This imports  all variables and  patient's number  and not the column "normal".    Many messages I get of this type:   NOTE: Invalid data for x_fold in line 8 14-16. NOTE: Invalid data for normal in line 8 23-31. RULE:     ----+----1----+----2----+----3----+----4----+----5----+----6----+----7----+----8----+----9-- 8         "8.472[ppm]","0","MI","3.58542","0.00113","0","0.000[abn/ppm]","0","0","1","3.58542","0.024[       93  ppm]","1","0","","","0","0","0","" 126 fname=C:\Users\hp\Desktop\data\7-PROKIDN-002-T1_141020_buf121020.csv pat_id=002 filname=C:\Users\hp\Desktop\data\7-PROKIDN -002-T1_141020_buf121020.csv i=8 x="8.472[ppm x_fold=. class="MI" normal=. _ERROR_=1 _N_=1 NOTE: Invalid data for x_fold in line 9 14-16. NOTE: Invalid data for normal in line 9 23-31. 9         "8.215[ppm]","0","MI","6.38263","0.00202","0","0.000[abn/ppm]","0","0","1","6.38263","0.050[       93  ppm]","1","0","","","0","0","0","" 126 fname=C:\Users\hp\Desktop\data\7-PROKIDN -002-T1_141020_buf121020.csv pat_id=002 filname=C:\Users\hp\Desktop\data\7-PROKIDN -002-T1_141020_buf121020.csv i=. x="8.215[ppm x_fold=. class="MI" normal=. _ERROR_=1 _N_=2   Is it possible to make the variables horizontally?             ... View more

The name of the spreadsheet is long and consists of: location-study's name-patient's number- time of measurement-date of measurement-buffer: 7-PROKIDN-001-T1-150222_buf12102020    7-PROKIDN-001-T1: This part is the same for all patients with exception of patient's number ( for patient 17: 7-PROKIDN-017-T1). Everything else can change because of date of measurement and type of buffer.    Patients are 120 but not in sequence numbering.  Tables has identical structure, all variables are in the same order and only column 1 and 4 are needed .        ... View more

I did this mistake just last week. and claimed great work done by someone else to myself. When you answer worked I  rushed to say thank you and replied to myself. So embarrassing. I'm so sorry.  ... View more

The data actually has more than 200 variables. I was just making something looks like the real data. ... View more

proc import datafile="C:\Users\hp\Desktop\data\T1.002.csv" dbms=csv out=out; run; ... View more

