HW2. Additional
Shchukina Polina
library(readr)
library(ggplot2)
library(randomForestExplainer)## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2library(randomForest)## randomForest 4.7-1.1## Type rfNews() to see new features/changes/bug fixes.##
## Присоединяю пакет: 'randomForest'## Следующий объект скрыт от 'package:ggplot2':
##
## marginlibrary(quanteda)## Package version: 3.2.4
## Unicode version: 13.0
## ICU version: 69.1## Parallel computing: 8 of 8 threads used.## See https://quanteda.io for tutorials and examples.library(quanteda.textplots)
library(tidytext)
library(janeaustenr)
library(dplyr)##
## Присоединяю пакет: 'dplyr'## Следующий объект скрыт от 'package:randomForest':
##
## combine## Следующие объекты скрыты от 'package:stats':
##
## filter, lag## Следующие объекты скрыты от 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(stringr)
library(tidyr)
library(scales)##
## Присоединяю пакет: 'scales'## Следующий объект скрыт от 'package:readr':
##
## col_factorlibrary(gt)
theme_set(theme_classic())Введение - скачивание данных
Задание заключалось в прогнозе баллов за дегустацию посредством использования текста. Так как в Kaggle данные не валидные (некоторые строчки разнесены по столбцам, некоторые нет), было принято решение скачать аналогичные данные с github. Для начала была проведена первичная аналитика: удалены NA, стоп-слова, а также выделены наиболее встречаемые слова в отзывах.
winemag <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-05-28/winemag-data-130k-v2.csv")## New names:
## Rows: 129971 Columns: 14
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (11): country, description, designation, province, region_1, region_2, t... dbl
## (3): ...1, points, price
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`data1 <- as.data.frame(winemag)
str(data1)## 'data.frame': 129971 obs. of 14 variables:
## $ ...1 : num 0 1 2 3 4 5 6 7 8 9 ...
## $ country : chr "Italy" "Portugal" "US" "US" ...
## $ description : chr "Aromas include tropical fruit, broom, brimstone and dried herb. The palate isn't overly expressive, offering un"| __truncated__ "This is ripe and fruity, a wine that is smooth while still structured. Firm tannins are filled out with juicy r"| __truncated__ "Tart and snappy, the flavors of lime flesh and rind dominate. Some green pineapple pokes through, with crisp ac"| __truncated__ "Pineapple rind, lemon pith and orange blossom start off the aromas. The palate is a bit more opulent, with note"| __truncated__ ...
## $ designation : chr "Vulkà Bianco" "Avidagos" NA "Reserve Late Harvest" ...
## $ points : num 87 87 87 87 87 87 87 87 87 87 ...
## $ price : num NA 15 14 13 65 15 16 24 12 27 ...
## $ province : chr "Sicily & Sardinia" "Douro" "Oregon" "Michigan" ...
## $ region_1 : chr "Etna" NA "Willamette Valley" "Lake Michigan Shore" ...
## $ region_2 : chr NA NA "Willamette Valley" NA ...
## $ taster_name : chr "Kerin O’Keefe" "Roger Voss" "Paul Gregutt" "Alexander Peartree" ...
## $ taster_twitter_handle: chr "@kerinokeefe" "@vossroger" "@paulgwine " NA ...
## $ title : chr "Nicosia 2013 Vulkà Bianco (Etna)" "Quinta dos Avidagos 2011 Avidagos Red (Douro)" "Rainstorm 2013 Pinot Gris (Willamette Valley)" "St. Julian 2013 Reserve Late Harvest Riesling (Lake Michigan Shore)" ...
