url_stem <- "https://raw.githubusercontent.com/higgi13425/rmrwr-book/master/"
haven::read_sas(glue(url_stem, "data/blood_storage.sas7bdat"))## # A tibble: 316 × 20
## rbc_age_group median_rbc_age age aa fam_hx p_vol t_vol t_stage b_gs
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 3 25 72.1 0 0 54 3 1 3
## 2 3 25 73.6 0 0 43.2 3 2 2
## 3 3 25 67.5 0 0 103. 1 1 3
## 4 2 15 65.8 0 0 46 1 1 1
## 5 2 15 63.2 0 0 60 2 1 2
## 6 3 25 65.4 0 0 45.9 2 1 1
## 7 3 25 65.5 1 0 42.6 2 1 1
## 8 1 10 67.1 0 0 40.7 3 1 1
## 9 1 10 63.9 0 0 45 2 1 1
## 10 2 15 63 1 0 67.6 2 1 2
## # ℹ 306 more rows
## # ℹ 11 more variables: bn <dbl>, organ_confined <dbl>, preop_psa <dbl>,
## # preop_therapy <dbl>, units <dbl>, s_gs <dbl>, any_adj_therapy <dbl>,
## # adj_rad_therapy <dbl>, recurrence <dbl>, censor <dbl>,
## # time_to_recurrence <dbl>#haven::read_dta(glue(url_stem, "data/blood_storage.dta"))
#haven::read_sav(glue(url_stem, "data/strep_tb.sav"))rio::import(glue(url_stem, "data/blood_storage.sas7bdat"))## rbc_age_group median_rbc_age age aa fam_hx p_vol t_vol t_stage b_gs bn
## 1 3 25 72.1 0 0 54.0 3 1 3 0
## 2 3 25 73.6 0 0 43.2 3 2 2 0
## 3 3 25 67.5 0 0 102.7 1 1 3 0
## 4 2 15 65.8 0 0 46.0 1 1 1 0
## 5 2 15 63.2 0 0 60.0 2 1 2 0
## 6 3 25 65.4 0 0 45.9 2 1 1 0
## 7 3 25 65.5 1 0 42.6 2 1 1 0
## 8 1 10 67.1 0 0 40.7 3 1 1 0
## 9 1 10 63.9 0 0 45.0 2 1 1 0
## 10 2 15 63.0 1 0 67.6 2 1 2 0
## 11 2 15 59.0 0 0 47.2 1 1 2 0
## 12 1 10 58.5 0 0 37.5 3 1 1 0
## 13 1 10 56.2 0 0 51.0 2 1 1 0
## 14 1 10 73.3 0 0 38.6 2 2 2 0
## 15 3 25 59.7 1 0 54.0 2 1 2 0
## 16 1 10 67.6 0 0 41.0 2 1 2 0
## 17 1 10 61.6 0 0 77.0 1 NA 1 0
## 18 1 10 61.2 1 0 76.0 2 1 1 0
## 19 2 15 59.7 1 0 63.0 2 1 2 0
## 20 2 15 65.2 0 0 113.0 2 1 1 0
## 21 2 15 67.1 0 0 54.2 3 1 2 0
## 22 2 15 64.4 0 0 42.0 2 1 2 0
## 23 3 25 61.5 1 0 29.3 2 1 1 0
## 24 2 15 57.9 0 1 24.0 2 NA 1 0
## 25 1 10 69.7 0 0 92.3 1 1 2 0
## 26 3 25 40.5 0 0 29.5 1 1 1 0
## 27 3 25 69.8 0 0 47.5 2 1 1 0
## 28 2 15 68.3 0 0 48.0 2 1 1 0
## 29 3 25 67.9 0 1 61.0 3 2 3 1
## 30 1 10 56.4 0 0 70.8 2 1 2 0
## 31 3 25 69.0 1 0 65.0 2 1 2 0
## 32 3 25 62.1 1 0 36.8 2 1 3 0
## 33 3 25 49.8 1 0 51.7 3 1 1 0
## 34 2 15 73.9 1 0 58.0 2 1 1 0
## 35 3 25 64.2 0 0 66.0 2 1 1 0
## 36 2 15 71.2 0 0 63.0 1 1 1 0
## 37 3 25 60.3 0 0 69.0 1 1 1 0
## 38 2 15 58.6 0 0 43.4 1 1 1 0
## 39 2 15 71.7 0 1 38.5 2 1 3 0
## 40 1 10 69.7 0 1 46.0 2 1 2 0
## 41 3 25 60.0 0 0 38.2 2 2 2 0
## 42 3 25 71.5 0 0 56.0 2 1 1 0
## 43 2 15 43.6 0 0 35.0 2 1 1 0
## 44 3 25 61.9 0 0 52.3 2 1 1 0
## 45 3 25 61.5 0 1 NA 2 1 2 0
## 46 2 15 63.4 1 0 47.9 3 1 3 0
## 47 1 10 70.4 1 0 73.0 3 1 2 0
## 48 3 25 67.4 0 0 66.5 2 1 1 0
## 49 2 15 62.4 1 0 74.0 1 1 1 0
## 50 2 15 69.4 0 0 57.2 3 1 1 0
## 51 1 10 65.3 1 0 43.0 NA 1 1 0
## 52 1 10 59.9 0 0 64.1 1 1 3 0
## 53 1 10 61.8 0 0 63.0 2 1 1 0
## 54 3 25 58.9 0 1 237.0 1 1 1 0
## 55 1 10 67.2 0 0 70.0 1 NA 1 0
## 56 2 15 74.6 0 0 90.2 1 NA 1 0
## 57 2 15 65.0 0 0 78.9 3 1 1 0
## 58 1 10 56.2 0 0 68.0 1 1 3 0
## 59 3 25 57.0 0 1 89.0 3 1 1 0
## 60 3 25 60.4 0 0 40.0 3 1 2 0
## 61 2 15 68.7 1 0 96.0 1 1 1 0
## 62 1 10 60.8 0 0 39.8 3 1 1 0
## 63 1 10 56.8 0 1 31.3 2 1 2 0
## 64 2 15 63.6 0 0 68.0 3 1 1 0
## 65 3 25 60.0 0 0 46.0 2 1 1 0
## 66 3 25 64.4 0 1 60.1 1 1 2 0
## 67 3 25 54.9 0 0 37.5 1 1 2 0
## 68 2 15 47.0 0 1 48.0 2 1 1 0
## 69 1 10 63.6 1 0 44.0 1 1 1 0
## 70 3 25 55.3 0 1 34.0 3 1 1 0
## 71 1 10 74.2 0 1 32.0 2 1 1 0
## 72 3 25 56.0 0 0 34.0 2 1 1 0
## 73 1 10 72.1 0 1 115.0 2 1 2 0
## 74 3 25 71.8 0 1 99.8 2 1 1 0
## 75 3 25 64.7 0 0 104.6 2 1 1 0
## 76 1 10 68.0 1 1 53.0 2 1 1 0
## 77 1 10 66.1 0 0 51.2 NA 1 1 1
## 78 2 15 59.0 1 0 66.0 2 1 1 0
## 79 3 25 61.8 0 0 44.0 1 1 1 0
## 80 1 10 52.8 1 0 43.8 2 1 1 0
## 81 1 10 57.5 1 0 43.0 3 1 2 1
## 82 2 15 74.4 0 0 42.0 3 1 1 0
## 83 3 25 49.6 0 0 67.5 1 1 1 0
## 84 3 25 68.7 0 0 35.0 1 1 3 0
## 85 1 10 47.9 1 0 45.0 2 1 1 1
## 86 2 15 63.7 1 0 43.0 2 1 1 0
## 87 2 15 60.0 0 1 46.2 2 1 1 0
## 88 2 15 61.7 0 0 42.0 NA 1 1 0
## 89 2 15 70.3 0 0 60.8 2 1 1 0
## 90 3 25 55.8 0 0 49.0 2 1 1 0
## 91 1 10 70.2 0 0 64.0 2 1 2 0
## 92 1 10 68.1 0 0 47.7 2 2 3 0
## 93 3 25 69.8 0 0 35.1 3 2 3 1
## 94 1 10 57.1 1 0 36.7 2 1 1 0
## 95 2 15 65.2 0 0 NA 1 1 1 0
## 96 2 15 60.8 0 0 49.0 1 1 1 0
## 97 1 10 49.9 0 0 52.2 3 1 2 0
## 98 1 10 55.2 0 0 82.3 1 1 1 0
## 99 2 15 57.6 0 0 57.5 2 1 1 0
## 100 2 15 57.4 0 0 NA 2 1 1 0
## 101 2 15 59.2 0 0 24.8 3 1 2 0
## 102 1 10 59.9 0 0 48.0 1 1 1 0
## 103 3 25 56.3 0 0 201.5 1 NA 1 0
## 104 3 25 70.6 0 0 102.0 2 1 1 0
## 105 1 10 71.8 0 0 90.1 2 1 1 0
## 106 1 10 58.2 1 0 35.1 2 1 1 0
## 107 2 15 78.3 0 0 135.0 1 1 1 0
## 108 3 25 55.9 0 1 47.1 1 1 1 0
## 109 1 10 53.9 0 0 42.0 2 1 2 0
## 110 1 10 51.7 0 0 43.2 2 1 1 0
## 111 3 25 64.2 0 0 39.0 1 1 1 0
## 112 1 10 67.4 0 0 65.7 2 1 2 0
## 113 1 10 62.3 0 0 52.1 1 1 1 0
## 114 1 10 62.4 0 0 46.0 2 1 1 0
## 115 2 15 71.0 0 0 NA 1 1 1 0
## 116 1 10 60.4 1 0 67.0 3 2 2 0
## 117 2 15 65.9 0 0 63.0 2 1 1 0
## 118 2 15 58.8 0 1 52.0 2 1 3 0
## 119 3 25 63.2 0 0 56.6 2 1 1 0
## 120 3 25 61.1 0 0 65.0 3 1 1 0
## 121 3 25 51.1 0 0 58.0 3 2 3 0
## 122 2 15 57.9 0 0 63.4 2 1 