library(tidyverse)
## ── Attaching packages ─────────────────────────── tidyverse 1.2.1 ──
## ✔ ggplot2 3.2.1 ✔ purrr 0.3.2
## ✔ tibble 2.1.3 ✔ dplyr 0.8.3
## ✔ tidyr 1.0.0 ✔ stringr 1.4.0
## ✔ readr 1.3.1 ✔ forcats 0.4.0
## ── Conflicts ────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
library(tidyr)
salary<-read.csv("NBA_season1718_salary (1).csv", header=TRUE,
stringsAsFactors = FALSE)
head(salary)
## X Player Tm season17_18
## 1 1 Stephen Curry GSW 34682550
## 2 2 LeBron James CLE 33285709
## 3 3 Paul Millsap DEN 31269231
## 4 4 Gordon Hayward BOS 29727900
## 5 5 Blake Griffin DET 29512900
## 6 6 Kyle Lowry TOR 28703704
summary(salary)
## X Player Tm season17_18
## Min. : 1 Length:573 Length:573 Min. : 17224
## 1st Qu.:144 Class :character Class :character 1st Qu.: 1312611
## Median :287 Mode :character Mode :character Median : 2386864
## Mean :287 Mean : 5858946
## 3rd Qu.:430 3rd Qu.: 7936509
## Max. :573 Max. :34682550
player_stats<-read.csv("nba_extra2.csv", header=TRUE,
stringsAsFactors = FALSE)
head(player_stats)
## Rk Player Pos Age Tm G GS MP FG FGA FG. X3P X3PA
## 1 NA NA NA NA NA NA NA NA NA NA
## 2 1 Alex Abrines:abrinal01 SG 24 OKC 75 8 1134 115 291 0.395 84 221
## 3 NA NA NA NA NA NA NA NA NA NA
## 4 2 Quincy Acy:acyqu01 PF 27 BRK 70 8 1359 130 365 0.356 102 292
## 5 NA NA NA NA NA NA NA NA NA NA
## 6 3 Steven Adams:adamsst01 C 24 OKC 76 76 2487 448 712 0.629 0 2
## X3P. X2P X2PA X2P. eFG. FT FTA FT. ORB DRB TRB AST STL BLK TOV PF
## 1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 2 0.380 31 70 0.443 0.540 39 46 0.848 26 88 114 28 38 8 25 124
## 3 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 4 0.349 28 73 0.384 0.496 49 60 0.817 40 217 257 57 33 29 60 149
## 5 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 6 0.000 448 710 0.631 0.629 160 286 0.559 384 301 685 88 92 78 128 215
## PTS
## 1 NA
## 2 353
## 3 NA
## 4 411
## 5 NA
## 6 1056
summary(player_stats)
## Rk Player Pos Age
## Min. : 1.0 Length:1328 Length:1328 Min. :19.00
## 1st Qu.:139.0 Class :character Class :character 1st Qu.:23.00
## Median :266.5 Mode :character Mode :character Median :26.00
## Mean :270.8 Mean :26.19
## 3rd Qu.:401.2 3rd Qu.:29.00
## Max. :540.0 Max. :41.00
## NA's :664 NA's :664
## Tm G GS MP
## Length:1328 Min. : 1.00 Min. : 0.00 Min. : 1.0
## Class :character 1st Qu.:17.00 1st Qu.: 0.00 1st Qu.: 186.0
## Mode :character Median :46.00 Median : 4.00 Median : 755.0
## Mean :43.28 Mean :19.71 Mean : 972.9
## 3rd Qu.:71.00 3rd Qu.:35.00 3rd Qu.:1651.5
## Max. :82.00 Max. :82.00 Max. :3026.0
## NA's :664 NA's :664 NA's :664
## FG FGA FG. X3P
## Min. : 0.0 Min. : 0.0 Min. :0.0000 Min. : 0.00
## 1st Qu.: 22.0 1st Qu.: 58.0 1st Qu.:0.3950 1st Qu.: 1.75
## Median :102.0 Median : 224.5 Median :0.4400 Median : 18.00
## Mean :159.5 Mean : 347.2 Mean :0.4414 Mean : 42.27
## 3rd Qu.:253.0 3rd Qu.: 554.0 3rd Qu.:0.4930 3rd Qu.: 64.25
## Max. :857.0 Max. :1687.0 Max. :1.0000 Max. :265.00
## NA's :664 