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# Load required libraries
library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.1.2## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.2 v dplyr 1.0.7
## v tidyr 1.1.3 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.1## Warning: package 'ggplot2' was built under R version 4.1.2## Warning: package 'stringr' was built under R version 4.1.2## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(dbplyr)##
## Attaching package: 'dbplyr'## The following objects are masked from 'package:dplyr':
##
## ident, sqllibrary(stringr)tournament_data <- read.delim("https://raw.githubusercontent.com/uzmabb182/Data_607_Project_1/main/tournament.txt", header=F, sep="|", quote = "")
tournament_data## V1
## 1 -----------------------------------------------------------------------------------------
## 2 Pair
## 3 Num
## 4 -----------------------------------------------------------------------------------------
## 5 1
## 6 ON
## 7 -----------------------------------------------------------------------------------------
## 8 2
## 9 MI
## 10 -----------------------------------------------------------------------------------------
## 11 3
## 12 MI
## 13 -----------------------------------------------------------------------------------------
## 14 4
## 15 MI
## 16 -----------------------------------------------------------------------------------------
## 17 5
## 18 MI
## 19 -----------------------------------------------------------------------------------------
## 20 6
## 21 OH
## 22 -----------------------------------------------------------------------------------------
## 23 7
## 24 MI
## 25 -----------------------------------------------------------------------------------------
## 26 8
## 27 MI
## 28 -----------------------------------------------------------------------------------------
## 29 9
## 30 ON
## 31 -----------------------------------------------------------------------------------------
## 32 10
## 33 MI
## 34 -----------------------------------------------------------------------------------------
## 35 11
## 36 MI
## 37 -----------------------------------------------------------------------------------------
## 38 12
## 39 MI
## 40 -----------------------------------------------------------------------------------------
## 41 13
## 42 MI
## 43 -----------------------------------------------------------------------------------------
## 44 14
## 45 MI
## 46 -----------------------------------------------------------------------------------------
## 47 15
## 48 MI
## 49 -----------------------------------------------------------------------------------------
## 50 16
## 51 MI
## 52 -----------------------------------------------------------------------------------------
## 53 17
## 54 MI
## 55 -----------------------------------------------------------------------------------------
## 56 18
## 57 MI
## 58 -----------------------------------------------------------------------------------------
## 59 19
## 60 MI
## 61 -----------------------------------------------------------------------------------------