1 0
## 123 2 15 63.4 0 0 60.4 3 1 2 0
## 124 3 25 67.7 0 0 42.1 2 1 2 0
## 125 2 15 58.2 0 0 50.6 1 1 1 0
## 126 2 15 62.1 0 0 59.0 2 1 2 0
## 127 1 10 55.6 0 0 56.0 3 1 2 0
## 128 2 15 64.5 0 0 60.0 2 1 1 0
## 129 2 15 52.8 0 0 64.0 2 1 1 0
## 130 1 10 55.8 0 0 47.0 2 1 1 0
## 131 2 15 68.9 0 0 29.0 3 2 2 0
## 132 2 15 63.5 0 1 42.0 3 1 1 0
## 133 3 25 68.0 0 1 67.0 2 1 2 0
## 134 2 15 64.9 0 0 66.4 2 1 1 0
## 135 1 10 62.7 0 0 33.0 2 1 2 0
## 136 3 25 57.5 0 1 51.0 2 1 1 0
## 137 3 25 70.7 0 0 43.4 3 1 2 0
## 138 3 25 61.7 0 0 105.0 2 1 1 0
## 139 3 25 64.2 0 0 37.5 3 1 2 0
## 140 1 10 69.8 0 1 56.0 3 2 2 0
## 141 3 25 68.0 0 0 96.3 1 1 1 0
## 142 2 15 63.1 0 0 48.0 1 1 2 0
## 143 2 15 64.1 0 0 57.6 2 1 3 0
## 144 3 25 58.1 0 0 36.6 2 1 2 0
## 145 3 25 60.1 0 0 NA 2 1 1 0
## 146 3 25 56.0 0 1 68.0 1 1 1 0
## 147 1 10 62.3 0 1 113.0 1 1 1 0
## 148 1 10 51.2 0 1 51.0 1 1 1 0
## 149 2 15 57.0 0 1 41.0 3 1 2 0
## 150 3 25 51.0 1 0 40.1 2 1 1 1
## 151 3 25 65.8 0 0 99.0 3 1 2 0
## 152 3 25 54.5 0 1 53.6 2 1 1 0
## 153 1 10 55.9 0 0 20.9 3 1 2 0
## 154 3 25 55.7 0 0 48.5 2 1 1 0
## 155 1 10 48.4 0 1 37.0 2 1 1 0
## 156 1 10 58.2 0 0 37.0 3 1 2 0
## 157 1 10 63.9 1 0 108.2 3 1 1 0
## 158 1 10 66.2 0 0 75.0 2 1 1 0
## 159 2 15 62.9 0 0 38.0 3 1 1 0
## 160 2 15 69.1 0 0 41.0 2 1 1 0
## 161 2 15 67.7 0 0 72.8 3 1 3 0
## 162 1 10 55.4 0 0 73.0 2 1 1 0
## 163 3 25 55.6 0 1 50.7 1 1 2 0
## 164 3 25 54.6 0 0 40.0 2 1 1 0
## 165 2 15 64.4 0 0 89.0 2 1 1 0
## 166 1 10 69.7 0 0 75.6 2 1 1 0
## 167 3 25 50.3 0 0 19.4 3 2 2 1
## 168 2 15 57.1 0 0 46.0 2 1 2 0
## 169 1 10 64.2 0 0 37.0 3 2 2 1
## 170 3 25 66.9 0 1 36.1 3 1 1 0
## 171 3 25 71.8 1 0 53.9 2 1 2 0
## 172 2 15 63.9 0 0 65.0 3 1 1 0
## 173 1 10 53.1 0 1 34.0 3 2 2 1
## 174 1 10 62.4 1 1 82.0 2 2 2 0
## 175 1 10 64.9 0 0 78.7 2 1 1 0
## 176 2 15 62.4 0 0 95.0 1 1 1 0
## 177 1 10 58.8 1 0 67.0 3 2 3 0
## 178 3 25 54.4 1 0 35.0 1 1 1 0
## 179 1 10 57.0 0 0 41.2 1 1 1 0
## 180 1 10 65.6 0 1 37.6 2 1 1 0
## 181 1 10 65.9 0 0 51.6 3 1 2 0
## 182 3 25 67.1 0 0 37.0 2 1 2 0
## 183 1 10 62.2 0 0 58.0 2 1 1 0
## 184 2 15 50.6 0 1 31.3 2 1 1 0
## 185 1 10 68.4 0 0 64.0 2 1 1 1
## 186 2 15 55.8 1 0 46.7 3 1 1 0
## 187 1 10 79.0 0 0 72.0 2 1 1 0
## 188 3 25 63.9 0 0 56.0 3 2 2 0
## 189 3 25 49.6 0 0 32.0 2 1 1 0
## 190 1 10 54.9 1 0 31.6 2 1 1 0
## 191 1 10 61.1 0 0 55.0 2 1 1 0
## 192 1 10 59.6 0 0 28.0 2 2 2 0
## 193 3 25 64.7 0 0 38.0 1 1 1 0
## 194 3 25 60.1 0 0 42.0 3 2 2 1
## 195 3 25 64.2 0 0 60.0 2 1 1 0
## 196 2 15 67.6 0 0 67.0 1 1 1 0
## 197 1 10 51.0 0 0 28.0 2 1 1 1
## 198 2 15 58.1 0 0 33.0 2 1 1 0
## 199 1 10 49.0 0 0 28.9 3 1 1 0
## 200 2 15 50.8 0 0 37.0 3 2 2 0
## 201 3 25 55.9 1 1 35.9 1 1 1 0
## 202 3 25 62.3 0 0 57.2 2 1 1 0
## 203 3 25 49.0 0 0 47.8 1 1 1 0
## 204 1 10 59.2 0 1 34.3 3 2 2 0
## 205 3 25 65.7 0 0 39.2 3 1 1 0
## 206 2 15 57.1 0 0 34.0 2 1 2 0
## 207 2 15 62.7 0 0 63.0 1 1 1 0
## 208 2 15 68.6 0 1 62.5 2 1 2 0
## 209 1 10 65.0 1 0 60.6 3 1 3 0
## 210 1 10 66.1 0 0 260.0 3 1 2 0
## 211 1 10 64.7 0 0 27.0 2 1 1 0
## 212 2 15 62.2 0 0 NA 2 1 1 0
## 213 2 15 55.9 0 1 53.4 2 1 1 0
## 214 2 15 45.7 0 0 42.0 2 1 1 0
## 215 1 10 71.4 0 0 164.0 1 1 2 0
## 216 1 10 55.4 0 0 35.8 2 1 1 0
## 217 2 15 54.8 0 0 34.1 2 1 2 0
## 218 2 15 56.1 0 0 111.0 3 2 3 1
## 219 1 10 67.5 0 1 53.1 2 1 1 0
## 220 3 25 58.2 1 0 85.9 1 1 1 0
## 221 3 25 65.3 0 1 42.0 2 1 2 0
## 222 2 15 56.2 0 0 51.0 3 1 2 0
## 223 1 10 66.8 0 0 62.0 1 1 1 0
## 224 2 15 61.5 1 0 37.9 2 NA 2 0
## 225 2 15 55.5 1 1 43.8 3 1 2 1
## 226 3 25 43.8 0 1 33.9 2 1 1 0
## 227 2 15 48.9 0 1 39.6 1 1 1 0
## 228 2 15 68.3 0 0 41.7 2 1 2 0
## 229 3 25 60.5 0 0 49.8 2 1 1 0
## 230 2 15 68.5 0 0 34.1 2 1 1 0
## 231 3 25 46.7 1 0 42.5 3 1 2 0
## 232 1 10 70.8 0 0 44.0 2 1 1 0
## 233 2 15 72.7 0 1 78.1 2 1 1 0
## 234 3 25 47.0 0 0 43.0 3 1 1 0
## 235 3 25 64.4 0 0 49.9 3 2 2 0
## 236 1 10 69.3 0 0 53.0 3 1 2 0
## 237 1 10 51.8 0 0 32.3 3 1 1 0
## 238 1 10 60.4 0 0 34.6 2 1 1 0
## 239 3 25 67.5 1 1 31.9 2 1 1 0
## 240 3 25 63.6 1 1 71.4 3 1 1 0
## 241 3 25 64.3 0 0 50.1 2 1 2 0
## 242 3 25 42.5 0 0 32.0 3 1 3 0
## 243 1 10 56.3 0 0 73.0 1 1 1 0
## 244 1 10 65.7 0 0 49.0 2 1 1 0
## 245 2 15 58.9 0 0 31.2 3 1 2 0
## 246 2 15 43.4 0 0 42.7 3 2 3 1
## 247 2 15 50.9 0 0 43.5 3 2 2 0
## 248 1 10 58.2 0 1 121.0 2 1 1 0
## 249 2 15 74.8 0 1 149.6 2 1 1 0
## 250 1 10 71.9 0 0 274.0 1 1 3 0
## 251 2 15 61.1 0 1 NA 2 1 2 0
## 252 2 15 76.3 0 0 NA NA NA NA 0
## 253 1 10 53.5 0 0 55.0 3 2 2 0
## 254 3 25 43.8 0 1 32.5 2 1 1 0
## 255 1 10 49.9 0 1 42.0 3 1 2 0
## 256 3 25 60.5 0 0 43.0 2 1 1 0
## 257 3 25 45.2 0 1 37.1 2 1 1 0
## 258 3 25 58.3 0 0 27.0 3 1 1 0
## 259 3 25 58.5 0 1 68.0 1 1 3 0
## 260 2 15 57.6 0 1 62.0 1 1 1 0
## 261 3 25 62.3 0 0 68.0 2 1 2 0
## 262 2 15 55.7 0 0 43.9 3 1 2 0
## 263 2 15 62.7 0 1 54.0 3 1 1 0
## 264 1 10 64.4 0 1 48.0 2 2 2 0
## 265 3 25 76.9 0 0 NA 1 2 1 0
## 266 2 15 55.4 0 0 64.8 2 1 1 0
## 267 1 10 47.7 1 1 41.9 2 1 2 0
## 268 3 25 49.1 0 0 63.0 2 1 1 0
## 269 2 15 53.2 0 0 28.0 3 1 1 0
## 270 1 10 54.8 0 1 44.5 3 1 1 0
## 271 2 15 62.2 0 0 43.4 1 1 1 0
## 272 1 10 46.5 0 0 48.2 2 1 2 0
## 273 3 25 