NA's :664 NA's :668 NA's :664
## X3PA X3P. X2P X2PA
## Min. : 0.0 Min. :0.0000 Min. : 0.0 Min. : 0.0
## 1st Qu.: 7.0 1st Qu.:0.2500 1st Qu.: 15.0 1st Qu.: 33.0
## Median : 56.5 Median :0.3370 Median : 71.0 Median : 143.0
## Mean :117.2 Mean :0.3100 Mean :117.2 Mean : 230.0
## 3rd Qu.:189.2 3rd Qu.:0.3795 3rd Qu.:181.2 3rd Qu.: 361.2
## Max. :722.0 Max. :1.0000 Max. :725.0 Max. :1361.0
## NA's :664 NA's :729 NA's :664 NA's :664
## X2P. eFG. FT FTA
## Min. :0.0000 Min. :0.000 Min. : 0.00 Min. : 0.00
## 1st Qu.:0.4422 1st Qu.:0.458 1st Qu.: 8.00 1st Qu.: 12.00
## Median :0.4980 Median :0.506 Median : 37.50 Median : 51.00
## Mean :0.4931 Mean :0.498 Mean : 66.93 Mean : 87.19
## 3rd Qu.:0.5468 3rd Qu.:0.551 3rd Qu.: 97.00 3rd Qu.:120.75
## Max. :1.0000 Max. :1.500 Max. :624.00 Max. :727.00
## NA's :682 NA's :668 NA's :664 NA's :664
## FT. ORB DRB TRB
## Min. :0.0000 Min. : 0.00 Min. : 0.0 Min. : 0.0
## 1st Qu.:0.6670 1st Qu.: 5.00 1st Qu.: 22.0 1st Qu.: 29.0
## Median :0.7680 Median : 21.50 Median : 95.5 Median : 121.5
## Mean :0.7411 Mean : 39.01 Mean :135.3 Mean : 174.3
## 3rd Qu.:0.8330 3rd Qu.: 53.00 3rd Qu.:208.0 3rd Qu.: 259.2
## Max. :1.0000 Max. :399.00 Max. :848.0 Max. :1247.0
## NA's :722 NA's :664 NA's :664 NA's :664
## AST STL BLK TOV
## Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.: 11.75 1st Qu.: 5.00 1st Qu.: 2.00 1st Qu.: 8.00
## Median : 51.00 Median : 23.00 Median : 10.00 Median : 36.00
## Mean : 93.18 Mean : 31.15 Mean : 19.01 Mean : 55.01
## 3rd Qu.:126.25 3rd Qu.: 47.00 3rd Qu.: 25.00 3rd Qu.: 86.00
## Max. :820.00 Max. :177.00 Max. :193.00 Max. :381.00
## NA's :664 NA's :664 NA's :664 NA's :664
## PF PTS
## Min. : 0.00 Min. : 0.0
## 1st Qu.: 16.75 1st Qu.: 59.0
## Median : 66.50 Median : 264.0
## Mean : 79.91 Mean : 428.1
## 3rd Qu.:132.00 3rd Qu.: 667.2
## Max. :285.00 Max. :2251.0
## NA's :664 NA's :664
player_stats2<-player_stats%>%
filter(is.na(Rk)==FALSE)
head(player_stats2)
## Rk Player Pos Age Tm G GS MP FG FGA FG. X3P X3PA
## 1 1 Alex Abrines:abrinal01 SG 24 OKC 75 8 1134 115 291 0.395 84 221
## 2 2 Quincy Acy:acyqu01 PF 27 BRK 70 8 1359 130 365 0.356 102 292
## 3 3 Steven Adams:adamsst01 C 24 OKC 76 76 2487 448 712 0.629 0 2
## 4 4 Bam Adebayo:adebaba01 C 20 MIA 69 19 1368 174 340 0.512 0 7
## 5 5 Arron Afflalo:afflaar01 SG 32 ORL 53 3 682 65 162 0.401 27 70
## 6 6 Cole Aldrich:aldrico01 C 29 MIN 21 0 49 5 15 0.333 0 0
## X3P. X2P X2PA X2P. eFG. FT FTA FT. ORB DRB TRB AST STL BLK TOV PF
## 1 0.380 31 70 0.443 0.540 39 46 0.848 26 88 114 28 38 8 25 124
## 2 0.349 28 73 0.384 0.496 49 60 0.817 40 217 257 57 33 29 60 149
## 3 0.000 448 710 0.631 0.629 160 286 0.559 384 301 685 88 92 78 128 215
## 4 0.000 174 333 0.523 0.512 129 179 0.721 118 263 381 101 32 41 66 138
## 5 0.386 38 92 0.413 0.485 22 26 0.846 4 62 66 30 4 9 21 56
## 6 NA 5 15 0.333 0.333 2 6 0.333 3 12 15 3 2 1 1 11
## PTS
## 1 353
## 2 411
## 3 1056
## 4 477
## 5 179
## 6 12
player_stats3 <- player_stats2[-c(24, 