Exam 1
Randy Reichenbach
2022-10-20
Section 1 of Exam 1: Regression Model
Read the csv file titled Training Data into R. All rows of this dataset should be used as your training set in the development of a regression model. This dataset contains information about some of Pfizer’s patent applications. Each patent application has a unique ID given to it, and this ID is listed in the Patent_Number column. The column titled Cites_Patent_Count lists the number of patent citations made within the corresponding patent application, and the column titled Cited_by_Patent_Count lists the number of patents which cite the document identified by the Patent_Number. The Cited_by_Patent_Count column can be seen as a measure of the influence and strength of the innovation, and as such, it can be useful to try to predict this column to help firms understand the potential influence of their innovations.
## Patent_Number Cites_Patent_Count Cited_by_Patent_Count
## Length:626 Min. : 0.000 Min. :0.0000
## Class :character 1st Qu.: 0.000 1st Qu.:0.0000
## Mode :character Median : 0.000 Median :0.0000
## Mean : 4.358 Mean :0.0639
## 3rd Qu.: 0.000 3rd Qu.:0.0000
## Max. :335.000 Max. :4.0000## Patent_Number Cites_Patent_Count Cited_by_Patent_Count
## 1 PL 3341367 T3 0 0
## 2 HR P20210871 T1 0 0
## 3 CR 20210284 A 0 0
## 4 US 2021/0205309 A1 0 0
## 5 JP 2021100972 A 3 0
## 6 AU 2021/203768 A1 0 0## [1] "Patent_Number" "Cites_Patent_Count" "Cited_by_Patent_Count"- Use regression to try to predict the Cited_by_Patent_Count, with Cites_Patent_Count used as a covariate. Based on your results, write an equation using the following format:
training$Patent_Number <- NULL
training_model <- lm(Cited_by_Patent_Count~.,data = training)
summary(training_model)##
## Call:
## lm(formula = Cited_by_Patent_Count ~ ., data = training)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.9469 -0.0523 -0.0523 -0.0523 3.7661
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0522603 0.0130896 3.993 7.32e-05 ***
## Cites_Patent_Count 0.0026705 0.0005322 5.018 6.81e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3223 on 624 degrees of freedom
## Multiple R-squared: 0.03879, Adjusted R-squared: 0.03725
## F-statistic: 25.18 on 1 and 624 DF, p-value: 6.811e-07Fill-in the blanks for the following statements:
- Your regression equation provides an “estimate” of the actual values for the numbers B1 and B2. There is a 90% probability that the actual value of B1 lies between 0.001793859 and 0.003547102.
- There is a 90% probability that the actual value of B2 lies between 0.030697775 and 0.073822761.
## 5 % 95 %
## (Intercept) 0.030697775 0.073822761
## Cites_Patent_Count 0.001793859 0.003547102- Based on Cook’s Distance, are there outliers that may possibly need to be removed and/or treated differently in the dataset? Create a plot using R to support your answer.
cooks.distance(training_model)## 1 2 3 4 5 6
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.810625e-05 2.174933e-05
## 7 8 9 10 11 12
## 2.174933e-05 2.372145e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 13 14 15 16 17 18
## 5.071376e-05 2.174933e-05 2.174933e-05 8.574799e-05 2.174933e-05 2.174933e-05
## 19 20 21 22 23 24
## 2.372145e-05 2.174933e-05 2.174933e-05 2.174933e-05 3.021095e-02 2.372145e-05
## 25 26 27 28 29 30
## 2.174933e-05 2.174933e-05 2.583357e-05 2.372145e-05 7.152876e-03 2.174933e-05
## 31 32 33 34 35 36
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 37 38 39 40 41 42
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 43 44 45 46 47 48
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 49 50 51 52 53 54
## 2.174933e-05 2.174933e-05 2.174933e-05 2.583357e-05 2.174933e-05 2.174933e-05
## 55 56 57 58 59 60
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 61 62 63 64 65 66
## 2.174933e-05 2.174933e-05 2.174933e-05 3.612723e-05 2.174933e-05 2.174933e-05
## 67 68 69 70 71 72
## 2.174933e-05 6.914970e-03 2.174933e-05 2.174933e-05 3.612723e-05 2.174933e-05
## 73 74 75 76 77 78
## 6.580877e-05 6.111181e-02 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 79 80 81 82 83 84
## 2.810625e-05 5.071376e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 85 86 87 88 89 90
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 3.322685e-05
## 91 92 93 94 95 96
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 7.152876e-03 6.580877e-05
## 97 98 99 100 101 102
## 2.174933e-05 2.174933e-05 2.372145e-05 2.174933e-05 4.284078e-04 2.174933e-05
## 103 104 105 106 107 108
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 109 110 111 112 113 114
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 115 116 117 118 119 120
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 121 122 123 124 125 126
## 2.174933e-05 2.174933e-05 2.174933e-05 2.810625e-05 2.174933e-05 4.275628e-05
## 127 128 129 130 131 132
## 9.239691e-02 5.071376e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.372145e-05
## 133 134 135 136 137 138
## 2.174933e-05 1.273912e-02 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 139 140 141 142 143 144
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 145 146 147 148 149 150
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 151 152 153 154 155 156
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 157 158 159 160 161 162
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 163 164 165 166 167 168
## 2.810625e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 169 170 171 172 173 174
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 175 176 177 178 179 180
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 7.381892e-02 2.174933e-05