62.4 0 0 26.5 2 NA 1 0
## 274 1 10 63.3 0 0 43.0 3 1 2 0
## 275 1 10 67.8 0 0 37.6 3 2 2 0
## 276 3 25 64.8 0 0 67.0 2 1 2 0
## 277 1 10 51.2 0 1 39.0 2 2 3 0
## 278 1 10 62.6 0 1 77.0 1 1 1 0
## 279 1 10 66.7 0 0 58.2 2 NA 1 0
## 280 2 15 63.5 0 0 67.2 3 NA 1 0
## 281 1 10 57.2 0 0 46.9 3 1 3 0
## 282 2 15 59.0 0 0 60.0 1 1 1 0
## 283 1 10 74.9 0 0 55.1 3 1 2 0
## 284 3 25 59.0 0 1 21.2 1 1 1 0
## 285 3 25 56.5 0 0 73.0 3 2 3 0
## 286 3 25 62.7 0 1 46.2 3 2 3 0
## 287 2 15 53.0 1 0 47.0 3 1 2 1
## 288 1 10 58.5 0 1 42.0 3 1 2 0
## 289 3 25 54.5 1 0 47.4 3 1 3 0
## 290 2 15 67.1 0 0 53.0 1 1 1 0
## 291 3 25 38.4 0 1 31.2 3 1 1 0
## 292 3 25 68.2 0 1 63.1 2 1 1 0
## 293 2 15 59.6 1 0 45.4 3 1 1 1
## 294 2 15 60.8 0 0 44.9 3 2 3 0
## 295 1 10 54.2 0 0 43.0 3 1 2 0
## 296 1 10 60.8 0 1 90.7 NA 1 1 0
## 297 1 10 72.8 0 0 43.8 2 2 3 0
## 298 3 25 64.8 0 0 44.3 2 1 2 0
## 299 2 15 51.9 0 0 68.3 3 2 3 1
## 300 2 15 55.1 0 0 50.1 3 1 3 0
## 301 3 25 52.9 0 1 29.9 2 NA 1 0
## 302 1 10 66.5 0 0 51.8 3 1 2 0
## 303 3 25 66.1 0 0 32.2 2 1 3 0
## 304 2 15 55.3 1 0 68.8 NA 1 1 0
## 305 2 15 55.7 1 0 75.8 2 NA 1 0
## 306 2 15 66.9 1 0 56.6 2 1 1 0
## 307 3 25 64.9 1 0 41.3 2 1 1 0
## 308 3 25 70.6 1 0 42.5 1 1 2 0
## 309 2 15 65.1 1 0 61.5 2 1 1 0
## 310 2 15 61.6 1 0 57.2 1 1 NA 0
## 311 1 10 64.1 1 0 62.0 3 1 1 0
## 312 1 10 54.8 0 0 25.6 2 1 1 0
## 313 3 25 62.3 1 0 42.3 2 1 1 0
## 314 3 25 62.4 1 0 50.0 2 NA 1 0
## 315 2 15 57.6 0 0 74.9 1 1 1 0
## 316 3 25 59.9 0 1 54.1 3 1 1 0
## organ_confined preop_psa preop_therapy units s_gs any_adj_therapy
## 1 0 14.08 1 6 1 0
## 2 1 10.50 0 2 3 0
## 3 1 6.98 1 1 1 0
## 4 1 4.40 0 2 3 0
## 5 1 21.40 0 3 3 0
## 6 0 5.10 0 1 3 0
## 7 1 6.03 0 2 3 0
## 8 0 8.70 0 4 3 0
## 9 1 3.83 0 1 3 0
## 10 1 7.98 0 2 3 0
## 11 1 4.50 0 2 3 0
## 12 1 5.30 0 2 3 0
## 13 1 2.70 0 2 2 0
## 14 0 30.60 1 4 1 0
## 15 1 10.40 0 2 3 0
## 16 1 5.60 1 4 1 0
## 17 1 4.30 0 5 2 0
## 18 1 7.00 0 4 2 0
## 19 1 4.20 0 2 2 0
## 20 1 8.48 0 1 2 0
## 21 0 5.30 0 4 4 0
## 22 0 5.40 1 2 1 0
## 23 1 4.10 0 2 2 0
## 24 1 5.00 0 2 3 0
## 25 1 5.70 0 2 2 0
## 26 1 1.48 0 2 3 0
## 27 1 6.70 0 2 3 0
## 28 0 2.90 0 1 3 0
## 29 0 29.00 1 4 1 1
## 30 1 6.90 0 3 3 0
## 31 1 7.20 0 5 3 0
## 32 0 5.58 0 2 4 0
## 33 1 6.38 0 1 3 0
## 34 1 8.10 0 2 4 0
## 35 1 5.50 0 2 3 0
## 36 1 5.30 0 4 3 0
## 37 1 5.70 0 2 3 0
## 38 1 5.40 0 1 2 0
## 39 1 9.05 0 2 3 0
## 40 0 4.90 0 8 3 0
## 41 0 5.00 0 1 3 0
## 42 1 2.09 0 2 3 0
## 43 1 5.50 0 2 2 0
## 44 0 4.10 0 19 3 0
## 45 1 4.10 0 1 3 0
## 46 0 15.70 1 2 1 0
## 47 0 6.20 0 3 3 0
## 48 1 5.20 0 1 3 0
## 49 1 6.27 0 3 2 0
## 50 1 5.00 0 1 3 0
## 51 0 14.90 0 9 2 0
## 52 0 4.40 1 4 1 0
## 53 0 5.20 0 7 3 0
## 54 1 9.00 0 5 2 0
## 55 1 5.92 0 2 2 0
## 56 1 4.30 0 1 2 0
## 57 1 15.70 0 2 2 0
## 58 1 5.20 0 2 3 0
## 59 0 14.00 0 1 3 0
## 60 1 8.99 0 1 3 0
## 61 1 4.20 0 2 3 0
## 62 0 13.50 0 3 3 0
## 63 0 4.70 0 2 3 0
## 64 1 5.10 0 2 3 0
## 65 1 6.80 0 2 2 0
## 66 1 5.87 0 2 3 0
## 67 1 7.40 0 2 2 0
## 68 1 5.80 0 3 3 0
## 69 1 5.30 0 3 3 0
## 70 1 4.70 0 2 3 0
## 71 1 4.70 0 2 2 0
## 72 1 6.50 0 4 2 0
## 73 1 10.10 0 4 4 0
## 74 1 5.40 0 1 2 0
## 75 0 13.70 0 2 3 0
## 76 0 20.70 0 2 3 0
## 77 1 5.90 0 6 3 0
## 78 1 8.70 0 2 3 0
## 79 1 6.30 0 2 2 0
## 80 1 4.20 0 2 3 0
## 81 0 15.90 0 2 3 1
## 82 0 4.30 0 2 4 0
## 83 1 5.85 0 10 2 0
## 84 1 5.70 1 2 1 0
## 85 1 6.00 1 2 1 0
## 86 0 4.70 0 2 3 0
## 87 0 5.90 0 2 3 0
## 88 1 3.50 0 1 2 0
## 89 1 6.30 1 2 1 0
## 90 0 10.00 0 2 3 0
## 91 0 6.10 0 2 3 0
## 92 0 4.17 1 1 1 0
## 93 0 6.20 1 1 1 1
## 94 1 10.00 0 2 3 0
## 95 1 4.10 0 2 2 0
## 96 1 4.90 0 2 2 0
## 97 1 29.70 0 4 3 0
## 98 1 4.10 1 6 1 0
## 99 1 7.70 0 2 3 0
## 100 1 4.02 0 2 3 0
## 101 0 5.00 1 2 1 0
## 102 1 4.20 0 2 2 0
## 103 1 9.32 0 13 2 0
## 104 1 11.90 0 2 3 0
## 105 0 8.30 0 2 3 0
## 106 1 4.60 0 2 2 0
## 107 1 9.00 0 3 2 0
## 108 1 7.20 0 1 2 0
## 109 1 4.90 0 2 3 0
## 110 1 7.00 0 4 3 0
## 111 1 NA 0 2 2 0
## 112 0 5.00 0 2 3 0
## 113 1 7.60 0 2 3 0
## 114 1 5.40 0 2 3 0
## 115 1 6.30 0 2 3 0
## 116 0 6.30 0 1 3 0
## 117 1 6.80 0 4 2 0
## 118 1 15.60 0 2 4 0
## 119 1 4.70 0 1 2 0
## 120 0 14.20 0 2 3 0
## 121 0 24.90 0 1 4 0
## 122 1 4.89 0 4 3 0
## 123 0 9.10 0 2 3 0
## 124 1 6.18 0 2 3 0
## 125 1 4.20 0 4 2 0
## 126 1 8.20 0 2 3 0
## 127 0 11.90 0 2 3 0
## 128 1 1.80 0 4 2 0
## 129 1 5.80 0 1 2 0
## 130 1 8.90 0 4 2 0
## 131 1 27.00 1 2 1 0
## 132 0 5.60 0 2 3 0
## 133 1 13.20 0 2 3 0
## 134 1 19.70 1 1 1 0
## 135 1 8.90 0 1 3 0
## 136 1 3.90 0 1 3 0
## 137 0 5.70 0 2 3 0
## 138 1 5.20 0 1 2 0
## 139 1 9.90 0 2 3 0
## 140 0 4.65 0 2 3 0
## 141 1 6.20 0 1 3 0
## 142 1 6.70 0 1 2 0
## 143 1 7.00 1 2 1 0
## 144 1 5.00 0 4 3 0
## 145 1 4.96 0 3 3 0
## 146 1 8.30 0 5 2 0
## 147 1 5.40 0 3 2 0
## 148 1 4.40 0 2 3 0
## 149 1 6.60 0 2 3 0
## 150 1 5.10 1 2 1 0
## 151 0 22.90 0 2 3 0
## 152 1 9.75 0 2 3 0
## 153 0 5.10 1 2 1 0
## 154 1 5.60 0 1 2 0
## 155 0 6.60 0 2 2 0
## 156 1 4.99 0 2 4 0
## 157 1 18.00 0 4 3 0
## 158 1 5.00 0 1 2 0
## 159 0 6.20 0 2 3 0
## 160 1 7.80 0 2 3 0
## 161 0 14.40 0 6 4 0
## 162 1 2.90 0 5 3 0
## 163 1 4.00 0 2 3 0
## 164 1 4.40 0 2 3 0
## 165 0 6.30 0 1 2 0
## 166 0 