25, 28, 29, 45, 46, 59, 60, 68, 69, 70, 73, 74, 77, 78, 97, 98, 102, 103, 118, 119, 122, 123, 142, 143, 145, 146, 167, 168, 174, 175, 180, 181, 190, 191, 202, 203, 229, 230, 237, 238, 242, 243, 250, 251, 252, 259, 260, 266, 267, 276, 277, 288, 289, 299, 300, 306, 307, 314, 315, 319, 320, 328, 329, 331, 332, 342, 342, 343, 344, 368, 369, 386, 387, 401, 402, 419, 420, 425, 426, 427, 439, 440, 442, 443, 450, 451, 454, 455, 471, 472, 481, 482, 493, 494, 496, 497, 523, 524, 535, 536, 550, 551, 568, 569, 585, 586, 602, 603, 604, 606, 607, 610, 611, 623, 624, 642, 643, 650, 651, 660, 661),]
head (player_stats3)
## Rk Player Pos Age Tm G GS MP FG FGA FG. X3P X3PA
## 1 1 Alex Abrines:abrinal01 SG 24 OKC 75 8 1134 115 291 0.395 84 221
## 2 2 Quincy Acy:acyqu01 PF 27 BRK 70 8 1359 130 365 0.356 102 292
## 3 3 Steven Adams:adamsst01 C 24 OKC 76 76 2487 448 712 0.629 0 2
## 4 4 Bam Adebayo:adebaba01 C 20 MIA 69 19 1368 174 340 0.512 0 7
## 5 5 Arron Afflalo:afflaar01 SG 32 ORL 53 3 682 65 162 0.401 27 70
## 6 6 Cole Aldrich:aldrico01 C 29 MIN 21 0 49 5 15 0.333 0 0
## X3P. X2P X2PA X2P. eFG. FT FTA FT. ORB DRB TRB AST STL BLK TOV PF
## 1 0.380 31 70 0.443 0.540 39 46 0.848 26 88 114 28 38 8 25 124
## 2 0.349 28 73 0.384 0.496 49 60 0.817 40 217 257 57 33 29 60 149
## 3 0.000 448 710 0.631 0.629 160 286 0.559 384 301 685 88 92 78 128 215
## 4 0.000 174 333 0.523 0.512 129 179 0.721 118 263 381 101 32 41 66 138
## 5 0.386 38 92 0.413 0.485 22 26 0.846 4 62 66 30 4 9 21 56
## 6 NA 5 15 0.333 0.333 2 6 0.333 3 12 15 3 2 1 1 11
## PTS
## 1 353
## 2 411
## 3 1056
## 4 477
## 5 179
## 6 12
playerS<-separate(data=player_stats3,col=Player,into=c("Player", "nickname"), sep=":")
head(playerS)
## Rk Player nickname Pos Age Tm G GS MP FG FGA FG. X3P X3PA
## 1 1 Alex Abrines abrinal01 SG 24 OKC 75 8 1134 115 291 0.395 84 221
## 2 2 Quincy Acy acyqu01 PF 27 BRK 70 8 1359 130 365 0.356 102 292
## 3 3 Steven Adams adamsst01 C 24 OKC 76 76 2487 448 712 0.629 0 2
## 4 4 Bam Adebayo adebaba01 C 20 MIA 69 19 1368 174 340 0.512 0 7
## 5 5 Arron Afflalo afflaar01 SG 32 ORL 53 3 682 65 162 0.401 27 70
## 6 6 Cole Aldrich aldrico01 C 29 MIN 21 0 49 5 15 0.333 0 0
## X3P. X2P X2PA X2P. eFG. FT FTA FT. ORB DRB TRB AST STL BLK TOV PF
## 1 0.380 31 70 0.443 0.540 39 46 0.848 26 88 114 28 38 8 25 124
## 2 0.349 28 73 0.384 0.496 49 60 0.817 40 217 257 57 33 29 60 149
## 3 0.000 448 710 0.631 0.629 160 286 0.559 384 301 685 88 92 78 128 215
## 4 0.000 174 333 0.523 0.512 129 179 0.721 118 263 381 101 32 41 66 138
## 5 0.386 38 92 0.413 0.485 22 26 0.846 4 62 66 30 4 9 21 56
## 6 NA 5 15 0.333 0.333 2 6 0.333 3 12 15 3 2 1 1 11
## PTS
## 1 353
## 2 411
## 3 1056
## 4 477
## 5 179
## 6 12
data<-left_join(playerS, salary)
## Joining, by = c("Player", "Tm")
data<-data%>%
mutate(MPG = MP/G, PPG = PTS/G, APG = AST/G, RPG = TRB/G, TOG = TOV/G)
data<-na.omit(data)
attach(data)
head(data)
