Exam 1
Julia Simioni
2022-10-13
Section 1 of Exam 1: Regression Model
TrainData <- read.csv("/Users/jusimioni/Desktop/TrainingData.csv")
colnames(TrainData)## [1] "Patent_Number" "Cites_Patent_Count" "Cited_by_Patent_Count"head(TrainData)## 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 0This dataset contains information about some of Pfizer’s patent
applications.
a) 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:
model <- lm(Cited_by_Patent_Count ~ Cites_Patent_Count, data = TrainData)
summary(model)##
## Call:
## lm(formula = Cited_by_Patent_Count ~ Cites_Patent_Count, data = TrainData)
##
## 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-07Cited_by_Patent_Count ≈ B1(Cites_Patent_Count) + B2, where B1 and B2
are real numbers
Fill-in the blanks for the following statements:
b) 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.
confint(model, level = 0.90)## 5 % 95 %
## (Intercept) 0.030697775 0.073822761
## Cites_Patent_Count 0.001793859 0.003547102- There is a 90% probability that the actual value of B2 lies between
0.030697775 and 0.073822761.
- 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(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-05max(cooks.distance(model))## [1] 2.635251Yes, there are values we should be concerned since according to cooks distance we have values above 1.
plot(model)
- Check the normality of residuals by creating a QQ-plot. Based on the plot, are the residuals normally distributed?
qqnorm(model$residuals, main = "model")
qqline(model$residuals)
The qq-plot show a nice amount of numbers allow the y-axis = o consider
well distributed but also some values that diverge.
- 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
New_Data = data.frame(Cites_Patent_Count = c(340))
predict(model, New_Data, type = "response")## 1
## 0.9602238- Cites_Patent_Count = 300
New_Data1 = data.frame(Cites_Patent_Count = c(300))
predict(model, New_Data, type = "response")## 1
## 0.9602238Datermining the largest leverage data point.
train <- cbind(TrainData, leverage = hatvalues(model))
head(train)## Patent_Number Cites_Patent_Count Cited_by_Patent_Count leverage
## 1 PL 3341367 T3 0 0 0.00164921
## 2 HR P20210871 T1 0 0 0.00164921
## 3 CR 20210284 A 0 0 0.00164921
## 4 US 2021/0205309 A1 0 0 0.00164921
## 5 JP 2021100972 A 3 0 0.00160247
## 6 AU 2021/203768 A1 0 0 0.00164921max(hatvalues(model))## [1] 0.2995991#Design Matrix
X= model.matrix(model)- Was the prediction made in part a of #3 considered to be extrapolation?
x_new = c(x = c(1,340))
t(x_new)%*%solve(t(X)%*%X)%*%x_new## [,1]
## [1,] 0.3086801Since it is greater than 0.29 is considered extrapolation.
- Was the prediction made in part b of #3 considered to be extrapolation?
x_new = c(x = c(1,300))
t(x_new)%*%solve(t(X)%*%X)%*%x_new## [,1]
## [1,] 0.2398486Since it is lower than 0.29 is not considered extrapolation. 5. 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 0.5434141 and 1.163395.
predict(model, New_Data1, interval = "confidence", level = 0.95, type = "response")## fit lwr upr
## 1 0.8534046 0.5434141 1.163395- When Cites_Patent_Count is 300, there is a 95% chance that the mean response will fall between 0.5434141 and 1.163395.