## 62 20
## 63 MI
## 64 -----------------------------------------------------------------------------------------
## 65 21
## 66 ON
## 67 -----------------------------------------------------------------------------------------
## 68 22
## 69 MI
## 70 -----------------------------------------------------------------------------------------
## 71 23
## 72 ON
## 73 -----------------------------------------------------------------------------------------
## 74 24
## 75 MI
## 76 -----------------------------------------------------------------------------------------
## 77 25
## 78 MI
## 79 -----------------------------------------------------------------------------------------
## 80 26
## 81 ON
## 82 -----------------------------------------------------------------------------------------
## 83 27
## 84 MI
## 85 -----------------------------------------------------------------------------------------
## 86 28
## 87 MI
## 88 -----------------------------------------------------------------------------------------
## 89 29
## 90 MI
## 91 -----------------------------------------------------------------------------------------
## 92 30
## 93 ON
## 94 -----------------------------------------------------------------------------------------
## 95 31
## 96 MI
## 97 -----------------------------------------------------------------------------------------
## 98 32
## 99 ON
## 100 -----------------------------------------------------------------------------------------
## 101 33
## 102 MI
## 103 -----------------------------------------------------------------------------------------
## 104 34
## 105 MI
## 106 -----------------------------------------------------------------------------------------
## 107 35
## 108 MI
## 109 -----------------------------------------------------------------------------------------
## 110 36
## 111 MI
## 112 -----------------------------------------------------------------------------------------
## 113 37
## 114 MI
## 115 -----------------------------------------------------------------------------------------
## 116 38
## 117 MI
## 118 -----------------------------------------------------------------------------------------
## 119 39
## 120 MI
## 121 -----------------------------------------------------------------------------------------
## 122 40
## 123 MI
## 124 -----------------------------------------------------------------------------------------
## 125 41
## 126 MI
## 127 -----------------------------------------------------------------------------------------
## 128 42
## 129 MI
## 130 -----------------------------------------------------------------------------------------
## 131 43
## 132 MI
## 133 -----------------------------------------------------------------------------------------
## 134 44
## 135 MI
## 136 -----------------------------------------------------------------------------------------
## 137 45
## 138 MI
## 139 -----------------------------------------------------------------------------------------
## 140 46
## 141 MI
## 142 -----------------------------------------------------------------------------------------
