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sdwfrost/nyjazz.txt

Last active August 29, 2015 14:02
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nyjazz.txt
ID netsize mycoupon coupon1 coupon2 coupon3 coupon4 coupon5 coupon6 coupon7 coupon8 gender.mf age airplay.yn
1 350 0 14250004 14250005 14250006 14256002 0 0 0 901 1 40 1
2 0 0 14250007 14250008 14250009 14256003 0 0 912 902 1 64 1
3 585 0 14250010 14250011 14250012 14256004 0 0 0 903 2 41 1
4 400 0 14250025 14250026 14250027 14256009 0 0 0 904 2 77 0
5 150 0 14250022 14250023 14250023 14256008 0 0 0 905 1 33 1
6 100 0 14250028 14250029 14250030 14256010 0 0 916 906 1 31 2
7 300 0 14250016 14250017 14250018 14256006 0 0 0 907 1 70 1
8 700 0 14250040 14250041 14250042 14256014 0 0 0 908 1 49 1
9 300 14256002 14250013 14250014 14250015 14256005 0 0 0 909 2 38 1
10 200 14250013 14250019 14250020 14250021 14256007 0 0 0 9010 1 37 1
11 200 14250005 14250031 14250032 14250033 14256011 0 0 0 9011 1 50 1
12 300 14250004 14250034 14250035 14250036 14256012 0 0 0 9012 1 41 1
13 100 14250012 14250103 14250102 14250101 14256101 0 0 0 9013 2 51 1
14 383 14250026 14250037 14250038 14250039 14256013 0 0 0 9014 2 46 1
15 700 14256007 14250043 14250044 14250045 14256015 0 0 0 9015 2 101 2
16 80 14250043 14250046 14250047 14250048 14256017 0 0 0 9016 2 50 1
17 300 14250033 14250104 14250105 14250106 14256016 0 0 0 9017 1 41 1
18 200 0 14250049 14250050 14250051 14256018 0 0 9118 9018 1 35 1
19 100 14250010 14250116 14250117 14250118 14256020 0 0 0 9019 1 34 1
20 400 14250029 14250107 14250108 14250109 14256102 0 0 0 9020 1 42 1
21 200 14250105 14250111 14250112 14250110 14256103 0 0 0 9021 1 33 2
22 150 14250037 14250113 14250114 14250115 14256019 0 0 0 9022 1 50 1
23 100 14250030 14250052 14250053 14250054 14256021 0 0 0 9023 1 34 1
24 150 14256004 14250057 14250056 14250055 14256022 0 0 0 9024 2 60 2
25 200 14250054 14250058 14250059 14250060 14256023 0 0 0 9025 1 34 1
26 50 14250053 14250061 14250062 14250063 14256024 0 0 0 9026 1 45 2
27 300 14250025 14250064 14250065 14250066 14256025 0 0 0 9027 2 40 1
28 850 14256009 14250122 14250123 14250124 14256027 0 0 0 9028 2 59 1
29 0 14256003 0 0 0 0 0 0 0 9029 2 0 1
30 500 14250066 14250067 14250068 14250069 900 0 0 0 9030 1 54 1
31 30 14250113 14250070 14250071 14250072 14256028 0 0 0 9031 1 48 1
32 200 14256025 14250125 14250126 14250127 14256034 0 0 0 9032 2 41 2
33 300 14256018 14250089 14250090 14250091 14256035 0 0 0 9033 2 46 1
34 500 14250059 14250098 14250099 14250100 14256038 0 0 0 9034 1 31 1
35 500 14250098 14250095 14250096 14250097 14256037 0 0 0 9035 1 52 1
36 25 14250049 14250133 14250134 14250135 14250136 14256040 0 0 9036 1 29 1
37 150 14250051 14250137 14250138 14250139 14250140 14256041 0 9137 9037 1 34 1
38 50 14250050 14250166 14250167 14250168 14250169 14256047 0 0 9038 1 42 1
39 500 14250089 14250162 14250163 14250164 14250165 14256046 0 0 9039 1 43 1
40 150 14256011 14250141 14250142 14250143 14250144 14256042 0 0 9040 2 31 1
41 150 14250007 14250145 14250146 14250147 14250148 14256043 0 0 9041 1 58 1
42 300 14250028 14250154 14250155 14250156 14250157 14256045 0 0 9042 1 38 1
43 300 14256043 14250150 14250151 14250152 14250153 14256044 0 0 9043 2 61 1
44 300 14256022 14250170 14250171 14250172 14250173 14256048 0 0 9044 2 48 1
45 0 902 14250174 14250175 14250176 14250177 14256049 0 0 9045 1 80 1
