Hi all
I have a data where the variables levels are in SPANISH language, what is the efficient way to convert the labels into English please
I have pasted few variables from the data using the dataex command please
------------------ copy up to and including the previous line ------------------
Listed 100 out of 14161 observations
Use the count() option to list more
I have a data where the variables levels are in SPANISH language, what is the efficient way to convert the labels into English please
I have pasted few variables from the data using the dataex command please
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input double(tepsi_int_coo tepsi_int_len tepsi_int_mot tepsi_int_t tvip_pb tvip_pt tvip_int asq_pb_6m asq_int_6m asq_pb_12m asq_int_12m asq_pb_18m asq_int_18m cbcl1_pb_1 cbcl1_pb_2 cbcl1_pb_3 cbcl1_pb_4 cbcl1_pb_5 cbcl1_pb_6) 1 1 1 1 28 101 3 . . . . . . 0 6 3 2 0 3 1 1 1 1 26 101 3 . . . . . . 7 5 4 2 1 5 1 1 1 1 38 116 5 . . . . . . 6 7 4 3 4 3 1 1 1 1 38 116 5 . . . . . . 6 6 2 5 6 5 1 1 1 1 39 114 4 . . . . . . 3 4 3 2 2 2 1 1 1 1 15 84 1 . . . . . . 0 3 2 2 2 6 1 1 1 1 31 104 3 . . . . . . 5 4 6 2 7 4 1 1 1 1 25 100 3 . . . . . . 4 6 3 3 6 5 1 1 1 1 33 107 4 . . . . . . 0 1 2 1 0 5 1 1 1 1 31 107 4 . . . . . . 6 5 4 2 2 6 1 1 1 1 26 101 3 . . . . . . 2 2 5 2 0 5 1 1 1 1 31 107 4 . . . . . . 9 10 7 3 5 6 1 1 1 1 44 124 5 . . . . . . 3 6 7 2 1 6 1 1 1 1 28 104 3 . . . . . . 2 5 1 1 1 3 2 1 1 1 30 106 4 . . . . . . 3 2 1 0 1 4 1 1 1 1 39 118 5 . . . . . . 5 4 2 4 1 5 1 1 2 1 25 103 3 . . . . . . 4 7 8 4 4 5 1 1 1 1 17 93 2 . . . . . . 5 6 1 6 2 4 1 1 1 1 2 70 1 . . . . . . 5 3 7 2 0 8 1 1 3 1 27 106 4 . . . . . . 0 1 3 4 2 5 1 1 1 1 46 130 5 . . . . . . 7 7 7 3 4 4 1 1 1 1 28 107 4 . . . . . . 7 4 5 3 3 7 1 1 1 1 35 116 5 . . . . . . 0 3 1 7 2 4 1 1 1 1 24 104 3 . . . . . . 6 6 2 6 4 6 1 1 1 1 47 134 6 . . . . . . 4 4 1 2 3 2 1 1 1 1 9 82 1 . . . . . . 3 8 0 4 8 6 1 1 1 1 11 86 2 . . . . . . 5 8 4 7 5 5 1 1 1 1 31 111 4 . . . . . . 1 4 0 2 3 5 1 1 1 1 15 94 3 . . . . . . 3 2 3 0 1 3 1 2 1 1 12 90 2 . . . . . . 1 3 5 8 2 3 1 3 1 2 12 90 2 . . . . . . 3 3 5 1 0 3 1 1 1 1 17 98 3 . . . . . . 3 3 2 1 2 3 1 1 1 1 3 77 1 . . . . . . 6 6 1 1 0 5 2 1 1 1 25 108 4 . . . . . . 3 4 5 8 10 5 1 1 1 1 13 90 2 . . . . . . 0 2 1 2 0 0 1 1 1 1 3 74 1 . . . . . . 3 3 4 0 6 7 2 1 1 1 3 79 1 . . . . . . 1 5 3 2 2 5 1 1 2 1 17 98 3 . . . . . . 4 2 2 3 3 4 1 1 1 1 15 94 3 . . . . . . 0 2 2 2 1 3 2 1 2 2 6 82 1 . . . . . . 1 5 1 3 3 5 1 1 1 1 47 139 6 . . . . . . 6 0 11 3 7 4 1 1 1 1 22 107 4 . . . . . . 8 10 5 4 4 7 1 1 1 1 11 93 2 . . . . . . 9 13 3 10 10 8 3 1 1 1 30 118 5 . . . . . . 7 6 3 4 6 3 1 1 2 1 15 97 3 . . . . . . 2 3 0 5 6 7 1 2 2 2 15 98 3 . . . . . . 2 5 5 3 1 3 1 1 1 1 24 110 4 . . . . . . 3 7 4 4 0 5 1 1 1 1 17 99 3 . . . . . . 6 8 7 4 1 4 1 1 1 1 9 91 2 . . . . . . 