## 181 182 183 184 185 186
## 2.838903e-02 2.174933e-05 2.174933e-05 2.174933e-05 1.570643e-03 2.174933e-05
## 187 188 189 190 191 192
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 193 194 195 196 197 198
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 199 200 201 202 203 204
## 2.174933e-05 1.335152e-04 7.152876e-03 2.174933e-05 3.612723e-05 2.174933e-05
## 205 206 207 208 209 210
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 211 212 213 214 215 216
## 2.372145e-05 2.174933e-05 2.810625e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 217 218 219 220 221 222
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.372145e-05 2.174933e-05
## 223 224 225 226 227 228
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.372145e-05
## 229 230 231 232 233 234
## 2.174933e-05 2.174933e-05 8.849406e-01 2.174933e-05 2.174933e-05 2.174933e-05
## 235 236 237 238 239 240
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 241 242 243 244 245 246
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 247 248 249 250 251 252
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 253 254 255 256 257 258
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 259 260 261 262 263 264
## 3.021095e-02 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 3.612723e-05
## 265 266 267 268 269 270
## 2.174933e-05 2.372145e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 271 272 273 274 275 276
## 2.174933e-05 2.174933e-05 2.372145e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 277 278 279 280 281 282
## 1.222411e-04 2.174933e-05 2.174933e-05 2.174933e-05 2.810625e-05 2.174933e-05
## 283 284 285 286 287 288
## 2.174933e-05 2.583357e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 289 290 291 292 293 294
## 2.174933e-05 2.174933e-05 2.174933e-05 3.929307e-05 2.174933e-05 2.174933e-05
## 295 296 297 298 299 300
## 2.174933e-05 9.727993e-02 9.369997e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 301 302 303 304 305 306
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 307 308 309 310 311 312
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 313 314 315 316 317 318
## 2.174933e-05 2.174933e-05 2.174933e-05 3.963990e-04 2.174933e-05 2.174933e-05
## 319 320 321 322 323 324
## 7.152876e-03 2.174933e-05 1.796726e-02 2.174933e-05 2.174933e-05 2.810625e-05
## 325 326 327 328 329 330
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 331 332 333 334 335 336
## 2.174933e-05 2.372145e-05 2.174933e-05 1.913967e-02 2.174933e-05 2.174933e-05
## 337 338 339 340 341 342
## 2.174933e-05 7.185379e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 343 344 345 346 347 348
## 2.174933e-05 2.485777e-03 2.174933e-05 3.322685e-05 2.174933e-05 1.118882e-04
## 349 350 351 352 353 354
## 2.174933e-05 2.174933e-05 7.702738e-04 2.372145e-05 2.174933e-05 2.174933e-05
## 355 356 357 358 359 360
## 6.996241e-03 2.174933e-05 2.174933e-05 2.372145e-05 2.174933e-05 2.174933e-05
## 361 362 363 364 365 366
## 2.174933e-05 2.174933e-05 3.322685e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 367 368 369 370 371 372
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 7.152876e-03 6.030046e-05
## 373 374 375 376 377 378
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 379 380 381 382 383 384
## 2.174933e-05 3.056231e-05 7.152876e-03 2.174933e-05 2.174933e-05 2.174933e-05
## 385 386 387 388 389 390
## 2.174933e-05 2.174933e-05 1.335152e-04 2.174933e-05 2.174933e-05 2.174933e-05
## 391 392 393 394 395 396
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.372145e-05 2.174933e-05
## 397 398 399 400 401 402
## 2.174933e-05 2.174933e-05 5.528323e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 403 404 405 406 407 408
## 2.174933e-05 2.174933e-05 3.021095e-02 2.174933e-05 2.372145e-05 2.174933e-05
## 409 410 411 412 413 414
## 3.056231e-05 7.152876e-03 2.174933e-05 2.174933e-05 3.929307e-05 2.174933e-05
## 415 416 417 418 419 420
## 2.174933e-05 2.174933e-05 7.111225e-02 2.174933e-05 5.528323e-05 7.152876e-03
## 421 422 423 424 425 426
## 2.174933e-05 4.655102e-05 8.574799e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 427 428 429 430 431 432
## 2.174933e-05 2.174933e-05 2.174933e-05 3.056231e-05 2.174933e-05 2.583357e-05
## 433 434 435 436 437 438
## 2.174933e-05 2.583357e-05 2.174933e-05 2.810625e-05 2.174933e-05 2.174933e-05
## 439 440 441 442 443 444
## 2.174933e-05 2.174933e-05 1.222411e-04 2.174933e-05 5.071376e-05 2.174933e-05
## 445 446 447 448 449 450
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 451 452 453 454 455 456
## 3.056231e-05 2.174933e-05 2.174933e-05 2.174933e-05 6.030046e-05 2.174933e-05
## 457 458 459 460 461 462
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 9.369997e-05
## 463 464 465 466 467 468
## 2.174933e-05 2.372145e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 469 470 471 472 473 474
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 475 476 477 478 479 480
## 2.174933e-05 2.810625e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 481 482 483 484 485 486
## 3.322685e-05 2.174933e-05 2.174933e-05 2.372145e-05 2.174933e-05 2.174933e-05
## 487 488 489 490 491 492
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.372145e-05 2.174933e-05
## 493 494 495 496 497 498
## 2.174933e-05 2.174933e-05 2.174933e-05 6.773844e-03 2.583357e-05 2.583357e-05
## 499 500 501 502 503 504
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 505 506 507 508 509 510
## 6.775879e-01 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 511 512 513 514 515 516
## 2.174933e-05 2.174933e-05 2.583357e-05 2.583357e-05 2.810625e-05 2.372145e-05
## 517 518 519 520 521 522
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 523 524 525 526 527 528