## $ variety : chr "White Blend" "Portuguese Red" "Pinot Gris" "Riesling" ...
## $ winery : chr "Nicosia" "Quinta dos Avidagos" "Rainstorm" "St. Julian" ...#удаляем ненужные столбцы - taster_twitter_handle, taster_name, region_2
names(data1)## [1] "...1" "country" "description"
## [4] "designation" "points" "price"
## [7] "province" "region_1" "region_2"
## [10] "taster_name" "taster_twitter_handle" "title"
## [13] "variety" "winery"data2 <- data1[,-c(10,11,9)]
data3_desc<- as.data.frame(data2[,3])
colnames(data3_desc) <- c("Description")
data4 <- unnest_tokens(data3_desc, output = word, input = Description)
#удалим стоп-слова
data(stop_words)
head(stop_words)## # A tibble: 6 × 2
## word lexicon
## <chr> <chr>
## 1 a SMART
## 2 a's SMART
## 3 able SMART
## 4 about SMART
## 5 above SMART
## 6 according SMARTdim(stop_words)## [1] 1149 2data5 <- data4 %>% anti_join(stop_words)## Joining with `by = join_by(word)`#размерность уменьшилась в два раза: удалили много стоп-слов
dim(data4); dim(data5)## [1] 5334954 1## [1] 2968316 1#наиболее часто встречающиеся слова
data5 %>% count(word, sort = TRUE)## word n
## 1 wine 78322
## 2 flavors 62791
## 3 fruit 49927
## 4 aromas 39639
## 5 palate 38437
## 6 acidity 34999
## 7 finish 34971
## 8 tannins 30877
## 9 drink 30318
## 10 cherry 29321
## 11 black 29020
## 12 ripe 27375
## 13 red 21782
## 14 spice 19233
## 15 notes 19045
## 16 oak 17758
## 17 fresh 17527
## 18 rich 17466
## 19 dry 17222
## 20 berry 16979
## 21 nose 16954
## 22 plum 14990
## 23 soft 13664
## 24 apple 13581
## 25 fruits 13548
## 26 sweet 13444
## 27 blend 13122
## 28 white 12912
## 29 crisp 12867
## 30 blackberry 12840
## 31 light 12682
## 32 offers 12675
## 33 dark 12403
## 34 texture 12402
## 35 citrus 11769
## 36 bodied 11558
## 37 vanilla 11062
## 38 bright 11000
## 39 cabernet 10769
## 40 pepper 10189
## 41 juicy 9773
## 42 green 9709
## 43 lemon 9594
## 44 fruity 9577
## 45 raspberry 9507
## 46 firm 9211
## 47 peach 8728
## 48 chocolate 8638
## 49 touch 8522
## 50 balanced 8509
## 51 character 8345
## 52 dried 8091
## 53 pear 7992
## 54 sauvignon 7698
## 55 structure 7612
## 56 spicy 7274
## 57 fine 7217
## 58 smooth 7202
## 59 wood 7055
## 60 medium 6852
## 61 pinot 6751
## 62 herb 6589
## 63 currant 6518
## 64 style 6507
## 65 mouth 6434
## 66 concentrated 6379
## 67 mineral 6369
## 68 tannic 6365
## 69 tart 6357
## 70 herbal 6274
## 71 note 6233
## 72 vineyard 6107
## 73 flavor 6050
## 74 licorice 6026
## 75 hint 5984
## 76 merlot 5982
## 77 lime 5918
## 78 orange 5841
## 79 bit 5837
## 80 clean 5773
## 81 creamy 5770
## 82 age 5704
## 83 toast 5661
## 84 balance 5621
## 85 syrah 5449
## 86 dense 5405
## 87 aging 5395
## 88 alongside 5394
## 89 structured 5353
## 90 leather 5285
## 91 lead 5282
## 92 savory 5225
## 93 tobacco 5210
## 94 earthy 5197
## 95 ready 5196
## 96 delicious 5186
## 97 tight 5166
## 98 slightly 5103
## 99 hints 5078
## 100 elegant 5024
## 101 easy 4992
## 102 chardonnay 4761
## 103 complex 4735
## 104 mouthfeel 4720
## 105 richness 4550
## 106 cassis 4545
## 107 freshness 4456
## 108 herbs 4427
## 109 time 4404
## 110 smoky 4399
## 111 lively 4377
## 112 yellow 4366
## 113 strawberry 4351
## 114 vintage 4342
## 115 cherries 4261
## 116 melon 4253
## 117 delivers 4233
## 118 coffee 4231
## 119 feels 4230
## 120 grapefruit 4222
## 121 cola 4215
## 122 color 4203
## 123 honey 4144
## 124 feel 4110
## 125 clove 4054
## 126 wild 4030
## 127 2018 3996
## 128 stone 3945
## 129 pineapple 3834
## 130 attractive 3833
## 131 weight 3825
## 132 simple 3821
## 133 intense 3813
## 134 tropical 3812
## 135 floral 3799
## 136 apricot 3790
## 137 earth 3775
## 138 solid 3775
## 139 barrel 3750
## 140 