <- "Other"
mydata$c_s4q16_other[1282] <- "Other"
mydata$c_s4q16_other[1299] <- "Other"
mydata$c_s4q16_other[1312] <- "Other"
mydata$c_s4q16_other[1378] <- "Other"
mydata$c_s4q16_other[1379] <- "Other"
mydata$c_s4q16_other[1380] <- "Other"
mydata$c_s4q16_other[1404] <- "Other"
mydata$c_s4q16_other[1411] <- "Other"
mydata$c_s4q16_other[1436] <- "Other"
mydata$c_s4q16_other[1446] <- "Other"
mydata$c_s4q16_other[1458] <- "Other"
mydata$c_s4q16_other[1466] <- "Other"
mydata$c_s4q16_other[1485] <- "Other"
mydata$c_s4q16_other[1497] <- "Other"
mydata$c_s4q16_other[1505] <- "Other"
mydata$c_s4q16_other[1507] <- "Other"
mydata$c_s4q16_other[1508] <- "Other"
mydata$c_s4q16_other[1530] <- "Other"
mydata$c_s4q16_other[1531] <- "Other"
mydata$c_s4q16_other[1541] <- "Other"
mydata$c_s4q16_other[1543] <- "Other"
mydata$c_s4q16_other[1556] <- "Other"
mydata$c_s4q16_other[1567] <- "Other"
mydata$c_s4q16_other[1571] <- "Other"
mydata$c_s4q16_other[1574] <- "Other"
mydata$c_s4q16_other[1577] <- "Other"
mydata$c_s4q16_other[1585] <- "Other"
mydata$c_s4q16_other[1617] <- "Other"
mydata$c_s4q16_other[1618] <- "Other"
mydata$c_s4q16_other[1648] <- "Other"
mydata$c_s4q16_other[1666] <- "Other"
mydata$c_s4q16_other[1668] <- "Other"
mydata$c_s4q16_other[1670] <- "Other"
mydata$c_s4q16_other[1723] <- "Other"
mydata$c_s4q16_other[1725] <- "Other"
mydata$c_s4q16_other[1756] <- "Other"
mydata$c_s4q16_other[1757] <- "Other"
mydata$c_s4q16_other[1773] <- "Other"
mydata$c_s4q16_other[1779] <- "Other"
mydata$c_s4q16_other[1788] <- "Other"
mydata$c_s4q16_other[1789] <- "Other"
mydata$c_s4q16_other[1792] <- "Other"
mydata$c_s4q16_other[1806] <- "Other"
mydata$c_s4q16_other[1809] <- "Other"
mydata$c_s4q16_other[1828] <- "Other"
mydata$c_s4q16_other[1829] <- "Other"
mydata$c_s4q16_other[1830] <- "Other"
mydata$c_s4q16_other[1836] <- "Other"
mydata$c_s4q16_other[1839] <- "Other"
mydata$c_s4q16_other[1842] <- "Other"
mydata$c_s4q16_other[1843] <- "Other"
mydata$c_s4q16_other[1845] <- "Other"
mydata$c_s4q16_other[1848] <- "Other"
mydata$c_s4q16_other[1852] <- "Other"
mydata$c_s4q16_other[1855] <- "Other"
mydata$c_s4q16_other[1857] <- "Other"
mydata$c_s4q16_other[1859] <- "Other"
mydata$c_s4q16_other[1866] <- "Other"
mydata$c_s4q16_other[1867] <- "Other"
mydata$c_s4q16_other[1872] <- "Other"
mydata$c_s4q16_other[1881] <- "Other"
mydata$c_s4q16_other[1882] <- "Other"
mydata$c_s4q16_other[1892] <- "Other"
mydata$c_s4q16_other[1896] <- "Other"
mydata$c_s4q16_other[1908] <- "Other"
mydata$c_s4q16_other[1911] <- "Other"
mydata$c_s4q16_other[1913] <- "Other"
mydata$c_s4q16_other[1917] <- "Other"
mydata$c_s4q16_other[1918] <- "Other"
mydata$c_s4q16_other[1919] <- "Other"
mydata$c_s4q16_other[1922] <- "Other"
mydata$c_s4q16_other[1939] <- "Other"
mydata$c_s4q16_other[1956] <- "Other"