## Rk Player nickname Pos Age Tm G GS MP FG FGA FG. X3P
## 1 1 Alex Abrines abrinal01 SG 24 OKC 75 8 1134 115 291 0.395 84
## 2 2 Quincy Acy acyqu01 PF 27 BRK 70 8 1359 130 365 0.356 102
## 3 3 Steven Adams adamsst01 C 24 OKC 76 76 2487 448 712 0.629 0
## 4 4 Bam Adebayo adebaba01 C 20 MIA 69 19 1368 174 340 0.512 0
## 5 5 Arron Afflalo afflaar01 SG 32 ORL 53 3 682 65 162 0.401 27
## 7 7 LaMarcus Aldridge aldrila01 C 32 SAS 75 75 2509 687 1347 0.510 27
## X3PA X3P. X2P X2PA X2P. eFG. FT FTA FT. ORB DRB TRB AST STL BLK
## 1 221 0.380 31 70 0.443 0.540 39 46 0.848 26 88 114 28 38 8
## 2 292 0.349 28 73 0.384 0.496 49 60 0.817 40 217 257 57 33 29
## 3 2 0.000 448 710 0.631 0.629 160 286 0.559 384 301 685 88 92 78
## 4 7 0.000 174 333 0.523 0.512 129 179 0.721 118 263 381 101 32 41
## 5 70 0.386 38 92 0.413 0.485 22 26 0.846 4 62 66 30 4 9
## 7 92 0.293 660 1255 0.526 0.520 334 399 0.837 246 389 635 152 43 90
## TOV PF PTS X season17_18 MPG PPG APG RPG
## 1 25 124 353 185 5725000 15.12000 4.706667 0.3733333 1.520000
## 2 60 149 411 350 1709538 19.41429 5.871429 0.8142857 3.671429
## 3 128 215 1056 32 22471910 32.72368 13.894737 1.1578947 9.013158
## 4 66 138 477 281 2490360 19.82609 6.913043 1.4637681 5.521739
## 5 21 56 179 291 2328652 12.86792 3.377358 0.5660377 1.245283
## 7 111 161 1735 36 21461010 33.45333 23.133333 2.0266667 8.466667
## TOG
## 1 0.3333333
## 2 0.8571429
## 3 1.6842105
## 4 0.9565217
## 5 0.3962264
## 7 1.4800000
stats_salary_cor <- data %>%
select(season17_18, PTS, TOV, PPG, MPG, TOG, RPG, APG)
pairs(stats_salary_cor)
cor(stats_salary_cor)[,"season17_18"]
## season17_18 PTS TOV PPG MPG TOG
## 1.0000000 0.5700031 0.5041297 0.6256246 0.5770201 0.5142743
## RPG APG
## 0.4696220 0.4472140
firstQT<-data%>%
filter(season17_18<1465920)
dim(firstQT)
## [1] 66 38
set.seed(1)
samp1<-sample(121, 10)
sampFirst<-firstQT[samp1,]
sampFirst
## Rk Player nickname Pos Age Tm G GS MP FG
## NA NA <NA> <NA> <NA> NA <NA> NA NA NA NA
## NA.1 NA <NA> <NA> <NA> NA <NA> NA NA NA NA
## 39 306 Timothe Luwawu-Cabarrot luwawti01 SF 22 PHI 52 7 807 100
## 1 26 Dwayne Bacon bacondw01 SG 22 CHO 53 6 713 72
## 34 252 Brandon Jennings jennibr01 PG 28 MIL 14 0 205 27
## NA.2 NA <NA> <NA> <NA> NA <NA> NA NA NA NA
## 43 330 Alfonzo McKinnie mckinal01 SF 25 TOR 14 0 53 8
## 14 92 Tyler Cavanaugh cavanty01 PF 23 ATL 39 1 518 67
## NA.3 NA <NA> <NA> <NA> NA <NA> NA NA NA NA
## 59 488 Tyler Ulis ulisty01 PG 22 PHO 71 43 1658 214
## FGA FG. X3P X3PA X3P. X2P X2PA X2P. eFG. FT FTA FT. ORB DRB
## NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## NA.1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 39 267 0.375 53 158 0.335 47 109 0.431 0.474 46 58 0.793 14 58
## 1 192 0.375 11 43 0.256 61 149 0.409 0.404 20 25 0.800 4 120
## 34 72 0.375 9 33 0.273 18 39 0.462 0.438 10 10 1.000 5 26
## NA.2 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 43 15 0.533 3 9 0.333 5 6 0.833 0.633 2 3 0.667 1 6
## 14 152 0.441 32 89 0.360 35 63 0.556 0.546 17 21 0.810 45 82
## NA.3 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 