Processing RDS data

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
56	100	14250214	14250222	14250223	14250224	14250225	14256056	0	0	9056	1	41	1
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
64	500	14250401	14250258	14250259	14250260	14250261	14256065	0	0	9064	1	53	2
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
104	200	14250256	14250440	14250441	14250442	14250443	14256116	0	0	90104	1	45	1
105	20	14250428	14250501	14250502	14250503	14250504	14256301	0	0	90105	2	47	2
106	100	14250166	14250505	14250506	14250507	14250508	14250509	0	0	90106	1	40	1
107	250	14250261	14250511	14250512	14250513	14250514	14256201	0	0	90107	1	43	1
108	250	14250423	14250444	14250445	14250446	14250447	14256117	0	0	90108	2	63	1
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
113	100	14250390	14250469	14250470	14250471	14250472	14256123	0	0	90113	1	25	2
114	150	14250432	14250539	14250540	14250541	14250542	14256208	0	0	90114	1	54	1
115	500	9081	14250535	14250536	14250537	14250538	14256207	0	0	90115	1	52	1
116	200	14250389	14250477	14250478	14250479	14250480	14256124	0	0	90116	1	25	1
117	200	90112	14250550	14250551	14250552	14250553	14256126	0	0	90117	1	54	2
118	100	9038	14250554	14250555	14250556	14250557	14256127	0	0	90118	1	50	0
119	20	9099	14250481	14250482	14250483	14250484	14256128	0	0	90119	2	51	2
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
124	100	9118	14250519	14250520	14250521	14250522	14256203	0	0	90124	1	33	1
125	150	14250108	14250574	14250575	14250576	14250577	14256134	0	0	90125	1	23	2
126	200	14250444	14250578	14250579	14250580	14250581	14256135	0	0	90126	2	58	1
127	0	14250294	14250489	14250490	14250491	14250492	14256209	0	0	90127	1	73	1
128	20	14250056	14250497	14250498	14250499	14250500	14256211	0	0	90128	2	29	2
129	70	14250157	14250461	14250462	14250463	143000000	14256121	0	0	90129	1	48	1
130	400	9055	14250586	14250587	14250588	14250589	0	0	0	90130	1	31	2
131	25	14250420	14250582	14250583	14250584	14250585	14256136	0	0	90131	1	54	2
132	50	14250322	14250531	14250532	14250533	14250534	14256206	0	0	90132	1	45	1
133	200	14250580	14250546	14250547	14250548	14250549	14256125	0	0	90133	1	55	1
134	300	9034	14250590	14250591	14250592	14250593	14256138	0	0	90134	1	38	1
135	250	14250594	14250598	14250599	14250600	14250601	14256140	0	0	90135	1	24	1
136	50	14250595	14250602	14250603	14250604	14250605	14256141	0	0	90136	1	21	2
137	200	14250562	14250594	14250595	14250596	14250597	14250139	0	0	90137	1	25	1
138	100	14250550	14250606	14250607	14250608	14250609	14256212	0	0	90138	1	66	1
139	500	14250587	14250363	14250364	14250365	14250366	14256090	0	0	90139	1	42	1
140	150	14250338	14250610	14250611	14250612	14250613	14256213	0	0	90140	2	65	2
141	150	14250109	14250614	14250615	14250616	14250617	14256214	0	0	90141	1	44	1
142	600	14250551	14250622	14250623	14250624	14250625	14256216	0	0	90142	1	47	1
143	0	14250339	14250626	14250627	14250628	14250629	14256217	0	0	90143	2	29	1
144	100	14250506	14250618	14250619	14250620	14250621	14256215	0	0	90144	1	31	1
145	30	14250563	14250634	14250635	14250636	14250367	14256219	0	0	90145	2	26	2
146	500	9181	14250523	14250524	14250525	14250526	14256204	0	0	90146	1	39	1
147	150	14250564	14250630	14250631	14250632	14250633	14256218	0	0	90147	1	22	1
148	200	14250308	14250638	14250639	14250640	14250641	14256302	0	0	90148	1	47	1
149	300	14256094	14250642	14250643	14250644	14250645	14256303	0	0	90149	1	38	2
150	200	14250445	14250646	14250647	14250648	14250649	14256304	0	0	90150	1	76	1
151	150	14250307	14250650	14250651	14250652	14250653	14256305	0	0	90151	1	47	2
152	300	14250446	14250654	14250655	14250656	14250657	14256306	0	0	90152	1	49	1
153	100	90141	14250658	14250659	14250660	14250661	14256307	0	0	90153	1	41	1
154	500	14250195	14250663	14250664	14250665	14250666	14256308	0	0	90154	1	59	1