6.80 0 2 3 0
## 167 0 19.60 1 2 1 0
## 168 0 4.10 0 1 3 0
## 169 0 3.70 1 1 1 0
## 170 0 2.50 1 1 1 0
## 171 1 11.90 0 2 3 0
## 172 1 6.30 0 2 3 0
## 173 1 4.80 0 2 3 0
## 174 0 5.60 0 2 3 0
## 175 0 5.30 0 2 3 0
## 176 1 13.60 0 1 2 0
## 177 0 8.20 0 1 3 0
## 178 1 5.10 1 1 1 0
## 179 1 5.10 0 2 3 0
## 180 0 5.20 0 2 2 0
## 181 0 8.10 0 2 3 0
## 182 1 7.10 0 2 3 0
## 183 1 3.88 0 2 3 0
## 184 1 11.06 0 2 3 0
## 185 1 8.40 0 2 3 0
## 186 1 5.80 0 1 3 0
## 187 0 8.10 0 2 3 0
## 188 0 10.07 0 2 4 0
## 189 1 5.30 0 1 2 0
## 190 0 15.00 0 2 3 0
## 191 1 5.60 0 2 2 0
## 192 0 7.90 1 1 1 0
## 193 1 5.40 0 14 3 0
## 194 0 22.30 1 1 1 1
## 195 1 5.70 0 2 2 0
## 196 1 6.80 0 1 2 0
## 197 1 2.80 0 1 2 0
## 198 1 6.80 0 2 3 0
## 199 1 8.10 0 6 3 0
## 200 0 37.00 1 4 1 0
## 201 1 4.70 0 1 3 0
## 202 1 6.40 0 4 2 0
## 203 1 6.60 0 1 2 0
## 204 0 4.20 0 1 3 1
## 205 0 2.10 0 2 3 0
## 206 1 11.80 0 1 3 0
## 207 1 6.80 0 5 2 0
## 208 0 5.86 0 3 3 0
## 209 1 11.09 0 2 3 0
## 210 0 26.10 0 2 3 0
## 211 1 4.90 0 2 3 0
## 212 1 5.00 0 4 2 0
## 213 1 9.20 0 3 2 0
## 214 0 6.70 0 2 3 0
## 215 1 14.90 0 2 2 0
## 216 1 4.70 0 2 3 0
## 217 1 11.40 0 5 3 0
## 218 0 8.30 0 2 4 0
## 219 1 6.00 0 2 3 0
## 220 1 14.20 0 2 2 0
## 221 1 9.40 0 2 3 0
## 222 0 5.00 0 2 3 0
## 223 1 5.30 1 4 1 0
## 224 1 6.90 0 1 3 0
## 225 0 13.00 0 2 3 0
## 226 0 5.20 0 1 3 0
## 227 1 6.20 0 4 2 0
## 228 0 6.75 0 3 3 0
## 229 1 8.20 0 1 3 0
## 230 0 5.50 0 2 3 0
## 231 1 14.93 1 1 1 0
## 232 1 4.20 0 2 3 0
## 233 0 5.30 0 2 3 0
## 234 1 8.00 0 2 2 0
## 235 0 10.80 0 1 3 0
## 236 0 7.10 0 2 3 0
## 237 1 5.10 0 2 3 0
## 238 1 2.81 0 3 3 0
## 239 1 7.00 0 3 3 0
## 240 0 7.00 0 2 3 0
## 241 0 4.60 0 2 3 0
## 242 0 14.70 1 2 1 1
## 243 1 6.20 0 1 2 0
## 244 1 2.20 0 2 3 0
## 245 0 4.20 0 2 4 0
## 246 0 6.88 1 2 1 0
## 247 0 26.20 1 4 1 0
## 248 1 20.00 0 3 2 0
## 249 1 10.10 0 2 2 0
## 250 1 27.74 0 4 2 0
## 251 1 6.40 0 2 3 0
## 252 0 NA 0 3 3 0
## 253 0 13.30 0 2 3 0
## 254 0 9.70 0 2 3 0
## 255 1 4.39 1 2 1 0
## 256 1 4.22 0 2 2 0
## 257 1 5.84 0 1 3 0
## 258 1 13.00 0 2 3 0
## 259 1 5.30 0 5 3 0
## 260 1 9.45 0 2 2 0
## 261 0 12.20 0 1 3 0
## 262 0 10.10 0 2 3 0
## 263 1 4.90 0 2 3 0
## 264 0 4.10 0 1 3 0
## 265 1 3.37 0 6 2 0
## 266 1 5.20 0 4 3 0
## 267 1 3.00 0 10 3 0
## 268 1 3.10 0 1 2 0
## 269 0 4.70 0 2 3 0
## 270 1 6.80 0 2 3 0
## 271 1 5.80 0 2 2 0
## 272 1 4.72 0 2 2 0
## 273 1 4.30 0 2 2 0
## 274 0 4.98 0 2 3 0
## 275 0 4.60 0 1 3 0
## 276 0 9.25 0 1 3 0
## 277 1 5.49 1 2 1 0
## 278 1 3.92 0 4 2 0
## 279 1 6.30 0 2 3 0
## 280 0 7.40 0 6 3 0
## 281 0 12.00 1 2 1 0
## 282 1 4.50 0 2 2 0
## 283 1 6.02 0 3 3 0
## 284 1 1.30 0 2 2 0
## 285 0 40.10 0 3 4 0
## 286 0 3.72 0 2 4 0
## 287 0 39.00 1 2 1 0
## 288 0 10.70 0 2 4 0
## 289 0 6.80 0 2 4 0
## 290 1 6.10 0 1 2 0
## 291 1 12.57 0 2 3 0
## 292 1 4.17 0 2 3 0
## 293 1 30.96 0 2 3 0
## 294 0 4.30 0 2 4 0
## 295 0 8.00 0 2 3 0
## 296 1 7.90 0 2 2 0
## 297 0 5.90 0 2 4 0
## 298 1 2.70 1 4 1 0
## 299 0 27.15 0 2 4 0
## 300 0 8.40 0 2 4 1
## 301 1 4.63 0 4 2 0
## 302 0 6.74 0 1 3 0
## 303 1 7.50 0 2 4 0
## 304 1 11.00 0 2 2 0
## 305 1 6.70 0 1 2 0
## 306 0 7.50 0 2 2 0
## 307 1 11.00 0 6 2 0
## 308 1 17.00 0 1 3 0
## 309 0 14.00 0 2 2 0
## 310 1 NA 1 4 1 0
## 311 1 8.40 0 2 3 0
## 312 1 5.00 0 1 3 0
## 313 1 7.60 0 1 2 0
## 314 0 9.30 0 2 3 0
## 315 1 4.50 0 2 2 0
## 316 1 6.80 0 1 3 0
## adj_rad_therapy recurrence censor time_to_recurrence
## 1 0 1 0 2.67
## 2 0 1 0 47.63
## 3 0 0 1 14.10
## 4 0 0 1 59.47
## 5 0 0 1 1.23
## 6 0 0 1 74.70
## 7 0 0 1 13.87
## 8 0 1 0 8.37
## 9 0 0 1 48.59
## 10 0 0 1 22.63
## 11 0 0 1 4.63
## 12 0 0 1 46.47
## 13 0 0 1 91.15
## 14 0 0 1 10.77
## 15 0 0 1 5.09
## 16 0 0 1 83.87
## 17 0 0 1 8.30
## 18 0 0 1 71.57
## 19 0 0 1 89.36
## 20 0 0 1 29.30
## 21 0 0 1 83.83
## 22 0 1 0 47.60
## 23 0 0 1 90.55
## 24 0 0 1 7.49
## 25 0 0 1 58.63
## 26 0 0 1 21.13
## 27 0 0 1 41.59
## 28 0 0 1 1.13
## 29 0 0 1 41.00
## 30 0 0 1 47.30
## 31 0 1 0 46.22
## 32 0 0 1 19.83
## 33 0 0 1 38.68
## 34 0 0 1 55.49
## 35 0 0 1 102.20
## 36 0 0 1 37.13
## 37 0 0 1 65.69
## 38 0 0 1 52.07
## 39 0 0 1 46.85
## 40 0 0 1 4.10
## 41 0 0 1 1.07
## 42 0 0 1 5.43
## 43 0 0 1 41.60
## 44 0 0 1 1.97
## 45 0 0 1 9.67
## 46 0 1 0 7.13
## 47 0 1 0 9.77
## 48 0 0 1 47.92
## 49 0 0 1 13.90
## 50 0 0 1 33.83
## 51 0 1 0 10.93
## 52 0 0 1 1.90
## 53 0 0 1 69.07
## 54 0 0 1 90.77
## 55 0 0 1 14.82
## 56 0 0 1 17.20
## 57 0 0 1 80.20
## 58 0 0 1 67.87
## 59 0 0 1 94.10
## 60 0 0 1 3.83
## 61 0 0 1 15.67
## 62 0 1 0 35.37
## 63 0 0 1 7.57
## 64 0 0 1 45.87
## 65 0 0 1 16.13
## 66 0 0 1 41.09
## 67 0 0 1 63.97
## 68 0 0 1 6.52
## 69 0 0 1 10.83
## 70 0 0 1 47.00
## 71 0 0 1 13.80
## 72 0 0 1 63.67
## 73 0 1 0 14.87
## 74 0 0 1 93.80
## 75 0 0 1 101.77
## 76 0 0 1 80.73
## 77 0 1 0 48.33
## 78 0 0 1 71.56
## 79 0 0 1 76.33
## 80 0 0 1 71.50
## 81 0 0 1 13.03
## 82 0 0 1 5.20
## 83 0 0 1 35.39
## 84 0 0 1 1.67
## 85 0 1 0 5.77
## 86 0 0 1 0.40
## 87 0 0 1 58.57
## 88 0 0 1 38.47
## 89 0 0 1 44.17
## 90 0 0 1 73.50
## 91 0 1 0 1.70
## 92 0 0 1 2.40
## 93 0 1 0 44.73
## 94 0 0 1 28.90
## 95 0 0 1 74.27
## 96 0 0 1 3.37
## 97 0 0 1 73.90
## 98 