46 200 14250039 14250203 14250204 14250205 14250206 14256104 0 0 9046 2 53 1
47 250 14256010 14250201 14250202 14250207 14250208 14250209 0 0 9047 1 36 1
48 400 14250162 14250210 14250211 14250212 14250213 14256105 0 0 9048 1 59 1
49 150 14256013 14250178 14250179 14250180 14250181 14256106 0 0 9049 2 44 1
50 100 14250038 14250182 14250183 14250184 14250185 14256107 14250401 9150 9050 2 54 1
51 0 14250206 14250186 14250187 14250188 14250189 14256050 0 0 9051 1 59 1
52 250 14250211 14250190 14250191 14250192 14250193 14256051 0 0 9052 1 64 1
53 100 0 14250194 14250195 14250196 14250197 14256052 0 0 9053 1 49 1
54 200 14250210 14250214 14250215 14250216 14250217 14256053 0 0 9054 1 50 1
55 0 14250194 14250218 14250219 14250220 14250221 14256055 0 0 9055 1 52 1
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57 150 14250183 14250226 14250227 14250228 14250229 14256057 0 0 9057 1 70 1
58 150 14250182 14250230 14250231 14250232 14250233 14256058 0 0 9058 1 29 1
59 150 14256107 14250234 14250235 14250236 14250237 14256059 0 0 9059 2 40 1
60 600 9050 14250242 14250243 14250244 14250245 14256061 0 0 9060 2 48 2
61 60 14250185 14250246 14250247 14250248 14256062 0 0 0 9061 1 49 1
62 300 14250184 14250250 14250251 14250252 14250253 14256063 0 0 9062 2 46 1
63 50 14256052 14250254 14250255 14250256 14250257 14256064 0 0 9063 2 56 1
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65 300 14256065 14250262 14250263 14250264 14250265 14256066 0 0 9065 2 39 2
66 100 14250205 14250266 14250267 14250268 14250269 14256067 0 0 9066 2 55 1
67 700 14250065 14250270 14250271 14250272 14250273 14256068 0 0 9067 2 61 1
68 100 14250138 14250274 14250275 14250276 14250277 14256069 0 0 9068 1 35 1
69 300 9037 14250278 14250279 14250280 14250281 14256070 0 0 9069 2 27 1
70 200 14250243 14250282 14250283 14250284 14250285 14256071 0 0 9070 1 49 1
71 200 9061 14250290 14250291 14250292 14256073 0 0 0 9071 1 55 1
72 100 14250253 14250294 14250295 14250296 14250297 14256074 0 0 9072 2 63 1
73 150 9071 14250298 14250299 14250300 14250301 1425075 0 0 9073 1 58 1
74 20 14250301 14250302 14250303 14250304 14250305 14256076 0 0 9074 1 47 1
75 0 14250297 14250306 14250307 14250308 14250309 14256077 0 0 9075 1 54 1
76 200 14250305 14250310 14250311 14250312 14250313 14256078 0 0 9076 1 30 1
77 500 14250309 14250314 14250315 14250316 14250317 14256079 0 0 9077 1 54 1
78 100 14250310 14250318 14250319 14250320 14250321 14256080 0 0 9078 1 59 1
79 35 14256104 14250322 14250324 14250325 14256081 0 0 0 9079 2 32 2
80 700 14250197 14250326 14250327 14250328 14250329 14256082 0 0 9080 1 64 1
81 70 14256056 14250286 14250287 14250288 14250289 14256072 0 9181 9081 2 43 1
82 300 14250107 14250330 14250331 14250332 14250333 14256083 0 0 9082 1 46 1
83 200 14250317 14250334 14250335 14250336 14250337 14256084 0 0 9083 1 60 1
84 500 14250258 14250338 14250339 14250340 14250341 14256085 0 0 9084 1 44 1
85 125 14256064 14250342 14250343 14250345 14250346 142560347 0 0 9085 1 57 1
86 500 14250304 14250355 14250356 14250367 14250378 14256088 0 0 9086 1 61 2
87 500 14250234 14250347 14250348 14250349 14250350 14256086 0 0 9087 2 32 1
88 250 9137 14250359 14250360 14250361 14250362 14256089 0 0 9088 1 30 1
89 100 14250064 14250351 14250352 14250353 14250354 14256087 0 0 9089 1 47 2
90 200 14250282 14250375 14250376 14250377 14250378 14256093 0 0 9090 1 42 1