59 551 0.388 42 146 0.288 172 405 0.425 0.426 84 101 0.832 24 104
## TRB AST STL BLK TOV PF PTS X season17_18 MPG PPG
## NA NA NA NA NA NA NA NA NA NA NA NA
## NA.1 NA NA NA NA NA NA NA NA NA NA NA
## 39 72 54 12 5 38 97 299 405 1386600 15.519231 5.750000
## 1 124 38 16 2 23 46 175 467 815615 13.452830 3.301887
## 34 31 44 6 4 18 11 73 511 119010 14.642857 5.214286
## NA.2 NA NA NA NA NA NA NA NA NA NA NA
## 43 7 1 1 1 3 8 21 471 815615 3.785714 1.500000
## 14 127 27 9 4 14 61 183 484 679919 13.282051 4.692308
## NA.3 NA NA NA NA NA NA NA NA NA NA NA
## 59 128 311 70 7 127 120 554 421 1312611 23.352113 7.802817
## APG RPG TOG
## NA NA NA NA
## NA.1 NA NA NA
## 39 1.03846154 1.384615 0.7307692
## 1 0.71698113 2.339623 0.4339623
## 34 3.14285714 2.214286 1.2857143
## NA.2 NA NA NA
## 43 0.07142857 0.500000 0.2142857
## 14 0.69230769 3.256410 0.3589744
## NA.3 NA NA NA
## 59 4.38028169 1.802817 1.7887324
secondQT<-data%>%
filter(season17_18>=1465920 & season17_18<3028410)
dim(secondQT)
## [1] 99 38
set.seed(2)
samp2<-sample(122, 10)
sampSecond<-secondQT[samp2,]
sampSecond
## Rk Player nickname Pos Age Tm G GS MP FG FGA
## 85 443 Mike Scott scottmi01 PF 29 WAS 76 1 1406 276 524
## 79 426 Josh Richardson richajo01 SF 24 MIA 81 81 2689 399 885
## 70 367 Raul Neto netora01 PG 25 UTA 41 0 498 69 151
## 6 13 Kyle Anderson anderky01 SF 24 SAS 74 67 1978 231 438
## 32 183 Jerami Grant grantje01 PF 23 OKC 81 1 1647 244 456
## 8 38 Malik Beasley beaslma01 SG 21 DEN 62 0 583 73 178
## 17 88 Michael Carter-Williams cartemi01 PG 26 CHO 52 2 835 76 229
## 93 516 Damien Wilkins wilkida02 SF 38 IND 19 1 152 13 39
## 81 435 Terry Rozier roziete01 PG 23 BOS 80 16 2068 316 800
## 76 407 Jakob Poeltl poeltja01 C 22 TOR 82 0 1524 253 384
## FG. X3P X3PA X3P. X2P X2PA X2P. eFG. FT FTA FT. ORB DRB TRB AST
## 85 0.527 66 163 0.405 210 361 0.582 0.590 50 76 0.658 50 197 247 80
## 79 0.451 127 336 0.378 272 549 0.495 0.523 120 142 0.845 69 216 285 231
## 70 0.457 19 47 0.404 50 104 0.481 0.520 26 35 0.743 7 42 49 75
## 6 0.527 19 57 0.333 212 381 0.556 0.549 104 146 0.712 84 312 396 202
## 32 0.535 32 110 0.291 212 346 0.613 0.570 162 240 0.675 86 233 319 57
## 8 0.410 28 82 0.341 45 96 0.469 0.489 22 33 0.667 14 57 71 31
## 17 0.332 14 59 0.237 62 170 0.365 0.362 73 89 0.820 37 101 138 116
## 93 0.333 4 18 0.222 9 21 0.429 0.385 3 4 0.750 5 11 16 9
## 81 0.395 153 402 0.381 163 398 0.410 0.491 115 149 0.772 63 313 376 232
## 76 0.659 1 2 0.500 252 382 0.660 0.660 60 101 0.594 165 228 393 57
## STL BLK TOV PF PTS X season17_18 MPG PPG APG
## 85 23 10 79 148 668 348 1709538 18.500000 8.789474 1.0526316
## 79 121 75 140 199 1045 380 1471382 33.197531 12.901235 2.8518519
## 70 13 4 36 38 183 387 1471382 12.146341 4.463415 1.8292683
## 6 115 60 94 114 585 313 2151704 26.729730 7.905405 2.7297297
## 32 31 77 54 155 682 366 1524305 20.333333 8.419753 0.7037037
## 8 15 7 24 36 196 351 1700640 9.403226 3.161290 0.5000000
## 17 