## 2.174933e-05 2.174933e-05 3.612723e-05 2.174933e-05 2.810625e-05 2.583357e-05
## 529 530 531 532 533 534
## 2.174933e-05 2.174933e-05 7.848345e-05 2.174933e-05 1.150560e-03 2.174933e-05
## 535 536 537 538 539 540
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 541 542 543 544 545 546
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 547 548 549 550 551 552
## 2.174933e-05 2.583357e-05 2.174933e-05 2.174933e-05 2.810625e-05 2.174933e-05
## 553 554 555 556 557 558
## 2.174933e-05 7.152876e-03 2.174933e-05 2.174933e-05 3.021095e-02 2.174933e-05
## 559 560 561 562 563 564
## 2.174933e-05 7.185379e-05 2.174933e-05 2.174933e-05 3.612723e-05 2.174933e-05
## 565 566 567 568 569 570
## 2.174933e-05 2.174933e-05 2.635251e+00 1.523294e-02 2.174933e-05 2.174933e-05
## 571 572 573 574 575 576
## 2.174933e-05 8.756098e-03 2.174933e-05 2.174933e-05 6.914970e-03 2.174933e-05
## 577 578 579 580 581 582
## 2.174933e-05 3.021095e-02 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 583 584 585 586 587 588
## 5.282779e-03 2.174933e-05 2.174933e-05 2.174933e-05 3.664304e-04 2.174933e-05
## 589 590 591 592 593 594
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 595 596 597 598 599 600
## 6.914970e-03 2.810625e-05 2.174933e-05 2.174933e-05 2.174933e-05 3.056231e-05
## 601 602 603 604 605 606
## 2.174933e-05 2.463827e+00 3.056231e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 607 608 609 610 611 612
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 613 614 615 616 617 618
## 2.174933e-05 2.174933e-05 4.275628e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 619 620 621 622 623 624
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 625 626
## 2.174933e-05 2.583357e-05cbind(training, cooks.distance(training_model))## Cites_Patent_Count Cited_by_Patent_Count cooks.distance(training_model)
## 1 0 0 2.174933e-05
## 2 0 0 2.174933e-05
## 3 0 0 2.174933e-05
## 4 0 0 2.174933e-05
## 5 3 0 2.810625e-05
## 6 0 0 2.174933e-05
## 7 0 0 2.174933e-05
## 8 1 0 2.372145e-05
## 9 0 0 2.174933e-05
## 10 0 0 2.174933e-05
## 11 0 0 2.174933e-05
## 12 0 0 2.174933e-05
## 13 10 0 5.071376e-05
## 14 0 0 2.174933e-05
## 15 0 0 2.174933e-05
## 16 16 0 8.574799e-05
## 17 0 0 2.174933e-05
## 18 0 0 2.174933e-05
## 19 1 0 2.372145e-05
## 20 0 0 2.174933e-05
## 21 0 0 2.174933e-05
## 22 0 0 2.174933e-05
## 23 0 2 3.021095e-02
## 24 1 0 2.372145e-05
## 25 0 0 2.174933e-05
## 26 0 0 2.174933e-05
## 27 2 0 2.583357e-05
## 28 1 0 2.372145e-05
## 29 0 1 7.152876e-03
## 30 0 0 2.174933e-05
## 31 0 0 2.174933e-05
## 32 0 0 2.174933e-05
## 33 0 0 2.174933e-05
## 34 0 0 2.174933e-05
## 35 0 0 2.174933e-05
## 36 0 0 2.174933e-05
## 37 0 0 2.174933e-05
## 38 0 0 2.174933e-05
## 39 0 0 2.174933e-05
## 40 0 0 2.174933e-05
## 41 0 0 2.174933e-05
## 42 0 0 2.174933e-05
## 43 0 0 2.174933e-05
## 44 0 0 2.174933e-05
## 45 0 0 2.174933e-05
## 46 0 0 2.174933e-05
## 47 0 0 2.174933e-05
## 48 0 0 2.174933e-05
## 49 0 0 2.174933e-05
## 50 0 0 2.174933e-05
## 51 0 0 2.174933e-05
## 52 2 0 2.583357e-05
## 53 0 0 2.174933e-05
## 54 0 0 2.174933e-05
## 55 0 0 2.174933e-05
## 56 0 0 2.174933e-05
## 57 0 0 2.174933e-05
## 58 0 0 2.174933e-05
## 59 0 0 2.174933e-05
## 60 0 0 2.174933e-05
## 61 0 0 2.174933e-05
## 62 0 0 2.174933e-05
## 63 0 0 2.174933e-05
## 64 6 0 3.612723e-05
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## 626 2 0 2.583357e-05plot(training_model)
There are three outliers that need to be removed or treated differently. Data point 231 sits just at 1 and data points 602 and 567 sit will outside of 1.
- Check the normality of residuals by creating a QQ-plot. Based on the plot, are the residuals normally distributed?
qqnorm(training_model$residuals)
qqline(training_model$residuals)
The residuals are not normally distributd; the plot is considered a heavily tailed distribution.
- Use the regression model that you created in #2 to predict the Cited_by_Patent_Count in each of the following scenarios:
- Cites_Patent_Count = 340 -> 0.9602238
- Cites_Patent_Count = 300 -> 0.8534046
Q3A = data.frame(Cites_Patent_Count = c(340))
Q3B = data.frame(Cites_Patent_Count = c(300))
predictA<-predict(training_model, Q3A, type = "response")
predictB<-predict(training_model, Q3B, type = "response")
predictA## 1
## 0.9602238predictB## 1
## 0.8534046- Was the prediction made in part a of #3 considered to be extrapolation? Yes, the result falls outside of the range of calculated leverage.
- Was the prediction made in part b of #3 considered to be extrapolation? Yes, the result falls outside of the range of calculated leverage.
min(hatvalues(training_model))## [1] 0.001597793mean(hatvalues(training_model))## [1] 0.003194888max(hatvalues(training_model))## [1] 0.2995991PRED = model.matrix(training_model)
pred_newA = c(.9602238, 0)
t(pred_newA)%*%solve(t(PRED)%*%PRED)%*%pred_newA## [,1]
## [1,] 0.001520621pred_newB = c(.8534046, 0)
t(pred_newB)%*%solve(t(PRED)%*%PRED)%*%pred_newB## [,1]
## [1,] 0.001201119- Fill in the blanks based on the regression model that you created in #2, and the corresponding prediction that you made in part b of #3:
- When Cites_Patent_Count is 300 (as in part b of #3), there is a 95% chance that Cited_by_Patent_Count will be between 0.1486074 and 1.558202.
predictB_prediction<-predict(training_model, Q3B, interval = "prediction", level = .95, type = "response")
predictB_prediction## fit lwr upr
## 1 0.8534046 0.1486074 1.558202- When Cites_Patent_Count is 300, there is a 95% chance that the mean response will fall between 0.5434141 and 1.163395.