91 75 14250262 14250379 14250380 14250381 14250382 14256094 0 0 9091 0 44 1
92 30 9070 14250383 14250384 14250385 14250386 14256095 0 0 9092 1 30 1
93 0 906 14250387 14250388 14250389 14250390 14256096 0 0 9093 1 73 1
94 150 14250264 14250391 14250392 14250393 14250394 14256097 0 0 9094 1 32 1
95 150 14250209 14250395 14250396 14250397 14250398 14256098 0 0 9095 2 31 1
96 200 14250259 14250404 14250405 14250406 14250407 14256099 0 0 9096 2 30 1
97 400 9018 14250412 14250413 14250414 14250415 14256109 0 0 9097 1 39 1
98 0 9150 14250416 14250417 14250418 14250419 14256110 0 0 9098 1 59 1
99 0 14250267 14250420 14250421 14250422 14250423 14256111 0 9199 9099 2 76 2
100 100 912 14250424 14250425 14250426 14250427 14256112 0 0 90100 1 65 1
101 0 14250422 14250428 14250429 14250430 14250431 14256113 0 0 90101 1 59 1
102 100 14250318 14250437 14250436 14250438 14250349 14256115 0 0 90102 1 45 1
103 100 14250436 14250432 14250433 14250434 14250435 14256114 0 0 90103 1 55 1
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109 50 14250388 14250448 14250449 14250450 14250451 14256118 0 0 90109 1 53 1
110 60 14250387 14250452 14250453 14250454 14250455 14256119 0 0 90110 1 32 2
111 100 916 14250456 14250457 14250458 14250459 14256120 0 0 90111 2 25 2
112 500 14256112 14250511 900 14250513 900 14256201 0 0 90112 2 49 1
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120 150 14250278 14250558 14250559 14250560 14250561 14256129 0 0 90120 2 55 1
121 100 14250505 14250562 14250563 14250564 14250565 14256130 0 0 90121 1 30 2
122 90 14250181 14250566 14250567 14250568 14250569 14256131 0 0 90122 2 55 2
123 0 14250168 14250570 14250571 14250572 14250573 14256132 0 0 90123 1 50 1
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127 0 14250294 14250489 14250490 14250491 14250492 14256209 0 0 90127 1 73 1
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129 70 14250157 14250461 14250462 14250463 143000000 14256121 0 0 90129 1 48 1
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252 300 14251046 14251076 14251077 14251078 14251079 14256198 0 0 90252 2 41 1
253 300 14251078 14251080 14251081 14251082 14251083 14256199 0 0 90253 1 33 1
254 70 14251077 0 0 0 0 0 0 0 90254 1 52 1
255 500 14256213 0 0 0 0 0 0 0 90255 2 67 1
256 100 14250414 0 0 0 0 0 0 0 90256 1 48 1
257 400 14256178 0 0 0 0 0 0 0 90257 2 42 1
258 100 14251047 0 0 0 0 0 0 0 90258 2 41 1
259 100 14251045 0 0 0 0 0 0 0 90259 1 39 1
260 50 14256189 0 0 0 0 0 0 0 90260 2 56 1
261 300 14250588 0 0 0 0 0 0 0 90261 1 49 1
262 75 14250831 0 0 0 0 0 0 0 90262 1 36 2
263 500 14250156 0 0 0 0 0 0 0 90263 1 46 1
264 40 14250893 14251040 14251041 14251042 14251043 14256190 0 0 90264 1 27 1
266 400 14250862 0 0 0 0 0 0 0 90266 1 55 1
Raw
processrds.Rmd
Data collected in RDS studies is often messy, especially when information on coupons given out and received is in hard copy, then entered into a computer later. A while ago, I wrote some code to process RDS data to check for some common errors. This doesn't get rid of all of them, by any means, but will catch quite a few. This code, and the dataset, are available as a [gist](https://gist.github.com/sdwfrost/1bb5f41d4dac317be22c) from my Github site.
# The data handling code
Firstly, here is the processing code. It's fairly ugly and inefficient, as I wasn't as familiar with R as I am now.