## 143 47
## 144 MI
## 145 -----------------------------------------------------------------------------------------
## 146 48
## 147 MI
## 148 -----------------------------------------------------------------------------------------
## 149 49
## 150 MI
## 151 -----------------------------------------------------------------------------------------
## 152 50
## 153 MI
## 154 -----------------------------------------------------------------------------------------
## 155 51
## 156 MI
## 157 -----------------------------------------------------------------------------------------
## 158 52
## 159 MI
## 160 -----------------------------------------------------------------------------------------
## 161 53
## 162 MI
## 163 -----------------------------------------------------------------------------------------
## 164 54
## 165 MI
## 166 -----------------------------------------------------------------------------------------
## 167 55
## 168 MI
## 169 -----------------------------------------------------------------------------------------
## 170 56
## 171 MI
## 172 -----------------------------------------------------------------------------------------
## 173 57
## 174 MI
## 175 -----------------------------------------------------------------------------------------
## 176 58
## 177 MI
## 178 -----------------------------------------------------------------------------------------
## 179 59
## 180 MI
## 181 -----------------------------------------------------------------------------------------
## 182 60
## 183 MI
## 184 -----------------------------------------------------------------------------------------
## 185 61
## 186 ON
## 187 -----------------------------------------------------------------------------------------
## 188 62
## 189 MI
## 190 -----------------------------------------------------------------------------------------
## 191 63
## 192 MI
## 193 -----------------------------------------------------------------------------------------
## 194 64
## 195 MI
## 196 -----------------------------------------------------------------------------------------
## V2 V3 V4 V5 V6 V7 V8 V9
## 1
## 2 Player Name Total Round Round Round Round Round Round
## 3 USCF ID / Rtg (Pre->Post) Pts 1 2 3 4 5 6
## 4
## 5 GARY HUA 6.0 W 39 W 21 W 18 W 14 W 7 D 12
## 6 15445895 / R: 1794 ->1817 N:2 W B W B W B
## 7
## 8 DAKSHESH DARURI 6.0 W 63 W 58 L 4 W 17 W 16 W 20
## 9 14598900 / R: 1553 ->1663 N:2 B W B W B W
## 10
## 11 ADITYA BAJAJ 6.0 L 8 W 61 W 25 W 21 W 11 W 13
## 12 14959604 / R: 1384 ->1640 N:2 W B W B W B
## 13
## 14 PATRICK H SCHILLING 5.5 W 23 D 28 W 2 W 26 D 5 W 19
## 15 12616049 / R: 1716 ->1744 N:2 W B W B W B
## 16
## 17 HANSHI ZUO 5.5 W 45 W 37 D 12 D 13 D 4 W 14
## 18 14601533 / R: 1655 ->1690 N:2 B W B W B W
## 19
## 20 HANSEN SONG 5.0 W 34 D 29 L 11 W 35 D 10 W 27
## 21 15055204 / R: 1686 ->1687 N:3 W B W B B W
## 22
## 23 GARY DEE SWATHELL 5.0 W 57 W 46 W 13 W 11 L 1 W 9
## 24 11146376 / R: 1649 ->1673 N:3 W B W B B W
## 25
## 26 EZEKIEL HOUGHTON 5.0 W 3 W 32 L 14 L 9 W 47 W 28
## 27 15142253 / R: 1641P17->1657P24 