tastes 3738
## 141 round 3725
## 142 refreshing 3719
## 143 core 3702
## 144 cinnamon 3658
## 145 bottling 3631
## 146 finishes 3605
## 147 baked 3518
## 148 lightly 3491
## 149 alcohol 3446
## 150 scents 3441
## 151 minerality 3426
## 152 generous 3417
## 153 complexity 3385
## 154 plenty 3384
## 155 edge 3363
## 156 tangy 3350
## 157 bitter 3280
## 158 noir 3271
## 159 zest 3269
## 160 crushed 3262
## 161 cranberry 3257
## 162 pretty 3224
## 163 glass 3211
## 164 toasty 3205
## 165 body 3200
## 166 flower 3110
## 167 blueberry 3094
## 168 drinking 3066
## 169 grapes 3050
## 170 cedar 3048
## 171 2017 3032
## 172 spices 3007
## 173 concentration 2987
## 174 toasted 2986
## 175 textured 2985
## 176 aged 2977
## 177 smoke 2977
## 178 almond 2948
## 179 silky 2943
## 180 mocha 2927
## 181 strong 2927
## 182 2020 2912
## 183 franc 2903
## 184 perfumed 2889
## 185 blanc 2884
## 186 rounded 2858
## 187 oaky 2851
## 188 powerful 2845
## 189 aftertaste 2831
## 190 mature 2824
## 191 zesty 2811
## 192 delicate 2806
## 193 jammy 2800
## 194 tones 2767
## 195 roasted 2755
## 196 wines 2747
## 197 nice 2722
## 198 bottle 2705
## 199 aromatic 2689
## 200 warm 2670
## 201 layers 2657
## 202 anise 2651
## 203 deep 2620
## 204 rose 2611
## 205 hard 2601
## 206 mint 2589
## 207 forward 2588
## 208 mix 2580
## 209 grape 2561
## 210 subtle 2559
## 211 bold 2558
## 212 vibrant 2543
## 213 dusty 2540
## 214 lush 2535
## 215 depth 2534
## 216 baking 2531
## 217 candied 2527
## 218 makes 2527
## 219 offering 2518
## 220 malbec 2500
## 221 estate 2489
## 222 caramel 2486
## 223 sweetness 2482
## 224 peppery 2467
## 225 fruitiness 2457
## 226 whiff 2451
## 227 riesling 2449
## 228 pair 2448
## 229 pure 2439
## 230 peel 2432
## 231 espresso 2430
## 232 power 2425
## 233 brings 2408
## 234 meat 2387
## 235 jam 2382
## 236 lean 2372
## 237 thick 2356
## 238 heavy 2354
## 239 exotic 2353
## 240 berries 2333
## 241 sour 2323
## 242 2016 2245
## 243 flowers 2232
## 244 velvety 2231
## 245 blackberries 2223
## 246 french 2222
## 247 bouquet 2214
## 248 tangerine 2213
## 249 lingering 2199
## 250 grenache 2188
## 251 close 2169
## 252 lovely 2162
## 253 polished 2158
## 254 layered 2155
## 255 grilled 2133
## 256 vineyards 2133
## 257 brisk 2123
## 258 2019 2108
## 259 tannin 2099
## 260 currants 2087
## 261 cut 2086
## 262 100 2083
## 263 valley 2080
## 264 violet 2074
## 265 quality 2042
## 266 chewy 2038
## 267 sangiovese 2034
## 268 acids 2032
## 269 wine's 2023
## 270 integrated 2017
## 271 cool 2012
## 272 enjoy 2010
## 273 nicely 2009
## 274 intensity 2004
## 275 petit 2001
## 276 giving 1999
## 277 supple 1997
## 278 packed 1981
## 279 varietal 1980
## 280 sharp 1977
## 281 price 1968
## 282 10 1967
## 283 tea 1963
## 284 rosé 1943
## 285 sugar 1942
## 286 fragrant 1938
## 287 taste 1930
## 288 develop 1929
## 289 verdot 1917
## 290 elegance 1897
## 291 vines 1897
## 292 driven 1863
## 293 impressive 1839
## 294 classic 1825
## 295 blue 1819
## 296 months 1813
## 297 food 1803
## 298 expression 1799
## 299 prune 1789
## 300 apples 1778
## 301 add 1777
## 302 sense 1770
## 303 pink 1769
## 304 sage 1769
## 305 accents 1755
## 306 adds 1744
## 307 lots 1742
## 308 provide 1739
## 309 nectarine 1737
## 310 variety 1714
## 311 cellar 1700
## 312 skin 1700
## 313 cocoa 1694
## 314 cab 1689
## 315 accented 1677
## 316 olive 1676
## 317 ripeness 1647
## 318 blossom 1615
## 319 pleasant 1615
## 320 pie 1613
## 321 forest 1610
## 322 length 1610
## 323 wet 1608
## 324 grained 1599
## 325 juice 1591
## 326 touches 1577
## 327 sip 1575
## 328 beautiful 1570
## 329 finishing 1566
## 330 raspberries 1557
## 331 produced 1550
## 332 straightforward 1540
## 