```{r}
prepareDataset <- function(data,idColumnName,netsizeColumnName,couponColumnName,seedCoupon,couponList){
result <- matchIds(data,idColumnName,couponColumnName,seedCoupon,couponList)
result$rds.size <- as.double(data[netsizeColumnName][,1])
if(length(result$rds.size[result$rds.size<1])>0){
print("Missing values/Network size of less than one found")
}
result
}
convertToFactor <- function(data,x){
data[x] <- as.factor(data[x])
}
makeCouponFactor <- function(data,couponColumnName){
couponVector <- data[couponColumnName][,1]
f <- rep(NA,length(couponVector))
for(i in 1:length(couponVector)){
f[i] <- strsplit(as.character(couponVector[i]),split="")[[1]][1]
}
as.factor(f)
}
checkForDups <- function(couponVector,seedCoupon){
tb <- table(couponVector)
dupCoupons <- names(tb)[tb>1]
dupCoupons[dupCoupons!=as.character(seedCoupon)]
}
matchIds <- function(data,idColumnName,couponColumnName,seedCoupon,couponList){
# Extract ID numbers; I convert to characters, as the code will fail if factors are used
toId <- as.character(data[idColumnName][,1])
numIds <- length(toId)
# Extract couponnumbers
couponVector <- data[couponColumnName][,1]
# Find seeds and replace their coupon number with missing
couponRecvdList=list()
for(i in 1:numIds){
if(paste(couponVector[i])==paste(seedCoupon)){
couponRecvdList[paste(toId[i])]=as.character(NA)
}
else{
couponRecvdList[paste(toId[i])]=paste(couponVector[i])
}
}
# Generate a list of all coupon ids, and the participant ids they were given to
couponGivenList=list()
for(i in 1:length(couponList)){
cupList=data[couponList[i]][,1]
for(j in 1:numIds){
cupNum=cupList[j][1]
if(is.na(cupNum)==F){
couponGivenList[[paste(cupNum)]]=toId[j]
}
}
}
# Cross-match coupons for each individual
# Sanity check; make sure that each coupon received was actually given out by someone
# Need to add check that coupon wasn't received twice
badCoupons=c()
matchedIdList=list()
seedList=list()
for(i in 1:length(couponRecvdList)){
tmpCoupon=couponRecvdList[i]
if(!is.na(tmpCoupon)){
matchedCouponNumber=intersect(tmpCoupon,names(couponGivenList))
validCoupon=(matchedCouponNumber==tmpCoupon)
if(length(validCoupon)==0){
badCoupons=c(badCoupons,tmpCoupon)
}
else{
matchedId=couponGivenList[matchedCouponNumber]
matchedIdList[names(tmpCoupon)]=matchedId
seedList[names(tmpCoupon)]=0
}
}
else{
matchedIdList[names(tmpCoupon)]=as.character(NA)
seedList[names(tmpCoupon)]=1
}
}
if(length(badCoupons)>0){
print("Bad coupons")
print(badCoupons)
print("Invalid coupons detected")
return()
}
# Assemble results
result=data
result$rds.toId=as.character(toId)
result$rds.fromId=as.character(matchedIdList)
result$rds.seed=as.integer(seedList)
result
}
```
This will return a data frame with three additional columns, representing the subject identifiers for the recruit (`rds.toId`), recruiter (`rds.fromID`), and a flag indicating whether the subject is a seed or not (`rds.seed`).
# An example: the New York Jazz dataset
These data are from the well-known NYC jazz musician dataset by [Heckathorn and Jeffri](http://www.respondentdrivensampling.org/reports/Heckathorn.pdf). These data are a subset of the full data, in a tab-delimited file that was generated by hand from the original RDSAT file.
```{r}
nyjazz <- read.table("nyjazz.txt",header=T,row.names=NULL,sep="\t")
head(nyjazz)
```
I then attempt to process the data. The arguments are as follows:
1. The name of the dataset
2. The name of the ID of the respondent
3. The name of the column with network size
4. The column of the coupon received}
5. The numeric code for a seed coupon
6. A vector of column names corresponding to the coupons given out to each participant
```{r}
nyjazz.prep <- prepareDataset(nyjazz,"ID","netsize","mycoupon",0,c("coupon1","coupon2","coupon3","coupon4","coupon5","coupon6","coupon7","coupon8"))
```
Note that this doesn't identify any bad coupons, but does raise a warning about some undesirable values for network size.
```{r}
table(nyjazz$netsize,exclude=NULL)
```
As numeric codes are used to represent missing data in RDSAT, one has to be careful. These may represent missing network sizes or real reports of no ties.
To see what happens when individuals come in with a coupon that is not given out, let's omit one of the columns.
```{r}
prepareDataset(nyjazz,"ID","netsize","mycoupon",0,c("coupon1","coupon2","coupon3","coupon4","coupon5","coupon6","coupon7"))
```
This now gives a list of IDs, with their coupon numbers, which one should then go and check. This code doesn't check for whether two people come in with the same coupon, but this should be apparent when one comes to plotting out the RDS recruitment tree.
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