N:3 B W B W B W
## 28
## 29 STEFANO LEE 5.0 W 25 L 18 W 59 W 8 W 26 L 7
## 30 14954524 / R: 1411 ->1564 N:2 W B W B W B
## 31
## 32 ANVIT RAO 5.0 D 16 L 19 W 55 W 31 D 6 W 25
## 33 14150362 / R: 1365 ->1544 N:3 W W B B W B
## 34
## 35 CAMERON WILLIAM MC LEMAN 4.5 D 38 W 56 W 6 L 7 L 3 W 34
## 36 12581589 / R: 1712 ->1696 N:3 B W B W B W
## 37
## 38 KENNETH J TACK 4.5 W 42 W 33 D 5 W 38 H D 1
## 39 12681257 / R: 1663 ->1670 N:3 W B W B W
## 40
## 41 TORRANCE HENRY JR 4.5 W 36 W 27 L 7 D 5 W 33 L 3
## 42 15082995 / R: 1666 ->1662 N:3 B W B B W W
## 43
## 44 BRADLEY SHAW 4.5 W 54 W 44 W 8 L 1 D 27 L 5
## 45 10131499 / R: 1610 ->1618 N:3 W B W W B B
## 46
## 47 ZACHARY JAMES HOUGHTON 4.5 D 19 L 16 W 30 L 22 W 54 W 33
## 48 15619130 / R: 1220P13->1416P20 N:3 B B W W B B
## 49
## 50 MIKE NIKITIN 4.0 D 10 W 15 H W 39 L 2 W 36
## 51 10295068 / R: 1604 ->1613 N:3 B W B W B
## 52
## 53 RONALD GRZEGORCZYK 4.0 W 48 W 41 L 26 L 2 W 23 W 22
## 54 10297702 / R: 1629 ->1610 N:3 W B W B W B
## 55
## 56 DAVID SUNDEEN 4.0 W 47 W 9 L 1 W 32 L 19 W 38
## 57 11342094 / R: 1600 ->1600 N:3 B W B W B W
## 58
## 59 DIPANKAR ROY 4.0 D 15 W 10 W 52 D 28 W 18 L 4
## 60 14862333 / R: 1564 ->1570 N:3 W B W B W W
## 61
## 62 JASON ZHENG 4.0 L 40 W 49 W 23 W 41 W 28 L 2
## 63 14529060 / R: 1595 ->1569 N:4 W B W B W B
## 64
## 65 DINH DANG BUI 4.0 W 43 L 1 W 47 L 3 W 40 W 39
## 66 15495066 / R: 1563P22->1562 N:3 B W B W W B
## 67
## 68 EUGENE L MCCLURE 4.0 W 64 D 52 L 28 W 15 H L 17
## 69 12405534 / R: 1555 ->1529 N:4 W B W B W
## 70
## 71 ALAN BUI 4.0 L 4 W 43 L 20 W 58 L 17 W 37
## 72 15030142 / R: 1363 ->1371 B W B W B W
## 73
## 74 MICHAEL R ALDRICH 4.0 L 28 L 47 W 43 L 25 W 60 W 44
## 75 13469010 / R: 1229 ->1300 N:4 B W B B W W
## 76
## 77 LOREN SCHWIEBERT 3.5 L 9 W 53 L 3 W 24 D 34 L 10
## 78 12486656 / R: 1745 ->1681 N:4 B W B W B W
## 79
## 80 MAX ZHU 3.5 W 49 W 40 W 17 L 4 L 9 D 32
## 81 15131520 / R: 1579 ->1564 N:4 B W B W B W
## 82
## 83 GAURAV GIDWANI 3.5 W 51 L 13 W 46 W 37 D 14 L 6
## 84 14476567 / R: 1552 ->1539 N:4 W B W B W B
## 85
## 86 SOFIA ADINA STANESCU-BELLU 3.5 W 24 D 4 W 22 D 19 L 20 L 8
## 87 14882954 / R: 1507 ->1513 N:3 W W B W B B
## 88
## 89 CHIEDOZIE OKORIE 3.5 W 50 D 6 L 38 L 34 W 52 W 48
## 90 15323285 / R: 1602P6 ->1508P12 N:4 B W B W W B
## 91
## 92 GEORGE AVERY JONES 3.5 L 52 D 64 L 15 W 55 L 31 W 61
## 93 12577178 / R: 1522 ->1444 W B B W W B
## 94
## 95 RISHI SHETTY 3.5 L 58 D 55 W 64 L 10 W 30 W 50
## 96 15131618 / R: 1494 ->1444 B W B W B W
## 97
## 98 JOSHUA PHILIP MATHEWS 3.5 W 61 L 8 W 44 L 18 W 51 D 26
## 99 14073750 / R: 1441 ->1433 N:4 W B W B W B
## 100
## 101 JADE GE 3.5 W 60 L 12 W 50 D 36 L 13 L 15
## 102 14691842 / R: 1449 ->1421 B W B W B W
## 103
## 104 MICHAEL JEFFERY THOMAS 3.5 L 6 W 60 L 37 W 29 D 25 L 11
## 105 15051807 / R: 1399 ->1400 B W B B W B
## 106
## 107 JOSHUA DAVID LEE 3.5 L 46 L 38 W 56 L 6 W 57 D 52
## 108 14601397 / R: 1438 ->1392 W W B W B B
## 109
## 110 SIDDHARTH JHA 3.5 L 13 W 57 W 51 D 33 H L 16
## 111 14773163 / R: 1355 ->1367 N:4 W B W B W
## 112
## 113 AMIYATOSH PWNANANDAM 3.5 B L 5 W 34 L 27 H L 23
## 114 