predictB_confidence<-predict(training_model, Q3B, interval = "confidence", level = .95, type = "response")
predictB_confidence## fit lwr upr
## 1 0.8534046 0.5434141 1.163395Section 2 of Exam 1: Joining Data
The four types of mutating joins learned in class are:
- Inner join
- Full join
- Left join
- Right join
- Open the Training Data and Sequence Counts csv files, and notice that each file has a column listing the patent application number. For full credit, perform the following joins in R without changing any column titles in either dataset:
library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.2.1## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.7 ✔ dplyr 1.0.9
## ✔ tidyr 1.2.0 ✔ stringr 1.4.0
## ✔ readr 2.1.2 ✔ forcats 0.5.1## Warning: package 'ggplot2' was built under R version 4.2.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()training <- read.csv("C:/Users/raze1/OneDrive/Desktop/UIndy/MSDA 621/Exams/Exam 1/Training Data.csv")
sequence <- read.csv("C:/Users/raze1/OneDrive/Desktop/UIndy/MSDA 621/Exams/Exam 1/Sequence Counts.csv")
colnames(training)## [1] "Patent_Number" "Cites_Patent_Count" "Cited_by_Patent_Count"colnames(sequence)## [1] "Patent_No." "Sequence_Count"colnames(sequence) <- c("Patent_Number", "Sequence_Count")
colnames(sequence)## [1] "Patent_Number" "Sequence_Count"- Perform an inner join
training%>%inner_join(sequence, by = "Patent_Number")->Inner_Join_TraSeq
Inner_Join_TraSeq## Patent_Number Cites_Patent_Count Cited_by_Patent_Count Sequence_Count
## 1 CA 189065 S 0 0 0
## 2 NI 202000072 A 0 0 0
## 3 KR 20210032013 A 0 0 80
## 4 PH 12020550461 A1 0 0 0
## 5 TW I722568 B 0 0 0
## 6 CN 112533674 A 0 2 0
## 7 CO 2021002954 A2 0 0 0
## 8 JP 2021040642 A 1 0 0
## 9 AU 2019/341683 A1 0 0 0
## 10 JP 2021042204 A 4 0 0
## 11 US 2021/0077463 A1 0 1 24
## 12 CA 3153676 A1 0 0 0
## 13 CA 200622 S 0 0 0
## 14 WO 2021/048736 A1 7 0 0
## 15 AU 2021/200232 A1 0 0 0
## 16 PE 20210554 A1 0 0 0
## 17 AR 115987 A1 0 0 0
## 18 US 10946086 B2 111 1 0
## 19 TW 202111125 A 0 0 0
## 20 US 10947494 B2 11 0 0
## 21 CN 112513070 A 0 1 0
## 22 TW 202110480 A 0 0 0
## 23 US 10946031 B2 9 0 0
## 24 US 10945994 B2 16 0 2
## 25 BR 112020025701 A2 0 0 0
## 26 TW 202110446 A 0 0 0
## 27 PE 20210488 A1 0 0 0
## 28 KR 20210029234 A 0 0 0
## 29 ES 2811523 T3 0 0 0
## 30 US 2021/0069219 A1 0 0 0
## 31 JP 2021038265 A 4 0 0
## 32 US 2021/0069326 A1 0 0 41
## 33 EA 037318 B1 2 0 0
## 34 AU 2019/329884 A1 0 0 0
## 35 AU 2016/286108 B2 2 0 0
## 36 AU 2019/323790 A1 0 0 0
## 37 JP 2021036873 A 3 0 0
## 38 EP 3334760 B1 0 0 0
## 39 UA 123270 C2 0 0 0
## 40 EP 3091981 B1 0 0 0
## 41 LT C2534153 I2 0 0 0
## 42 US 10941216 B2 20 0 82
## 43 BR 112020024956 A2 0 0 0
## 44 US 10941118 B2 10 0 0
## 45 CN 112469473 A 0 0 0
## 46 PL 3096785 T3 0 0 0
## 47 PL 3310784 T3 0 0 0
## 48 CO 2021002230 A2 0 0 0
## 49 CO 2021001893 A2 0 0 0
## 50 CO 2020015305 A2 0 0 0
## 51 PE 20210468 A1 0 0 0
## 52 EA 037288 B1 4 0 0
## 53 PE 20210412 A1 0 0 0
## 54 US 2021/0061902 A1 0 0 225
## 55 US 2021/0061923 A1 0 0 0
## 56 EP 2968155 B1 12 0 0
## 57 EP 3784664 A1 0 0 0
## 58 MA 52360 A 0 0 0
## 59 BR 112014018192 A8 0 0 0
## 60 BR 112020020941 A2 0 0 0
## 61 ES 2808987 T3 0 0 0
## 62 BR 112020024470 A2 0 0 0
## 63 US 10934279 B2 17 0 0
## 64 TW 202108167 A 0 0 0
## 65 TW I720272 B 1 0 0
## 66 IL 280292 A 0 0 0
## 67 TW I720448 B 0 0 0
## 68 IL 279591 A 0 0 0
## 69 IL 280317 A 0 0 0
## 70 IL 279926 A 0 0 0
## 71 DO P2020000191 A 0 0 0
## 72 IL 248667 B 0 0 0
## 73 IL 254131 B 0 0 0
## 74 IL 244437 B 0 0 0
## 75 IL 255444 B 0 0 0
## 76 PT 3137114 T 0 0 0
## 77 RU 2743793 C1 3 0 0
## 78 CL 2020002507 A1 0 0 0
## 79 UY 38786 A 0 0 0
## 80 SG 11202100888W A 0 0 0
## 81 US 2021/0054087 A1 0 0 502
## 82 AU 2018/208844 B2 5 0 0
## 83 EP 3781132 A1 0 0 0
## 84 EP 3341367 B1 0 0 0
## 85 EP 3148587 B1 1 0 0
## 86 ES 2807591 T3 0 0 0
## 87 US 10927178 B2 0 0 173
## 88 BR 112020022897 A2 0 0 0
## 89 KR 20210020095 A 0 0 0
## 90 KR 20210019529 A 0 0 0
## 91 CN 112384282 A 0 0 0
## 92 AU 2018/349259 B2 1 0 0
## 93 US 2021/0047298 A1 0 0 0
## 94 JP 2021020890 A 0 0 0
## 95 AU 2019/325400 A1 0 0 0
## 96 AU 