155	200	14250581	14250667	14250668	14250669	14250670	14256309	0	0	90155	1	64	1
156	200	14250507	14250371	14250672	14250673	14250674	14256310	0	0	90156	1	48	1
157	100	14256110	14250675	14250676	14250677	14250678	14256311	0	0	90157	2	66	1
158	100	14250490	14250679	14250680	14250681	14250682	14256312	0	0	90158	1	64	2
159	200	14250613	14250683	14250684	14250685	14250686	14256313	0	0	90159	2	64	2
160	125	14250571	14250687	14250688	14250689	14250690	14256314	0	0	90160	1	44	1
161	50	14250572	14250691	14250692	14250693	14250694	14256315	0	0	90161	1	51	1
162	500	14250514	14250699	14250700	14250701	14250702	14256371	0	0	90162	1	50	1
163	50	14250133	14250703	14250704	14250705	14250706	14256318	0	0	90163	1	35	1
164	200	9199	14250707	14250708	14250709	14250710	14256319	0	0	90164	1	54	2
165	500	14250663	14250695	14250696	14250697	14250698	14256316	0	0	90165	1	60	0
166	500	14250196	14250715	14250716	14250717	14250718	14256321	0	0	90166	1	74	1
167	100	14256313	14250719	14250720	14250721	14250722	14256322	0	0	90167	2	51	2
168	300	90160	14250723	14250724	14250725	14250726	14256323	0	0	90168	1	62	1
169	200	14250578	14250727	14250728	14250729	14250730	14256324	0	0	90169	1	48	1
170	100	14250221	14250731	14250732	14250733	14250734	14256325	0	0	90170	1	54	1
171	70	14250565	14250735	14250736	14250737	14250738	14256326	0	0	90171	1	31	1
172	300	14250615	14250739	14250740	14250741	14250742	14256327	0	0	90172	1	35	1
173	75	14250636	14250743	14250744	14250745	14250746	14256328	0	0	90173	1	29	1
174	300	14250356	14250408	14250409	14250410	14250411	14256100	0	0	90174	1	62	1
175	0	14250664	14250711	14250712	14250713	14250714	14256320	0	0	90175	1	52	0
176	500	14250658	14250751	14250752	14250753	14250751	14256330	0	0	90176	1	43	1
177	100	14256218	14250755	14250756	14250757	14250758	14256331	0	0	90177	1	20	1
178	100	14250610	14250759	14250760	14250761	14250762	14256332	0	0	90178	1	39	1
179	60	14250579	14250763	14250764	14250765	14250766	14256333	0	0	90179	2	39	1
180	125	14250169	14250767	14250768	14250769	14250770	14256334	0	0	90180	1	33	1
181	500	14250512	14250771	14250772	14250773	14250774	142565335	0	0	90181	1	44	2
182	0	14250725	14250775	14250776	14250777	14250778	14256336	0	0	90182	1	54	1
184	200	14256324	14250783	14250784	14250785	14250786	14256338	0	0	90184	1	48	1
185	200	14250561	14250787	14250788	14250789	14250790	14256339	0	0	90185	1	59	1
186	500	14250732	14250791	14250792	14250793	14250794	14256142	0	0	90186	1	52	1
187	600	0	14250795	14250796	14250797	14250798	14256163	0	0	90187	2	59	1
188	25	14250770	14250799	14250800	14250801	14250802	14256334	0	0	90188	1	36	1
189	80	14250730	14250807	14250808	14250809	14250810	14256146	0	0	90189	1	28	1
190	150	14250703	14250811	14250812	14250813	14250814	14256147	0	0	90190	1	29	1
191	120	0	14250815	14250816	14250817	14250818	14256147	0	0	90191	1	55	0
192	100	14256074	14250819	14250820	14250821	14250822	14256149	0	0	90192	2	44	1
193	150	14250570	14250823	14250824	14250826	14256150	14250825	0	0	90193	1	45	1
194	400	14250721	14250827	14250828	14250829	14250830	14256151	0	0	90194	0	42	1
195	60	14250683	14250831	14250832	14250833	14250834	14256152	0	0	90195	0	30	1
196	0	14250482	14250835	14250836	14250837	14250838	14256153	0	0	90196	1	50	2
197	500	14250774	14250843	14250844	14250845	14250846	14256155	0	0	90197	1	48	0
198	250	14250799	14250839	14250840	14250841	14250842	14256154	0	0	90198	1	55	1
199	0	14250155	14250960	14250961	14250962	14250963	14256221	0	0	90199	1	36	1
200	150	0	14250851	14250852	14250853	14250854	14256157	0	0	90200	1	28	1
201	25	14250694	14250847	14250848	14250849	14250850	14256156	0	0	90201	1	53	1
202	30	14250558	14250863	14250864	14250865	14250866	14256160	0	0	90202	1	34	1
203	50	14250783	14250867	14250868	14250869	14250870	14256161	0	0	90203	1	27	1
