Code:
* Example generated by -dataex-. To install: ssc install dataex clear input double avg_gross_hourly_ea float qdate byte prf_group float(gender ter) 18.86 224 6 3 1 26.34 232 2 3 1 17.28 223 2 2 2 21.42 218 6 3 1 12.95 229 4 3 2 26.3 212 2 2 1 17.82 221 2 1 2 14.81 215 5 1 2 22.54 233 6 1 1 11.19 228 4 1 2 15.18 231 3 1 2 12.29 233 4 2 2 17.15 241 4 2 1 15.65 225 6 3 2 21.49 235 3 3 2 37.76 241 1 3 2 . 239 6 2 2 21.78 214 3 3 1 26.31 225 2 2 1 14.43 208 5 3 1 . 225 5 2 2 29.59 225 6 2 1 41.86 237 6 2 1 19.67 219 2 3 2 36.5 228 6 2 1 44.11 226 1 2 2 43.53 213 6 3 1 11.2 217 5 1 2 . 218 1 1 2 . 221 3 2 2 20.73 226 6 1 1 26.4 218 2 3 1 43.87 225 1 2 1 10.26 213 4 3 2 14.22 225 3 1 2 39.72 239 2 1 1 23.69 211 2 1 1 12.18 225 4 3 2 33.14 217 1 2 1 17.56 226 3 2 2 27.92 225 3 3 1 12.59 221 5 1 1 32.4 239 1 3 2 . 238 6 3 2 14.11 242 5 3 2 32.25 219 1 3 2 12.28 229 5 3 2 20.68 208 3 3 1 22.05 213 1 1 2 41.56 227 1 2 1 24.78 214 2 1 1 21.33 211 6 2 1 12.06 217 3 2 2 40.32 226 1 2 1 29.81 219 2 2 1 . 228 1 1 2 16.96 233 4 1 1 30.7 234 6 3 1 14.21 220 5 2 1 17.63 225 3 3 2 35.37 209 1 1 2 15.57 233 4 1 1 21.97 224 1 1 2 22.46 239 3 2 1 12.28 218 3 3 2 14.74 216 3 1 2 16.43 240 4 3 1 20.62 224 3 1 1 . 221 6 1 1 17.3 233 3 1 2 17.36 216 2 1 2 12.26 211 5 2 2 22.42 240 6 3 2 18.86 236 4 1 1 8.7 209 5 1 2 22.42 225 6 2 2 28.31 208 3 2 1 21.86 225 2 3 1 13.27 212 4 1 1 42.24 232 1 1 1 14.02 215 3 2 2 16.51 242 3 3 2 14.24 221 4 3 1 26.82 217 2 2 1 10.45 231 5 2 2 22.86 233 6 1 1 14.34 208 6 2 2 35.19 221 1 2 2 44.58 237 1 1 1 14.13 210 4 3 1 17.35 235 4 2 1 20.66 243 3 3 1 22.55 225 3 3 1 16.75 242 6 1 2 22.55 221 2 2 1 32.53 225 1 3 2 14.48 209 5 1 1 15.74 241 3 2 2 12.43 231 4 1 2 26.64 236 2 1 2 end format %tq qdate label values prf_group prf_group label def prf_group 1 "unskilled", modify label def prf_group 2 "semiskilled", modify label def prf_group 3 "skilled", modify label def prf_group 4 "highly qualified", modify label def prf_group 5 "executive staff", modify label def prf_group 6 "total", modify label values gender gender label def gender 1 "Female", modify label def gender 2 "Male", modify label def gender 3 "Total", modify label values ter ter1 label def ter1 1 "Former territory of the Federal Republic", modify label def ter1 2 "New Lander", modify
Thanks to anyone you who can share some tips/advices on how to handle this dataset.
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