mydata$c_s4q16_other[1960] <- "Other"
mydata$c_s4q16_other[1961] <- "Other"
mydata$c_s4q16_other[1964] <- "Other"
mydata$c_s4q16_other[1974] <- "Other"
mydata$c_s4q16_other[1976] <- "Other"
mydata$c_s4q16_other[1978] <- "Other"
mydata$c_s4q16_other[1979] <- "Other"
mydata$c_s4q16_other[1981] <- "Other"
mydata$c_s4q16_other[1984] <- "Other"
mydata$c_s4q16_other[1986] <- "Other"
mydata$c_s4q16_other[1990] <- "Other"
mydata$c_s4q16_other[1993] <- "Other"
mydata$c_s4q16_other[1995] <- "Other"
mydata$c_s4q16_other[1996] <- "Other"
mydata$c_s4q16_other[1997] <- "Other"
mydata$c_s4q16_other[2004] <- "Other"
mydata$c_s4q16_other[2006] <- "Other"
mydata$c_s4q16_other[2007] <- "Other"
mydata$c_s4q16_other[2015] <- "Other"
mydata$c_s4q16_other[2023] <- "Other"
mydata$c_s4q16_other[2033] <- "Other"
mydata$c_s4q16_other[2040] <- "Other"
mydata$c_s4q16_other[2041] <- "Other"
mydata$c_s4q16_other[2042] <- "Other"
mydata$c_s4q16_other[2100] <- "Other"
mydata$c_s4q16_other[2102] <- "Other"
mydata$c_s4q16_other[2103] <- "Other"
mydata$c_s4q16_other[2104] <- "Other"
mydata$c_s4q16_other[2108] <- "Other"
mydata$c_s4q16_other[2111] <- "Other"
mydata$c_s4q16_other[2112] <- "Other"
mydata$c_s4q16_other[2113] <- "Other"
mydata$c_s4q16_other[2115] <- "Other"
mydata$c_s4q16_other[2132] <- "Other"
mydata$c_s4q16_other[2133] <- "Other"
mydata$c_s4q16_other[2143] <- "Other"
mydata$c_s4q16_other[2144] <- "Other"
mydata$c_s4q16_other[2150] <- "Other"
mydata$c_s4q16_other[2168] <- "Other"
mydata$c_s4q16_other[2169] <- "Other"
mydata$c_s4q16_other[2223] <- "Other"
mydata$c_s4q16_other[2259] <- "Other"
mydata$c_s4q16_other[2260] <- "Other"
mydata$c_s4q16_other[2277] <- "Other"
mydata$c_s4q16_other[2283] <- "Other"
mydata$c_s4q16_other[2298] <- "Other"
mydata$c_s4q16_other[2299] <- "Other"
mydata$c_s4q16_other[2300] <- "Other"
mydata$c_s4q16_other[2309] <- "Other"
mydata$c_s4q17noresponse[100] <- "[language]"
mydata$c_s4q19noresponse[100] <- "[language]"
mydata$c_s4q20noresponse[100] <- "[language]"
mydata$c_s4q27[99] <- "[language]"
mydata$c_s4q27[118] <- "[language]"
mydata$c_s4q27[378] <- "[language]"
mydata$c_s4q27[526] <- "[language]"
mydata$c_s4q27[564] <- "[language]"
mydata$c_s4q27[579] <- "[language]"
mydata$c_s4q27[1120] <- "[language]"
mydata$c_s4q27[1425] <- "[language]"
mydata$c_s4q27[1455] <- "[language]"
mydata$c_s4q27[1680] <- "[language]"
mydata$c_s4q29noresponse[100] <- "[language]"
mydata$c_s4q29noresponse[874] <- "[language]"
mydata$c_s4q35[99] <- "[language]"
mydata$c_s4q35[2068] <- "She once cleaned the house of her grandmother for [amount of money] over the past year."
# !!!No GPS data
haven::write_dta(mydata, paste0(filename, "_PU.dta"))
haven::write_sav(mydata, paste0(filename, "_PU.sav"))
# Add report title dynamically
title_var <- paste0("DOL-ILAB SDC - ", filename)