#Se cargan los datos de interes desde la base de datos
load("~/Especializacion_geomatica/tratamiento_datos/clase8/YDRAY-datos_fresno3.RData")
datos_fresno3
## Monitoreo Fruto_Total_Dano Temperature Relative_Humidity Altitude
## 1 0021-08-20 0 25.7 33.6 1.896
## 2 0021-08-20 3 20.8 36.8 1.895
## 3 0021-08-20 0 23.7 31.5 1.889
## 4 0021-08-20 0 25.0 33.2 1.890
## 5 0021-08-20 1 25.0 34.3 1.894
## 6 0021-08-20 0 25.0 33.8 1.900
## 7 0021-08-20 0 24.9 34.9 1.900
## 8 0021-08-20 0 22.9 34.2 1.899
## 9 0021-08-20 0 26.2 33.6 1.897
## 10 0021-08-20 21 24.6 34.0 1.892
## 11 0021-08-20 0 25.5 29.5 1.891
## 12 0021-08-20 1 26.7 30.6 1.895
## 13 0021-08-20 0 28.8 29.6 1.897
## 14 0021-08-20 0 27.4 32.7 1.901
## 15 0021-08-20 0 26.8 29.7 1.902
## 16 0021-08-20 0 28.0 31.0 1.900
## 17 0021-08-20 0 27.3 29.1 1.901
## 18 0021-08-20 0 28.2 32.0 1.899
## 19 0021-08-20 0 28.4 29.6 1.899
## 20 0021-08-20 0 26.5 28.3 1.895
## 21 0021-08-20 2 26.9 27.2 1.895
## 22 0021-08-20 0 27.5 31.8 1.897
## 23 0021-08-20 0 28.0 30.9 1.899
## 24 0021-08-20 0 27.9 31.9 1.900
## 25 0021-08-20 0 26.4 33.3 1.900
## 26 0021-08-20 0 28.1 30.0 1.901
## 27 0021-08-20 27 28.1 35.6 1.901
## 28 0021-08-20 14 29.4 34.4 1.898
## 29 0021-08-20 0 27.8 31.5 1.898
## 30 0021-08-20 0 28.7 30.9 1.899
## 31 0021-08-20 1 30.1 28.5 1.898
## 32 0021-08-20 2 30.2 32.3 1.898
## 33 0021-08-20 0 27.6 40.0 1.899
## 34 0021-08-20 0 29.6 31.2 1.898
## 35 0021-08-20 0 28.9 32.6 1.898
## 36 0021-08-20 0 29.0 32.4 1.895
## 37 0021-08-20 0 30.6 31.8 1.894
## 38 0021-08-20 0 28.0 29.7 1.895
## 39 0021-08-20 0 29.5 30.8 1.896
## 40 0021-08-20 0 27.8 30.0 1.896
## 41 0021-08-20 0 29.1 33.3 1.896
## 42 0021-08-20 0 27.6 36.0 1.895
## 43 0021-08-20 0 28.4 33.5 1.895
## 44 0021-08-20 0 30.4 32.8 1.895
## 45 0021-08-20 0 28.6 28.9 1.896
## 46 0021-08-20 0 29.0 31.1 1.896
## 47 0021-08-20 3 26.5 33.8 1.896
## 48 0021-08-20 0 29.3 31.7 1.893
## 49 0021-08-20 1 29.3 31.8 1.892
## 50 0021-08-20 0 30.2 32.2 1.892
## 51 0021-08-20 18 27.6 31.2 1.893
## 52 0021-08-20 1 29.8 30.8 1.892
## 53 0021-08-20 0 29.1 30.0 1.893
## 54 0021-08-20 0 27.2 29.1 1.893
## 55 0021-08-20 11 29.2 30.7 1.893
## 56 0021-08-20 12 28.1 33.8 1.894
## 57 0021-08-20 0 29.6 32.6 1.894
## 58 0021-08-20 0 27.3 32.2 1.894
## 59 0021-08-20 11 27.3 32.8 1.894
## 60 0021-08-20 2 27.5 34.3 1.893
## 61 0021-08-20 0 24.9 31.4 1.894
## 62 0021-08-20 0 26.9 35.6 1.893
## 63 0021-08-20 0 27.0 32.9 1.891
## 64 0021-08-20 0 28.0 30.5 1.891
## 65 0021-08-20 0 27.8 29.4 1.892
## 66 0021-08-20 0 26.4 35.7 1.894
## 67 0021-08-20 1 27.3 43.1 1.893
## 68 0021-08-20 0 26.8 31.3 1.892
## 69 0021-08-20 0 30.8 26.4 1.893
## 70 0021-08-20 0 28.8 28.3 1.893
## 71 0021-08-20 0 29.5 33.0 1.893
## 72 0021-08-20 5 31.0 26.7 1.894
## 73 0021-08-20 14 28.1 31.7 1.893
## 74 0021-08-20 15 26.5 34.8 1.892
## 75 0021-08-20 5 28.8 31.8 1.892
## 76 0021-08-20 1 29.1 31.1 1.891
## 77 0021-08-20 0 30.0 33.3 1.891
## 78 0021-08-20 0 33.9 36.1 1.891
## 79 0021-08-20 3 31.4 43.7 1.893
## 80 0021-08-20 2 30.0 33.2 1.894
## 81 0021-08-20 0 30.9 31.2 1.894
## 82 0021-08-20 0 29.5 31.8 1.892
## 83 0021-08-20 0 26.5 37.1 1.892
## 84 0021-08-20 0 26.0 38.7 1.891
## 85 0021-08-20 2 27.0 35.3 1.890
## 86 0021-08-20 3 27.9 31.7 1.887
## 87 0021-08-20 0 28.7 31.5 1.888
## 88 0021-08-20 9 28.8 31.8 1.887
## 89 0021-08-20 0 32.8 29.5 1.889
## 90 0021-08-20 1 28.8 30.7 1.890
## 91 0021-08-20 0 29.3 31.3 1.891
## 92 0021-08-20 0 33.3 34.2 1.891
## 93 0021-08-20 0 29.2 31.4 1.891
## 94 0021-08-20 1 28.3 30.6 1.892
## 95 0021-08-20 0 29.7 34.5 1.894
## 96 0021-08-20 1 30.2 30.5 1.894
## 97 0021-08-20 0 28.5 31.1 1.895
## 98 0021-08-20 0 26.6 31.7 1.895
## 99 0021-08-20 3 28.8 32.2 1.892
## 100 0021-08-20 0 26.8 29.0 1.893
## 101 0021-08-20 0 27.5 30.1 1.895
## 102 0021-08-20 0 27.0 32.7 1.895
## 103 0021-08-20 0 27.8 33.3 1.894
## 104 0021-08-20 0 26.2 31.5 1.893
## 105 0021-08-20 0 26.2 33.9 1.892
## 106 0021-08-20 0 27.7 33.9 1.892
## 107 0021-08-20 0 29.6 31.7 1.891
## 108 0021-08-20 0 28.8 34.7 1.891
## 109 0021-08-20 2 29.0 30.2 1.890
## 110 0021-08-20 0 29.7 29.7 1.890
## 111 0021-08-20 0 30.3 30.5 1.891
## 112 0021-08-20 1 30.7 30.3 1.891
## 113 0021-08-20 0 29.6 29.0 1.890
## 114 0021-08-20 6 29.6 31.7 1.889
## 115 0021-08-20 25 31.0 31.9 1.891
## 116 0021-08-20 0 29.9 29.2 1.891
## 117 0021-08-20 1 32.0 30.5 1.893
## 118 0021-08-20 0 30.4 29.3 1.895
## 119 0021-08-20 0 31.1 33.4 1.897
## 120 0021-08-20 0 31.2 30.0 1.894
## 121 0021-08-20 0 30.5 27.8 1.893
## 122 0021-08-20 0 33.6 25.1 1.892
## 123 0021-08-20 0 31.0 30.6 1.893
## 124 0021-08-20 0 30.8 29.0 1.894
## 125 0021-08-20 2 31.6 28.4 1.895
## 126 0021-08-20 0 29.8 26.9 1.897
## 127 0021-08-20 0 29.2 27.9 1.898
## 128 0021-08-20 0 29.0 27.8 1.899
## 129 0021-08-20 1 29.9 30.2 1.899
## 130 0021-08-20 0 30.7 31.5 1.901
## 131 0021-08-20 0 30.5 30.6 1.905
## 132 0021-08-20 1 28.4 31.8 1.903
## 133 0021-08-20 0 30.5 27.5 1.903
## 134 0021-08-20 1 31.5 27.4 1.900
## 135 0021-08-20 0 33.0 27.0 1.903
## 136 0021-08-20 0 28.6 27.6 1.904
## 137 0021-08-20 2 31.7 27.2 1.903
## 138 0021-08-20 0 29.0 31.1 1.903
## 139 0021-08-20 0 29.7 27.5 1.903
## 140 0021-08-20 1 29.2 30.5 1.900
## 141 0021-08-20 0 28.8 30.6 1.900
## 142 0021-08-20 0 29.2 31.4 1.900
## 143 0021-08-20 0 31.8 31.7 1.899
## 144 0021-08-20 0 31.3 29.9 1.896
## 145 0021-08-20 1 32.0 29.4 1.897
## 146 0021-08-20 9 29.8 28.2 1.896
## 147 0021-08-20 7 27.9 29.7 1.898
## 148 0021-08-20 7 26.2 35.2 1.899
## 149 0021-08-20 0 26.0 35.1 1.900
## 150 0021-08-20 0 24.8 37.8 1.902
## 151 0021-08-20 3 27.0 34.6 1.903
## 152 0021-08-20 0 27.1 43.3 1.904
## 153 0021-08-20 0 27.0 32.3 1.904
## 154 0021-08-20 1 25.5 33.4 1.903
## 155 0021-08-20 0 25.5 32.7 1.902
## 156 0021-08-20 0 25.6 36.5 1.900
## 157 0021-08-20 0 25.2 33.9 1.901
## 158 0021-08-20 0 25.8 32.1 1.902
## 159 0021-08-20 0 25.4 31.8 1.902
## 160 0021-08-20 0 23.2 34.6 1.902
## 161 0021-08-20 0 24.3 37.2 1.900
## 162 0021-08-20 0 22.8 41.6 1.899
## 163 0021-08-20 0 22.7 40.5 1.897
## 164 0021-08-20 0 22.8 40.5 1.895
## 165 0021-08-20 20 23.1 42.4 1.895
## 166 0021-08-20 2 22.8 40.8 1.897
## 167 0021-08-20 0 23.1 39.3 1.897
## 168 0021-08-20 0 22.9 38.7 1.897
## 169 0021-08-20 0 23.5 39.7 1.899
## 170 0021-08-20 0 23.3 42.8 1.901
## 171 0021-08-20 0 23.0 41.3 1.901
## 172 0021-08-20 2 24.0 41.0 1.899
## 173 0021-08-20 0 23.9 40.6 1.899
## 174 0021-08-20 0 23.6 42.5 1.897
## 175 0021-08-20 0 24.3 