2019/336197 A1 0 0 0
## 97 WO 2021/029854 A1 4 1 0
## 98 JP 2021019632 A 2 0 0
## 99 US 2021/0047325 A1 2 0 2
## 100 CA 3150424 A1 0 0 0
## 101 BR PI0915166 B1 0 0 0
## 102 KR 20210018529 A 0 0 6
## 103 EP 3774863 A1 0 0 0
## 104 KR 20210018341 A 0 0 0
## 105 BR 112020019368 A2 0 0 0
## 106 US 10918708 B2 139 2 40
## 107 PT 3327027 T 0 0 0
## 108 DK 3242888 T3 0 0 0
## 109 PH 12020550596 A1 0 0 0
## 110 DK 3328849 T5 0 0 0
## 111 CL 2020002748 A1 0 0 0
## 112 US 2021/0040182 A1 0 0 518
## 113 US 2021/0040045 A1 0 0 0
## 114 TW I718758 B 2 0 0
## 115 TW I718644 B 2 0 0
## 116 TW I718427 B 3 0 0
## 117 TW I718144 B 1 0 0
## 118 CN 112336854 A 0 0 0
## 119 BR 112020022595 A2 0 0 0
## 120 KR 20210014763 A 0 0 0
## 121 BR 112020023891 A2 0 0 0
## 122 BR 112020017935 A2 0 0 0
## 123 KR 20210013777 A 0 0 62
## 124 US 2021/0032308 A1 0 0 130
## 125 AU 2019/304198 A1 0 0 0
## 126 AU 2016/280137 B2 6 0 0
## 127 AU 2019/290212 A1 0 0 0
## 128 RU 2742260 C2 3 0 0
## 129 JP 2021011493 A 2 0 0
## 130 KR 20210013167 A 0 0 200
## 131 KR 20210013165 A 0 0 890
## 132 US 10906955 B2 15 0 25
## 133 CN 112313219 A 0 0 0
## 134 US 10906888 B2 49 0 0
## 135 TW 202104224 A 0 0 0
## 136 IL 254073 B 0 0 0
## 137 IL 279396 A 0 0 0
## 138 IL 278924 A 0 0 0
## 139 IL 278926 A 0 0 0
## 140 IL 279224 A 0 0 0
## 141 IL 279300 A 0 0 0
## 142 IL 255224 B 0 0 0
## 143 IL 279270 A 0 0 0
## 144 IL 252158 B 0 0 0
## 145 SI 3500564 T1 0 0 0
## 146 UY 38768 A 0 0 0
## 147 EC SP20078651 A 0 0 0
## 148 UY 38752 A 0 0 0
## 149 KR 20210011076 A 2 0 798
## 150 SG 11202100021T A 0 0 0
## 151 US 2021/0024497 A1 0 0 0
## 152 AU 2019/210579 B2 3 0 0
## 153 US 2021/0023134 A1 0 0 10
## 154 JP 2021008452 A 0 0 0
## 155 US 2021/0024903 A1 0 1 761
## 156 US 2021/0024589 A1 0 0 81
## 157 US 2021/0023200 A1 0 0 326
## 158 US 2021/0023193 A1 0 2 40
## 159 PT 3143019 T 0 0 0
## 160 HU E050811 T2 0 0 0
## 161 WO 2021/014360 A1 14 0 0
## 162 US 2021/0024536 A1 0 0 0
## 163 AR 115498 A1 0 0 0
## 164 EP 3770164 A1 6 0 0
## 165 EP 3768669 A1 0 0 0
## 166 BR 112020021689 A2 0 0 0
## 167 CN 112263677 A 0 0 0
## 168 US 10899802 B2 335 0 22
## 169 US 10899840 B2 36 1 36
## 170 DK 3137114 T3 0 0 0
## 171 PL 3339303 T3 0 0 0
## 172 GE P20217211 B 0 0 0
## 173 WO 2021/009676 A1 20 1 0
## 174 CA 3147266 A1 0 0 0
## 175 US 2021/0017206 A1 0 0 0
## 176 US 2021/0017553 A1 2 1 5
## 177 US 2021/0017205 A1 0 0 0
## 178 US 2021/0017254 A1 0 0 12
## 179 US 2021/0017172 A1 0 2 0
## 180 CR 20200612 A 0 0 0
## 181 EP 3765474 A1 0 0 0
## 182 KR 20210008177 A 0 0 4
## 183 EP 3765027 A1 0 0 0
## 184 EP 3766885 A1 77 0 0
## 185 EP 3765019 A1 0 0 0
## 186 SA 516371030 B1 0 0 0
## 187 MA 52012 A 0 0 0
## 188 US 10894083 B2 33 0 10
## 189 PE 20210127 A1 0 0 0
## 190 BR 112016029612 B1 0 0 0
## 191 DK 3327027 T3 0 0 0
## 192 MY 182282 A 0 0 0
## 193 MY 182168 A 0 0 0
## 194 CN 112236446 A 0 0 0
## 195 US 2021/0009601 A1 0 0 15
## 196 US 2021/0009657 A1 2 1 36
## 197 RU 2740309 C2 3 0 0
## 198 EP 3761954 A1 0 0 0
## 199 PT 3328867 T 0 0 0
## 200 CA 2928908 C 0 0 66
## 201 CN 112218618 A 4 0 0
## 202 MY 181891 A 0 0 0
## 203 US 10888611 B2 331 0 22
## 204 RU 2740135 C1 4 0 0
## 205 NI 202000058 A 0 0 0
## 206 KR 20210002757 A 0 0 46
## 207 CN 112195169 A 0 0 0
## 208 KR 20210002642 A 0 0 2
## 209 US 2021/0002369 A1 0 0 11
## 210 US 2021/0002371 A1 0 0 9
## 211 US 2021/0002251 A1 0 0 0
## 212 JP 2021001170 A 0 0 0
## 213 AU 2020/281105 A1 0 0 0
## 214 US 2021/0000930 A1 0 0 3
## 215 AU 2019/299666 A1 0 0 0
## 216 WO 2021/001741 A1 8 0 11
## 217 EP 3327027 B1 0 0 0
## 218 EP 3759129 A1 0 0 0
## 219 EP 3328849 B9 0 0 0
## 220 EP 3759134 A1 0 0 0
## 221 EP 3758753 A1 0 0 0
## 222 BR 112020015641 A2 0 0 0- Perform a full join
training%>%full_join(sequence, by = "Patent_Number")->Full_Join_TraSeq
Full_Join_TraSeq## Patent_Number Cites_Patent_Count Cited_by_Patent_Count Sequence_Count