0 0 1 48.33
## 99 0 0 1 29.53
## 100 0 0 1 12.49
## 101 0 1 0 18.40
## 102 0 0 1 86.47
## 103 0 0 1 23.73
## 104 0 0 1 82.43
## 105 0 0 1 83.02
## 106 0 1 0 1.67
## 107 0 1 0 4.23
## 108 0 0 1 23.17
## 109 0 0 1 12.23
## 110 0 0 1 49.07
## 111 0 0 1 66.69
## 112 0 0 1 99.47
## 113 0 0 1 42.62
## 114 0 0 1 86.70
## 115 0 0 1 11.57
## 116 0 0 1 95.25
## 117 0 0 1 63.52
## 118 0 1 0 13.30
## 119 0 0 1 13.77
## 120 0 0 1 10.47
## 121 0 1 0 1.43
## 122 0 0 1 22.26
## 123 0 0 1 70.90
## 124 0 0 1 14.23
## 125 0 0 1 17.03
## 126 0 0 1 20.30
## 127 0 0 1 101.70
## 128 0 0 1 103.60
## 129 0 0 1 24.27
## 130 0 0 1 79.37
## 131 0 1 0 43.77
## 132 0 1 0 1.77
## 133 0 0 1 25.30
## 134 0 0 1 57.83
## 135 0 0 1 68.57
## 136 0 0 1 58.73
## 137 0 0 1 88.25
## 138 0 0 1 41.57
## 139 0 0 1 86.79
## 140 0 0 1 11.20
## 141 0 0 1 67.76
## 142 0 0 1 83.07
## 143 0 0 1 39.37
## 144 0 0 1 9.82
## 145 0 0 1 1.30
## 146 0 0 1 65.00
## 147 0 0 1 95.73
## 148 0 0 1 84.87
## 149 0 0 1 91.39
## 150 0 0 1 37.03
## 151 0 0 1 54.27
## 152 0 0 1 1.27
## 153 0 1 0 24.60
## 154 0 0 1 54.00
## 155 0 0 1 53.07
## 156 0 0 1 5.03
## 157 0 0 1 1.50
## 158 0 0 1 37.97
## 159 0 0 1 91.07
## 160 0 0 1 98.60
## 161 0 1 0 1.47
## 162 0 0 1 59.37
## 163 0 0 1 3.93
## 164 0 1 0 68.07
## 165 0 0 1 64.55
## 166 0 0 1 15.37
## 167 0 1 0 26.70
## 168 0 0 1 81.63
## 169 0 0 1 33.80
## 170 0 0 1 74.50
## 171 0 1 0 30.70
## 172 0 0 1 14.00
## 173 0 0 1 57.23
## 174 0 0 1 0.27
## 175 0 0 1 51.43
## 176 0 0 1 70.13
## 177 0 1 0 12.60
## 178 0 0 1 64.92
## 179 0 0 1 39.03
## 180 0 0 1 33.47
## 181 0 0 1 68.63
## 182 0 1 0 5.53
## 183 0 0 1 10.27
## 184 0 0 1 0.73
## 185 0 0 1 70.52
## 186 0 0 1 1.90
## 187 0 1 0 37.43
## 188 0 1 0 63.53
## 189 0 0 1 61.77
## 190 0 0 1 52.00
## 191 0 1 0 NA
## 192 0 1 0 9.57
## 193 0 0 1 42.63
## 194 0 0 1 32.67
## 195 0 0 1 50.77
## 196 0 0 1 56.43
## 197 0 0 1 58.60
## 198 0 0 1 24.37
## 199 0 1 0 23.13
## 200 0 1 0 18.17
## 201 0 0 1 30.33
## 202 0 0 1 59.27
## 203 0 0 1 27.07
## 204 0 0 1 41.29
## 205 0 0 1 28.33
## 206 0 0 1 47.50
## 207 0 0 1 18.47
## 208 0 1 0 56.67
## 209 0 0 1 35.45
## 210 0 1 0 0.43
## 211 0 0 1 36.27
## 212 0 0 1 19.23
## 213 0 0 1 43.87
## 214 0 0 1 1.65
## 215 0 0 1 41.13
## 216 0 0 1 44.93
## 217 0 0 1 39.27
## 218 0 1 0 1.43
## 219 0 0 1 34.70
## 220 0 0 1 10.73
## 221 0 1 0 7.60
## 222 0 0 1 47.50
## 223 0 0 1 23.40
## 224 0 0 1 1.50
## 225 0 0 1 1.43
## 226 0 0 1 44.23
## 227 0 0 1 9.33
## 228 0 0 1 4.73
## 229 0 0 1 35.90
## 230 0 0 1 29.89
## 231 0 1 0 7.47
## 232 0 0 1 44.37
## 233 0 0 1 16.50
## 234 0 0 1 16.87
## 235 0 0 1 34.70
## 236 0 0 1 44.88
## 237 0 0 1 23.23
## 238 0 0 1 33.48
## 239 0 0 1 7.77
## 240 0 0 1 28.99
## 241 0 0 1 46.87
## 242 0 1 0 26.69
## 243 0 0 1 35.93
## 244 0 0 1 35.20
## 245 0 1 0 21.00
## 246 0 1 0 9.23
## 247 0 0 1 30.90
## 248 0 0 1 23.62
## 249 0 0 1 27.80
## 250 0 0 1 12.17
## 251 0 1 0 28.59
## 252 0 1 0 25.30
## 253 0 1 0 20.97
## 254 0 0 1 32.77
## 255 0 0 1 14.03
## 256 0 0 1 22.83
## 257 0 0 1 31.50
## 258 0 0 1 17.29
## 259 0 0 1 19.13
## 260 0 0 1 12.30
## 261 0 0 1 11.23
## 262 0 0 1 1.27
## 263 0 0 1 9.28
## 264 0 0 1 4.63
## 265 0 0 1 11.73
## 266 0 0 1 1.43
## 267 0 0 1 1.30
## 268 0 0 1 3.90
## 269 0 0 1 10.13
## 270 0 0 1 4.99
## 271 0 0 1 13.53
## 272 0 0 1 1.47
## 273 0 0 1 4.10
## 274 0 0 1 2.83
## 275 0 0 1 1.70
## 276 0 0 1 6.60
## 277 0 0 1 1.93
## 278 0 0 1 10.43
## 279 0 0 1 13.60
## 280 0 0 1 4.83
## 281 1 1 0 8.40
## 282 0 0 1 4.17
## 283 0 0 1 2.40
## 284 0 0 1 2.13
## 285 0 1 0 0.30
## 286 0 0 1 5.90
## 287 0 0 1 5.63
## 288 0 0 1 4.40
## 289 0 1 0 1.57
## 290 0 0 1 1.87
## 291 0 0 1 8.30
## 292 0 0 1 1.23
## 293 0 0 1 1.83
## 294 0 0 1 4.33
## 295 0 0 1 1.20
## 296 0 0 1 1.20
## 297 0 0 1 2.03
## 298 0 0 1 3.67
## 299 0 1 0 1.73
## 300 0 0 1 1.17
## 301 0 0 1 1.43
## 302 0 0 1 1.60
## 303 0 1 0 2.60
## 304 0 0 1 86.37
## 305 0 0 1 43.43
## 306 0 0 1 55.53
## 307 0 1 0 3.67
## 308 0 0 1 6.43
## 309 0 0 1 22.83
## 310 0 1 0 21.47
## 311 0 1 0 15.60
## 312 0 0 1 76.03
## 313 0 0 1 65.30
## 314 0 1 0 52.30
## 315 0 0 1 26.77
## 316 0 0 1 24.87glimpse(medicaldata::scurvy)## Rows: 12
## Columns: 8
## $ study_id <chr> "001", "002", "003", "004", "005", "006", "0…
## $ treatment <fct> cider, cider, dilute_sulfuric_acid, dilute_s…
## $ dosing_regimen_for_scurvy <chr> "1 quart per day", "1 quart per day", "25 dr…
## $ gum_rot_d6 <fct> 2_moderate, 2_moderate, 1_mild, 2_moderate, …
## $ skin_sores_d6 <fct> 2_moderate, 1_mild, 3_severe, 3_severe, 3_se…
## $ weakness_of_the_knees_d6 <fct> 2_moderate, 2_moderate, 3_severe, 3_severe, …
## $ lassitude_d6 <fct> 2_moderate, 3_severe, 3_severe, 3_severe, 3_…
## $ fit_for_duty_d6 <fct> 0_no, 0_no, 0_no, 0_no, 0_no, 0_no, 0_no, 0_…str(medicaldata::scurvy)## tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
## $ study_id : chr [1:12] "001" "002" "003" "004" ...
## $ treatment : Factor w/ 6 levels "cider","citrus",..: 1 1 3 3 6 6 5 5 2 2 ...
## $ dosing_regimen_for_scurvy: chr [1:12] "1 quart per day" "1 quart per day" "25 drops of elixir of vitriol, three times a day" "25 drops of elixir of vitriol, three times a day" ...