333 potential 1535
## 334 acidic 1503
## 335 based 1503
## 336 smells 1501
## 337 plump 1498
## 338 mild 1485
## 339 leaf 1484
## 340 front 1482
## 341 bordeaux 1480
## 342 tightly 1469
## 343 5 1462
## 344 carry 1461
## 345 tomato 1460
## 346 midpalate 1442
## 347 racy 1441
## 348 beautifully 1433
## 349 raisin 1430
## 350 fermented 1417
## 351 2015 1416
## 352 flavored 1412
## 353 aroma 1411
## 354 focused 1404
## 355 plums 1392
## 356 grown 1389
## 357 raw 1383
## 358 set 1383
## 359 ample 1376
## 360 buttered 1372
## 361 purple 1371
## 362 astringent 1370
## 363 chunky 1368
## 364 fleshy 1368
## 365 rustic 1357
## 366 top 1357
## 367 short 1319
## 368 lemony 1317
## 369 plush 1317
## 370 citrusy 1309
## 371 honeysuckle 1309
## 372 graphite 1306
## 373 lot 1295
## 374 20 1289
## 375 elements 1280
## 376 underbrush 1277
## 377 acid 1272
## 378 opulent 1271
## 379 hot 1265
## 380 spiced 1265
## 381 viognier 1264
## 382 mango 1261
## 383 candy 1260
## 384 framed 1253
## 385 single 1251
## 386 approachable 1247
## 387 menthol 1241
## 388 streak 1239
## 389 friendly 1237
## 390 appealing 1235
## 391 beef 1231
## 392 suggest 1231
## 393 low 1221
## 394 pomegranate 1219
## 395 meaty 1218
## 396 soil 1211
## 397 extra 1203
## 398 2022 1199
## 399 heat 1199
## 400 cream 1198
## 401 lift 1197
## 402 appellation 1192
## 403 excellent 1191
## 404 smoked 1190
## 405 honeyed 1188
## 406 petite 1186
## 407 boysenberry 1178
## 408 steely 1173
## 409 coconut 1170
## 410 grip 1169
## 411 dominate 1166
## 412 perfect 1162
## 413 gentle 1158
## 414 drying 1155
## 415 winery 1150
## 416 robust 1147
## 417 boasts 1145
## 418 leathery 1141
## 419 nutmeg 1127
## 420 fairly 1125
## 421 refined 1124
## 422 winemaker 1122
## 423 broad 1121
## 424 50 1117
## 425 zinfandel 1116
## 426 butter 1115
## 427 density 1113
## 428 thyme 1106
## 429 mourvèdre 1099
## 430 taut 1096
## 431 3 1092
## 432 sirah 1092
## 433 minty 1087
## 434 restrained 1081
## 435 support 1081
## 436 bean 1077
## 437 lend 1074
## 438 tar 1067
## 439 intensely 1063
## 440 background 1061
## 441 brown 1060
## 442 salty 1058
## 443 slight 1053
## 444 fig 1050
## 445 floor 1050
## 446 woody 1048
## 447 golden 1047
## 448 features 1042
## 449 hold 1041
## 450 charred 1040
## 451 rubbery 1035
## 452 dominated 1034
## 453 rind 1032
## 454 linger 1030
## 455 accent 1025
## 456 chalky 1025
## 457 element 1022
## 458 soften 1022
## 459 richly 1017
## 460 2025 1014
## 461 pressed 1014
## 462 appeal 1013
## 463 family 1011
## 464 yeasty 1007
## 465 bacon 1006
## 466 doles 1006
## 467 tasty 1003
## 468 region 996
## 469 luscious 988
## 470 de 981
## 471 suggests 980
## 472 tasting 978
## 473 15 977
## 474 producer 976
## 475 winery's 976
## 476 level 974
## 477 coming 971
## 478 30 969
## 479 bracing 969
## 480 mouthwatering 965
## 481 inviting 964
## 482 balsamic 963
## 483 sandalwood 963
## 484 late 961
## 485 colored 960
## 486 modest 960
## 487 lasting 956
## 488 succulent 956
## 489 bone 954
## 490 carries 952
## 491 star 951
## 492 sourced 949
## 493 starts 946
## 494 typical 942
## 495 thin 940
## 496 stage 939
## 497 wrapped 937
## 498 backbone 935
## 499 pears 935
## 500 california 934
## 501 intriguing 931
## 502 4 930
## 503 blended 928
## 504 toned 928
## 505 buttery 926
## 506 firmly 925
## 507 easygoing 924
## 508 lifted 920
## 509 grass 918
## 510 finely 914
## 511 laced 911
## 512 banana 909
## 513 game 909
## 514 basic 907
## 515 skinned 906
## 516 minerally 904
## 517 profile 902
## 518 softly 898
## 519 extracted 894
## 520 austere 892
## 521 tough 892
## 522 bitterness 890
## 523 mountain 890
## 524 immediately 889
## 525 moderate 889
## 526 drinkable 888
## 527 linear 