47.6 1.894
## 176 0021-08-20 0 24.2 39.5 1.897
## 177 0021-08-20 5 24.7 41.9 1.899
## 178 0021-08-20 0 23.5 38.1 1.900
## 179 0021-08-20 0 23.1 40.0 1.900
## 180 0021-08-20 0 24.9 43.1 1.899
## 181 0021-08-20 0 24.3 48.2 1.899
## 182 0021-08-20 0 24.8 38.5 1.898
## 183 0021-08-20 0 24.0 35.9 1.897
## 184 0021-08-20 0 24.6 38.7 1.896
## 185 0021-08-20 0 24.5 44.7 1.894
## 186 0021-08-20 0 23.8 46.4 1.895
## 187 0021-08-20 0 23.7 39.6 1.896
## 188 0021-08-20 0 24.1 41.1 1.897
## 189 0021-08-20 0 23.1 62.1 1.898
## 190 0021-08-20 0 24.4 44.0 1.899
## 191 0021-08-20 4 25.5 42.4 1.899
## 192 0021-08-20 0 26.1 42.5 1.900
## 193 0021-08-20 0 25.4 38.7 1.898
## 194 0021-08-20 0 26.0 40.3 1.897
## 195 0021-08-20 0 26.2 37.2 1.895
## 196 0021-08-20 0 24.9 76.1 1.893
## 197 0021-08-20 0 24.8 40.3 1.890
## 198 0021-08-20 0 25.6 51.4 1.894
## 199 0021-08-20 0 25.3 40.6 1.896
## 200 0021-08-20 0 26.3 45.1 1.898
## 201 0021-08-20 0 25.9 39.1 1.901
## 202 0021-08-20 0 29.1 31.2 1.901
## 203 0021-08-20 0 28.2 47.1 1.900
## 204 0021-08-20 0 27.6 35.4 1.901
## 205 0021-08-20 0 28.0 37.9 1.900
## 206 0021-08-20 0 25.4 34.1 1.900
## 207 0021-08-20 0 26.2 35.8 1.898
## 208 0021-08-20 0 26.5 38.9 1.899
## 209 0021-08-20 3 26.9 37.9 1.898
## 210 0021-08-20 0 28.8 34.6 1.898
## 211 0021-08-20 0 27.5 33.2 1.897
## 212 0021-08-20 5 26.9 36.2 1.896
## 213 0021-08-20 2 27.4 40.4 1.896
## 214 0021-08-20 2 27.4 63.9 1.896
## 215 0021-08-20 0 26.4 44.5 1.898
## 216 0021-08-20 8 27.4 41.5 1.898
## 217 0021-08-20 0 25.8 36.4 1.899
## 218 0021-08-20 0 25.8 44.7 1.900
## 219 0021-08-20 3 26.5 36.0 1.900
## 220 0021-08-20 0 28.1 52.2 1.900
## 221 0021-08-20 0 25.5 54.5 1.900
## 222 0021-08-20 1 25.5 39.3 1.898
## 223 0021-08-20 1 25.8 40.8 1.897
## 224 0021-08-20 0 24.9 42.2 1.896
## 225 0021-08-20 0 25.2 37.7 1.894
## 226 0021-08-20 0 24.6 38.1 1.891
## 227 0021-08-20 0 24.4 36.1 1.893
## 228 0021-08-20 0 23.9 35.9 1.895
## 229 0021-08-20 0 24.2 35.3 1.896
## 230 0021-08-20 0 23.7 37.8 1.896
## 231 0021-08-20 0 25.4 35.9 1.898
## 232 0021-08-20 0 25.8 33.9 1.899
## 233 0021-08-20 0 26.0 33.2 1.899
## 234 0021-08-20 0 26.2 36.5 1.898
## 235 0021-08-20 4 26.5 35.6 1.899
## 236 0021-08-20 2 27.5 33.8 1.900
## 237 0021-08-20 0 28.1 33.8 1.899
## 238 0021-08-20 1 26.6 36.1 1.899
## 239 0021-08-20 1 28.1 46.7 1.899
## 240 0021-08-20 1 28.0 45.6 1.898
## 241 0021-08-20 0 27.2 34.2 1.897
## 242 0021-08-20 0 27.1 40.2 1.896
## 243 0021-08-20 1 27.8 37.7 1.895
## 244 0021-08-20 0 27.3 32.6 1.896
## 245 0021-08-20 0 27.0 34.8 1.897
## 246 0021-08-20 0 27.3 36.2 1.896
## 247 0021-08-20 0 28.4 32.3 1.898
## 248 0021-08-20 0 27.8 36.7 1.897
## 249 0021-08-20 0 27.9 31.7 1.898
## 250 0021-08-20 1 25.8 35.5 1.898
## 251 0021-08-20 0 27.0 33.2 1.898
## 252 0021-08-20 0 24.5 45.1 1.899
## 253 0021-08-20 0 25.7 40.7 1.899
## 254 0021-08-20 0 26.2 35.0 1.896
## 255 0021-08-20 0 26.0 37.8 1.896
## 256 0021-08-20 0 25.9 35.9 1.894
## 257 0021-08-20 2 24.2 32.5 1.893
## 258 0021-08-20 0 27.4 38.7 1.891
## 259 0021-08-20 0 27.3 31.1 1.894
## 260 0021-08-20 0 27.0 68.2 1.895
## 261 0021-08-20 0 28.0 37.8 1.897
## 262 0021-08-20 0 26.6 36.0 1.897
## 263 0021-08-20 0 28.3 33.1 1.898
## 264 0021-08-20 0 27.9 32.1 1.898
## 265 0021-08-20 1 26.0 34.1 1.898
## 266 0021-08-20 0 26.8 36.6 1.897
## 267 0021-08-20 0 25.7 31.0 1.897
## 268 0021-08-20 0 25.6 34.7 1.898
## 269 0021-08-20 0 28.1 49.4 1.896
## 270 0021-08-20 0 26.6 34.8 1.895
## 271 0021-08-20 5 27.3 33.3 1.895
## 272 0021-08-20 0 27.1 31.1 1.895
## 273 0021-08-20 0 26.9 32.0 1.894
## 274 0021-08-20 3 25.9 32.2 1.893
## 275 0021-08-20 2 26.0 41.6 1.893
## 276 0021-08-20 0 27.0 42.8 1.894
## 277 0021-08-20 0 25.9 44.1 1.895
## 278 0021-08-20 0 25.8 35.8 1.895
## 279 0021-08-20 0 25.1 59.6 1.895
## 280 0021-08-20 0 27.5 36.1 1.896
## 281 0021-08-20 0 28.0 33.8 1.895
## 282 0021-08-20 0 27.9 36.4 1.894
## 283 0021-08-20 0 27.0 35.4 1.895
## 284 0021-08-20 0 27.2 40.8 1.894
## 285 0021-08-20 0 26.4 35.4 1.893
## 286 0021-08-20 0 27.3 36.5 1.891
## 287 0021-08-20 0 27.2 34.3 1.890
## 288 0021-08-20 0 27.6 35.0 1.892
## 289 0021-08-20 0 27.8 31.9 1.895
## 290 0021-08-20 0 27.4 32.4 1.896
## 291 0021-08-20 2 27.6 30.8 1.899
## 292 0021-08-20 0 30.8 28.9 1.892
## 293 0021-08-20 0 29.1 27.7 1.893
## 294 0021-08-20 0 28.2 30.8 1.894
## 295 0021-08-20 0 27.4 33.7 1.894
## 296 0021-08-20 0 26.5 29.6 1.893
## 297 0021-08-20 1 26.5 48.1 1.893
## 298 0021-08-20 5 26.9 34.6 1.892
## 299 0021-08-20 6 27.3 33.8 1.892
## 300 0021-08-20 0 27.0 64.9 1.891
## 301 0021-08-20 0 26.4 38.7 1.891
## 302 0021-08-20 1 26.9 35.5 1.892
## 303 0021-08-20 0 26.6 33.9 1.892
## 304 0021-08-20 2 25.3 32.7 1.892
## 305 0021-08-20 0 25.4 39.9 1.892
## 306 0021-08-20 0 27.9 57.0 1.892
## 307 0021-08-20 2 26.6 33.5 1.892
## 308 0021-08-20 0 26.5 32.2 1.889
## 309 0021-08-20 0 27.2 34.5 1.891
## 310 0021-08-20 0 27.8 33.3 1.890
## 311 0021-08-20 0 26.8 31.8 1.888
## 312 0021-08-20 0 26.8 31.5 1.890
## 313 0021-08-20 0 27.1 31.9 1.891
## 314 0021-08-20 0 25.7 31.2 1.890
## 315 0021-08-20 0 25.2 36.4 1.891
## 316 0021-08-20 0 25.2 43.9 1.890
## 317 0021-08-20 0 25.4 37.3 1.891
## 318 0021-08-20 0 25.9 35.2 1.891
## 319 0021-08-20 0 26.0 52.8 1.891
## 320 0021-08-20 0 25.4 43.2 1.890
## 321 0021-08-20 0 26.6 33.6 1.887
## 322 0021-08-20 2 26.6 41.7 1.888
## 323 0021-08-20 0 26.4 39.2 1.888
## 324 0021-08-20 0 26.0 34.2 1.889
## 325 0021-08-20 0 25.5 33.1 1.889
## 326 0021-08-20 0 25.6 33.5 1.889
## 327 0021-08-20 0 24.4 39.1 1.889
## 328 0021-08-20 0 24.4 37.2 1.889
## 329 0021-08-20 0 24.9 38.8 1.888
## 330 0021-08-20 0 24.9 42.4 1.888
## 331 0021-08-20 0 26.1 35.6 1.888
## 332 0021-08-20 0 25.8 34.2 1.884
## 333 0021-08-20 0 26.1 38.1 1.886
## 334 0021-08-20 0 25.9 43.8 1.888
## 335 0021-08-20 2 25.2 37.0 1.889
## 336 0021-08-20 0 24.7 37.0 1.889
## 337 0021-08-20 0 24.0 36.7 1.888
## 338 0021-08-20 0 24.7 39.1 1.889
## 339 0021-08-20 0 25.5 35.9 1.888
## 340 0021-08-20 0 25.3 38.2 1.888
## 341 0021-08-20 0 25.3 39.4 1.884
## 342 0021-08-20 0 25.3 38.3 1.884
## 343 0021-08-20 0 25.8 37.8 1.886
## 344 0021-08-20 6 25.9 37.3 1.886
## 345 0021-08-20 0 25.0 37.2 1.887
## 346 0021-08-20 0 24.8 33.8 1.887
## 347 0021-08-20 3 23.4 67.2 1.886
## 348 0021-08-20 2 23.5 42.4 1.887
## 349 0021-08-20 0 24.0 48.2 1.887
## 350 0021-08-20 2 24.2 39.4 1.886
## 351 0021-08-20 0 23.7 40.0 1.885
## 352 0021-08-20 1 24.4 36.9 1.884
## 353 0021-08-20 0 24.8 38.1 1.882