## $ gum_rot_d6 : Factor w/ 4 levels "0_none","1_mild",..: 3 3 2 3 4 4 4 4 2 1 ...
## $ skin_sores_d6 : Factor w/ 4 levels "0_none","1_mild",..: 3 2 4 4 4 4 4 4 2 1 ...
## $ weakness_of_the_knees_d6 : Factor w/ 4 levels "0_none","1_mild",..: 3 3 4 4 4 4 4 4 1 1 ...
## $ lassitude_d6 : Factor w/ 4 levels "0_none","1_mild",..: 3 4 4 4 4 4 4 4 2 1 ...
## $ fit_for_duty_d6 : Factor w/ 2 levels "0_no","1_yes": 1 1 1 1 1 1 1 1 1 2 ...mean(medicaldata::strep_tb$patient_id)## [1] 54str(medicaldata::strep_tb$patient_id)## int [1:107] 1 2 3 4 5 6 7 8 9 10 ...is.numeric(medicaldata::strep_tb$patient_id)## [1] TRUEhead(medicaldata::scurvy)## # A tibble: 6 × 8
## study_id treatment dosing_regimen_for_sc…¹ gum_rot_d6 skin_sores_d6
## <chr> <fct> <chr> <fct> <fct>
## 1 001 cider 1 quart per day 2_moderate 2_moderate
## 2 002 cider 1 quart per day 2_moderate 1_mild
## 3 003 dilute_sulfuric_acid 25 drops of elixir of … 1_mild 3_severe
## 4 004 dilute_sulfuric_acid 25 drops of elixir of … 2_moderate 3_severe
## 5 005 vinegar two spoonfuls, three t… 3_severe 3_severe
## 6 006 vinegar two spoonfuls, three t… 3_severe 3_severe
## # ℹ abbreviated name: ¹dosing_regimen_for_scurvy
## # ℹ 3 more variables: weakness_of_the_knees_d6 <fct>, lassitude_d6 <fct>,
## # fit_for_duty_d6 <fct>tail(medicaldata::strep_tb)## patient_id arm dose_strep_g dose_PAS_g gender baseline_condition
## 102 100 Streptomycin 2 0 M 3_Poor
## 103 101 Streptomycin 2 0 F 3_Poor
## 104 104 Streptomycin 2 0 M 3_Poor
## 105 105 Streptomycin 2 0 F 3_Poor
## 106 106 Streptomycin 2 0 F 3_Poor
## 107 107 Streptomycin 2 0 F 3_Poor
## baseline_temp baseline_esr baseline_cavitation strep_resistance
## 102 2_99-99.9F/37.3-37.7C 4_51+ yes 3_resist_100+
## 103 4_>=101F/38.3C 4_51+ yes 3_resist_100+
## 104 4_>=101F/38.3C 4_51+ yes 3_resist_100+
## 105 4_>=101F/38.3C 4_51+ yes 3_resist_100+
## 106 4_>=101F/38.3C 4_51+ yes 3_resist_100+
## 107 4_>=101F/38.3C 4_51+ yes 3_resist_100+
## radiologic_6m rad_num improved
## 102 4_No_change 4 FALSE
## 103 2_Considerable_deterioration 2 FALSE
## 104 5_Moderate_improvement 5 TRUE
## 105 2_Considerable_deterioration 2 FALSE
## 106 1_Death 1 FALSE
## 107 6_Considerable_improvement 6 TRUEhead(as.data.frame(medicaldata::scurvy))## study_id treatment
## 1 001 cider
## 2 002 cider
## 3 003 dilute_sulfuric_acid
## 4 004 dilute_sulfuric_acid
## 5 005 vinegar
## 6 006 vinegar
## dosing_regimen_for_scurvy gum_rot_d6 skin_sores_d6
## 1 1 quart per day 2_moderate 2_moderate
## 2 1 quart per day 2_moderate 1_mild
## 3 25 drops of elixir of vitriol, three times a day 1_mild 3_severe
## 4 25 drops of elixir of vitriol, three times a day 2_moderate 3_severe
## 5 two spoonfuls, three times daily 3_severe 3_severe
## 6 two spoonfuls, three times daily 3_severe 3_severe
## weakness_of_the_knees_d6 lassitude_d6 fit_for_duty_d6
## 1 2_moderate 2_moderate 0_no
## 2 2_moderate 3_severe 0_no
## 3 3_severe 3_severe 0_no
## 4 3_severe 3_severe 0_no
## 5 3_severe 3_severe 0_no
## 6 3_severe 3_severe 0_no# 查看全列，width = Inf
print(tail(medicaldata::strep_tb, width = Inf))## patient_id arm dose_strep_g dose_PAS_g gender baseline_condition
## 102 100 Streptomycin 2 0 M 3_Poor
## 103 101 Streptomycin 2 0 F 3_Poor
## 104 104 Streptomycin 2 0 M 3_Poor
## 105 105 Streptomycin 2 0 F 3_Poor
## 106 106 Streptomycin 2 0 F 3_Poor
## 107 107 Streptomycin 2 0 F 3_Poor
## baseline_temp baseline_esr baseline_cavitation strep_resistance
## 102 2_99-99.9F/37.3-37.7C 4_51+ yes 3_resist_100+
## 103 4_>=101F/38.3C 4_51+ yes 3_resist_100+
## 104 4_>=101F/38.3C 4_51+ yes 3_resist_100+
## 105 4_>=101F/38.3C 4_51+ yes 3_resist_100+
## 106 4_>=101F/38.3C 4_51+ yes 3_resist_100+
## 107 4_>=101F/38.3C 4_51+ yes 3_resist_100+
## radiologic_6m rad_num improved
## 102 4_No_change 4 FALSE
## 103 2_Considerable_deterioration 2 FALSE
## 104 5_Moderate_improvement 5 TRUE
## 105 2_Considerable_deterioration 2 FALSE
## 106 1_Death 1 FALSE
## 107 6_Considerable_improvement 6 TRUE# 查看全行，n = Inf
print(tail(medicaldata::strep_tb, n = Inf))## patient_id arm dose_strep_g dose_PAS_g gender baseline_condition
## 1 1 Control 0 0 M 1_Good
## 2 2 Control 0 0 F 1_Good
## 3 3 Control 0 0 F 1_Good
## 4 4 Control 0 0 M 1_Good
## 5 5 Control 0 0 F 1_Good
## 6 6 Control 0 0 M 1_Good
## 7 7 Control 0 0 F 1_Good
## 8 8 Control 0 0 M 1_Good
## 9 9 Control 0 0 F 2_Fair
## 10 10 Control 0 0 M 2_Fair
## 11 11 Control 0 0 F 2_Fair
## 12 12 Control 0 0 M 2_Fair
## 13 13 Control 0 0 F 2_Fair
## 14 14 Control 0 0 M 2_Fair
## 15 15 Control 0 0 F 2_Fair
## 16 16 Control 0 0 M 2_Fair
## 17 17 Control 0 0 F 2_Fair
## 18 18 Control 0 0 M 2_Fair
## 19 19 Control 0 0 F 2_Fair
## 20 20 Control 0 0 M 2_Fair
## 21 21 Control 0 0 F 2_Fair
## 22 22 Control 0 0 M 2_Fair
## 23 23 Control 0 0 F 2_Fair
## 24 24 Control 0 0 M 2_Fair
## 25 25 Control 0 0 F 2_Fair
## 26 26 Control 0 0 M 2_Fair
## 27 27 Control 0 0 F 2_Fair
## 28 28 Control 0 0 M 2_Fair
## 29 29 Control 0 0 F 3_Poor
## 30 30 Control 0 0 F 3_Poor
## 31 31 Control 0 0 M 3_Poor
## 32 32 Control 0 0 F 3_Poor
## 33 33 Control 0 0 M 3_Poor
## 34 34 Control 0 0 F 3_Poor
## 35 35 Control 0 0 M 3_Poor
## 36 36 Control 0 0 F 3_Poor
## 37 37 Control 0 0 M 3_Poor
## 38 38 Control 0 0 F 3_Poor
## 39 39 Control 0 0 F 3_Poor
## 40 40 Control 0 0 M 3_Poor
## 41 41 Control 0 0 F 3_Poor
## 42 42 Control 0 0 M 3_Poor
## 43 43 Control 0 0 F 3_Poor
## 44 44 Control 0 0 M 3_Poor
## 45 45 Control 0 0 F 3_Poor
## 46 46 Control 0 0 F 3_Poor
## 47 47 Control 0 0 F 3_Poor
## 48 48 Control 0 0 M 3_Poor
## 49 49 Control 0 0 F 3_Poor
## 50 50 Control 0 0 M 3_Poor
## 51 51 Control 0 0 F 3_Poor
## 52 52 Control 0 0 M 3_Poor
## 53 53 Streptomycin 2 0 M 1_Good
## 54 55 Streptomycin 2 0 F 1_Good
## 55 56 Streptomycin 2 0 M 1_Good
## 56 57 Streptomycin 2 0 F 1_Good
## 57 58 Streptomycin 2 0 M 1_Good
## 58 59 Streptomycin 2 0 F 1_Good
## 59 60 Streptomycin 2 0 M 1_Good
## 60 67 Streptomycin 2 0 F 1_Good
## 61 74 Streptomycin 2 0 M 2_Fair
## 62 54 Streptomycin 2 0 F 2_Fair
## 63 61 Streptomycin 2 0 F 2_Fair
## 64 68 Streptomycin 2 0 M 2_Fair
## 65 75 Streptomycin 2 0 F 2_Fair
## 66 62 Streptomycin 2 0 M 2_Fair
## 67 63 Streptomycin 2 0 F 2_Fair
## 68 64 Streptomycin 2 0 M 2_Fair
## 69 65 Streptomycin 2 0 F 2_Fair
## 70 66 Streptomycin 2 0 M 2_Fair
## 71 69 Streptomycin 2 0 F 2_Fair
## 72 70 Streptomycin 2 0 M 2_Fair
## 73 71 Streptomycin 2 0 F 2_Fair
## 74 72 Streptomycin 2 0 M 2_Fair
## 75 73 Streptomycin 2 0 F 2_Fair
## 76 81 Streptomycin 2 0 F 2_Fair
## 77 88 Streptomycin 2 0 F 2_Fair
## 78 82 Streptomycin 2 0 F 3_Poor
## 79 89 Streptomycin 2 0 M 3_Poor
## 80 76 Streptomycin 2 0 M 3_Poor
## 81 77 Streptomycin 2 0 F 3_Poor
## 82 78 Streptomycin 2 0 M 3_Poor
## 83 79 Streptomycin 2 0 F 3_Poor
## 84 80 Streptomycin 2 0 M 3_Poor
## 85 83 Streptomycin 2 0 M 3_Poor
## 86 84 Streptomycin 2 0 F 3_Poor
## 87 85 Streptomycin 2 0 M 3_Poor
## 88 86 Streptomycin 2 0 F 3_Poor
## 89 87 Streptomycin 2 0 M 3_Poor
## 90 90 Streptomycin 2 0 F 3_Poor
## 91 91 Streptomycin 2 0 F 3_Poor
## 92 95 Streptomycin 2 0 F 3_Poor
## 93 102 Streptomycin 2 0 M 3_Poor
## 94 96 Streptomycin 2 0 M 3_Poor
## 95 103 Streptomycin 2 0 F 3_Poor