44 23 52 99 239 270 2700000 16.057692 4.596154 2.2307692
## 93 2 1 5 7 33 324 2116955 8.000000 1.736842 0.4736842
## 81 80 19 80 123 900 333 1988520 25.850000 11.250000 2.9000000
## 76 39 100 85 212 567 267 2825640 18.585366 6.914634 0.6951220
## RPG TOG
## 85 3.2500000 1.0394737
## 79 3.5185185 1.7283951
## 70 1.1951220 0.8780488
## 6 5.3513514 1.2702703
## 32 3.9382716 0.6666667
## 8 1.1451613 0.3870968
## 17 2.6538462 1.0000000
## 93 0.8421053 0.2631579
## 81 4.7000000 1.0000000
## 76 4.7926829 1.0365854
thirdQT<-data%>%
filter(season17_18>=3028410 & season17_18<9539057)
dim(thirdQT)
## [1] 99 38
set.seed(3)
samp3<-sample(122, 10)
sampThird<-thirdQT[samp3,]
sampThird
## Rk Player nickname Pos Age Tm G GS MP FG FGA FG.
## 5 24 D.J. Augustin augusdj01 PG 30 ORL 75 36 1760 244 540 0.452
## 58 333 Jodie Meeks meeksjo01 SG 30 WAS 77 0 1119 157 393 0.399
## 12 43 Dragan Bender bendedr01 PF 20 PHO 82 37 2069 187 484 0.386
## NA NA <NA> <NA> <NA> NA <NA> NA NA NA NA NA NA
## 36 179 Aaron Gordon gordoaa01 PF 22 ORL 58 57 1909 375 865 0.434
## NA.1 NA <NA> <NA> <NA> NA <NA> NA NA NA NA NA NA
## NA.2 NA <NA> <NA> <NA> NA <NA> NA NA NA NA NA NA
## NA.3 NA <NA> <NA> <NA> NA <NA> NA NA NA NA NA NA
## 95 487 Ekpe Udoh udohek01 C 30 UTA 63 3 810 60 120 0.500
## 8 30 J.J. Barea bareajo01 PG 33 DAL 69 10 1603 303 690 0.439
## X3P X3PA X3P. X2P X2PA X2P. eFG. FT FTA FT. ORB DRB TRB AST STL
## 5 114 272 0.419 130 268 0.485 0.557 164 189 0.868 30 130 160 287 54
## 58 72 210 0.343 85 183 0.464 0.491 101 117 0.863 15 111 126 70 29
## 12 118 322 0.366 69 162 0.426 0.508 39 51 0.765 40 321 361 130 22
## NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 36 115 342 0.336 260 523 0.497 0.500 157 225 0.698 87 370 457 136 59
## NA.1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## NA.2 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## NA.3 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 95 0 1 0.000 60 119 0.504 0.500 42 56 0.750 68 82 150 53 43
## 8 115 313 0.367 188 377 0.499 0.522 80 102 0.784 15 186 201 434 35
## BLK TOV PF PTS X season17_18 MPG PPG APG
## 5 0 123 94 766 156 7250000 23.46667 10.213333 3.8266667
## 58 4 37 55 487 246 3290000 14.53247 6.324675 0.9090909
## 12 53 112 166 531 214 4468800 25.23171 6.475610 1.5853659
## NA NA NA NA NA NA NA NA NA NA
## 36 45 107 111 1022 190 5504420 32.91379 17.620690 2.3448276
## NA.1 NA NA NA NA NA NA NA NA NA
## NA.2 NA NA NA NA NA NA NA NA NA
## NA.3 NA NA NA NA NA NA NA NA NA
## 95 74 20 105 162 252 3200000 12.85714 2.571429 0.8412698
## 8 3 143 83 801 229 3903900 23.23188 11.608696 6.2898551
## RPG TOG
## 5 2.133333 1.6400000
## 58 1.636364 0.4805195
## 12 4.402439 1.3658537
## NA NA NA
## 36 7.879310 1.8448276
## NA.1 NA NA
## NA.2 NA NA
## NA.3 NA NA
## 95 2.380952 0.3174603
## 8 2.913043 2.0724638
fourthQT<-data%>%
filter(season17_18>=9539097 & season17_18<=34682550)
dim(fourthQT)
## [1] 104 38
set.seed(4)
samp4<-sample(122, 10)
sampFourth<-fourthQT[samp4,]