## 354 0021-08-20 0 24.2 36.6 1.884
## 355 0021-08-20 0 23.9 39.0 1.885
## 356 0021-08-20 0 24.0 39.4 1.885
## 357 0021-08-20 0 24.1 42.6 1.885
## 358 0021-08-20 5 23.6 42.3 1.885
## 359 0021-08-20 0 23.9 41.9 1.885
## 360 0021-08-20 0 24.7 42.6 1.885
## 361 0021-08-20 0 25.2 39.7 1.885
## 362 0021-08-20 0 24.7 38.6 1.883
## 363 0021-08-20 0 24.3 38.3 1.882
## 364 0021-08-20 0 24.6 38.9 1.880
## 365 0021-08-20 0 24.5 38.4 1.878
## 366 0021-08-20 0 24.7 39.7 1.877
## 367 0021-08-20 0 24.6 37.6 1.879
## 368 0021-08-20 0 24.1 37.2 1.879
## 369 0021-08-20 0 23.7 38.5 1.881
## 370 0021-08-20 1 23.0 38.7 1.882
## 371 0021-08-20 0 23.3 40.6 1.882
## 372 0021-08-20 6 23.3 46.7 1.883
## 373 0021-08-20 0 24.1 42.5 1.883
## 374 0021-08-20 2 23.1 40.2 1.883
## 375 0021-08-20 1 22.9 41.1 1.882
## 376 0021-08-20 2 23.0 44.2 1.882
## 377 0021-08-20 5 22.2 41.7 1.880
## 378 0021-08-20 0 22.4 47.6 1.881
## 379 0021-08-20 4 22.3 43.5 1.881
## 380 0021-08-20 0 22.5 41.8 1.882
## 381 0021-08-20 3 22.7 41.8 1.882
## 382 0021-08-20 0 22.6 42.8 1.881
## 383 0021-08-20 0 22.4 41.7 1.881
## 384 0021-08-20 0 21.9 41.6 1.879
## 385 0021-08-20 0 22.0 42.5 1.878
## 386 0021-08-20 0 22.0 42.4 1.878
## 387 0021-08-20 0 22.1 43.6 1.877
## 388 0021-08-20 4 22.3 45.3 1.876
## 389 0021-08-20 0 21.6 43.1 1.874
## 390 0021-08-20 0 21.8 45.2 1.879
## 391 0021-08-20 0 22.0 66.3 1.880
## 392 0021-08-20 0 21.7 49.1 1.879
## 393 0021-08-20 0 21.7 47.7 1.880
## 394 0021-08-20 2 21.3 48.5 1.880
## 395 0004-09-20 0 26.4 47.2 1.930
## 396 0004-09-20 5 26.5 46.5 1.925
## 397 0004-09-20 0 26.9 47.4 1.923
## 398 0004-09-20 0 25.8 44.9 1.925
## 399 0004-09-20 0 26.3 45.1 1.927
## 400 0004-09-20 0 27.7 45.0 1.932
## 401 0004-09-20 0 28.5 46.2 1.934
## 402 0004-09-20 0 28.4 40.4 1.925
## 403 0004-09-20 0 28.4 38.4 1.924
## 404 0004-09-20 3 29.5 35.2 1.921
## 405 0004-09-20 0 29.1 36.6 1.918
## 406 0004-09-20 1 30.5 38.5 1.916
## 407 0004-09-20 0 28.9 39.0 1.919
## 408 0004-09-20 0 27.3 39.1 1.920
## 409 0004-09-20 0 26.6 38.5 1.923
## 410 0004-09-20 0 26.9 34.3 1.924
## 411 0004-09-20 0 25.8 45.1 1.922
## 412 0004-09-20 0 25.9 39.0 1.920
## 413 0004-09-20 0 27.1 43.7 1.919
## 414 0004-09-20 0 26.7 39.7 1.918
## 415 0004-09-20 0 26.5 43.5 1.915
## 416 0004-09-20 0 25.6 42.3 1.914
## 417 0004-09-20 0 25.5 43.0 1.916
## 418 0004-09-20 0 24.9 41.8 1.917
## 419 0004-09-20 0 25.4 40.9 1.918
## 420 0004-09-20 0 25.7 41.1 1.918
## 421 0004-09-20 0 26.1 40.5 1.917
## 422 0004-09-20 3 26.0 39.8 1.918
## 423 0004-09-20 0 26.5 38.7 1.915
## 424 0004-09-20 0 26.9 37.8 1.916
## 425 0004-09-20 0 26.7 38.5 1.915
## 426 0004-09-20 0 25.8 40.9 1.915
## 427 0004-09-20 0 26.5 38.7 1.916
## 428 0004-09-20 0 27.2 39.8 1.915
## 429 0004-09-20 0 27.8 38.2 1.913
## 430 0004-09-20 0 28.2 39.1 1.912
## 431 0004-09-20 0 27.9 41.6 1.911
## 432 0004-09-20 0 27.0 40.9 1.910
## 433 0004-09-20 0 26.9 41.0 1.911
## 434 0004-09-20 0 26.5 41.8 1.911
## 435 0004-09-20 0 26.3 40.1 1.913
## 436 0004-09-20 0 25.7 39.9 1.912
## 437 0004-09-20 0 25.3 38.6 1.911
## 438 0004-09-20 0 26.1 38.2 1.912
## 439 0004-09-20 0 26.6 37.6 1.913
## 440 0004-09-20 0 26.4 37.4 1.912
## 441 0004-09-20 0 26.8 38.2 1.912
## 442 0004-09-20 0 26.8 40.0 1.911
## 443 0004-09-20 0 26.5 41.0 1.909
## 444 0004-09-20 0 26.4 43.6 1.908
## 445 0004-09-20 1 26.4 38.3 1.908
## 446 0004-09-20 3 28.1 40.6 1.907
## 447 0004-09-20 0 27.7 36.9 1.907
## 448 0004-09-20 1 27.8 40.9 1.909
## 449 0004-09-20 0 27.2 35.8 1.908
## 450 0004-09-20 0 28.2 38.0 1.909
## 451 0004-09-20 0 28.9 39.6 1.908
## 452 0004-09-20 0 28.8 38.7 1.908
## 453 0004-09-20 2 28.8 37.9 1.909
## 454 0004-09-20 0 27.9 38.5 1.910
## 455 0004-09-20 0 27.2 35.8 1.907
## 456 0004-09-20 0 30.1 38.2 1.908
## 457 0004-09-20 0 30.1 39.9 1.906
## 458 0004-09-20 0 28.3 40.9 1.905
## 459 0004-09-20 0 27.5 41.4 1.906
## 460 0004-09-20 0 26.8 41.1 1.906
## 461 0004-09-20 0 27.4 38.3 1.907
## 462 0004-09-20 0 27.3 39.0 1.906
## 463 0004-09-20 0 26.4 39.6 1.904
## 464 0004-09-20 0 25.9 38.2 1.904
## 465 0004-09-20 0 27.2 36.5 1.904
## 466 0004-09-20 0 27.5 35.9 1.904
## 467 0004-09-20 0 27.3 36.0 1.903
## 468 0004-09-20 4 28.8 33.4 1.903
## 469 0004-09-20 3 29.5 35.5 1.903
## 470 0004-09-20 0 29.0 35.8 1.902
## 471 0004-09-20 0 28.3 34.9 1.903
## 472 0004-09-20 1 29.7 36.9 1.903
## 473 0004-09-20 1 27.5 33.3 1.904
## 474 0004-09-20 1 29.3 35.7 1.906
## 475 0004-09-20 0 31.8 45.5 1.906
## 476 0004-09-20 0 30.9 39.3 1.904
## 477 0004-09-20 0 28.7 37.9 1.903
## 478 0004-09-20 1 29.1 33.6 1.902
## 479 0004-09-20 1 31.5 31.1 1.901
## 480 0004-09-20 0 34.6 36.0 1.900
## 481 0004-09-20 0 32.8 38.4 1.900
## 482 0004-09-20 0 32.0 46.1 1.901
## 483 0004-09-20 0 30.7 38.5 1.901
## 484 0004-09-20 1 31.1 35.4 1.902
## 485 0004-09-20 0 28.8 29.9 1.902
## 486 0004-09-20 0 33.3 31.0 1.902
## 487 0004-09-20 0 29.7 35.4 1.903
## 488 0004-09-20 0 29.0 35.6 1.903
## 489 0004-09-20 0 31.1 44.7 1.905
## 490 0004-09-20 0 29.6 32.3 1.906
## 491 0004-09-20 0 33.3 35.3 1.905
## 492 0004-09-20 0 30.8 35.5 1.905
## 493 0004-09-20 0 30.5 36.0 1.901
## 494 0004-09-20 0 28.8 39.9 1.903
## 495 0004-09-20 2 24.7 38.9 1.904
## 496 0004-09-20 0 27.2 38.1 1.904
## 497 0004-09-20 0 28.3 36.4 1.902
## 498 0004-09-20 0 29.1 38.3 1.902
## 499 0004-09-20 0 29.2 36.5 1.901
## 500 0004-09-20 0 28.4 37.6 1.899
## 501 0004-09-20 0 27.1 35.5 1.898
## 502 0004-09-20 0 27.2 34.8 1.898
## 503 0004-09-20 0 26.3 43.2 1.898
## 504 0004-09-20 0 28.3 44.6 1.897
## 505 0004-09-20 0 28.9 40.6 1.896
## 506 0004-09-20 0 29.5 39.0 1.897
## 507 0004-09-20 0 30.4 38.5 1.896
## 508 0004-09-20 0 29.9 40.2 1.895
## 509 0004-09-20 3 29.1 38.4 1.896
## 510 0004-09-20 0 29.8 37.5 1.896
## 511 0004-09-20 1 29.5 35.3 1.897
## 512 0004-09-20 0 30.8 30.7 1.900
## 513 0004-09-20 0 32.7 28.5 1.900
## 514 0004-09-20 0 26.7 48.8 1.901
## 515 0004-09-20 0 26.9 48.8 1.898
## 516 0004-09-20 0 25.9 46.6 1.896
## 517 0004-09-20 0 25.6 48.7 1.892
## 518 0004-09-20 0 25.8 48.1 1.893
## 519 0004-09-20 0 28.0 53.6 1.893
## 520 0004-09-20 0 26.0 49.5 1.894
## 521 0004-09-20 0 26.2 49.7 1.894
## 522 0004-09-20 0 27.0 51.0 1.896
## 523 0004-09-20 0 26.0 51.9 1.896
## 524 0004-09-20 0 26.3 55.7 1.897
## 525 0004-09-20 0 24.3 54.9 1.898
## 526 0004-09-20 0 23.8 52.1 1.899
## 527 0004-09-20 0 24.4 51.0 1.898
## 528 0004-09-20 0 25.1 55.2 1.897
## 529 0004-09-20 0 25.4 51.0 1.896
## 530 0004-09-20 0 25.3 55.0 1.897
## 531 0004-09-20 0 24.5 58.7 1.898
## 532 