## 96 92 Streptomycin 2 0 M 3_Poor
## 97 93 Streptomycin 2 0 F 3_Poor
## 98 94 Streptomycin 2 0 M 3_Poor
## 99 97 Streptomycin 2 0 F 3_Poor
## 100 98 Streptomycin 2 0 F 3_Poor
## 101 99 Streptomycin 2 0 F 3_Poor
## 102 100 Streptomycin 2 0 M 3_Poor
## 103 101 Streptomycin 2 0 F 3_Poor
## 104 104 Streptomycin 2 0 M 3_Poor
## 105 105 Streptomycin 2 0 F 3_Poor
## 106 106 Streptomycin 2 0 F 3_Poor
## 107 107 Streptomycin 2 0 F 3_Poor
## baseline_temp baseline_esr baseline_cavitation
## 1 1_<=98.9F/37.2C 2_11-20 yes
## 2 3_100-100.9F/37.8-38.2C 2_11-20 no
## 3 1_<=98.9F/37.2C 3_21-50 no
## 4 1_<=98.9F/37.2C 3_21-50 no
## 5 2_99-99.9F/37.3-37.7C 3_21-50 no
## 6 3_100-100.9F/37.8-38.2C 3_21-50 no
## 7 2_99-99.9F/37.3-37.7C 3_21-50 yes
## 8 2_99-99.9F/37.3-37.7C 3_21-50 yes
## 9 2_99-99.9F/37.3-37.7C 3_21-50 yes
## 10 4_>=101F/38.3C 3_21-50 yes
## 11 3_100-100.9F/37.8-38.2C 3_21-50 no
## 12 2_99-99.9F/37.3-37.7C 3_21-50 yes
## 13 2_99-99.9F/37.3-37.7C 3_21-50 yes
## 14 4_>=101F/38.3C 3_21-50 yes
## 15 2_99-99.9F/37.3-37.7C 3_21-50 yes
## 16 3_100-100.9F/37.8-38.2C 3_21-50 no
## 17 3_100-100.9F/37.8-38.2C 3_21-50 no
## 18 1_<=98.9F/37.2C 3_21-50 no
## 19 3_100-100.9F/37.8-38.2C 3_21-50 no
## 20 3_100-100.9F/37.8-38.2C 3_21-50 no
## 21 3_100-100.9F/37.8-38.2C 3_21-50 no
## 22 3_100-100.9F/37.8-38.2C 3_21-50 no
## 23 2_99-99.9F/37.3-37.7C 4_51+ yes
## 24 2_99-99.9F/37.3-37.7C 4_51+ no
## 25 2_99-99.9F/37.3-37.7C 4_51+ no
## 26 3_100-100.9F/37.8-38.2C 4_51+ yes
## 27 3_100-100.9F/37.8-38.2C 4_51+ yes
## 28 3_100-100.9F/37.8-38.2C 4_51+ yes
## 29 2_99-99.9F/37.3-37.7C 4_51+ yes
## 30 4_>=101F/38.3C 4_51+ yes
## 31 4_>=101F/38.3C 4_51+ yes
## 32 4_>=101F/38.3C 4_51+ yes
## 33 4_>=101F/38.3C 4_51+ no
## 34 4_>=101F/38.3C 4_51+ no
## 35 4_>=101F/38.3C 4_51+ no
## 36 4_>=101F/38.3C 4_51+ no
## 37 4_>=101F/38.3C 4_51+ no
## 38 4_>=101F/38.3C 4_51+ yes
## 39 4_>=101F/38.3C 4_51+ no
## 40 4_>=101F/38.3C 4_51+ yes
## 41 3_100-100.9F/37.8-38.2C 4_51+ no
## 42 3_100-100.9F/37.8-38.2C 4_51+ yes
## 43 3_100-100.9F/37.8-38.2C <NA> yes
## 44 3_100-100.9F/37.8-38.2C 4_51+ yes
## 45 3_100-100.9F/37.8-38.2C 4_51+ yes
## 46 2_99-99.9F/37.3-37.7C 4_51+ yes
## 47 4_>=101F/38.3C 4_51+ yes
## 48 4_>=101F/38.3C 4_51+ yes
## 49 4_>=101F/38.3C 4_51+ yes
## 50 4_>=101F/38.3C 4_51+ yes
## 51 4_>=101F/38.3C 4_51+ yes
## 52 4_>=101F/38.3C 4_51+ yes
## 53 4_>=101F/38.3C 2_11-20 no
## 54 1_<=98.9F/37.2C 2_11-20 no
## 55 1_<=98.9F/37.2C 3_21-50 no
## 56 1_<=98.9F/37.2C 3_21-50 no
## 57 2_99-99.9F/37.3-37.7C 3_21-50 no
## 58 2_99-99.9F/37.3-37.7C 3_21-50 no
## 59 2_99-99.9F/37.3-37.7C 3_21-50 no
## 60 2_99-99.9F/37.3-37.7C 3_21-50 no
## 61 3_100-100.9F/37.8-38.2C 3_21-50 no
## 62 2_99-99.9F/37.3-37.7C 2_11-20 no
## 63 2_99-99.9F/37.3-37.7C 3_21-50 no
## 64 4_>=101F/38.3C 3_21-50 no
## 65 3_100-100.9F/37.8-38.2C 4_51+ no
## 66 2_99-99.9F/37.3-37.7C 3_21-50 no
## 67 4_>=101F/38.3C 3_21-50 no
## 68 2_99-99.9F/37.3-37.7C/37.3-37.7C 3_21-50 no
## 69 2_99-99.9F/37.3-37.7C 3_21-50 no
## 70 2_99-99.9F/37.3-37.7C 3_21-50 no
## 71 4_>=101F/38.3C 3_21-50 no
## 72 4_>=101F/38.3C 3_21-50 no
## 73 4_>=101F/38.3C 4_51+ no
## 74 4_>=101F/38.3C 4_51+ no
## 75 3_100-100.9F/37.8-38.2C 4_51+ no
## 76 3_100-100.9F/37.8-38.2C 4_51+ yes
## 77 3_100-100.9F/37.8-38.2C 4_51+ yes
## 78 3_100-100.9F/37.8-38.2C 4_51+ yes
## 79 3_100-100.9F/37.8-38.2C 4_51+ yes
## 80 3_100-100.9F/37.8-38.2C 4_51+ yes
## 81 4_>=101F/38.3C 4_51+ yes
## 82 3_100-100.9F/37.8-38.2C 4_51+ yes
## 83 3_100-100.9F/37.8-38.2C 4_51+ yes
## 84 3_100-100.9F/37.8-38.2C 4_51+ yes
## 85 3_100-100.9F/37.8-38.2C/37.8-38.2C 4_51+ yes
## 86 3_100-100.9F/37.8-38.2C 4_51+ yes
## 87 3_100-100.9F/37.8-38.2C 4_51+ yes
## 88 4_>=101F/38.3C 4_51+ yes
## 89 4_>=101F/38.3C 4_51+ yes
## 90 4_>=101F/38.3C 4_51+ yes
## 91 3_100-100.9F/37.8-38.2C 4_51+ yes
## 92 2_99-99.9F/37.3-37.7C 4_51+ yes
## 93 4_>=101F/38.3C 4_51+ yes
## 94 4_>=101F/38.3C 4_51+ yes
## 95 4_>=101F/38.3C 4_51+ yes
## 96 4_>=101F/38.3C 4_51+ yes
## 97 4_>=101F/38.3C 4_51+ yes
## 98 2_99-99.9F/37.3-37.7C 4_51+ yes
## 99 4_>=101F/38.3C 4_51+ yes
## 100 4_>=101F/38.3C 4_51+ yes
## 101 4_>=101F/38.3C 4_51+ yes
## 102 2_99-99.9F/37.3-37.7C 4_51+ yes
## 103 4_>=101F/38.3C 4_51+ yes
## 104 4_>=101F/38.3C 4_51+ yes
## 105 4_>=101F/38.3C 4_51+ yes
## 106 4_>=101F/38.3C 4_51+ yes
## 107 4_>=101F/38.3C 4_51+ yes
## strep_resistance radiologic_6m rad_num improved
## 1 1_sens_0-8 6_Considerable_improvement 6 TRUE
## 2 1_sens_0-8 5_Moderate_improvement 5 TRUE
## 3 1_sens_0-8 5_Moderate_improvement 5 TRUE
## 4 1_sens_0-8 5_Moderate_improvement 5 TRUE
## 5 1_sens_0-8 5_Moderate_improvement 5 TRUE
## 6 1_sens_0-8 6_Considerable_improvement 6 TRUE
## 7 1_sens_0-8 5_Moderate_improvement 5 TRUE
## 8 1_sens_0-8 5_Moderate_improvement 5 TRUE
## 9 1_sens_0-8 5_Moderate_improvement 5 TRUE
## 10 1_sens_0-8 5_Moderate_improvement 5 TRUE
## 11 1_sens_0-8 6_Considerable_improvement 6 TRUE
## 12 1_sens_0-8 5_Moderate_improvement 5 TRUE
## 13 1_sens_0-8 5_Moderate_improvement 5 TRUE
## 14 1_sens_0-8 5_Moderate_improvement 5 TRUE
## 15 1_sens_0-8 5_Moderate_improvement 5 TRUE
## 16 1_sens_0-8 6_Considerable_improvement 6 TRUE
## 17 1_sens_0-8 5_Moderate_improvement 5 TRUE
## 18 1_sens_0-8 4_No_change 4 FALSE
## 19 1_sens_0-8 3_Moderate_deterioration 3 FALSE
## 20 1_sens_0-8 3_Moderate_deterioration 3 FALSE
## 21 1_sens_0-8 3_Moderate_deterioration 3 FALSE
## 22 1_sens_0-8 3_Moderate_deterioration 3 FALSE
## 23 1_sens_0-8 4_No_change 4 FALSE
## 24 1_sens_0-8 3_Moderate_deterioration 3 FALSE
## 25 1_sens_0-8 3_Moderate_deterioration 3 FALSE
## 26 1_sens_0-8 3_Moderate_deterioration 3 FALSE
## 27 1_sens_0-8 3_Moderate_deterioration 3 FALSE
## 28 1_sens_0-8 3_Moderate_deterioration 3 FALSE
## 29 1_sens_0-8 4_No_change 4 FALSE
## 30 1_sens_0-8 3_Moderate_deterioration 3 FALSE
## 31 1_sens_0-8 3_Moderate_deterioration 3 FALSE
## 32 1_sens_0-8 3_Moderate_deterioration 3 FALSE
## 33 1_sens_0-8 2_Considerable_deterioration 2 FALSE
## 34 1_sens_0-8 2_Considerable_deterioration 2 FALSE
## 35 1_sens_0-8 2_Considerable_deterioration 2 FALSE
## 36 1_sens_0-8 2_Considerable_deterioration 2 FALSE
## 37 1_sens_0-8 1_Death 1 FALSE
## 38 1_sens_0-8 2_Considerable_deterioration 2 FALSE
## 39 1_sens_0-8 1_Death 1 FALSE
## 40 1_sens_0-8 2_Considerable_deterioration 2 FALSE
## 41 1_sens_0-8 1_Death 1 FALSE
## 42 1_sens_0-8 1_Death 1 FALSE
## 43 1_sens_0-8 1_Death 1 FALSE
## 44 1_sens_0-8 1_Death 1 FALSE
## 45 1_sens_0-8 1_Death 1 FALSE
## 46 1_sens_0-8 1_Death 1 FALSE
## 47 1_sens_0-8 1_Death 1 FALSE
## 48 1_sens_0-8 1_Death 1 FALSE
## 49 1_sens_0-8 1_Death 1 FALSE
## 50 1_sens_0-8 1_Death 1 FALSE
## 51 1_sens_0-8 1_Death 1 FALSE
## 52 1_sens_0-8 1_Death 1 FALSE
## 53 1_sens_0-8 6_Considerable_improvement 6 TRUE
## 54 1_sens_0-8 6_Considerable_improvement 6 TRUE
## 55 1_sens_0-8 6_Considerable_improvement 6 TRUE
## 56 1_sens_0-8 6_Considerable_improvement 6 TRUE
## 57 1_sens_0-8 6_Considerable_improvement 6 TRUE
## 58 1_sens_0-8 6_Considerable_improvement 6 TRUE
## 59 1_sens_0-8 6_Considerable_improvement 6 TRUE
## 60 1_sens_0-8 6_Considerable_improvement 6 TRUE
## 61 1_sens_0-8 6_Considerable_improvement 