## 1 PL 3341367 T3 0 0 NA
## 2 HR P20210871 T1 0 0 NA
## 3 CR 20210284 A 0 0 NA
## 4 US 2021/0205309 A1 0 0 NA
## 5 JP 2021100972 A 3 0 NA
## 6 AU 2021/203768 A1 0 0 NA
## 7 ES 2841400 T3 0 0 NA
## 8 US 2021/0206757 A1 1 0 NA
## 9 PE 20211235 A1 0 0 NA
## 10 US 2021/0205463 A1 0 0 NA
## 11 AR 117073 A1 0 0 NA
## 12 EP 3327027 B9 0 0 NA
## 13 EP 3096786 B1 10 0 NA
## 14 EP 3844288 A1 0 0 NA
## 15 EP 3843740 A1 0 0 NA
## 16 EP 3096783 B1 16 0 NA
## 17 BR 112021006223 A2 0 0 NA
## 18 DK 2909307 T3 0 0 NA
## 19 AU 2018/372109 B2 1 0 NA
## 20 TW 202124414 A 0 0 NA
## 21 US 2021/0198365 A1 0 0 NA
## 22 TW 202123937 A 0 0 NA
## 23 US 2021/0196810 A1 0 2 NA
## 24 AU 2018/275359 B2 1 0 NA
## 25 US 2021/0196823 A1 0 0 NA
## 26 AU 2019/404250 A1 0 0 NA
## 27 JP 2021098716 A 2 0 NA
## 28 TW I732431 B 1 0 NA
## 29 US 2021/0198305 A1 0 1 NA
## 30 TW 202123940 A 0 0 NA
## 31 IL 267288 B 0 0 NA
## 32 IL 270760 B 0 0 NA
## 33 EC SP21034668 A 0 0 NA
## 34 IL 282446 A 0 0 NA
## 35 EP 3233054 B1 0 0 NA
## 36 DO P2021000088 A 0 0 NA
## 37 IL 283032 A 0 0 NA
## 38 IL 282771 A 0 0 NA
## 39 EP 3841121 A2 0 0 NA
## 40 KR 20210080468 A 0 0 NA
## 41 PT 3478679 T 0 0 NA
## 42 RS 61896 B1 0 0 NA
## 43 IL 283149 A 0 0 NA
## 44 ZA 201505468 B 0 0 NA
## 45 EP 3840777 A2 0 0 NA
## 46 EP 3571202 B1 0 0 NA
## 47 IL 283058 A 0 0 NA
## 48 RS 61897 B1 0 0 NA
## 49 EC SP21037191 A 0 0 NA
## 50 ES 2837018 T3 0 0 NA
## 51 SG 11202104585Y A 0 0 NA
## 52 RU 2750454 C2 2 0 NA
## 53 PL 3327027 T3 0 0 NA
## 54 PL 3137114 T3 0 0 NA
## 55 PH 12020500671 A1 0 0 NA
## 56 HU E053911 T4 0 0 NA
## 57 HU E053287 T2 0 0 NA
## 58 HR P20210769 T1 0 0 NA
## 59 NZ 748652 A 0 0 NA
## 60 GE P20217265 B 0 0 NA
## 61 CN 113039178 A 0 0 NA
## 62 HR P20210770 T1 0 0 NA
## 63 LT 3318565 T 0 0 NA
## 64 WO 2021/124210 A1 6 0 NA
## 65 CA 3165135 A1 0 0 NA
## 66 US 2021/0186974 A1 0 0 NA
## 67 US 2021/0188991 A1 0 0 NA
## 68 WO 2021/124155 A1 2 1 NA
## 69 CR 20210163 A 0 0 NA
## 70 US 2021/0188829 A1 0 0 NA
## 71 WO 2021/124267 A1 6 0 NA
## 72 CA 3164623 A1 0 0 NA
## 73 WO 2021/124096 A1 13 0 NA
## 74 WO 2021/124073 A1 147 0 NA
## 75 JP 2021095402 A 0 0 NA
## 76 US 2021/0188979 A1 0 0 NA
## 77 SA 516380328 B1 0 0 NA
## 78 CA 3164804 A1 0 0 NA
## 79 WO 2021/124095 A1 3 0 NA
## 80 WO 2021/124152 A1 10 0 NA
## 81 NI 202100012 A 0 0 NA
## 82 BR 112016002008 B1 0 0 NA
## 83 CA 2810359 C 0 0 NA
## 84 ES 2834927 T3 0 0 NA
## 85 DK 3478679 T3 0 0 NA
## 86 KR 20210074335 A 0 0 NA
## 87 CA 3103120 A1 0 0 NA
## 88 JP 2021091683 A 0 0 NA
## 89 US 2021/0177857 A1 0 0 NA
## 90 JP 2021091692 A 5 0 NA
## 91 JP 2021091663 A 0 0 NA
## 92 CA 3163979 A1 0 0 NA
## 93 US 2021/0180008 A1 0 0 NA
## 94 PT 3497103 T 0 0 NA
## 95 US 2021/0179716 A1 0 1 NA
## 96 WO 2021/116874 A1 13 0 NA
## 97 ES 2834060 T3 0 0 NA
## 98 AR 116797 A1 0 0 NA
## 99 EP 3630789 A4 1 0 NA
## 100 CN 112969461 A 0 0 NA
## 101 US 11034678 B2 35 0 NA
## 102 PE 20211093 A1 0 0 NA
## 103 DK 3497103 T3 0 0 NA
## 104 PE 20211095 A1 0 0 NA
## 105 PH 12020552006 A1 0 0 NA
## 106 CN 112955147 A 0 0 NA
## 107 ES 2832893 T3 0 0 NA
## 108 JP 2021087420 A 0 0 NA
## 109 CO 2021006924 A2 0 0 NA
## 110 JP 2021088558 A 0 0 NA
## 111 JP 2021088548 A 0 0 NA
## 112 LT 3397631 T 0 0 NA
## 113 KR 20210068532 A 0 0 NA
## 114 PE 20211070 A1 0 0 NA
## 115 CU 24517 B1 0 0 NA
## 116 CU 24522 B1 0 0 NA
## 117 KR 20210066837 A 0 0 NA
## 118 PH 12020500648 A1 0 0 NA
## 119 MY 185765 A 0 0 NA
## 120 CN 112912395 A 0 0 NA
## 121 US 2021/0163455 A1 0 0 NA
## 122 US 2021/0163588 A1 0 0 NA
## 123 AU 2019/376078 A1 0 0 NA
## 124 RU 2748949 C2 3 0 NA
## 125 EP 3242888 B8 0 0 NA
## 126 EP 3828198 A1 8 0 NA
## 127 US 11020469 B2 142 1 NA
## 128 US 11020376 B2 10 0 NA
## 129 SA 516371506 B1 0 0 NA
## 130 BR 112021004058 A2 0 0 NA
## 131 CN 112888691 A 0 0 NA
## 132 TW I729530 B 1 0 NA
## 133 BR 112021004935 A2 0 0 NA
## 134 US 11021528 B2 98 0 NA
## 135 TW 202120482 A 0 0 NA
## 136 PH 12020552069 A1 0 0 NA
## 137 IL 281634 A 0 0 NA
## 138 IL 264818 B 0 0 NA
## 139 IL 282215 A 0 0 NA
## 140 IL 257731 B 0 0 NA
## 141 IL 281670 A 0 0 NA
## 142 DK 3091981 T3 0 0 NA
## 143 IL 259563 B 0 0 NA
## 144 SI 3341367 T1 0 0 NA
## 145 IL 270090 B 0 0 NA
## 146 IL 282167 A 0 0 NA
## 147 MA 43518 B1 0 0 NA
## 148 IL 282083 A 0 0 NA
## 149 PH 12020551160 A1 0 0 NA
## 150 IL 281968 A 0 0 NA
## 151 RS 61719 B1 0 0 NA
## 152 EC SP21023404 A 0 0 NA
## 153 CO 2021001926 A2 0 0 NA
## 154 IL 267967 B 0 0 NA
## 155 CO 2021006114 A2 0 0 NA