0004-09-20 0 23.9 53.9 1.898
## 533 0004-09-20 0 24.6 55.6 1.897
## 534 0004-09-20 0 24.4 56.7 1.896
## 535 0004-09-20 0 24.5 58.3 1.894
## 536 0004-09-20 0 23.6 61.2 1.894
## 537 0004-09-20 0 22.8 60.8 1.893
## 538 0004-09-20 0 22.6 61.9 1.892
## 539 0004-09-20 0 23.2 61.9 1.890
## 540 0004-09-20 3 23.2 58.3 1.889
## 541 0004-09-20 0 21.3 59.9 1.888
## 542 0004-09-20 2 21.7 60.5 1.891
## 543 0004-09-20 0 21.8 60.7 1.891
## 544 0004-09-20 0 22.0 62.9 1.892
## 545 0004-09-20 0 22.1 63.3 1.894
## 546 0004-09-20 0 21.8 60.9 1.896
## 547 0004-09-20 0 22.4 61.3 1.896
## 548 0004-09-20 0 21.7 63.4 1.896
## 549 0004-09-20 0 21.9 72.9 1.895
## 550 0004-09-20 0 22.0 68.4 1.892
## 551 0004-09-20 0 20.7 63.5 1.891
## 552 0004-09-20 0 22.4 61.4 1.893
## 553 0004-09-20 0 22.0 61.2 1.895
## 554 0004-09-20 0 22.0 61.3 1.895
## 555 0004-09-20 0 21.9 62.8 1.894
## 556 0004-09-20 0 22.1 64.9 1.892
## 557 0004-09-20 0 21.8 69.4 1.891
## 558 0004-09-20 0 22.8 68.0 1.889
## 559 0004-09-20 4 22.8 65.6 1.887
## 560 0004-09-20 0 22.4 69.1 1.889
## 561 0004-09-20 0 21.9 64.6 1.887
## 562 0004-09-20 0 22.2 66.3 1.889
## 563 0004-09-20 0 22.3 71.4 1.891
## 564 0004-09-20 0 21.5 69.8 1.892
## 565 0004-09-20 0 22.7 73.1 1.893
## 566 0004-09-20 0 22.2 64.6 1.893
## 567 0004-09-20 0 23.4 63.5 1.892
## 568 0004-09-20 0 21.3 65.3 1.893
## 569 0004-09-20 0 21.0 66.4 1.891
## 570 0004-09-20 7 20.3 66.9 1.887
## 571 0004-09-20 0 20.0 66.7 1.890
## 572 0004-09-20 0 20.0 66.7 1.892
## 573 0004-09-20 0 20.8 69.2 1.892
## 574 0004-09-20 0 20.1 73.4 1.891
## 575 0004-09-20 0 20.4 66.7 1.891
## 576 0004-09-20 0 20.8 66.5 1.890
## 577 0004-09-20 0 20.7 68.2 1.889
## 578 0004-09-20 0 20.7 73.3 1.888
## 579 0004-09-20 0 21.7 66.9 1.887
## 580 0004-09-20 0 21.6 63.6 1.886
## 581 0004-09-20 0 21.5 63.1 1.887
## 582 0004-09-20 0 21.2 69.3 1.887
## 583 0004-09-20 0 21.9 62.6 1.888
## 584 0004-09-20 0 20.1 61.3 1.889
## 585 0004-09-20 0 22.2 58.0 1.889
## 586 0004-09-20 0 22.9 62.7 1.891
## 587 0004-09-20 0 23.0 61.1 1.890
## 588 0004-09-20 0 23.0 56.7 1.888
## 589 0004-09-20 0 24.6 57.5 1.887
## 590 0004-09-20 0 24.9 59.3 1.885
## 591 0004-09-20 0 23.9 64.7 1.884
## 592 0004-09-20 0 23.9 81.3 1.879
## 593 0004-09-20 0 23.4 63.0 1.882
## 594 0004-09-20 0 23.9 59.7 1.884
## 595 0004-09-20 0 23.7 58.7 1.886
## 596 0004-09-20 0 23.7 57.5 1.888
## 597 0004-09-20 0 23.2 59.6 1.889
## 598 0004-09-20 0 22.0 62.1 1.889
## 599 0004-09-20 1 21.5 60.6 1.889
## 600 0004-09-20 0 23.5 59.1 1.887
## 601 0004-09-20 0 23.3 60.4 1.886
## 602 0004-09-20 0 22.8 62.2 1.885
## 603 0004-09-20 2 22.0 59.5 1.884
## 604 0004-09-20 0 23.0 61.7 1.883
## 605 0004-09-20 0 21.6 62.5 1.883
## 606 0004-09-20 0 21.1 62.7 1.883
## 607 0004-09-20 0 21.0 65.4 1.882
## 608 0004-09-20 0 21.0 69.0 1.882
## 609 0004-09-20 0 20.8 73.2 1.884
## 610 0004-09-20 2 20.5 66.8 1.884
## 611 0004-09-20 0 20.6 67.8 1.885
## 612 0004-09-20 0 20.8 67.3 1.886
## 613 0004-09-20 1 20.2 67.2 1.886
## 614 0004-09-20 0 20.8 68.1 1.886
## 615 0004-09-20 0 20.3 69.4 1.887
## 616 0004-09-20 0 19.3 70.3 1.885
## 617 0004-09-20 0 20.2 68.7 1.885
## 618 0004-09-20 0 20.5 70.9 1.883
## 619 0004-09-20 0 20.4 76.4 1.882
## 620 0004-09-20 0 20.5 69.2 1.880
## 621 0004-09-20 0 20.2 66.9 1.876
## 622 0004-09-20 0 20.3 66.5 1.875
## 623 0004-09-20 0 19.9 66.3 1.878
## 624 0004-09-20 0 20.2 69.3 1.880
## 625 0004-09-20 0 19.4 67.0 1.882
## 626 0004-09-20 0 19.7 68.1 1.883
## 627 0004-09-20 0 19.4 70.9 1.884
## 628 0004-09-20 0 18.4 69.2 1.885
## 629 0004-09-20 0 19.0 69.8 1.885
## 630 0004-09-20 0 19.0 73.2 1.885
## 631 0004-09-20 0 18.9 73.0 1.885
## 632 0004-09-20 0 18.7 75.9 1.884
## 633 0004-09-20 0 18.6 76.8 1.882
## 634 0004-09-20 0 19.2 71.4 1.881
## 635 0004-09-20 0 18.7 71.5 1.882
## 636 0004-09-20 0 18.5 71.0 1.881
## 637 0004-09-20 0 18.4 78.7 1.879
## 638 0004-09-20 0 19.1 71.7 1.879
## 639 0004-09-20 0 19.2 74.5 1.880
## 640 0004-09-20 0 18.8 76.2 1.881
## 641 0004-09-20 0 18.8 74.8 1.880
## 642 0004-09-20 0 19.0 73.5 1.881
## 643 0004-09-20 0 19.1 72.4 1.881
## 644 0004-09-20 0 18.9 72.9 1.883
## 645 0004-09-20 0 19.0 74.8 1.882
## 646 0004-09-20 0 18.9 83.1 1.883
## 647 0004-09-20 0 18.5 73.8 1.883
## 648 0004-09-20 0 18.4 70.8 1.882
## 649 0004-09-20 0 18.5 69.6 1.881
## 650 0004-09-20 0 19.0 68.7 1.879
## 651 0004-09-20 0 19.3 69.6 1.878
## 652 0004-09-20 0 19.0 71.4 1.876
## 653 0004-09-20 0 18.5 70.0 1.876
## 654 0004-09-20 0 18.9 71.1 1.877
## 655 0004-09-20 0 18.8 71.6 1.879
## 656 0004-09-20 0 18.9 69.7 1.881
## 657 0004-09-20 0 18.8 69.1 1.880
## 658 0004-09-20 0 18.6 70.2 1.880
## 659 0004-09-20 0 18.4 70.7 1.880
## 660 0004-09-20 1 18.5 71.3 1.880
## 661 0004-09-20 0 18.6 72.5 1.880
## 662 0004-09-20 0 18.3 75.0 1.880
## 663 0004-09-20 0 18.7 73.1 1.880
## 664 0004-09-20 0 18.5 70.1 1.879
## 665 0004-09-20 0 18.7 72.8 1.878
## 666 0004-09-20 0 19.2 75.4 1.878
## 667 0004-09-20 0 18.9 69.7 1.877
## 668 0004-09-20 0 18.9 68.8 1.877
## 669 0004-09-20 0 19.3 68.3 1.877
## 670 0004-09-20 0 19.8 68.1 1.877
## 671 0004-09-20 0 19.7 67.0 1.878
## 672 0004-09-20 0 19.7 67.6 1.878
## 673 0004-09-20 0 18.7 64.6 1.879
## 674 0004-09-20 0 19.9 63.4 1.879
## 675 0004-09-20 0 20.0 63.8 1.879
## 676 0004-09-20 0 20.6 63.9 1.879
## 677 0004-09-20 0 19.7 61.5 1.879
## 678 0004-09-20 0 20.1 60.9 1.878
## 679 0004-09-20 0 20.9 68.7 1.880
## 680 0004-09-20 0 20.3 64.2 1.878
## 681 0004-09-20 0 20.0 62.8 1.877
## 682 0004-09-20 0 20.1 63.5 1.876
## 683 0004-09-20 0 20.0 62.4 1.877
## 684 0004-09-20 0 20.1 62.6 1.878
## 685 0004-09-20 0 19.8 64.4 1.878
## 686 0004-09-20 0 19.8 62.7 1.879
## 687 0004-09-20 0 19.8 63.7 1.879
## 688 0004-09-20 0 19.8 65.4 1.878
## 689 0004-09-20 0 19.7 66.6 1.878
## 690 0004-09-20 0 19.6 62.6 1.878
## 691 0004-09-20 0 19.6 62.0 1.878
## 692 0004-09-20 2 20.1 64.5 1.878
## 693 0004-09-20 0 20.0 64.4 1.878
## 694 0004-09-20 0 19.9 64.9 1.878
## 695 0004-09-20 0 19.8 65.0 1.877
## 696 0004-09-20 0 19.9 67.7 1.877
## 697 0004-09-20 0 19.9 67.4 1.878
## 698 0004-09-20 0 19.5 65.2 1.878
## 699 0004-09-20 0 19.3 64.9 1.877
## 700 0004-09-20 0 19.3 64.8 1.878
## 701 0004-09-20 0 19.3 69.2 1.877
## 702 0004-09-20 0 19.7 69.0 1.878
## 703 0004-09-20 0 20.1 73.4 1.877
## 704 0004-09-20 0 20.1 70.0 1.876
## 705 0004-09-20 0 19.6 73.4 1.875
## 706 0004-09-20 0 19.8 68.5 1.874
## 707 0004-09-20 0 19.6 66.6 1.876
## 708 0004-09-20 0 19.3 66.7 1.876
## 709 0004-09-20 0 18.9 64.1 1.877
## 710 0004-09-20 