The same number of measurements and same variables for all patients and in the same order. The variables are numeric because they are the peaks in the spectrum. I want to change these variables to Var001,..., Var200.    The analytical equipment works with JEOL format, so  when converting to csv the column col1 showed up but has no relevance. The data for 2 patients looks like this:   Patient1:   col1,variable,value 1,277 .5,28.3 2,276 .5,28 3,275 .5,27.7 4,274 .5,27.4 5,273 .5,27.1 6,272 .5,26.8 7,271 .5,26.5 8,270 .5,26.2 9,269 .5,25.9 10,268 .5,25.6 11,267 .5,25.3 12,266 .5,25 13,265 .5,24.7 14,264 .5,24.4 15,263 .5,24.1 16,262 .5,23.8 17,261 .5,23.5 18,260 .5,23.2 19,259 .5,22.9 20,258 .5,22.6 21,257 .5,22.3 23,256 .5,22 24,255 .5,21.7 25,254 .5,21.4 26,253 .5,21.1 27,252 .5,20.8 28,251 .5,20.5 29,250 .5,20.2 30,249 .5,19.9 31,248 .5,19.6 32,247 .5,19.3 33,246 .5,19 34,245 .5,18.7 35,244 .5,18.4 36,243 .5,18.1 37,242 .5,17.8 38,241 .5,17.5 39,240 .5,17.2 40,239 .5,16.9 41,238 .5,16.6 42,237 .5,16.3 43,236 .5,16 44,235 .5,15.7 45,234 .5,15.4 46,233 .5,15.1 47,232 .5,14.8 48,231 .5,14.5 49,230 .5,14.2 50,229 .5,13.9 51,228 .5,13.6 52,227 .5,13.3 53,226 .5,13 54,225 .5,12.7 55,224 .5,12.4 56,223 .5,12.1 57,222 .5,11.8 58,221 .5,11.5 59,220 .5,11.2 60,219 .5,10.9 61,218 .5,10.6 62,217 .5,10.3 63,216 .5,10 64,215 .5,9.7 65,214 .5,9.4 66,213 .5,9.1 67,212 .5,8.8 68,211 .5,8.5 69,210 .5,8.2 70,209 .5,7.9 71,208 .5,7.6 72,207 .5,7.3 73,206 .5,7 74,205 .5,6.7 75,204 .5,6.4 76,203 .5,6.1 77,202 .5,5.8 78,201 .5,5.5 79,200 .5,5.2 80,199 .5,4.9 81,198 .5,4.6 82,197 .5,4.3 83,196 .5,4 84,195 .5,3.7 85,194 .5,3.4 86,193 .5,3.1 87,192 .5,2.8 88,191 .5,2.5 89,190 .5,2.2 90,189 .5,1.9 91,188 .5,1.6 92,187 .5,1.3 93,186 .5,1 94,185 .5,0.7 95,184 .5,0.4 96,183 .5,0.1 97,182 .5,-0.2 98,181 .5,-0.5 99,180 .5,-0.8 100,179 .5,-1.1 101,178 .5,-1.4 102,177 .5,-1.7 103,176 .5,-2 104,175 .5,-2.3 105,174 .5,-2.6 106,173 .5,-2.9 107,172 .5,-3.2 108,171 .5,-3.5 109,170 .5,-3.8 110,169 .5,-4.1 111,168 .5,-4.4 112,167 .5,-4.7 113,166 .5,-5 114,165 .5,-5.3 115,164 .5,-5.6 116,163 .5,-5.9 117,162 .5,-6.2 118,161 .5,-6.5 119,160 .5,-6.8 120,159 .5,-7.1 121,158 .5,-7.4 122,157 .5,-7.7 123,156 .5,-8 124,155 .5,-8.3 125,154 .5,-8.6 126,153 .5,-8.9 127,152 .5,-9.2 128,151 .5,-9.5 129,150 .5,-9.8 130,149 .5,-10.1 131,148 .5,-10.4 132,147 .5,-10.7 133,146 .5,-11 134,145 .5,-11.3 135,144 .5,-11.6 136,143 .5,-11.9 137,142 .5,-12.2 138,141 .5,-12.5 139,140 .5,-12.8 140,139 .5,-13.1 141,138 .5,-13.4 142,137 .5,-13.7 143,136 .5,-14 144,135 .5,-14.3 145,134 .5,-14.6 146,133 .5,-14.9 147,132 .5,-15.2 148,131 .5,-15.5 149,130 .5,-15.8 150,129 .5,-16.1 151,128 .5,-16.4 152,127 .5,-16.7 153,126 .5,-17 154,125 .5,-17.3 155,124 .5,-17.6 156,123 .5,-17.9 157,122 .5,-18.2 158,121 .5,-18.5 159,120 .5,-18.8 160,119 .5,-19.1 161,118 .5,-19.4 162,117 .5,-19.7 163,116 .5,-20 164,115 .5,-20.3 165,114 .5,-20.6 166,113 .5,-20.9 167,112 .5,-21.2 168,111 .5,-21.5 169,110 .5,-21.8 170,109 .5,-22.1 171,108 .5,-22.4 172,107 .5,-22.7 173,106 .5,-23 174,105 .5,-23.3 175,104 .5,-23.6 176,103 .5,-23.9 177,102 .5,-24.2 178,101 .5,-24.5 179,100 .5,-24.8 180,99 .5,-25.1 181,98 .5,-25.4 182,97 .5,-25.7 183,96 .5,-26 184,95 .5,-26.3 185,94 .5,-26.6 186,93 .5,-26.9 187,92 .5,-27.2 188,91 .5,-27.5 189,90 .5,-27.8 190,89 .5,-28.1 191,88 .5,-28.4 192,87 .5,-28.7 193,86 .5,-29 194,85 .5,-29.3 195,84 .5,-29.6 196,83 .5,-29.9 197,82 .5,-30.2 198,81 .5,-30.5 199,80 .5,-30.8 200,79 .5,-31.1 201,78 .5,-31.4   Patient2:   col1,variable,value 1,277 .5,31.3 2,276 .5,31 3,275 .5,30.7 4,274 .5,30.4 5,273 .5,30.1 6,272 .5,29.8 7,271 .5,29.5 8,270 .5,29.2 9,269 .5,28.9 10,268 .5,28.6 11,267 .5,28.3 12,266 .5,28 13,265 .5,27.7 14,264 .5,27.4 15,263 .5,27.1 16,262 .5,26.8 17,261 .5,26.5 18,260 .5,26.2 19,259 .5,25.9 20,258 .5,25.6 21,257 .5,25.3 23,256 .5,25 24,255 .5,24.7 25,254 .5,24.4 26,253 .5,24.1 27,252 .5,23.8 28,251 .5,23.5 29,250 .5,23.2 30,249 .5,22.9 31,248 .5,22.6 32,247 .5,22.3 33,246 .5,22 34,245 .5,21.7 35,244 .5,21.4 36,243 .5,21.1 37,242 .5,20.8 38,241 .5,20.5 39,240 .5,20.2 40,239 .5,19.9 41,238 .5,19.6 42,237 .5,19.3 43,236 .5,19 44,235 .5,18.7 45,234 .5,18.4 46,233 .5,18.1 47,232 .5,17.8 48,231 .5,17.5 49,230 .5,17.2 50,229 .5,16.9 51,228 .5,16.6 52,227 .5,16.3 53,226 .5,16 54,225 .5,15.7 55,224 .5,15.4 56,223 .5,15.1 57,222 .5,14.8 58,221 .5,14.5 59,220 .5,14.2 60,219 .5,13.9 61,218 .5,13.6 62,217 .5,13.3 63,216 .5,13 64,215 .5,12.7 65,214 .5,12.4 66,213 .5,12.1 67,212 .5,11.8 68,211 .5,11.5 69,210 .5,11.2 70,209 .5,10.9 71,208 .5,10.6 72,207 .5,10.3 73,206 .5,10 74,205 .5,9.7 75,204 .5,9.4 76,203 .5,9.1 77,202 .5,8.8 78,201 .5,8.5 79,200 .5,8.2 80,199 .5,7.9 81,198 .5,7.6 82,197 .5,7.3 83,196 .5,7 84,195 .5,6.7 85,194 .5,6.4 86,193 .5,6.1 87,192 .5,5.8 88,191 .5,5.5 89,190 .5,5.2 90,189 .5,4.9 91,188 .5,4.6 92,187 .5,4.3 93,186 .5,4 94,185 .5,3.7 95,184 .5,3.4 96,183 .5,3.1 97,182 .5,2.8 98,181 .5,2.5 99,180 .5,2.2 100,179 .5,1.9 101,178 .5,1.6 102,177 .5,1.3 103,176 .5,1 104,175 .5,0.7 105,174 .5,0.4 106,173 .5,0.1 107,172 .5,-0.2 108,171 .5,-0.5 109,170 .5,-0.8 110,169 .5,-1.1 111,168 .5,-1.4 112,167 .5,-1.7 113,166 .5,-2 114,165 .5,-2.3 115,164 .5,-2.6 116,163 .5,-2.9 117,162 .5,-3.2 118,161 .5,-3.5 119,160 .5,-3.8 120,159 .5,-4.1 121,158 .5,-4.4 122,157 .5,-4.7 123,156 .5,-5 124,155 .5,-5.3 125,154 .5,-5.6 126,153 .5,-5.9 127,152 .5,-6.2 128,151 .5,-6.5 129,150 .5,-6.8 130,149 .5,-7.1 131,148 .5,-7.4 132,147 .5,-7.7 133,146 .5,-8 134,145 .5,-8.3 135,144 .5,-8.6 136,143 .5,-8.9 137,142 .5,-9.2 138,141 .5,-9.5 139,140 .5,-9.8 140,139 .5,-10.1 141,138 .5,-10.4 142,137 .5,-10.7 143,136 .5,-11 144,135 .5,-11.3 145,134 .5,-11.6 146,133 .5,-11.9 147,132 .5,-12.2 148,131 .5,-12.5 149,130 .5,-12.8 150,129 .5,-13.1 151,128 .5,-13.4 152,127 .5,-13.7 153,126 .5,-14 154,125 .5,-14.3 155,124 .5,-14.6 156,123 .5,-14.9 157,122 .5,-15.2 158,121 .5,-15.5 159,120 .5,-15.8 160,119 .5,-16.1 161,118 .5,-16.4 162,117 .5,-16.7 163,116 .5,-17 164,115 .5,-17.3 165,114 .5,-17.6 166,113 .5,-17.9 167,112 .5,-18.2 168,111 .5,-18.5 169,110 .5,-18.8 170,109 .5,-19.1 171,108 .5,-19.4 172,107 .5,-19.7 173,106 .5,-20 174,105 .5,-20.3 175,104 .5,-20.6 176,103 .5,-20.9 177,102 .5,-21.2 178,101 .5,-21.5 179,100 .5,-21.8 180,99 .5,-22.1 181,98 .5,-22.4 182,97 .5,-22.7 183,96 .5,-23 184,95 .5,-23.3 185,94 .5,-23.6 186,93 .5,-23.9 187,92 .5,-24.2 188,91 .5,-24.5 189,90 .5,-24.8 190,89 .5,-25.1 191,88 .5,-25.4 192,87 .5,-25.7 193,86 .5,-26 194,85 .5,-26.3 195,84 .5,-26.6 196,83 .5,-26.9 197,82 .5,-27.2 198,81 .5,-27.5 199,80 .5,-27.8 200,79 .5,-28.1 201,78 .5,-28.4     I want to replace these variables by:   Var001 Var002 Var003 Var004 Var005 Var006 Var007 Var008 Var009 Var010 Var011 Var012 Var013 Var014 Var015 Var016 Var017 Var018 Var019 Var020 Var021 Var022 Var023 Var024 Var025 Var026 Var027 Var028 Var029 Var030 Var031 Var032 Var033 Var034 Var035 Var036 Var037 Var038 Var039 Var040 Var041 Var042 Var043 Var044 Var045 Var046 Var047 Var048 Var049 Var050 Var051 Var052 Var053 Var054 Var055 Var056 Var057 Var058 Var059 Var060 Var061 Var062 Var063 Var064 Var065 Var066 Var067 Var068 Var069 Var070 Var071 Var072 Var073 Var074 Var075 Var076 Var077 Var078 Var079 Var080 Var081 Var082 Var083 Var084 Var085 Var086 Var087 Var088 Var089 Var090 Var091 Var092 Var093 Var094 Var095 Var096 Var097 Var098 Var099 Var100 Var101 Var102 Var103 Var104 Var105 Var106 Var107 Var108 Var109 Var110 Var111 Var112 Var113 Var114 Var115 Var116 Var117 Var118 Var119 Var120 Var121 Var122 Var123 Var124 Var125 Var126 Var127 Var128 Var129 Var130 Var131 Var132 Var133 Var134 Var135 Var136 Var137 Var138 Var139 Var140 Var141 Var142 Var143 Var144 Var145 Var146 Var147 Var148 Var149 Var150 Var151 Var152 Var153 Var154 Var155 Var156 Var157 Var158 Var159 Var160 Var161 Var162 Var163 Var164 Var165 Var166 Var167 Var168 Var169 Var170 Var171 Var172 Var173 Var174 Var175 Var176 Var177 Var178 Var179 Var180 Var181 Var182 Var183 Var184 Var185 Var186 Var187 Var188 Var189 Var190 Var191 Var192 Var193 Var194 Var195 Var196 Var197 Var198 Var199 Var200 ... 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I accepted the answer provided by  "mkeintz".   Answer was clear, straighforward and solves the questions from the very begining, and applies to any dimension of data that has identical structure as mine.    It  was a simple and irrelevant mistake from my part when applying the codes on the original size and  names of variables.    So the accepted answer is as initially provided by "mkeintz":    data want (drop=_: time); set have (where=(time='post')) have (where=(time='Baseline')) have (where=(time='Control')) ; by subject; array vars var1-var4; array diftwo _diftwo_1 - _diftwo_4; do over vars; diftwo=dif2(vars); vars=dif(vars); end; length contrast $19; contrast=catx(' ',time,'-',lag(time)); if contrast in('Control - Baseline','Baseline - post') then output; if time='Control' and lag2(time)='post' then do; contrast='Control - post'; do over vars; vars=diftwo; end; output; end; run; data want (drop=time); array var var1-var4; do contrast='Control - Baseline','Control - post','Baseline - post'; do until (dif(e1) or dif(e2) or dif(e3)); if contrast='Control - Baseline' then do; set have (where=(time='Baseline')) have (where=(time='Control')) end=e1; by subject; end; else if contrast='Control - post' then do; set have (where=(time='post')) have (where=(time='Control')) end=e2; by subject; end; else if contrast='Baseline - post' then do; set have (where=(time='post')) have (where=(time='Baseline')) end=e3; by subject; end; do over var; var=dif(var); end; if last.subject then output; end; end; run;   ... View more