## 156 PT 3397631 T 0 0 NA
## 157 HU E052790 T2 0 0 NA
## 158 SG 11202103667Q A 0 0 NA
## 159 HR P20210579 T1 0 0 NA
## 160 SG 11202104394X A 0 0 NA
## 161 PT 3318565 T 0 0 NA
## 162 HU E052692 T2 0 0 NA
## 163 AU 2017/311645 B2 3 0 NA
## 164 AU 2021/202540 A1 0 0 NA
## 165 AU 2019/387290 A1 0 0 NA
## 166 AU 2021/202522 A1 0 0 NA
## 167 AU 2019/378184 A1 0 0 NA
## 168 ZA 201905543 B 0 0 NA
## 169 EP 3823723 A1 0 0 NA
## 170 KR 20210060549 A 0 0 NA
## 171 EP 2909307 B1 0 0 NA
## 172 ZA 202002207 B 0 0 NA
## 173 AR 116603 A1 0 0 NA
## 174 ZA 201904616 B 0 0 NA
## 175 BR PI0114704 B8 0 0 NA
## 176 BR PI0918841 B8 0 0 NA
## 177 BR PI0811280 B8 0 0 NA
## 178 BR PI0417810 B8 0 0 NA
## 179 US 11014911 B2 13 3 NA
## 180 BR PI0414105 B8 0 0 NA
## 181 US 11014909 B2 121 0 NA
## 182 BR PI0915166 B8 0 0 NA
## 183 BR PI0414130 B8 0 0 NA
## 184 BR PI0208811 B8 0 0 NA
## 185 US 11014908 B2 54 0 NA
## 186 DK 3318565 T3 0 0 NA
## 187 BR PI0620081 B8 0 0 NA
## 188 BR PI0110955 B8 0 0 NA
## 189 BR PI0813412 B8 0 0 NA
## 190 BR PI0608819 B8 0 0 NA
## 191 DK 3397631 T3 0 0 NA
## 192 BR 112021003956 A2 0 0 NA
## 193 BR PI9916853 B8 0 0 NA
## 194 BR PI0807346 B8 0 0 NA
## 195 BR PI0514537 B8 0 0 NA
## 196 BR PI9917007 B8 0 0 NA
## 197 DK 3431475 T3 0 0 NA
## 198 KR 20210057726 A 0 0 NA
## 199 CR 20210146 A 0 0 NA
## 200 WO 2021/094953 A1 21 0 NA
## 201 US 2021/0145957 A1 0 1 NA
## 202 US 2021/0145817 A1 0 0 NA
## 203 WO 2021/094917 A1 6 0 NA
## 204 CA 3160806 A1 0 0 NA
## 205 CA 3161153 A1 0 0 NA
## 206 BR 112021003039 A2 0 0 NA
## 207 CN 112823167 A 0 0 NA
## 208 PH 12020550908 A1 0 0 NA
## 209 DO P2021000055 A 0 0 NA
## 210 HR P20210472 T1 0 0 NA
## 211 AU 2016/222928 B2 1 0 NA
## 212 PT 3431475 T 0 0 NA
## 213 JP 2021073274 A 3 0 NA
## 214 CR 20210110 A 0 0 NA
## 215 AR 116504 A1 0 0 NA
## 216 CU 20200070 A7 0 0 NA
## 217 KR 20210053929 A 0 0 NA
## 218 EP 3817810 A1 0 0 NA
## 219 AR 116464 A1 0 0 NA
## 220 CU 24511 B1 0 0 NA
## 221 TW I726942 B 1 0 NA
## 222 BR 112021002530 A2 0 0 NA
## 223 TW I727380 B 0 0 NA
## 224 BR 112021003173 A2 0 0 NA
## 225 BR PI0811280 B1 0 0 NA
## 226 PH 12020551666 A1 0 0 NA
## 227 KR 20210049840 A 0 0 NA
## 228 US 2021/0128729 A1 1 0 NA
## 229 CA 3159573 A1 0 0 NA
## 230 KR 20210049866 A 0 0 NA
## 231 WO 2021/084429 A1 68 4 NA
## 232 ES 2823279 T3 0 0 NA
## 233 AR 116417 A1 0 0 NA
## 234 ES 2823049 T3 0 0 NA
## 235 EP 3497103 B1 0 0 NA
## 236 TW 202116770 A 0 0 NA
## 237 SI 3328867 T1 0 0 NA
## 238 CL 2020003222 A1 0 0 NA
## 239 UY 38899 A 0 0 NA
## 240 SI 3145934 T1 0 0 NA
## 241 DO P2021000045 A 0 0 NA
## 242 JP 2021066727 A 0 0 NA
## 243 UY 38892 A 0 0 NA
## 244 SG 11202101158T A 0 0 NA
## 245 SG 11202101827R A 0 0 NA
## 246 SG 11202103111T A 0 0 NA
## 247 IL 280963 A 0 0 NA
## 248 EC SP21018584 A 0 0 NA
## 249 SG 11202102674R A 0 0 NA
## 250 SG 11202102047P A 0 0 NA
## 251 IL 281490 A 0 0 NA
## 252 IL 270761 B 0 0 NA
## 253 AU 2019/351960 A1 0 0 NA
## 254 IL 246853 B 0 0 NA
## 255 IL 281093 A 0 0 NA
## 256 IL 281094 A 0 0 NA
## 257 IL 263505 B 0 0 NA
## 258 IL 258311 B 0 0 NA
## 259 US 2021/0121555 A1 0 2 NA
## 260 IL 246985 B 0 0 NA
## 261 EC SP20057847 A 0 0 NA
## 262 IL 270764 B 0 0 NA
## 263 AU 2019/359540 A1 0 0 NA
## 264 AU 2019/204623 B2 6 0 NA
## 265 IL 264687 B 0 0 NA
## 266 EP 3668495 A4 1 0 NA
## 267 EP 3810192 A1 0 0 NA
## 268 ZA 201800197 B 0 0 NA
## 269 EP 3810089 A1 0 0 NA
## 270 EP 3119395 B1 0 0 NA
## 271 HU E052146 T2 0 0 NA
## 272 EP 3810761 A1 0 0 NA
## 273 EP 3630788 A4 1 0 NA
## 274 ZA 201903620 B 0 0 NA
## 275 MA 52987 A 0 0 NA
## 276 KR 20210046717 A 0 0 NA
## 277 US 10988463 B2 20 0 NA
## 278 CR 20200484 A 0 0 NA
## 279 LT 3341367 T 0 0 NA
## 280 PT 3334760 T 0 0 NA
## 281 JP 2021061851 A 3 0 NA
## 282 ES 2820824 T3 0 0 NA
## 283 US 2021/0115159 A1 0 0 NA
## 284 JP 2021061845 A 2 0 NA
## 285 US 2021/0115413 A1 0 0 NA
## 286 PT 2968155 T 0 0 NA
## 287 AR 116295 A1 0 0 NA
## 288 EP 3806855 A1 0 0 NA
## 289 EP 3806955 A1 0 0 NA
## 290 MA 52856 A 0 0 NA
## 291 MA 52882 A 0 0 NA
## 292 EP 3808768 A1 7 0 NA
## 293 EP 3478679 B1 0 0 NA
## 294 ME 03793 B 0 0 NA
## 295 CA 2859755 C 0 0 NA
## 296 US 10982198 B2 151 1 NA
## 297 US 10980815 B2 17 0 NA
## 298 ME 03743 B 0 0 NA
## 299 PT 3341367 T 0 0 NA
## 300 DK 3334760 T3 0 0 NA
## 301 PL 3328867 T3 0 0 NA
## 302 DK 2968155 T3 0 0 NA
## 303 ES 2819676 T3 0 0 NA
## 304 CO 2021003928 A2 0 0 NA
## 305 TW 202115048 A 0 0 NA
## 306 CN 112672791 A 0 0 NA
## 307 TW 202115120 A 0 0 NA
## 308 HR P20210292 T1 0 0 NA
## 309 PE 20210708 A1 0 0 NA
## 310 TW 202115086 A 0 0 NA
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## 622 BR 112020015603 A2 0 0 NA