0 19.4 75.6 1.878
## 711 0004-09-20 0 19.1 68.6 1.878
## 712 0004-09-20 0 19.4 72.5 1.878
## 713 0004-09-20 0 19.4 76.4 1.878
## 714 0004-09-20 0 19.1 64.0 1.877
## 715 0004-09-20 0 19.3 60.3 1.876
## 716 0004-09-20 0 19.8 63.4 1.875
## 717 0004-09-20 0 19.5 63.8 1.876
## 718 0004-09-20 0 19.3 64.5 1.877
## 719 0004-09-20 0 19.1 65.1 1.878
## 720 0004-09-20 0 20.0 64.0 1.878
## 721 0004-09-20 0 19.5 63.2 1.879
## 722 0004-09-20 0 19.6 65.6 1.878
## 723 0004-09-20 0 19.7 62.2 1.878
## 724 0004-09-20 0 19.9 60.6 1.878
## 725 0004-09-20 0 20.1 59.0 1.876
## 726 0004-09-20 0 20.0 60.3 1.877
## 727 0004-09-20 0 19.5 62.2 1.874
## 728 0004-09-20 0 19.3 66.6 1.875
## 729 0004-09-20 0 19.7 64.2 1.876
## 730 0004-09-20 0 19.7 62.3 1.876
## 731 0004-09-20 0 19.5 63.4 1.877
## 732 0004-09-20 0 19.2 68.5 1.877
## 733 0004-09-20 0 19.2 69.0 1.876
## 734 0004-09-20 0 18.8 67.5 1.876
## 735 0004-09-20 0 18.7 69.0 1.875
## 736 0004-09-20 0 18.9 67.5 1.874
## 737 0004-09-20 0 18.8 68.9 1.875
## 738 0004-09-20 0 18.7 66.1 1.877
## 739 0004-09-20 0 19.0 64.3 1.878
## 740 0004-09-20 0 18.2 66.3 1.878
## 741 0004-09-20 0 19.2 69.5 1.880
## 742 0004-09-20 0 19.5 67.7 1.879
## 743 0004-09-20 0 19.1 69.0 1.879
## 744 0004-09-20 23 19.2 70.7 1.879
## 745 0004-09-20 0 19.4 69.8 1.878
## 746 0004-09-20 0 19.4 71.7 1.878
## 747 0004-09-20 0 18.3 75.7 1.876
## 748 0004-09-20 0 18.5 91.4 1.875
## 749 0004-09-20 0 17.6 69.8 1.876
## 750 0004-09-20 0 17.6 70.8 1.877
## 751 0004-09-20 3 17.5 75.3 1.877
## 752 0004-09-20 15 17.4 82.7 1.878
## 753 0004-09-20 0 17.5 73.3 1.878
## 754 0004-09-20 0 17.3 73.2 1.877
## 755 0004-09-20 0 17.3 74.0 1.877
## 756 0004-09-20 0 17.1 76.3 1.877
## 757 0004-09-20 0 17.0 77.8 1.876
## 758 0004-09-20 0 16.9 75.4 1.875
## 759 0004-09-20 0 16.8 76.5 1.872
## 760 0004-09-20 0 17.1 74.9 1.870
## 761 0004-09-20 0 17.2 74.6 1.873
## 762 0004-09-20 0 17.0 74.2 1.874
## 763 0004-09-20 0 16.9 72.2 1.875
## 764 0004-09-20 0 16.9 70.5 1.876
## 765 0004-09-20 0 16.8 70.6 1.877
## 766 0004-09-20 0 16.8 71.6 1.877
## 767 0004-09-20 0 16.5 78.9 1.878
## 768 0004-09-20 0 16.8 83.3 1.878
## 769 0004-09-20 0 16.8 76.1 1.877
## 770 0004-09-20 0 16.9 78.6 1.877
## 771 0004-09-20 0 16.6 67.5 1.876
## 772 0004-09-20 0 16.8 68.7 1.875
## 773 0004-09-20 0 16.7 65.8 1.876
## 774 0004-09-20 0 16.6 66.2 1.876
## 775 0004-09-20 0 16.6 67.4 1.876
## 776 0004-09-20 0 16.5 65.9 1.877
## 777 0004-09-20 0 16.5 66.3 1.876
## 778 0004-09-20 0 16.4 66.8 1.876
## 779 0004-09-20 0 16.4 66.9 1.874
## 780 0004-09-20 0 16.5 67.0 1.873
## 781 0004-09-20 0 16.3 69.3 1.872
## 782 0004-09-20 2 16.0 68.2 1.871
## 783 0004-09-20 0 16.2 68.7 1.870
## 784 0004-09-20 0 16.6 65.2 1.875
## 785 0004-09-20 0 16.6 66.0 1.877
## 786 0004-09-20 0 16.5 63.7 1.878
## 787 0004-09-20 0 16.7 63.0 1.877
## 788 0004-09-20 0 16.9 61.3 1.876
## Wind_Speed Latitude Longitude Barometric_Pressure Crosswind
## 1 0.8 2.381290 -76.61264 805.0 0.2
## 2 5.1 2.381320 -76.61259 805.2 3.6
## 3 0.8 2.381353 -76.61254 805.7 0.4
## 4 0.9 2.381374 -76.61261 805.7 0.6
## 5 0.6 2.381335 -76.61266 805.2 0.4
## 6 0.5 2.381313 -76.61271 804.7 0.5
## 7 0.6 2.381298 -76.61282 804.5 0.6
## 8 3.7 2.381342 -76.61276 804.7 2.9
## 9 0.5 2.381366 -76.61270 805.0 0.4
## 10 1.0 2.381415 -76.61263 805.4 0.7
## 11 0.0 2.381459 -76.61268 805.5 0.0
## 12 0.0 2.381429 -76.61271 805.2 0.0
## 13 0.0 2.381400 -76.61275 805.0 0.0
## 14 0.5 2.381336 -76.61283 804.5 0.4
## 15 1.3 2.381292 -76.61286 804.5 1.0
## 16 0.8 2.381310 -76.61293 804.7 0.6
## 17 0.0 2.381354 -76.61288 804.5 0.0
## 18 0.4 2.381396 -76.61284 804.7 0.3
## 19 0.8 2.381428 -76.61280 804.7 0.8
## 20 2.1 2.381484 -76.61273 805.2 1.9
## 21 1.0 2.381517 -76.61280 805.2 0.7
## 22 0.0 2.381486 -76.61282 805.0 0.0
## 23 0.4 2.381453 -76.61286 804.7 0.4
## 24 0.4 2.381405 -76.61290 804.7 0.4
## 25 0.6 2.381355 -76.61292 804.7 0.5
## 26 1.1 2.381295 -76.61296 804.5 0.1
## 27 0.5 2.381266 -76.61301 804.5 0.3
## 28 1.0 2.381211 -76.61310 804.7 1.0
## 29 0.7 2.381251 -76.61306 804.7 0.6
## 30 0.5 2.381306 -76.61303 804.7 0.1
## 31 0.0 2.381364 -76.61299 804.7 0.0
## 32 0.0 2.381411 -76.61294 804.7 0.0
## 33 0.4 2.381464 -76.61291 804.7 0.3
## 34 0.4 2.381514 -76.61288 804.9 0.2
## 35 0.4 2.381542 -76.61284 804.7 0.4
## 36 0.0 2.381568 -76.61282 805.0 0.0
## 37 0.0 2.381621 -76.61284 805.2 0.0
## 38 0.5 2.381598 -76.61287 805.0 0.5
## 39 0.7 2.381561 -76.61291 805.0 0.7
## 40 0.8 2.381508 -76.61294 805.0 0.4
## 41 0.4 2.381460 -76.61298 805.0 0.4
## 42 0.9 2.381415 -76.61301 805.0 0.2
## 43 0.4 2.381366 -76.61304 805.2 0.3
## 44 0.0 2.381309 -76.61308 805.0 0.0
## 45 0.0 2.381264 -76.61311 805.0 0.0
## 46 0.8 2.381207 -76.61315 805.0 0.7
## 47 0.6 2.381144 -76.61319 805.0 0.5
## 48 0.4 2.380892 -76.61341 805.2 0.3
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## 50 0.0 2.380991 -76.61333 805.5 0.0
## 51 0.7 2.381045 -76.61330 805.4 0.6
## 52 0.0 2.381096 -76.61325 805.4 0.0
## 53 0.0 2.381201 -76.61319 805.2 0.0
## 54 0.5 2.381255 -76.61316 805.2 0.1
## 55 0.0 2.381313 -76.61313 805.4 0.0
## 56 0.0 2.381361 -76.61309 805.2 0.0
## 57 0.4 2.381414 -76.61305 805.2 0.2
## 58 0.8 2.381482 -76.61302 805.2 0.7
## 59 0.6 2.381522 -76.61299 805.2 0.3
## 60 0.8 2.381580 -76.61296 805.4 0.5
## 61 0.0 2.381630 -76.61292 805.2 0.0
## 62 0.4 2.381655 -76.61289 805.2 0.1
## 63 0.4 2.381673 -76.61286 805.5 0.4
## 64 0.0 2.381715 -76.61292 805.5 0.0
## 65 0.4 2.381688 -76.61294 805.5 0.1
## 66 0.0 2.381630 -76.61298 805.2 0.0
## 67 0.0 2.381581 -76.61301 805.4 0.0
## 68 0.5 2.381528 -76.61304 805.4 0.2
## 69 0.6 2.381467 -76.61307 805.2 0.3
## 70 0.0 2.381419 -76.61311 805.2 0.0
## 71 0.4 2.381319 -76.61319 805.2 0.3
## 72 0.0 2.381265 -76.61322 805.2 0.0
## 73 0.5 2.381210 -76.61325 805.2 0.4
## 74 0.6 2.381153 -76.61328 805.4 0.5
## 75 0.0 2.381113 -76.61333 805.5 0.0
## 76 0.4 2.381052 -76.61336 805.5 0.4
## 77 0.7 2.380998 -76.61339 805.5 0.4
## 78 0.4 2.380950 -76.61343 805.5 0.0
## 79 0.4 2.380900 -76.61346 805.2 0.1
## 80 0.0 2.380856 -76.61350 805.2 0.0
## 81 0.4 2.380799 -76.61353 805.2 0.0
## 82 0.0 2.380830 -76.61357 805.4 0.0
## 83 0.4 2.380869 -76.61354 805.4 0.4
## 84 0.7 2.380917 -76.61351 805.5 0.3
## 85 0.0 2.381013 -76.61344 805.7 0.0
## 86 0.4 2.381068 -76.61340 805.9 0.2
## 87 0.0 2.381111 -76.61336 805.9 0.0
## 88 0.6 2.381169 -76.61333 805.9 0.3
## 89 0.4 2.381216 -76.61330 805.7 0.4
## 90 0.6 2.381268 -76.61326 805.7 0.3
## 91 0.0 