Your response worked very well. I mistakenly accepted my reply. Please tell me how  can I fix this mistake so the right answer is credited to you. ... View more

Here is the data for 3 observations:   SUBJECT,SUBJECT,TIME,VAR1,VAR2,VAR3,VAR4 1,1,Control ,17.20,18.50,10.10,-6.20 2,2,Control ,16.30,2.30,8.20,5.00 3,2,Control ,4.50,4.50,6.30,2.00 1,1,Baseline ,-48.09,-37.88,53.97,144.39 2,2,Baseline ,-49.34,-38.86,54.90,147.33 3,3,Baseline ,-50.59,-39.84,55.82,150.27 1,1,post,-110.52,-86.74,100.26,291.53 2,2,post,-111.77,-87.72,101.18,294.47 3,3,post,-113.02,-88.69,102.11,297.41   Samples are paired. Classification using my mutivariate method requires to calculate the difference in observations between timepoints. For example for patient 1 I have 3 effects per VAR1.   I did the substraction for only 3 samples:     SUBJECT VAR1 VAR2 VAR3 VAR4 1 (control-baseline) 65.29 56.38 -43.87 -150.59 2 (control-baseline) 65.64 41.16 -46.70 -142.33 3 (control-baseline) 55.09 44.34 -49.52 -148.27 1 (control-post) 127.72 105.24 -90.16 -297.73 2 (control-post) 128.07 90.02 -92.98 -289.47 3 (control-post) 117.52 93.19 -95.81 -295.41 1 (baseline-post) 62.43 48.86 -46.29 -147.14 2 (baseline-post) 62.43 48.86 -46.29 -147.14 3 (baseline-post) 62.43 48.86 -46.29 -147.14 ... View more

Sorry for not explaining straight to the point what I needed.  It was so simple to say I want to keep the last 3 digits.  ... View more

I didn't well explain what I needed. As you put it: S[digit]-[number]:  I needed only to keep the "number".  I have another column in the dataset to control when measurement was taken. This is longitudinal study  so I should have 3 identical ID number per subjet.      ... View more