predict(model, New_Data1, interval = "confidence", level = 0.95, type = "response")## 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:
SCount <- read.csv("/Users/jusimioni/Desktop/SequenceCounts.csv")
colnames(SCount)## [1] "Patent_No." "Sequence_Count"head(SCount)## Patent_No. Sequence_Count
## 1 CA 189065 S 0
## 2 NI 202000072 A 0
## 3 KR 20210032013 A 80
## 4 PH 12020550461 A1 0
## 5 TW I722568 B 0
## 6 CN 112533674 A 0- Perform an inner join
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ dplyr 1.0.9
## ✔ tidyr 1.2.0 ✔ stringr 1.4.1
## ✔ readr 2.1.2 ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()TrainData %>% inner_join(SCount, by = c("Patent_Number" = "Patent_No.")) -> joined_data
joined_data## 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
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## 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
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## 19 TW 202111125 A 0 0 0
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## 21 CN 112513070 A 0 1 0
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## 36 AU 2019/323790 A1 0 0 0
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## 131 KR 20210013165 A 0 0 890
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## 150 SG 11202100021T A 0 0 0
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## 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
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## 159 PT 3143019 T 0 0 0
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## 221 EP 3758753 A1 0 0 0
## 222 BR 112020015641 A2 0 0 0- Perform a full join
TrainData %>% full_join(SCount, by = c("Patent_Number" = "Patent_No.")) -> joined_data
joined_data## Patent_Number Cites_Patent_Count Cited_by_Patent_Count Sequence_Count
## 1 PL 3341367 T3 0 0 NA
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## 479 SG 11202100888W A 0 0 0
## 480 US 2021/0054087 A1 0 0 502
## 481 AU 2018/208844 B2 5 0 0
## 482 EP 3781132 A1 0 0 0
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## 487 BR 112020022897 A2 0 0 0
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## 491 AU 2018/349259 B2 1 0 0
## 492 US 2021/0047298 A1 0 0 0
## 493 JP 2021020890 A 0 0 0
## 494 AU 2019/325400 A1 0 0 0
## 495 AU 2019/336197 A1 0 0 0
## 496 WO 2021/029854 A1 4 1 0
## 497 JP 2021019632 A 2 0 0
## 498 US 2021/0047325 A1 2 0 2
## 499 CA 3150424 A1 0 0 0
## 500 BR PI0915166 B1 0 0 0
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## 506 PT 3327027 T 0 0 0
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## 509 DK 3328849 T5 0 0 0
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## 512 US 2021/0040045 A1 0 0 0
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## 516 TW I718144 B 1 0 0
## 517 CN 112336854 A 0 0 0
## 518 BR 112020022595 A2 0 0 0
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## 520 BR 112020023891 A2 0 0 0
## 521 BR 112020017935 A2 0 0 0
## 522 KR 20210013777 A 0 0 62
## 523 US 2021/0032308 A1 0 0 130
## 524 AU 2019/304198 A1 0 0 0
## 525 AU 2016/280137 B2 6 0 0
## 526 AU 2019/290212 A1 0 0 0
## 527 RU 2742260 C2 3 0 0
## 528 JP 2021011493 A 2 0 0
## 529 KR 20210013167 A 0 0 200
## 530 KR 20210013165 A 0 0 890
## 531 US 10906955 B2 15 0 25
## 532 CN 112313219 A 0 0 0
## 533 US 10906888 B2 49 0 0
## 534 TW 202104224 A 0 0 0
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## 548 KR 20210011076 A 2 0 798
## 549 SG 11202100021T A 0 0 0
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## 553 JP 2021008452 A 0 0 0
## 554 US 2021/0024903 A1 0 1 761
## 555 US 2021/0024589 A1 0 0 81
## 556 US 2021/0023200 A1 0 0 326
## 557 US 2021/0023193 A1 0 2 40
## 558 PT 3143019 T 0 0 0
## 559 HU E050811 T2 0 0 0
## 560 WO 2021/014360 A1 14 0 0
## 561 US 2021/0024536 A1 0 0 0
## 562 AR 115498 A1 0 0 0
## 563 EP 3770164 A1 6 0 0
## 564 EP 3768669 A1 0 0 0
## 565 BR 112020021689 A2 0 0 0
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## 568 US 10899840 B2 36 1 36
## 569 DK 3137114 T3 0 0 0
## 570 PL 3339303 T3 0 0 0
## 571 GE P20217211 B 0 0 0
## 572 WO 2021/009676 A1 20 1 0
## 573 CA 3147266 A1 0 0 0
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## 575 US 2021/0017553 A1 2 1 5
## 576 US 2021/0017205 A1 0 0 0
## 577 US 2021/0017254 A1 0 0 12
## 578 US 2021/0017172 A1 0 2 0
## 579 CR 20200612 A 0 0 0
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## 581 KR 20210008177 A 0 0 4