2.381324 -76.61323 805.5 0.0
## 92 0.3 2.381377 -76.61319 805.5 0.1
## 93 0.6 2.381428 -76.61316 805.5 0.3
## 94 0.5 2.381487 -76.61312 805.4 0.4
## 95 0.7 2.381590 -76.61306 805.2 0.1
## 96 0.0 2.381647 -76.61303 805.2 0.0
## 97 0.4 2.381697 -76.61299 805.2 0.4
## 98 0.5 2.381750 -76.61296 805.0 0.4
## 99 0.5 2.381833 -76.61295 805.4 0.1
## 100 1.3 2.381804 -76.61299 805.4 0.6
## 101 0.7 2.381751 -76.61302 805.0 0.1
## 102 0.0 2.381697 -76.61306 805.2 0.0
## 103 0.0 2.381633 -76.61309 805.2 0.0
## 104 0.0 2.381590 -76.61312 805.2 0.0
## 105 0.0 2.381542 -76.61315 805.4 0.0
## 106 0.0 2.381486 -76.61319 805.5 0.0
## 107 0.4 2.381426 -76.61322 805.5 0.4
## 108 0.0 2.381364 -76.61325 805.5 0.0
## 109 0.6 2.381322 -76.61328 805.5 0.1
## 110 0.0 2.381276 -76.61331 805.5 0.0
## 111 0.0 2.381214 -76.61334 805.5 0.0
## 112 0.6 2.381156 -76.61337 805.5 0.0
## 113 0.3 2.381103 -76.61341 805.5 0.2
## 114 0.0 2.381059 -76.61346 805.7 0.0
## 115 0.0 2.381014 -76.61350 805.5 0.0
## 116 0.0 2.380968 -76.61355 805.5 0.0
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## 118 0.4 2.380876 -76.61362 805.2 0.2
## 119 0.0 2.380837 -76.61365 805.0 0.0
## 120 0.4 2.380887 -76.61366 805.2 0.3
## 121 0.0 2.380931 -76.61362 805.2 0.0
## 122 0.0 2.381209 -76.61341 805.4 0.0
## 123 0.7 2.381247 -76.61337 805.4 0.1
## 124 0.4 2.381320 -76.61333 805.2 0.0
## 125 0.8 2.381371 -76.61329 805.0 0.4
## 126 0.0 2.381426 -76.61327 805.0 0.0
## 127 0.0 2.381493 -76.61323 804.7 0.0
## 128 0.0 2.381536 -76.61321 804.7 0.0
## 129 0.0 2.381583 -76.61317 804.7 0.0
## 130 0.3 2.381636 -76.61314 804.5 0.1
## 131 0.0 2.381751 -76.61307 804.2 0.0
## 132 0.5 2.381799 -76.61305 804.2 0.0
## 133 0.4 2.381855 -76.61301 804.4 0.4
## 134 0.0 2.381912 -76.61304 804.5 0.0
## 135 0.4 2.381864 -76.61307 804.4 0.0
## 136 0.0 2.381810 -76.61311 804.2 0.0
## 137 0.0 2.381759 -76.61313 804.2 0.0
## 138 1.2 2.381701 -76.61316 804.2 0.9
## 139 0.4 2.381644 -76.61321 804.4 0.1
## 140 0.0 2.381594 -76.61324 804.5 0.0
## 141 0.6 2.381538 -76.61326 804.5 0.3
## 142 0.5 2.381485 -76.61330 804.5 0.4
## 143 0.0 2.381434 -76.61333 804.7 0.0
## 144 0.5 2.381383 -76.61336 805.0 0.1
## 145 0.0 2.381325 -76.61340 805.0 0.0
## 146 0.0 2.381448 -76.61338 805.0 0.0
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## 148 0.0 2.381545 -76.61331 804.7 0.0
## 149 0.4 2.381598 -76.61329 804.5 0.1
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## 154 0.0 2.381913 -76.61308 804.2 0.0
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## 156 0.0 2.382037 -76.61308 804.5 0.0
## 157 0.7 2.381975 -76.61311 804.5 0.1
## 158 0.0 2.381930 -76.61315 804.5 0.0
## 159 0.7 2.381872 -76.61317 804.4 0.1
## 160 0.5 2.381814 -76.61321 804.5 0.2
## 161 0.6 2.381707 -76.61328 804.7 0.1
## 162 1.9 2.381606 -76.61334 804.7 1.8
## 163 1.4 2.381541 -76.61337 805.0 0.6
## 164 0.0 2.381488 -76.61341 805.2 0.0
## 165 0.0 2.381606 -76.61338 805.2 0.0
## 166 0.6 2.381665 -76.61335 805.0 0.2
## 167 0.4 2.381724 -76.61332 805.0 0.0
## 168 0.6 2.381770 -76.61329 804.9 0.4
## 169 0.6 2.381828 -76.61326 804.7 0.3
## 170 0.6 2.381882 -76.61324 804.5 0.6
## 171 0.8 2.381932 -76.61320 804.5 0.0
## 172 0.0 2.381988 -76.61316 804.7 0.0
## 173 0.4 2.382038 -76.61312 804.7 0.1
## 174 0.5 2.382097 -76.61310 805.0 0.3
## 175 0.0 2.382195 -76.61309 805.2 0.0
## 176 0.5 2.382148 -76.61312 804.9 0.1
## 177 0.0 2.382037 -76.61318 804.7 0.0
## 178 0.6 2.381989 -76.61322 804.7 0.1
## 179 0.0 2.381935 -76.61324 804.5 0.0
## 180 0.4 2.381876 -76.61328 804.7 0.2
## 181 0.5 2.381821 -76.61333 804.7 0.4
## 182 0.0 2.381782 -76.61335 804.9 0.0
## 183 2.5 2.381724 -76.61338 805.0 2.3
## 184 0.8 2.381663 -76.61342 805.0 0.0
## 185 0.5 2.381676 -76.61347 805.2 0.2
## 186 1.0 2.381738 -76.61343 805.0 0.5
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## 188 0.0 2.381840 -76.61336 805.0 0.0
## 189 0.6 2.381896 -76.61334 804.7 0.3
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## 191 0.0 2.381983 -76.61327 804.7 0.0
## 192 0.0 2.382044 -76.61323 804.5 0.0
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## 194 0.7 2.382200 -76.61315 804.9 0.4
## 195 0.3 2.382245 -76.61311 805.2 0.1
## 196 0.9 2.382297 -76.61307 805.2 0.3
## 197 0.0 2.382402 -76.61306 805.7 0.0
## 198 0.3 2.382333 -76.61311 805.2 0.2
## 199 0.0 2.382300 -76.61314 805.0 0.0
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## 203 0.0 2.382098 -76.61327 804.5 0.0
## 204 0.0 2.382049 -76.61329 804.5 0.0
## 205 0.4 2.381987 -76.61334 804.7 0.1
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## 210 0.5 2.381727 -76.61350 804.7 0.4
## 211 0.3 2.381671 -76.61352 804.9 0.3
## 212 0.6 2.381738 -76.61354 805.0 0.6
## 213 0.0 2.381798 -76.61351 805.0 0.0
## 214 0.7 2.381851 -76.61347 805.0 0.2
## 215 0.5 2.381892 -76.61344 804.9 0.4
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## 222 0.5 2.382263 -76.61322 804.7 0.5
## 223 0.0 2.382322 -76.61319 805.0 0.0
## 224 0.0 2.382362 -76.61316 805.0 0.0
## 225 0.3 2.382405 -76.61312 805.2 0.2
## 226 0.4 2.382451 -76.61307 805.5 0.4
## 227 0.0 2.382497 -76.61310 805.2 0.0
## 228 0.5 2.382458 -76.61315 805.2 0.1
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## 230 0.3 2.382367 -76.61322 805.0 0.3
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## 232 0.3 2.382266 -76.61326 804.7 0.2
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## 235 0.6 2.382111 -76.61337 804.7 0.6
## 236 0.7 2.382053 -76.61340 804.7 0.7
## 237 0.5 2.382002 -76.61343 804.7 0.5
## 238 0.0 2.381950 -76.61347 804.7 0.0
## 239 0.5 2.381889 -76.61351 804.7 0.4
## 240 0.3 2.381839 -76.61354 804.9 0.3
## 241 0.3 2.381777 -76.61358 805.0 0.3
## 242 0.0 2.381766 -76.61362 805.0 0.0
## 243 0.0 2.381813 -76.61364 805.0 0.0
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## 248 0.9 2.382058 -76.61346 804.9 0.4
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## 250 0.6 2.382166 -76.61339 804.7 0.6
## 251 0.5 2.382219 -76.61336 804.9 0.4
## 252 0.9 2.382270 -76.61332 804.7 0.6
## 253 0.4 2.382326 -76.61329 804.7 0.4
## 254 0.7 2.382380 -76.61326 805.0 0.6
## 255 0.9 2.382442 -76.61324 805.0 0.0
## 256 0.4 2.382480 -76.61320 805.2 0.3
## 257 0.9 2.382527 -76.61316 805.4 0.2
## 258 0.0 2.382524 -76.61322 805.5 0.0
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## 276 0.5 2.382025 -76.61361 805.2 0.2
## 277 0.0 2.382074 -76.61357 805.2 0.0
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## 686 0.5 2.382271 -76.61351 806.7 0.1