15489571 / R: 980P12->1077P17 B W W B
## 115
## 116 BRIAN LIU 3.0 D 11 W 35 W 29 L 12 H L 18
## 117 15108523 / R: 1423 ->1439 N:4 W B W W B
## 118
## 119 JOEL R HENDON 3.0 L 1 W 54 W 40 L 16 W 44 L 21
## 120 12923035 / R: 1436P23->1413 N:4 B W B W B W
## 121
## 122 FOREST ZHANG 3.0 W 20 L 26 L 39 W 59 L 21 W 56
## 123 14892710 / R: 1348 ->1346 B B W W B W
## 124
## 125 KYLE WILLIAM MURPHY 3.0 W 59 L 17 W 58 L 20 X U
## 126 15761443 / R: 1403P5 ->1341P9 B W B W
## 127
## 128 JARED GE 3.0 L 12 L 50 L 57 D 60 D 61 W 64
## 129 14462326 / R: 1332 ->1256 B W B B W W
## 130
## 131 ROBERT GLEN VASEY 3.0 L 21 L 23 L 24 W 63 W 59 L 46
## 132 14101068 / R: 1283 ->1244 W B W W B B
## 133
## 134 JUSTIN D SCHILLING 3.0 B L 14 L 32 W 53 L 39 L 24
## 135 15323504 / R: 1199 ->1199 W B B W B
## 136
## 137 DEREK YAN 3.0 L 5 L 51 D 60 L 56 W 63 D 55
## 138 15372807 / R: 1242 ->1191 W B W B W B
## 139
## 140 JACOB ALEXANDER LAVALLEY 3.0 W 35 L 7 L 27 L 50 W 64 W 43
## 141 15490981 / R: 377P3 ->1076P10 B W B W B W
## 142
## 143 ERIC WRIGHT 2.5 L 18 W 24 L 21 W 61 L 8 D 51
## 144 12533115 / R: 1362 ->1341 W B W B W B
## 145
## 146 DANIEL KHAIN 2.5 L 17 W 63 H D 52 H L 29
## 147 14369165 / R: 1382 ->1335 B W B W
## 148
## 149 MICHAEL J MARTIN 2.5 L 26 L 20 D 63 D 64 W 58 H
## 150 12531685 / R: 1291P12->1259P17 W W B W B
## 151
## 152 SHIVAM JHA 2.5 L 29 W 42 L 33 W 46 H L 31
## 153 14773178 / R: 1056 ->1111 W B W B B
## 154
## 155 TEJAS AYYAGARI 2.5 L 27 W 45 L 36 W 57 L 32 D 47
## 156 15205474 / R: 1011 ->1097 B W B W B W
## 157
## 158 ETHAN GUO 2.5 W 30 D 22 L 19 D 48 L 29 D 35
## 159 14918803 / R: 935 ->1092 N:4 B W B W B W
## 160
## 161 JOSE C YBARRA 2.0 H L 25 H L 44 U W 57
## 162 12578849 / R: 1393 ->1359 B W W
## 163
## 164 LARRY HODGE 2.0 L 14 L 39 L 61 B L 15 L 59
## 165 12836773 / R: 1270 ->1200 B B W W B
## 166
## 167 ALEX KONG 2.0 L 62 D 31 L 10 L 30 B D 45
## 168 15412571 / R: 1186 ->1163 W B W B W
## 169
## 170 MARISA RICCI 2.0 H L 11 L 35 W 45 H L 40
## 171 14679887 / R: 1153 ->1140 B W W B
## 172
## 173 MICHAEL LU 2.0 L 7 L 36 W 42 L 51 L 35 L 53
## 174 15113330 / R: 1092 ->1079 B W W B W B
## 175
## 176 VIRAJ MOHILE 2.0 W 31 L 2 L 41 L 23 L 49 B
## 177 14700365 / R: 917 -> 941 W B W B W
## 178
## 179 SEAN M MC CORMICK 2.0 L 41 B L 9 L 40 L 43 W 54
## 180 12841036 / R: 853 -> 878 W B B W W
## 181
## 182 JULIA SHEN 1.5 L 33 L 34 D 45 D 42 L 24 H
## 183 14579262 / R: 967 -> 984 W B B W B
## 184
## 185 JEZZEL FARKAS 1.5 L 32 L 3 W 54 L 47 D 42 L 30
## 186 15771592 / R: 955P11-> 979P18 B W B W B W
## 187
## 188 ASHWIN BALAJI 1.0 W 55 U U U U U
## 189 15219542 / R: 1530 ->1535 B
## 190
## 191 THOMAS JOSEPH HOSMER 1.0 L 2 L 48 D 49 L 43 L 45 H
## 192 15057092 / R: 1175 ->1125 W B W B B
## 193
## 194 BEN LI 1.0 L 22 D 30 L 31 D 49 L 46 L 42
## 195 15006561 / R: 1163 ->1112 B W W B W B
## 196
## V10 V11
## 1 NA
## 2 Round NA
## 3 7 NA
## 4 NA
## 5 D 4 NA
## 6 W NA
## 7 NA
## 8 W 7 NA
## 9 B NA
## 10 NA
## 11 W 12 NA
## 12 W NA
## 13 NA
## 14 D 1 NA
## 15 B NA
## 16 NA
## 17 W 17 NA
## 18 B NA
## 19 NA
## 20 W 21 NA
## 21 B NA
## 22 NA
## 23 L 2 NA
## 24 W NA
## 25 NA