## 582 EP 3765027 A1 0 0 0
## 583 EP 3766885 A1 77 0 0
## 584 EP 3765019 A1 0 0 0
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## 588 PE 20210127 A1 0 0 0
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## 590 DK 3327027 T3 0 0 0
## 591 MY 182282 A 0 0 0
## 592 MY 182168 A 0 0 0
## 593 CN 112236446 A 0 0 0
## 594 US 2021/0009601 A1 0 0 15
## 595 US 2021/0009657 A1 2 1 36
## 596 RU 2740309 C2 3 0 0
## 597 EP 3761954 A1 0 0 0
## 598 PT 3328867 T 0 0 0
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## 600 CN 112218618 A 4 0 0
## 601 MY 181891 A 0 0 0
## 602 US 10888611 B2 331 0 22
## 603 RU 2740135 C1 4 0 0
## 604 NI 202000058 A 0 0 0
## 605 KR 20210002757 A 0 0 46
## 606 CN 112195169 A 0 0 0
## 607 KR 20210002642 A 0 0 2
## 608 US 2021/0002369 A1 0 0 11
## 609 US 2021/0002371 A1 0 0 9
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## 611 JP 2021001170 A 0 0 0
## 612 AU 2020/281105 A1 0 0 0
## 613 US 2021/0000930 A1 0 0 3
## 614 AU 2019/299666 A1 0 0 0
## 615 WO 2021/001741 A1 8 0 11
## 616 EP 3327027 B1 0 0 0
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## 619 EP 3759134 A1 0 0 0
## 620 EP 3758753 A1 0 0 0
## 621 BR 112020015641 A2 0 0 0
## 622 BR 112020015603 A2 0 0 NA
## 623 CN 112168957 A 0 0 NA
## 624 DK 3143019 T3 0 0 NA
## 625 CN 112156176 A 0 0 NA
## 626 TW I715285 B 2 0 NA- Join the two datasets so that only the patents in the Sequence Counts dataset are included
TrainData %>% right_join(SCount, by = c("Patent_Number" = "Patent_No.")) -> joined_data
joined_data## 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
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## 64 TW 202108167 A 0 0 0
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## 76 PT 3137114 T 0 0 0
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## 78 CL 2020002507 A1 0 0 0
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## 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
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## 85 EP 3148587 B1 1 0 0
## 86 ES 2807591 T3 0 0 0
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## 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- 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:
ttdata <- read.csv("/Users/jusimioni/Desktop/TrainingandTestingData.csv")
head(ttdata)## 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- Group the dataset by Partition. Summarize each grouping by calculating the standard deviation of Cites_Patent_Count for each group. (I don’t understand what columns do you want in the group)
partition1 <- group_by(ttdata, Partition, Patent_Number)
patent_mean <- summarise(partition1, Patent_Count_mean = mean(Cites_Patent_Count, na.rm = TRUE))## `summarise()` has grouped output by 'Partition'. You can override using the
## `.groups` argument.patent_mean## # A tibble: 725 × 3
## # Groups: Partition [2]
## Partition Patent_Number Patent_Count_mean
## <int> <chr> <dbl>
## 1 0 AR 115498 A1 0
## 2 0 AR 115987 A1 0
## 3 0 AR 116010 A1 0
## 4 0 AR 116046 A1 0
## 5 0 AR 116295 A1 0
## 6 0 AR 116417 A1 0
## 7 0 AR 116464 A1 0
## 8 0 AR 116504 A1 0
## 9 0 AR 116603 A1 0
## 10 0 AR 116797 A1 0
## # … with 715 more rows- Identify the top 3 patents with the highest number of citations in the Cited_by_Patent_Count column.
desc <- arrange(patent_mean, desc(Patent_Count_mean))
desc## # A tibble: 725 × 3
## # Groups: Partition [2]
## Partition Patent_Number Patent_Count_mean
## <int> <chr> <dbl>
## 1 0 US 10899802 B2 335
## 2 0 US 10888611 B2 331
## 3 1 US 11208633 B2 161
## 4 0 US 10982198 B2 151
## 5 0 WO 2021/124073 A1 147
## 6 0 US 11020469 B2 142
## 7 0 US 10918708 B2 139
## 8 0 US 11014909 B2 121
## 9 0 US 10946086 B2 111
## 10 0 US 11021528 B2 98
## # … with 715 more rows- Filter the dataset to display only those rows for which the Cites_Patent_Count is greater than zero.
filter(desc, Patent_Count_mean > 0)## # A tibble: 137 × 3
## # Groups: Partition [2]
## Partition Patent_Number Patent_Count_mean
## <int> <chr> <dbl>
## 1 0 US 10899802 B2 335
## 2 0 US 10888611 B2 331
## 3 1 US 11208633 B2 161
## 4 0 US 10982198 B2 151
## 5 0 WO 2021/124073 A1 147
## 6 0 US 11020469 B2 142
## 7 0 US 10918708 B2 139
## 8 0 US 11014909 B2 121
## 9 0 US 10946086 B2 111
## 10 0 US 11021528 B2 98
## # … with 127 more rows