6 TRUE
## 62 2_mod_8-99 6_Considerable_improvement 6 TRUE
## 63 2_mod_8-99 6_Considerable_improvement 6 TRUE
## 64 2_mod_8-99 6_Considerable_improvement 6 TRUE
## 65 2_mod_8-99 5_Moderate_improvement 5 TRUE
## 66 3_resist_100+ 2_Considerable_deterioration 2 FALSE
## 67 3_resist_100+ 2_Considerable_deterioration 2 FALSE
## 68 3_resist_100+ 6_Considerable_improvement 6 TRUE
## 69 3_resist_100+ 5_Moderate_improvement 5 TRUE
## 70 3_resist_100+ 6_Considerable_improvement 6 TRUE
## 71 3_resist_100+ 3_Moderate_deterioration 3 FALSE
## 72 3_resist_100+ 6_Considerable_improvement 6 TRUE
## 73 3_resist_100+ 6_Considerable_improvement 6 TRUE
## 74 3_resist_100+ 5_Moderate_improvement 5 TRUE
## 75 3_resist_100+ 6_Considerable_improvement 6 TRUE
## 76 1_sens_0-8 6_Considerable_improvement 6 TRUE
## 77 1_sens_0-8 5_Moderate_improvement 5 TRUE
## 78 2_mod_8-99 6_Considerable_improvement 6 TRUE
## 79 2_mod_8-99 5_Moderate_improvement 5 TRUE
## 80 3_resist_100+ 6_Considerable_improvement 6 TRUE
## 81 3_resist_100+ 1_Death 1 FALSE
## 82 3_resist_100+ 5_Moderate_improvement 5 TRUE
## 83 3_resist_100+ 6_Considerable_improvement 6 TRUE
## 84 3_resist_100+ 3_Moderate_deterioration 3 FALSE
## 85 3_resist_100+ 6_Considerable_improvement 6 TRUE
## 86 3_resist_100+ 6_Considerable_improvement 6 TRUE
## 87 3_resist_100+ 6_Considerable_improvement 6 TRUE
## 88 3_resist_100+ 1_Death 1 FALSE
## 89 3_resist_100+ 4_No_change 4 FALSE
## 90 3_resist_100+ 3_Moderate_deterioration 3 FALSE
## 91 3_resist_100+ 3_Moderate_deterioration 3 FALSE
## 92 1_sens_0-8 2_Considerable_deterioration 2 FALSE
## 93 1_sens_0-8 6_Considerable_improvement 6 TRUE
## 94 2_mod_8-99 6_Considerable_improvement 6 TRUE
## 95 2_mod_8-99 5_Moderate_improvement 5 TRUE
## 96 3_resist_100+ 6_Considerable_improvement 6 TRUE
## 97 3_resist_100+ 5_Moderate_improvement 5 TRUE
## 98 3_resist_100+ 2_Considerable_deterioration 2 FALSE
## 99 3_resist_100+ 5_Moderate_improvement 5 TRUE
## 100 3_resist_100+ 3_Moderate_deterioration 3 FALSE
## 101 3_resist_100+ 1_Death 1 FALSE
## 102 3_resist_100+ 4_No_change 4 FALSE
## 103 3_resist_100+ 2_Considerable_deterioration 2 FALSE
## 104 3_resist_100+ 5_Moderate_improvement 5 TRUE
## 105 3_resist_100+ 2_Considerable_deterioration 2 FALSE
## 106 3_resist_100+ 1_Death 1 FALSE
## 107 3_resist_100+ 6_Considerable_improvement 6 TRUEView(medicaldata::strep_tb)
#check that radiologic_6m, rad_num, and improved all match,用slice_sample随机阅览数据
medicaldata::strep_tb %>%
slice_sample(prop = 0.1) %>%
select(radiologic_6m, rad_num, improved)## radiologic_6m rad_num improved
## 1 3_Moderate_deterioration 3 FALSE
## 2 6_Considerable_improvement 6 TRUE
## 3 5_Moderate_improvement 5 TRUE
## 4 6_Considerable_improvement 6 TRUE
## 5 6_Considerable_improvement 6 TRUE
## 6 6_Considerable_improvement 6 TRUE
## 7 4_No_change 4 FALSE
## 8 6_Considerable_improvement 6 TRUE
## 9 2_Considerable_deterioration 2 FALSE
## 10 4_No_change 4 FALSElibrary(skimr)
library(DataExplorer)
skimr::skim(medicaldata::strep_tb)| Name | medicaldata::strep_tb |
| Number of rows | 107 |
| Number of columns | 13 |
| _______________________ | |
| Column type frequency: | |
| character | 8 |
| logical | 1 |
| numeric | 4 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| arm | 0 | 1.00 | 7 | 12 | 0 | 2 | 0 |
| gender | 0 | 1.00 | 1 | 1 | 0 | 2 | 0 |
| baseline_condition | 0 | 1.00 | 6 | 6 | 0 | 3 | 0 |
| baseline_temp | 0 | 1.00 | 14 | 34 | 0 | 6 | 0 |
| baseline_esr | 1 | 0.99 | 5 | 7 | 0 | 3 | 0 |
| baseline_cavitation | 0 | 1.00 | 2 | 3 | 0 | 2 | 0 |
| strep_resistance | 0 | 1.00 | 10 | 13 | 0 | 3 | 0 |
| radiologic_6m | 0 | 1.00 | 7 | 28 | 0 | 6 | 0 |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| improved | 0 | 1 | 0.51 | TRU: 55, FAL: 52 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| patient_id | 0 | 1 | 54.00 | 31.03 | 1 | 27.5 | 54 | 80.5 | 107 | ▇▇▇▇▇ |
| dose_strep_g | 0 | 1 | 1.03 | 1.00 | 0 | 0.0 | 2 | 2.0 | 2 | ▇▁▁▁▇ |
| dose_PAS_g | 0 | 1 | 0.00 | 0.00 | 0 | 0.0 | 0 | 0.0 | 0 | ▁▁▇▁▁ |
| rad_num | 0 | 1 | 3.93 | 1.89 | 1 | 2.0 | 5 | 6.0 | 6 | ▇▅▁▆▇ |
DataExplorer::create_report(medicaldata::strep_tb)##
##
## processing file: report.rmd## | | | 0% | |. | 2% | |.. | 5% [global_options] | |... | 7% | |.... | 10% [introduce] | |.... | 12% | |..... | 14% [plot_intro]## | |...... | 17% | |....... | 19% [data_structure] | |........ | 21% | |......... | 24% [missing_profile]## | |.......... | 26% | |........... | 29% [univariate_distribution_header] | |........... | 31% | |............ | 33% [plot_histogram]## | |............. | 36% | |.............. | 38% [plot_density] | |............... | 40% | |................ | 43% [plot_frequency_bar]## | |................. | 45% | |.................. | 48% [plot_response_bar] | |.................. | 50% | |................... | 52% [plot_with_bar] | |.................... | 55% | |..................... | 57% [plot_normal_qq]## | |...................... | 60% | |....................... | 62% [plot_response_qq] | |........................ | 64% | |......................... | 67% [plot_by_qq] | |.......................... | 69% | |.......................... | 71% [correlation_analysis]## | |........................... | 74% | |............................ | 76% [principal_component_analysis]## | |............................. | 79% | |.............................. | 81% [bivariate_distribution_header] | |............................... | 83% | |................................ | 86% [plot_response_boxplot] | |................................. | 88% | |................................. | 90% [plot_by_boxplot] | |.................................. | 93% | |................................... | 95% [plot_response_scatterplot] | |.................................... | 98% | |.....................................| 100% [plot_by_scatterplot] ## output file: D:/rstudio workspace/study/report.knit.md## "D:/RStudio/resources/app/bin/quarto/bin/tools/pandoc" +RTS -K512m -RTS "D:\RSTUDI~1\study\REPORT~1.MD" --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output pandoc583c51d95a8a.html --lua-filter "D:\R-4.4.1\library\rmarkdown\rmarkdown\lua\pagebreak.lua" --lua-filter "D:\R-4.4.1\library\rmarkdown\rmarkdown\lua\latex-div.lua" --embed-resources --standalone --variable bs3=TRUE --section-divs --table-of-contents --toc-depth 6 --template "D:\R-4.4.1\library\rmarkdown\rmd\h\default.html" --no-highlight --variable highlightjs=1 --variable theme=yeti --mathjax --variable "mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML" --include-in-header "C:\Users\86186\AppData\Local\Temp\RtmpiuNIvy\rmarkdown-str583c90446f.html"##
## Output created: report.html| Variable Type | Long form | Abbreviation |
|---|---|---|
| Logical (TRUE/FALSE) | col_logical() | l |
| Integer | col_integer() | i |
| Double | col_double() | d |
| Character | col_character() | c |
| Factor (nominal or ordinal) | col_factor(levels, ordered) | f |
| Date | col_date(format) | D |
| Time | col_time(format) | t |
| Date & Time | col_datetime(format) | T |
| Number | col_number() | n |
| Don’t import | col_skip() | - |
| Default Guessing | col_guess() | ? |