sampFourth
## Rk Player nickname Pos Age Tm G GS MP FG FGA FG.
## NA NA <NA> <NA> <NA> NA <NA> NA NA NA NA NA NA
## 75 341 Paul Millsap millspa01 PF 32 DEN 38 37 1143 202 435 0.464
## 51 234 Andre Iguodala iguodan01 SF 34 GSW 64 7 1622 148 320 0.463
## 3 14 Ryan Anderson anderry01 PF 29 HOU 66 50 1725 207 480 0.431
## 71 311 Ian Mahinmi mahinia01 C 31 WAS 77 0 1145 138 248 0.556
## 44 195 Maurice Harkless harklma01 SF 24 POR 59 36 1264 147 297 0.495
## NA.1 NA <NA> <NA> <NA> NA <NA> NA NA NA NA NA NA
## 58 257 James Johnson johnsja01 PF 30 MIA 73 41 1943 309 614 0.503
## NA.2 NA <NA> <NA> <NA> NA <NA> NA NA NA NA NA NA
## 56 250 Al Jefferson jeffeal01 C 33 IND 36 1 484 111 208 0.534
## X3P X3PA X3P. X2P X2PA X2P. eFG. FT FTA FT. ORB DRB TRB AST STL
## NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 75 39 113 0.345 163 322 0.506 0.509 112 161 0.696 65 180 245 105 39
## 51 33 117 0.282 115 203 0.567 0.514 55 87 0.632 50 196 246 210 54
## 3 131 339 0.386 76 141 0.539 0.568 72 93 0.774 94 237 331 60 24
## 71 0 2 0.000 138 246 0.561 0.556 90 128 0.703 135 177 312 53 38
## 44 49 118 0.415 98 179 0.547 0.577 42 59 0.712 46 116 162 53 48
## NA.1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 58 57 185 0.308 252 429 0.587 0.550 113 162 0.698 61 297 358 280 70
## NA.2 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 56 0 3 0.000 111 205 0.541 0.534 30 36 0.833 30 113 143 30 16
## BLK TOV PF PTS X season17_18 MPG PPG APG RPG
## NA NA NA NA NA NA NA NA NA NA NA
## 75 44 73 99 555 3 31269231 30.07895 14.605263 2.7631579 6.447368
## 51 38 67 99 384 75 14814815 25.34375 6.000000 3.2812500 3.843750
## 3 21 42 126 617 42 19578455 26.13636 9.348485 0.9090909 5.015152
## 71 42 100 230 366 63 16661641 14.87013 4.753247 0.6883117 4.051948
## 44 43 40 103 385 122 10162922 21.42373 6.525424 0.8983051 2.745763
## NA.1 NA NA NA NA NA NA NA NA NA NA
## 58 51 141 192 788 85 13954000 26.61644 10.794521 3.8356164 4.904110
## NA.2 NA NA NA NA NA NA NA NA NA NA
## 56 23 21 66 252 128 9769821 13.44444 7.000000 0.8333333 3.972222
## TOG
## NA NA
## 75 1.9210526
## 51 1.0468750
## 3 0.6363636
## 71 1.2987013
## 44 0.6779661
## NA.1 NA
## 58 1.9315068
## NA.2 NA
## 56 0.5833333
college<-read.csv("College:International_Stats.csv", header=TRUE,
stringsAsFactors = FALSE)
college