888
## 528 40 883
## 529 quickly 883
## 530 loaded 879
## 531 bring 876
## 532 burnt 876
## 533 milk 875
## 534 mushroom 875
## 535 sauce 874
## 536 stalky 872
## 537 marked 871
## 538 final 870
## 539 sparkling 865
## 540 residual 864
## 541 mellow 863
## 542 peaches 862
## 543 pleasing 862
## 544 subdued 858
## 545 2021 854
## 546 chopped 851
## 547 combine 851
## 548 remains 851
## 549 cake 850
## 550 lacks 849
## 551 feeling 848
## 552 highlights 844
## 553 leads 844
## 554 unusual 844
## 555 dishes 843
## 556 mark 842
## 557 summer 842
## 558 steel 840
## 559 gritty 837
## 560 lingers 836
## 561 ground 835
## 562 suggesting 835
## 563 youthful 834
## 564 butterscotch 833
## 565 screwcap 833
## 566 napa 831
## 567 ginger 829
## 568 briny 827
## 569 flat 827
## 570 leafy 825
## 571 leaves 823
## 572 pencil 823
## 573 zin 822
## 574 brooding 821
## 575 natural 816
## 576 assertive 815
## 577 crispness 814
## 578 wound 812
## 579 effort 808
## 580 accessible 807
## 581 varieties 807
## 582 grippy 805
## 583 offer 800
## 584 selection 799
## 585 chocolaty 794
## 586 nuances 794
## 587 skins 794
## 588 frame 788
## 589 neutral 787
## 590 nut 785
## 591 climate 784
## 592 asian 783
## 593 bite 782
## 594 personality 782
## 595 freshly 780
## 596 pale 780
## 597 grassy 779
## 598 slate 776
## 599 strongly 773
## 600 lychee 772
## 601 overripe 771
## 602 lavender 769
## 603 lighter 768
## 604 impression 763
## 605 stewed 762
## 606 deliciously 760
## 607 tempranillo 759
## 608 hearty 757
## 609 pith 757
## 610 dessert 756
## 611 2 754
## 612 start 754
## 613 rough 752
## 614 emerge 750
## 615 enjoyable 749
## 616 6 748
## 617 violets 746
## 618 char 745
## 619 enticing 745
## 620 pine 741
## 621 compact 740
## 622 barrels 737
## 623 line 733
## 624 passion 731
## 625 sparkler 730
## 626 spring 730
## 627 warmth 730
## 628 stainless 726
## 629 planted 723
## 630 play 723
## 631 extremely 722
## 632 rubber 721
## 633 modern 720
## 634 rhubarb 717
## 635 everyday 711
## 636 term 711
## 637 brambly 709
## 638 pithy 708
## 639 elegantly 707
## 640 filling 706
## 641 herbaceous 704
## 642 60 702
## 643 2023 700
## 644 eucalyptus 700
## 645 form 700
## 646 purity 700
## 647 center 699
## 648 wait 699
## 649 apéritif 697
## 650 closes 697
## 651 persistent 695
## 652 bread 694
## 653 cru 693
## 654 flavorful 693
## 655 2010 689
## 656 dryness 686
## 657 reserve 685
## 658 perfectly 684
## 659 adding 680
## 660 finesse 680
## 661 oily 680
## 662 steak 678
## 663 syrup 678
## 664 citric 677
## 665 gorgeous 677
## 666 reveals 676
## 667 easily 675
## 668 generic 675
## 669 layer 673
## 670 displays 672
## 671 deeply 671
## 672 include 671
## 673 range 671
## 674 grabby 670
## 675 focus 669
## 676 petal 668
## 677 surprisingly 668
## 678 satisfying 667
## 679 site 665
## 680 half 664
## 681 heady 660
## 682 entry 659
## 683 tang 659
## 684 blends 658
## 685 perfume 657
## 686 developing 656
## 687 richer 656
## 688 jasmine 655
## 689 nutty 653
## 690 mousse 652
## 691 yeast 652
## 692 2014 651
## 693 mixed 650
## 694 gris 649
## 695 meet 648
## 696 papaya 648
## 697 takes 646
## 698 25 641
## 699 honeydew 638
## 700 mediterranean 635
## 701 production 634
## 702 amount 633
## 703 cured 633
## 704 resiny 633
## 705 priced 632
## 706 sophisticated 632
## 707 slowly 631
## 708 sugary 631
## 709 2012 630
## 710 leading 630
## 711 smelling 629
## 712 combines 626
## 713 edges 625
## 714 slender 625
## 715 southern 625
## 716 expressive 624
## 717 additional 622
## 718 shiraz 622
## 719 truffle 620
## 720 supported 619
## 721 label 617
## 722 snappy 617
## 723 loads 616
## 724 real 612
## 725 fennel 611
## 726 bell 609
## 727 named 609
## 728 