## 623 CN 112168957 A 0 0 NA
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## 626 TW I715285 B 2 0 NA- Join the two datasets so that only the patents in the Sequence Counts dataset are included
training%>%right_join(sequence, by = "Patent_Number")->right_Join_TraSeq
right_Join_TraSeq## Patent_Number Cites_Patent_Count Cited_by_Patent_Count Sequence_Count
## 1 CA 189065 S 0 0 0
## 2 NI 202000072 A 0 0 0
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## 5 TW I722568 B 0 0 0
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## 8 JP 2021040642 A 1 0 0
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## 10 JP 2021042204 A 4 0 0
## 11 US 2021/0077463 A1 0 1 24
## 12 CA 3153676 A1 0 0 0
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## 222 BR 112020015641 A2 0 0 0- Read the Training and Testing Data csv file into R. This file contains a binary column called Partition which is 0 when the patent was part of the training set used to develop the regression model in #2, and is equal to 1 otherwise. Use R to perform the following on this dataset:
tratest <- read.csv("C:/Users/raze1/OneDrive/Desktop/UIndy/MSDA 621/Exams/Exam 1/Training and Testing Data.csv")
tratest## Patent_Number Cites_Patent_Count Cited_by_Patent_Count Partition
## 1 PL 3341367 T3 0 0 0
## 2 HR P20210871 T1 0 0 0
## 3 CR 20210284 A 0 0 0
## 4 US 2021/0205309 A1 0 0 0
## 5 JP 2021100972 A 3 0 0
## 6 AU 2021/203768 A1 0 0 0
## 7 ES 2841400 T3 0 0 0
## 8 US 2021/0206757 A1 1 0 0
## 9 PE 20211235 A1 0 0 0
## 10 US 2021/0205463 A1 0 0 0
## 11 AR 117073 A1 0 0 0
## 12 EP 3327027 B9 0 0 0
## 13 EP 3096786 B1 10 0 0
## 14 EP 3844288 A1 0 0 0
## 15 EP 3843740 A1 0 0 0
## 16 EP 3096783 B1 16 0 0
## 17 BR 112021006223 A2 0 0 0
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## 19 AU 2018/372109 B2 1 0 0
## 20 TW 202124414 A 0 0 0
## 21 US 2021/0198365 A1 0 0 0
## 22 TW 202123937 A 0 0 0
## 23 US 2021/0196810 A1 0 2 0
## 24 AU 2018/275359 B2 1 0 0
## 25 US 2021/0196823 A1 0 0 0
## 26 AU 2019/404250 A1 0 0 0
## 27 JP 2021098716 A 2 0 0
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## 40 KR 20210080468 A 0 0 0
## 41 PT 3478679 T 0 0 0
## 42 RS 61896 B1 0 0 0
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## 44 ZA 201505468 B 0 0 0
## 45 EP 3840777 A2 0 0 0
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## 725 UA 124804 C2 0 0 1- Group the dataset by Partition. Summarize each grouping by calculating the standard deviation of Cites_Patent_Count for each group.
colnames(tratest)## [1] "Patent_Number" "Cites_Patent_Count" "Cited_by_Patent_Count"
## [4] "Partition"by_partition <- group_by(tratest, Cites_Patent_Count, Cited_by_Patent_Count, Partition)
summarise(by_partition, Cites_Patent_Count = sd(Cites_Patent_Count, na.rm = TRUE))## `summarise()` has grouped output by 'Cites_Patent_Count',
## 'Cited_by_Patent_Count'. You can override using the `.groups` argument.## # A tibble: 64 × 3
## # Groups: Cites_Patent_Count, Cited_by_Patent_Count [9]
## Cites_Patent_Count Cited_by_Patent_Count Partition
## <dbl> <int> <int>
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## 2 0 0 1
## 3 0 1 0
## 4 0 2 0
## 5 0 0 0
## 6 NA 0 1
## 7 0 0 0
## 8 0 1 0
## 9 0 0 0
## 10 0 0 1
## # … with 54 more rows- Identify the top 3 patents with the highest number of citations in the Cited_by_Patent_Count column.
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## 725 UA 124804 C2 0 0 1- Filter the dataset to display only those rows for which the Cites_Patent_Count is greater than zero.
filter(tratest, Cites_Patent_Count>0)## Patent_Number Cites_Patent_Count Cited_by_Patent_Count Partition
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