## 687 0.0 2.382211 -76.61355 806.7 0.0
## 688 0.4 2.382162 -76.61357 806.7 0.2
## 689 0.0 2.382118 -76.61361 806.7 0.0
## 690 0.5 2.382067 -76.61365 806.7 0.1
## 691 0.0 2.382013 -76.61367 806.7 0.0
## 692 0.0 2.381961 -76.61372 806.9 0.0
## 693 0.3 2.381922 -76.61375 806.7 0.2
## 694 0.3 2.381871 -76.61379 806.9 0.3
## 695 0.4 2.381960 -76.61378 806.9 0.4
## 696 0.4 2.382011 -76.61374 807.0 0.4
## 697 0.0 2.382054 -76.61370 806.9 0.0
## 698 0.0 2.382114 -76.61367 806.7 0.0
## 699 0.0 2.382220 -76.61360 806.9 0.0
## 700 0.4 2.382270 -76.61356 806.9 0.4
## 701 0.6 2.382361 -76.61350 806.9 0.5
## 702 0.4 2.382426 -76.61347 806.9 0.1
## 703 0.6 2.382471 -76.61344 806.9 0.5
## 704 0.0 2.382535 -76.61339 807.0 0.0
## 705 0.0 2.382526 -76.61346 807.2 0.0
## 706 0.0 2.382476 -76.61350 807.2 0.0
## 707 0.0 2.382430 -76.61353 807.0 0.0
## 708 0.0 2.382378 -76.61356 807.0 0.0
## 709 0.5 2.382214 -76.61366 807.0 0.1
## 710 0.5 2.382173 -76.61370 806.9 0.4
## 711 0.3 2.382116 -76.61373 806.7 0.2
## 712 0.0 2.382066 -76.61377 806.9 0.0
## 713 0.0 2.382011 -76.61381 806.9 0.0
## 714 0.4 2.381955 -76.61385 806.9 0.2
## 715 0.0 2.381983 -76.61389 807.0 0.0
## 716 0.0 2.382025 -76.61386 807.2 0.0
## 717 0.0 2.382080 -76.61383 807.0 0.0
## 718 0.3 2.382119 -76.61379 806.9 0.3
## 719 0.0 2.382171 -76.61376 806.7 0.0
## 720 0.0 2.382228 -76.61371 806.7 0.0
## 721 0.0 2.382286 -76.61369 806.7 0.0
## 722 0.0 2.382331 -76.61365 806.7 0.0
## 723 0.0 2.382436 -76.61359 806.7 0.0
## 724 0.0 2.382486 -76.61355 806.9 0.0
## 725 0.0 2.382525 -76.61352 807.0 0.0
## 726 0.5 2.382587 -76.61354 807.0 0.0
## 727 0.4 2.382533 -76.61358 807.2 0.1
## 728 0.0 2.382485 -76.61361 807.0 0.0
## 729 0.4 2.382441 -76.61365 807.0 0.2
## 730 0.0 2.382274 -76.61375 807.0 0.0
## 731 0.0 2.382226 -76.61378 807.0 0.0
## 732 0.5 2.382176 -76.61382 807.0 0.3
## 733 0.0 2.382119 -76.61386 807.0 0.0
## 734 0.0 2.382062 -76.61389 807.0 0.0
## 735 0.0 2.382030 -76.61400 807.0 0.0
## 736 0.0 2.382076 -76.61398 807.2 0.0
## 737 0.0 2.382136 -76.61393 807.2 0.0
## 738 0.0 2.382175 -76.61390 806.9 0.0
## 739 0.0 2.382224 -76.61386 806.9 0.0
## 740 0.4 2.382271 -76.61382 806.7 0.4
## 741 0.0 2.382325 -76.61377 806.7 0.0
## 742 0.0 2.382368 -76.61374 806.7 0.0
## 743 0.3 2.382424 -76.61371 806.7 0.3
## 744 0.0 2.382486 -76.61367 806.7 0.0
## 745 0.0 2.382538 -76.61364 806.9 0.0
## 746 0.0 2.382581 -76.61360 806.9 0.0
## 747 0.0 2.382629 -76.61363 807.0 0.0
## 748 0.4 2.382582 -76.61368 807.2 0.0
## 749 0.0 2.382540 -76.61371 807.0 0.0
## 750 0.0 2.382481 -76.61373 807.0 0.0
## 751 0.3 2.382433 -76.61376 806.9 0.3
## 752 0.0 2.382375 -76.61382 806.9 0.0
## 753 0.0 2.382331 -76.61384 806.9 0.0
## 754 0.0 2.382293 -76.61388 806.9 0.0
## 755 0.0 2.382226 -76.61391 807.0 0.0
## 756 0.0 2.382176 -76.61397 806.9 0.0
## 757 0.0 2.382129 -76.61397 807.0 0.0
## 758 0.0 2.382061 -76.61405 807.2 0.0
## 759 0.0 2.382066 -76.61408 807.5 0.0
## 760 0.0 2.382136 -76.61405 807.5 0.0
## 761 0.0 2.382188 -76.61402 807.4 0.0
## 762 0.0 2.382236 -76.61398 807.2 0.0
## 763 0.0 2.382279 -76.61395 807.0 0.0
## 764 0.4 2.382331 -76.61391 807.0 0.4
## 765 0.0 2.382377 -76.61387 806.9 0.0
## 766 0.0 2.382436 -76.61382 806.9 0.0
## 767 0.0 2.382487 -76.61379 806.9 0.0
## 768 0.0 2.382535 -76.61376 806.9 0.0
## 769 0.0 2.382582 -76.61374 806.9 0.0
## 770 0.0 2.382639 -76.61370 807.0 0.0
## 771 0.4 2.382694 -76.61373 807.0 0.4
## 772 0.0 2.382641 -76.61375 807.2 0.0
## 773 0.0 2.382588 -76.61379 807.0 0.0
## 774 0.0 2.382536 -76.61383 807.0 0.0
## 775 0.0 2.382484 -76.61385 807.0 0.0
## 776 0.0 2.382430 -76.61389 807.0 0.0
## 777 0.0 2.382374 -76.61395 807.0 0.0
## 778 0.3 2.382333 -76.61396 807.0 0.3
## 779 0.0 2.382278 -76.61401 807.2 0.0
## 780 0.0 2.382220 -76.61406 807.2 0.0
## 781 0.0 2.382183 -76.61408 807.5 0.0
## 782 0.0 2.382121 -76.61412 807.5 0.0
## 783 0.0 2.382056 -76.61415 807.7 0.0
## 784 0.0 2.382486 -76.61391 807.0 0.0
## 785 0.0 2.382543 -76.61388 806.9 0.0
## 786 0.3 2.382596 -76.61385 806.9 0.3
## 787 0.0 2.382643 -76.61382 807.0 0.0
## 788 0.3 2.382693 -76.61379 807.0 0.3
## La tabla muestra variables las cuales pueden ayudar a un insecto a generar los daños en los arboles o frutos
## Se realiza una tabla para saber cuantos monitoreos se realizaron en las fechas del trabajo de campo
table(datos_fresno3$Monitoreo)
##
## 0004-09-20 0021-08-20
## 394 394
## Se obtienen dos monitoreos con fecha de 0004-09-20 y 0021-08-20 con 394 cada uno
## Se cargan las siguientes librerias, se debe tener en cuenta que la libreria "plottly" se encuentra
## disponible desde la versión 4.05 versiones anteriores traen problemas.
require(ggplot2)
## Loading required package: ggplot2
require(plotly)
## Loading required package: plotly
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
## Se realiza un primer mapa "map1" se plotea la tabla, los datos geograficos y la columna de fruto total daño en puntos
map1=ggplot(datos_fresno3,aes(x=Longitude,y=Latitude,color=Fruto_Total_Dano,size=Fruto_Total_Dano))+geom_point()+theme_bw()+ggtitle("Daño de Frutos")
ggplotly(map1)
## Se obtiene un grafico el cual indica diferentes tamaños de puntos, siendo los mas grandes los de mayor afectacion
## Las zonas donde se presenta mayor afectación es en la parte sur de la finca con algunas afectaciones dispersas al norte y centro
## Realizamos una figura para observar la distribuon del daño total, existe un conteo de 656
fig1=ggplot(datos_fresno3,aes(x=Fruto_Total_Dano))+geom_bar()+theme_bw()
ggplotly(fig1)
## Se realiza otro grafico que representa la relación entre la temperatura y daño del fruto
fig2=ggplot(datos_fresno3,aes(x=Temperature,y=Fruto_Total_Dano))+geom_smooth(se = FALSE)+theme_bw()
ggplotly(fig2)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Se puede observar que el daño del fruto aumenta a medida que la temperatura sube, hubo una pequeña
##disminución sobre los 23° sin embargo, al aumentar por encima de los 25° el daño es elevado
#Se realiza un grafico 3 donde se relaciona la humedad relativa vs el daño del fruto
fig3=ggplot(datos_fresno3,aes(x=Relative_Humidity,y=Fruto_Total_Dano))+geom_smooth(se = FALSE)+theme_bw()
ggplotly(fig3)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Se observa que cuando la humedad es de 60% el daño del fruto es muy bajo, por otro lado al aumentar
## la humedad aumenta el daño del fruto
## instalamos libreria mgvc
require(mgcv)