apricots 608
## 729 improve 608
## 730 meyer 608
## 731 wonderful 607
## 732 lees 606
## 733 result 606
## 734 flesh 605
## 735 stony 605
## 736 moderately 602
## 737 rhône 602
## 738 syrupy 601
## 739 closed 600
## 740 medicinal 600
## 741 true 600
## 742 minerals 599
## 743 bramble 597
## 744 drunk 596
## 745 meats 594
## 746 vivid 588
## 747 whiffs 588
## 748 leave 587
## 749 compelling 585
## 750 mildly 585
## 751 shy 585
## 752 champagne 582
## 753 combination 582
## 754 salt 582
## 755 drinks 581
## 756 sea 581
## 757 lends 580
## 758 tongue 580
## 759 cheese 579
## 760 softness 579
## 761 release 578
## 762 wonderfully 578
## 763 astringency 577
## 764 future 575
## 765 holds 574
## 766 funky 572
## 767 italy 571
## 768 riper 571
## 769 dominates 565
## 770 appetizing 564
## 771 equal 564
## 772 balances 563
## 773 gooseberry 562
## 774 8 557
## 775 mingle 557
## 776 barbecue 555
## 777 oaked 555
## 778 dominant 554
## 779 includes 554
## 780 river 554
## 781 nero 552
## 782 strawberries 551
## 783 burst 548
## 784 direct 548
## 785 oregano 548
## 786 substantial 548
## 787 fat 547
## 788 petals 546
## 789 caramelized 545
## 790 follow 543
## 791 tone 543
## 792 penetrating 542
## 793 tilled 542
## 794 cigar 541
## 795 drop 541
## 796 aromatics 540
## 797 hazelnut 540
## 798 kiwi 539
## 799 table 539
## 800 built 538
## 801 hay 537
## 802 orchard 536
## 803 overly 536
## 804 root 536
## 805 brightened 535
## 806 bubbles 535
## 807 waves 534
## 808 2011 533
## 809 considerable 532
## 810 70 531
## 811 sample 531
## 812 liqueur 530
## 813 standard 530
## 814 80 528
## 815 huge 526
## 816 stand 526
## 817 vegetal 524
## 818 decent 522
## 819 recall 522
## 820 delicately 521
## 821 super 521
## 822 raisiny 520
## 823 underlying 520
## 824 fermentation 519
## 825 pleasantly 519
## 826 worth 519
## 827 inky 518
## 828 leaving 518
## 829 tension 517
## 830 2030 516
## 831 filled 516
## 832 terms 516
## 833 defined 514
## 834 dill 514
## 835 saturated 514
## 836 pleasure 513
## 837 streaks 510
## 838 including 509
## 839 complete 507
## 840 resin 507
## 841 gold 506
## 842 edgy 505
## 843 shape 503
## 844 fill 502
## 845 peppercorn 500
## 846 rare 499
## 847 distinctive 498
## 848 framework 498
## 849 promise 498
## 850 rock 498
## 851 90 497
## 852 sensations 497
## 853 2024 496
## 854 bringing 495
## 855 coating 495
## 856 fun 494
## 857 totally 494
## 858 touriga 494
## 859 blossoms 493
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## 1718 ava 182
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## 1739 fiery 179
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## 1800 clarity 169
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## 1806 measured 168
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## 1809 abundance 167
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## 1811 breathing 167
## 1812 control 167
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## 1814 lovers 167
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## 1817 playful 167
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## 1822 complemented 166
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## 1825 understated 166
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## 1832 iodine 165
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## 1836 plastic 165
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## 1840 bubblegum 164
## 1841 eye 164
## 1842 it’s 164
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## 1845 pinch 164
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## 1847 barbaresco 163
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## 1854 maturing 163