204	0	14250552	14250871	14250872	14250873	14250874	14256162	0	0	90204	2	49	0
205	30	14250810	14250875	14250876	14250877	14250878	14256163	0	0	90205	1	27	1
206	200	90155	14250880	14250881	14250882	14250883	14256164	0	0	90206	1	47	1
207	0	14250724	14250885	14250886	14250887	14250888	14256165	0	0	90207	1	69	1
208	30	14250871	14250888	14250889	14250890	14250891	14256166	0	0	90208	0	40	0
209	70	14250795	14250892	14250893	14250894	14250895	14256167	0	0	90209	1	29	2
210	0	14250729	14250900	14250901	14250902	14250903	14256192	0	0	90210	1	64	1
211	100	14250733	14250904	14250905	14250906	14250907	14256170	0	0	90211	1	63	1
212	100	14250295	14250908	14250909	14250910	14250911	14256171	0	0	90212	1	34	1
213	375	14250773	14250912	14250913	14250914	14250915	14256172	0	0	90213	2	40	1
214	50	14250892	14250916	14250917	14250918	14250919	14256345	0	0	90214	1	30	1
215	100	14250771	14250920	14250921	14250922	14250923	14256344	0	0	90215	1	42	1
216	100	14250900	14250952	14250953	14250954	14250955	14256180	0	0	90216	1	31	1
217	100	14250611	14250924	14250925	14250926	14250927	14256346	0	0	90217	1	62	1
218	50	14250838	14250896	14250897	14250898	14250899	14256174	0	0	90218	1	59	0
219	50	90210	14250859	14250860	14250861	14250862	14256159	0	0	90219	2	48	2
220	100	14250786	14250932	14250933	14250934	14250935	14256175	0	0	90220	1	49	1
221	400	14250923	14250515	14250516	14250517	14250518	14256202	0	0	90221	1	58	1
222	25	14250272	14250968	14250969	14250970	14250971	14256223	0	0	90222	1	55	1
223	200	14250296	14250855	14250856	14250856	14250858	14256158	0	0	90223	2	30	1
224	200	14250785	14250948	14250949	14250950	14250951	142560179	0	0	90224	1	38	1
225	800	14250920	14250957	14250958	14250959	14256220	14250956	0	0	90225	1	55	2
226	30	14250612	14250940	14250941	14250942	14250943	14256177	0	0	90226	1	35	1
227	400	14256211	14250936	14250937	14250938	14250939	14256176	0	0	90227	1	38	1
228	300	14250179	14250944	14250945	14250946	14250947	14256178	0	0	90228	2	51	1
229	100	90187	14250964	14250965	14250967	14256222	14250966	0	0	90229	1	47	2
230	100	14250560	14250972	14250973	14250974	14250975	14256224	0	0	90230	1	21	1
231	150	14250859	14250976	14250977	14250978	14250979	14256225	0	0	90231	1	68	1
232	40	14250948	14250980	14250981	14250982	14250983	14256223	0	0	90232	1	41	1
233	250	14250946	14251000	14251001	14251002	14251003	14256231	0	0	90233	1	44	2
234	300	14250492	14250984	14250985	14250986	14250987	14256227	0	0	90234	1	50	1
235	100	14250090	14250992	14250993	14250994	14250995	14256229	0	0	90235	2	53	1
236	100	14250178	14250996	14250997	14250998	14250999	142596230	0	0	90236	2	44	2
237	200	14250860	14251008	14251009	14251010	14251011	14256182	0	0	90237	1	74	1
238	0	14250555	14251012	14251013	14251014	14251015	14256183	0	0	90238	1	65	0
239	200	14250396	14251016	14251017	14251018	14251019	14256184	0	0	90239	2	29	1
240	75	14256035	14251020	14251021	14251022	14251023	14256185	0	0	90240	2	30	0
241	300	14250984	14251004	14251005	14251006	14251007	14256181	0	0	90241	1	53	1
242	200	14250861	0	0	0	0	0	0	0	90242	1	65	1
243	800	14250491	14251028	14251029	41251030	14251031	14256187	0	0	90243	1	65	1
244	400	14250734	14251036	14251037	14251038	14251039	14256189	0	0	90244	1	62	1
245	150	14250832	14251056	14251057	14251058	14251059	14256194	0	0	90245	0	45	2
246	400	14250731	14251060	14251061	14251062	14251063	14256200	0	0	90246	1	45	1
247	100	14250559	0	0	0	0	0	0	0	90247	2	62	1
248	500	14251056	0	0	0	0	0	0	0	90248	1	28	2
249	450	14250944	14251044	14251045	14251046	14251047	14256191	0	0	90249	1	40	1
250	600	14250413	0	0	0	0	0	0	0	90250	1	35	1
251	220	14250945	14251048	14251049	14251050	14251051	14256192	0	0	90251	2	30	1
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

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.