## Loading required package: mgcv
## Loading required package: nlme
## This is mgcv 1.8-35. For overview type 'help("mgcv-package")'.
## Se incluyen las demás co-variables en relación al daño del fruto a traves de una función suave
mod1= gam(Fruto_Total_Dano~s(Temperature)+s(Relative_Humidity)+s(Altitude)+s(Wind_Speed)+s(Latitude)+s(Longitude)+s(Barometric_Pressure)+s(Crosswind),data=datos_fresno3,family = "poisson")
summary(mod1)
##
## Family: poisson
## Link function: log
##
## Formula:
## Fruto_Total_Dano ~ s(Temperature) + s(Relative_Humidity) + s(Altitude) +
## s(Wind_Speed) + s(Latitude) + s(Longitude) + s(Barometric_Pressure) +
## s(Crosswind)
##
## Parametric coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.904 1.046 -2.776 0.0055 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df Chi.sq p-value
## s(Temperature) 8.902 8.995 69.907 < 2e-16 ***
## s(Relative_Humidity) 8.286 8.856 64.898 < 2e-16 ***
## s(Altitude) 6.552 7.365 10.930 0.164
## s(Wind_Speed) 7.719 7.837 61.222 < 2e-16 ***
## s(Latitude) 8.613 8.957 204.481 < 2e-16 ***
## s(Longitude) 8.565 8.944 87.070 < 2e-16 ***
## s(Barometric_Pressure) 6.572 7.364 8.553 0.347
## s(Crosswind) 5.400 5.579 31.322 2.55e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.181 Deviance explained = 37.1%
## UBRE = 1.0986 Scale est. = 1 n = 788
## el resultado muestra que solomente las presión bariometrica y la altura sobre el nivel del mar no afectan
## o no influyen directamente en el daño del fruto
##Generamos otro modelo para predecir y ver su error, se ocomodan los datos quitando la coli¿umna 1 y 2
modelo=round(predict(mod1,newdata = datos_fresno3[,-c(1:2)],type = "response"))
real=datos_fresno3$Fruto_Total_Dano
MAE=round(mean(abs(modelo-real)),1)
RMSE=round(sqrt(mean((modelo-real)^2)),1)
MAE
## [1] 0.8
RMSE
## [1] 2.2
##Se calculo la matrica de ajuste y se obtiene que el modelo presenta
##un error MAE de 0.8 y un RMSE de 2.2
##Agregamos los datos del modelo a la tabla para generar un nuevo grafico
datos_fresno3$Fruto_Total_Dano_modelo=modelo
datos_fresno3
## # A tibble: 788 x 11
## Monitoreo Fruto_Total_Dano Temperature Relative_Humidi~ Altitude Wind_Speed
## <date> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0021-08-20 0 25.7 33.6 1.90 0.8
## 2 0021-08-20 3 20.8 36.8 1.90 5.1
## 3 0021-08-20 0 23.7 31.5 1.89 0.8
## 4 0021-08-20 0 25 33.2 1.89 0.9
## 5 0021-08-20 1 25 34.3 1.89 0.6
## 6 0021-08-20 0 25 33.8 1.9 0.5
## 7 0021-08-20 0 24.9 34.9 1.9 0.6
## 8 0021-08-20 0 22.9 34.2 1.90 3.7
## 9 0021-08-20 0 26.2 33.6 1.90 0.5
## 10 0021-08-20 21 24.6 34 1.89 1
## # ... with 778 more rows, and 5 more variables: Latitude <dbl>,
## # Longitude <dbl>, Barometric_Pressure <dbl>, Crosswind <dbl>,
## # Fruto_Total_Dano_modelo <dbl>
##Realizamos un "map2" con los datos predichos por el modelo en el paso anterior
map2=ggplot(datos_fresno3,aes(x=Longitude,y=Latitude,color=Fruto_Total_Dano_modelo,size=Fruto_Total_Dano_modelo))+geom_point()+theme_bw()+ggtitle("Modelo GAM")
ggplotly(map2)
##El modelo muestra una afectacción mas severa sobre la parte sur y un amuento en la parte norte y centro del lote
##Se realiza un ploteo de ambos datos el observado vs el predicho
require(gridExtra)
## Loading required package: gridExtra
gridExtra::grid.arrange(map1, map2, ncol = 1)
## el modelo presenta un diferencia en la magnitud
##Otra metodologia "Arbol de regresion""
require(rpart)
## Loading required package: rpart
##se juntan todas las covariebles y se realiza un grafico
mod2= rpart(Fruto_Total_Dano~Temperature+Relative_Humidity+Altitude+Wind_Speed+Latitude+Longitude+Barometric_Pressure+Crosswind,data=datos_fresno3)
## El grafico representa una jerarquia
plot(mod2)
text(mod2, use.n = TRUE)##Reglas de desicion
## Realizar la prediccion e incluimos los datos predichos
modelo2=predict(mod2)
datos_fresno3$Fruto_Total_Dano_modelo2=modelo2
datos_fresno3
## # A tibble: 788 x 12
## Monitoreo Fruto_Total_Dano Temperature Relative_Humidi~ Altitude Wind_Speed
## <date> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0021-08-20 0 25.7 33.6 1.90 0.8
## 2 0021-08-20 3 20.8 36.8 1.90 5.1
## 3 0021-08-20 0 23.7 31.5 1.89 0.8
## 4 0021-08-20 0 25 33.2 1.89 0.9
## 5 0021-08-20 1 25 34.3 1.89 0.6
## 6 0021-08-20 0 25 33.8 1.9 0.5
## 7 0021-08-20 0 24.9 34.9 1.9 0.6
## 8 0021-08-20 0 22.9 34.2 1.90 3.7
## 9 0021-08-20 0 26.2 33.6 1.90 0.5
## 10 0021-08-20 21 24.6 34 1.89 1
## # ... with 778 more rows, and 6 more variables: Latitude <dbl>,
## # Longitude <dbl>, Barometric_Pressure <dbl>, Crosswind <dbl>,
## # Fruto_Total_Dano_modelo <dbl>, Fruto_Total_Dano_modelo2 <dbl>
##Mapa de predicción de Daño en Frutos
map3=ggplot(datos_fresno3,aes(x=Longitude,y=Latitude,color=Fruto_Total_Dano_modelo2,size=Fruto_Total_Dano_modelo2))+geom_point()+theme_bw()+ggtitle("Arbol de Regresión")
ggplotly(map3)
##ploteamos los resultados entre el original y los dos modelos
require(gridExtra)
gridExtra::grid.arrange(map1,map2, map3, ncol = 1)
##realizamos el modelo de maquina de soporte vectoial
require(e1071)
## Loading required package: e1071
mod3= svm(Fruto_Total_Dano~Temperature+Relative_Humidity+Altitude+Wind_Speed+Latitude+Longitude+Barometric_Pressure+Crosswind,data=datos_fresno3,kernel="polynomial",degree=10)
modelo3=predict(mod3)
datos_fresno3$Fruto_Total_Dano_modelo3=modelo3
datos_fresno3
## # A tibble: 788 x 13
## Monitoreo Fruto_Total_Dano Temperature Relative_Humidi~ Altitude Wind_Speed
## <date> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0021-08-20 0 25.7 33.6 1.90 0.8
## 2 0021-08-20 3 20.8 36.8 1.90 5.1
## 3 0021-08-20 0 23.7 31.5 1.89 0.8
## 4 0021-08-20 0 25 33.2 1.89 0.9
## 5 0021-08-20 1 25 34.3 1.89 0.6
## 6 0021-08-20 0 25 33.8 1.9 0.5
## 7 0021-08-20 0 24.9 34.9 1.9 0.6
## 8 0021-08-20 0 22.9 34.2 1.90 3.7
## 9 0021-08-20 0 26.2 33.6 1.90 0.5
## 10 0021-08-20 21 24.6 34 1.89 1
## # ... with 778 more rows, and 7 more variables: Latitude <dbl>,
## # Longitude <dbl>, Barometric_Pressure <dbl>, Crosswind <dbl>,
## # Fruto_Total_Dano_modelo <dbl>, Fruto_Total_Dano_modelo2 <dbl>,
## # Fruto_Total_Dano_modelo3 <dbl>
##Mapa de predicción de Daño en Frutos
map4=ggplot(datos_fresno3,aes(x=Longitude,y=Latitude,color=Fruto_Total_Dano_modelo3,size=Fruto_Total_Dano_modelo3))+geom_point()+theme_bw()+ggtitle("Maquina de Soporte Vectorial")
ggplotly(map4)
## con este modelo se disminuye los daños del fruto en la parte sur
require(gridExtra)
gridExtra::grid.arrange(map1,map2, map3,map4, ncol = 2)
##Modelo con Red Neuronal
require(neuralnet)
## Loading required package: neuralnet
datos_fresno4=datos_fresno3
datos_fresno4[,3:10]=scale(datos_fresno4[,3:10])
datos_fresno4=data.frame(datos_fresno4)
mod4= neuralnet(Fruto_Total_Dano~Temperature+Relative_Humidity+Altitude+Wind_Speed+Latitude+Longitude+Barometric_Pressure+Crosswind,hidden = c(1),data=datos_fresno4)
plot(mod4)
modelo4=predict(mod4,datos_fresno4)
datos_fresno4$Fruto_Total_Dano_modelo4=modelo4
##Mapa de predicción de Daño en Frutos
map5=ggplot(datos_fresno4,aes(x=Longitude,y=Latitude,color=Fruto_Total_Dano_modelo4,size=Fruto_Total_Dano_modelo4))+geom_point()+theme_bw()+ggtitle("Red Neuronal")
ggplotly(map5)
require(gridExtra)
gridExtra::grid.arrange(map1,map2, map3,map4,map5, ncol = 2)
##Se pudo observar que el modelo 1 y 2 son los que predicen mejor, el modelo 3, 4
##no representan en mayor medida el posible comprotamiento de las covariables frente
## a la afectacion del fruto, cada modelo se ajusta a una serie de parametros
## ya nos queda a nosotros seleccionar el que mejor se ajuste a los objetivos de la investigacion
## y soportar con la validación cruzada.