## 1855 beet 162
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## 1857 confected 162
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## 1859 lusciously 162
## 1860 zestiness 162
## 1861 florals 161
## 1862 imparts 161
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## 1866 masculine 161
## 1867 missing 161
## 1868 nettle 161
## 1869 nonvintage 161
## 1870 releases 161
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## 1872 confectionary 160
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## 1880 lasts 159
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## 1893 sophistication 158
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## 1933 pulls 153
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## 1936 delightfully 152
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## 1958 blood 150
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## 1968 located 149
## 1969 risotto 149
## 1970 viura 149
## 1971 waiting 149
## 1972 2004 148
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## 1975 flood 148
## 1976 grapey 148
## 1977 lower 148
## 1978 semi 148
## 1979 slopes 148
## 1980 spray 148
## 1981 strange 148
## 1982 veers 148
## 1983 woodsy 148
## 1984 word 148
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## 1986 dances 147
## 1987 ease 147
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## 1989 alvarinho 146
## 1990 bouncy 146
## 1991 deft 146
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## 1993 fried 146
## 1994 mélange 146
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## 1996 supportive 146
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## 1998 volatile 146
## 1999 welcoming 146
## 2000 boned 145
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## 2007 poultry 145
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## 2011 mixture 144
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## 2014 soupy 144
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## 2018 irresistible 143
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## 32800 ürzig 1
## 32801 žilavka 1#визуализация частот
#оставим только те, которые больше 20 000
data6 <- data5 %>%
count(word, sort = TRUE) %>% filter(n > 20000) %>%
mutate(word = reorder(word, n))
ggplot(data6, aes(word, n)) +
geom_col(fill = "slateblue") +
xlab(NULL) +
coord_flip() + ylab("Количество") + geom_label(aes(label = n)) 
#целевая переменная - points (баллы за дегустацию)
table(data1$points)##
## 80 81 82 83 84 85 86 87 88 89 90 91 92
## 397 692 1836 3025 6480 9530 12600 16933 17207 12226 15410 11359 9613
## 93 94 95 96 97 98 99 100
## 6489 3758 1535 523 229 77 33 19#посмотрим на другие переменные
#Большее количество наблюдений из США (54504 наблюдения)
table(data1$country)##
## Argentina Armenia Australia
## 3800 2 2329
## Austria Bosnia and Herzegovina Brazil
## 3345 2 52
## Bulgaria Canada Chile
## 141 257 4472
## China Croatia Cyprus
## 1 73 11
## Czech Republic Egypt England
## 12 1 74
## France Georgia Germany
## 22093 86 2165
## Greece Hungary India
## 466 146 9
## Israel Italy Lebanon
## 505 19540 35
## Luxembourg Macedonia Mexico
## 6 12 70
## Moldova Morocco New Zealand
## 59 28 1419
## Peru Portugal Romania
## 16 5691 120
## Serbia Slovakia Slovenia
## 12 1 87
## South Africa Spain Switzerland
## 1401 6645 7
## Turkey Ukraine Uruguay
## 90 14 109
## US
## 54504data1_1 <- as.data.frame(table(data1$country))
ordered_df <- data1_1[order(data1_1$Freq, data1_1$Var1), ]
ggplot(data = tail(ordered_df), aes(x = reorder(Var1,Freq), y = Freq)) + geom_col(fill = "lightblue") + ylab("Количество наблюдений") + xlab("Страны") + geom_label(aes(label = Freq))
#Большее количество баллов за дегустацию находится около 90 очков.