4 1 6 4 3 5 1 1 1 1 15 98 3 . . . . . . 0 2 1 4 0 6 2 1 1 1 26 111 4 . . . . . . 2 0 5 2 0 1 1 1 1 1 20 107 4 . . . . . . 1 3 4 3 2 3 1 3 2 2 2 82 1 . . . . . . 5 4 5 0 4 6 1 1 1 1 26 114 4 . . . . . . 0 0 2 4 0 0 1 1 1 1 19 106 4 . . . . . . 9 8 5 6 0 6 1 1 1 1 36 130 5 . . . . . . 2 4 0 1 4 3 1 1 1 1 31 124 5 . . . . . . 0 4 3 8 6 5 1 1 1 1 15 103 3 . . . . . . 10 12 8 7 5 8 2 1 1 1 0 81 1 . . . . . . 5 10 4 3 2 6 1 1 1 1 17 104 3 . . . . . . 3 6 3 2 6 5 1 1 1 1 6 89 2 . . . . . . 1 4 4 4 1 3 3 3 3 3 0 82 1 . . . . . . 9 6 6 11 4 8 1 1 1 1 35 131 6 . . . . . . 0 0 0 1 3 5 1 1 1 1 14 102 3 . . . . . . 5 8 2 7 2 6 1 1 2 1 20 111 4 . . . . . . 5 6 3 6 1 5 1 1 1 1 23 115 5 . . . . . . 2 5 2 2 0 2 2 1 2 2 17 107 4 . . . . . . 0 2 3 2 0 6 1 1 1 1 24 118 5 . . . . . . 2 5 1 2 5 5 1 1 1 1 29 126 5 . . . . . . 0 4 3 1 1 3 1 1 1 1 24 118 5 . . . . . . 7 4 5 6 1 6 3 3 3 3 0 86 2 . . . . . . 4 4 4 3 1 2 2 3 3 3 0 87 2 . . . . . . 2 5 1 7 0 2 1 2 2 1 0 87 2 . . . . . . 6 8 13 4 7 5 2 2 1 2 4 89 2 . . . . . . 0 2 4 4 3 3 1 2 1 1 15 107 4 . . . . . . 4 5 3 4 2 4 1 1 1 1 8 98 3 . . . . . . 4 7 4 8 0 4 1 2 1 1 0 87 2 . . . . . . 7 10 8 3 2 5 1 1 1 1 15 108 4 . . . . . . 3 4 1 1 1 6 1 2 1 1 3 93 2 . . . . . . 1 4 3 4 1 3 2 2 1 2 0 88 2 . . . . . . 5 8 2 8 4 6 1 1 1 1 23 121 5 . . . . . . 9 3 7 5 5 7 1 1 1 1 3 90 2 . . . . . . 0 4 4 3 4 2 1 1 1 1 4 92 2 . . . . . . 2 1 8 2 0 6 1 1 1 1 10 101 3 . . . . . . 0 4 3 2 2 4 1 1 1 1 14 107 4 . . . . . . 6 6 4 5 2 7 1 1 1 1 3 93 2 . . . . . . 7 6 0 8 4 7 1 1 1 1 0 88 2 . . . . . . 0 2 2 3 1 2 1 2 1 1 23 123 5 . . . . . . 4 5 2 2 4 3 2 2 2 3 4 95 3 . . . . . . 5 5 7 2 6 2 1 1 1 1 3 94 3 . . . . . . 9 8 4 6 6 4 1 1 1 1 19 117 5 . . . . . . 1 3 4 5 1 4 2 1 1 1 7 100 3 . . . . . . 4 4 0 0 0 6 1 2 1 1 9 104 3 . . . . . . 6 6 8 7 3 5 1 1 1 1 2 93 2 . . . . . . 4 6 5 1 8 5 1 2 1 1 0 91 2 . . . . . . 0 2 0 2 2 4 2 2 1 2 0 92 2 . . . . . . 1 3 1 0 3 6 1 1 1 1 11 107 4 . . . . . . 3 6 2 8 6 6 1 1 1 1 8 102 3 . . . . . . . . . . . . 1 1 1 1 2 95 3 . . . . . . 1 1 2 3 0 5 2 2 1 1 12 109 4 . . . . . . 2 4 8 1 8 5 end label values tepsi_int_coo tepsi_categ label values tepsi_int_len tepsi_categ label values tepsi_int_mot tepsi_categ label values tepsi_int_t tepsi_categ label def tepsi_categ 1 "Normalidad", modify label def tepsi_categ 2 "Riesgo", modify label def tepsi_categ 3 "Retraso", modify label values tvip_int lblppvt label def lblppvt 1 "Moderadamente Bajo", modify label def lblppvt 2 "Promedio Bajo", modify label def lblppvt 3 "Promedio", modify label def lblppvt 4 "Promedio Alto", modify label def lblppvt 5 "Moderadamente Alto", modify label def lblppvt 6 "Extremadamente Alto", modify label values asq_int_6m lblol label values asq_int_12m lblol label values asq_int_18m lblol
Listed 100 out of 14161 observations
Use the count() option to list more
Comment