## Player Team cMPG cPPG cFGM cFGA
## 1 Willy Hernangomez Real Madrid 12.6 18.8 7.5 11.2
## 2 Ivan Rabb Cal 32.6 14.0 4.9 10.1
## 3 David Nwaba Cal Poly 28.0 12.5 4.4 9.5
## 4 Tyler Cavanaugh George Washington 32.2 18.3 5.7 12.7
## 5 Tyler Ulis Kentucky 36.8 17.3 5.5 12.6
## 6 Ersan Ilyasova FC Barcelona 29.0 10.7 4.0 8.0
## 7 Travis Wear UCLA 23.9 7.2 3.1 5.8
## 8 James Micheal McAdoo North Carolina 30.1 14.2 5.1 11.1
## 9 Khem Birch UNLV 31.4 11.5 3.8 7.4
## 10 Frank Mason Kansas 36.1 20.9 6.7 13.7
## 11 Jusuf Nurkic Cedevita Zagreb (Croatia) 15.4 8.9 3.2 5.5
## 12 Shabazz Muhammad UCLA 30.8 17.9 6.3 14.3
## 13 TJ McConnell Arizona 30.5 10.4 4.1 8.2
## 14 Kyle Anderson UCLA 33.2 14.6 5.0 10.5
## 15 Tyler Ennis Syracuse 35.7 12.9 4.4 10.6
## 16 Luke Babbit Nevada 37.1 21.9 7.4 14.8
## 17 Norman Powell UCLA 34.6 16.4 5.9 13.0
## 18 Bruno Caboclo Pinheiros (Brazil) 12.8 5.0 1.7 3.3
## 19 Shabazz Napier UConn 35.1 18.0 5.3 12.4
## 20 Donovan Mitchell Louisville 32.3 15.6 5.3 13.1
## 21 Darrell Arthur Kansas 24.7 12.8 5.4 9.9
## 22 Wesley Johnson Syracuse 35.0 16.5 5.9 11.8
## 23 Aron Baynes Washington State 28.8 12.7 4.6 8.0
## 24 Ish Smith Wake Forest 36.8 13.2 5.7 13.5
## 25 Tyreke Evans Memphis 29.0 17.1 6.2 13.6
## 26 Brandan Wright North Carolina 27.4 14.7 6.2 9.5
## 27 Jason Smith Colorado State 30.2 16.8 5.8 10.1
## 28 Jayson Tatum Duke 33.3 16.8 5.7 12.6
## 29 Rajon Rondo Kentuck 31.0 11.2 4.3 9.0
## 30 Lonzo Ball UCLA 35.1 14.6 5.3 9.5
## 31 Marvin Williams North Carolina 22.2 11.3 3.5 6.9
## 32 Meyers Leonard Illinois 31.8 13.6 5.3 9.1
## 33 James Harden Arizona States 35.8 20.1 6.3 12.9
## 34 Ryan Anderson Cal 32.8 21.1 7.0 14.2
## 35 Rudy Gobert Cholet (France) 22.7 8.4 3.3 4.6
## 36 Enes Kanter Fenerbahce (Turkey) 7.8 2.0 0.8 1.8
## 37 Dion Waiters Syracuse 24.1 12.6 4.6 9.6
## 38 Jrue Holiday UCLA 27.1 8.5 3.2 7.1
## 39 Cody Zeller Indiana 29.5 16.5 5.5 9.8
## 40 George Hill IUPUI 36.8 21.5 6.9 12.6
## cFGp cAPG cRPG
## 1 0.667 1.2 11.0
## 2 0.484 1.5 10.5
## 3 0.462 3.5 6.3
## 4 0.448 2.0 8.4
## 5 0.434 7.0 3.0
## 6 0.500 0.7 7.6
## 7 0.530 1.4 3.2
## 8 0.458 1.7 6.8
## 9 0.510 1.2 10.2
## 10 0.490 5.2 4.2
## 11 0.585 0.6 3.3
## 12 0.443 0.8 5.2
## 13 0.500 6.3 3.8
## 14 0.480 6.5 8.8
## 15 0.411 5.5 3.4
## 16 0.500 2.1 8.9
## 17 0.456 2.1 4.7
## 18 0.507 0.2 2.8
## 19 0.429 4.9 5.9
## 20 0.408 2.7 4.9
## 21 0.543 0.8 6.3
## 22 0.502 2.2 8.5
## 23 0.580 0.6 7.5
## 24 0.420 6.0 4.9
## 25 0.455 3.9 5.4
## 26 0.646 1.0 6.2
## 27 0.579 1.9 10.1
## 28 0.452 2.1 7.3
## 29 0.482 4.9 6.1
## 30 0.551 7.6 6.0
## 31 0.506 0.7 6.6
## 32 0.584 1.3 8.2
## 33 0.489 4.2 5.6
## 34 0.490 1.4 9.9
## 35 0.718 0.4 5.4
## 36 0.429 0.0 1.5
## 37 0.476 2.5 2.3
## 38 0.450 3.7 3.8
## 39 0.564 1.3 8.0
## 40 0.545 4.3 6.8