#гистограмма распределения
ggplot(data = data1, aes(x = points)) + geom_histogram(bins = 20, fill = "slateblue") + xlab("Баллы за дегустацию") + ylab("Количество")
#создадим новую дату - удалив ненужные переменные
str(data1)## 'data.frame': 129971 obs. of 14 variables:
## $ ...1 : num 0 1 2 3 4 5 6 7 8 9 ...
## $ country : chr "Italy" "Portugal" "US" "US" ...
## $ description : chr "Aromas include tropical fruit, broom, brimstone and dried herb. The palate isn't overly expressive, offering un"| __truncated__ "This is ripe and fruity, a wine that is smooth while still structured. Firm tannins are filled out with juicy r"| __truncated__ "Tart and snappy, the flavors of lime flesh and rind dominate. Some green pineapple pokes through, with crisp ac"| __truncated__ "Pineapple rind, lemon pith and orange blossom start off the aromas. The palate is a bit more opulent, with note"| __truncated__ ...
## $ designation : chr "Vulkà Bianco" "Avidagos" NA "Reserve Late Harvest" ...
## $ points : num 87 87 87 87 87 87 87 87 87 87 ...
## $ price : num NA 15 14 13 65 15 16 24 12 27 ...
## $ province : chr "Sicily & Sardinia" "Douro" "Oregon" "Michigan" ...
## $ region_1 : chr "Etna" NA "Willamette Valley" "Lake Michigan Shore" ...
## $ region_2 : chr NA NA "Willamette Valley" NA ...
## $ taster_name : chr "Kerin O’Keefe" "Roger Voss" "Paul Gregutt" "Alexander Peartree" ...
## $ taster_twitter_handle: chr "@kerinokeefe" "@vossroger" "@paulgwine " NA ...
## $ title : chr "Nicosia 2013 Vulkà Bianco (Etna)" "Quinta dos Avidagos 2011 Avidagos Red (Douro)" "Rainstorm 2013 Pinot Gris (Willamette Valley)" "St. Julian 2013 Reserve Late Harvest Riesling (Lake Michigan Shore)" ...
## $ variety : chr "White Blend" "Portuguese Red" "Pinot Gris" "Riesling" ...
## $ winery : chr "Nicosia" "Quinta dos Avidagos" "Rainstorm" "St. Julian" ...names(data1)## [1] "...1" "country" "description"
## [4] "designation" "points" "price"
## [7] "province" "region_1" "region_2"
## [10] "taster_name" "taster_twitter_handle" "title"
## [13] "variety" "winery"data2 <- data1[,-c(1,3,9,11,10,12)]
data3 <- na.omit(data2)
str(data3)## 'data.frame': 70175 obs. of 8 variables:
## $ country : chr "US" "US" "Spain" "Italy" ...
## $ designation: chr "Reserve Late Harvest" "Vintner's Reserve Wild Child Block" "Ars In Vitro" "Belsito" ...
## $ points : num 87 87 87 87 87 87 87 87 87 87 ...
## $ price : num 13 65 15 16 27 19 30 13 28 23 ...
## $ province : chr "Michigan" "Oregon" "Northern Spain" "Sicily & Sardinia" ...
## $ region_1 : chr "Lake Michigan Shore" "Willamette Valley" "Navarra" "Vittoria" ...
## $ variety : chr "Riesling" "Pinot Noir" "Tempranillo-Merlot" "Frappato" ...
## $ winery : chr "St. Julian" "Sweet Cheeks" "Tandem" "Terre di Giurfo" ...
## - attr(*, "na.action")= 'omit' Named int [1:59796] 1 2 3 8 9 12 13 14 15 16 ...
## ..- attr(*, "names")= chr [1:59796] "1" "2" "3" "8" ...Создание случайного леса
В дополнение к заданию я решила сделать случайный лес на всех переменных кроме Description (у меня не потянул компьютер совместить два в одном). Вышло, что с точки зрения глубины дерева важными переменными являются price и country, variety. Поэтому при изучении совокупности переменных (с использованием Description) важно будет учесть данный топ-3.
forest1 <- randomForest(data = data3, points ~., localImp = TRUE, ntree = 50)
min_depth_frame1 <- min_depth_distribution(forest1)
plot_min_depth_distribution(min_depth_frame1) +
xlab("Переменные") +
ylab("Количество деревьев")+
scale_fill_manual(values = c("#003f5c", "#2f4b7c", "#665191", "#a05195", "#d45087", "#f95d6a", "#ff7c43", "#ffa600",
"#e45149", "#b6d2dd", "#259086", "#0a463c", "darkblue", "darkred"))