## 26 W 19 NA
## 27 W NA
## 28 NA
## 29 W 20 NA
## 30 B NA
## 31 NA
## 32 W 18 NA
## 33 W NA
## 34 NA
## 35 W 26 NA
## 36 B NA
## 37 NA
## 38 L 3 NA
## 39 B NA
## 40 NA
## 41 W 32 NA
## 42 B NA
## 43 NA
## 44 W 31 NA
## 45 W NA
## 46 NA
## 47 W 38 NA
## 48 W NA
## 49 NA
## 50 U NA
## 51 NA
## 52 NA
## 53 L 5 NA
## 54 W NA
## 55 NA
## 56 L 10 NA
## 57 B NA
## 58 NA
## 59 L 8 NA
## 60 B NA
## 61 NA
## 62 L 9 NA
## 63 W NA
## 64 NA
## 65 L 6 NA
## 66 W NA
## 67 NA
## 68 W 40 NA
## 69 B NA
## 70 NA
## 71 W 46 NA
## 72 B NA
## 73 NA
## 74 W 39 NA
## 75 B NA
## 76 NA
## 77 W 47 NA
## 78 B NA
## 79 NA
## 80 L 11 NA
## 81 W NA
## 82 NA
## 83 U NA
## 84 NA
## 85 NA
## 86 D 36 NA
## 87 W NA
## 88 NA
## 89 U NA
## 90 NA
## 91 NA
## 92 W 50 NA
## 93 B NA
## 94 NA
## 95 L 14 NA
## 96 B NA
## 97 NA
## 98 L 13 NA
## 99 W NA
## 100 NA
## 101 W 51 NA
## 102 B NA
## 103 NA
## 104 W 52 NA
## 105 W NA
## 106 NA
## 107 W 48 NA
## 108 W NA
## 109 NA
## 110 D 28 NA
## 111 B NA
## 112 NA
## 113 W 61 NA
## 114 W NA
## 115 NA
## 116 L 15 NA
## 117 B NA
## 118 NA
## 119 L 24 NA
## 120 W NA
## 121 NA
## 122 L 22 NA
## 123 W NA
## 124 NA
## 125 U NA
## 126 NA
## 127 NA
## 128 W 56 NA
## 129 B NA
## 130 NA
## 131 W 55 NA
## 132 W NA
## 133 NA
## 134 W 59 NA
## 135 W NA
## 136 NA
## 137 W 58 NA
## 138 W NA
## 139 NA
## 140 L 23 NA
## 141 W NA
## 142 NA
## 143 L 25 NA
## 144 W NA
## 145 NA
## 146 L 35 NA
## 147 B NA
## 148 NA
## 149 U NA
## 150 NA
## 151 NA
## 152 L 30 NA
## 153 W NA
## 154 NA
## 155 L 33 NA
## 156 W NA
## 157 NA
## 158 L 34 NA
## 159 B NA
## 160 NA
## 161 U NA
## 162 NA
## 163 NA
## 164 W 64 NA
## 165 W NA
## 166 NA
## 167 L 43 NA
## 168 B NA
## 169 NA
## 170 L 42 NA
## 171 W NA
## 172 NA
## 173 B NA
## 174 NA
## 175 NA
## 176 L 45 NA
## 177 B NA
## 178 NA
## 179 L 44 NA
## 180 B NA
## 181 NA
## 182 U NA
## 183 NA
## 184 NA
## 185 L 37 NA
## 186 B NA
## 187 NA
## 188 U NA
## 189 NA
## 190 NA
## 191 U NA
## 192 NA
## 193 NA
## 194 L 54 NA
## 195 B NA
## 196 NArow_count <- nrow(tournament_data)
row_count## [1] 196tournament_data <- tournament_data[-c(seq(1, row_count, by=3)),]
tournament_data## V1 V2 V3 V4 V5 V6 V7
## 2 Pair Player Name Total Round Round Round Round
## 3 Num USCF ID / Rtg (Pre->Post) Pts 1 2 3 4
## 5 1 GARY HUA 6.0 W 39 W 21 W 18 W 14
## 6 ON 15445895 / R: 1794 ->1817 N:2 W B W B
## 8 2 DAKSHESH DARURI 6.0 W 63 W 58 L 4 W 17
## 9 MI 14598900 / R: 1553 ->1663 N:2 B W B W
## 11 3 ADITYA BAJAJ 6.0 L 8 W 61 W 25 W 21
## 12 MI 14959604 / R: 1384 ->1640 N:2 W B W B
## 14 4 PATRICK H SCHILLING 5.5 W 23 D 28 W 2 W 26
## 15 MI 12616049 / R: 1716 ->1744 N:2 W B W B
## 17 5 HANSHI ZUO 5.5 W 45 W 37 D 12 D 13
## 18 MI 14601533 / R: 1655 ->1690 N:2 B W B W
## 20 6 HANSEN SONG 5.0 W 34 D 29 L 11 W 35
## 21 OH 15055204 / R: 1686 ->1687 N:3 W B W B
## 23 7 GARY DEE SWATHELL 5.0 W 57 W 46 W 13 W 11
## 24 MI 11146376 / R: 1649 ->1673 N:3 W B W B
## 26 8 EZEKIEL HOUGHTON 5.0 W 3 W 32 L 14 L 9
## 27 MI 15142253 / R: 1641P17->1657P24 N:3 B W B W
## 29 9 STEFANO LEE 5.0 W 25 L 18 W 59 W 8
## 30 ON 14954524 / R: 1411 ->1564 N:2 W B W B
## 32 10 ANVIT RAO 5.0 D 16 L 19 W 55 W 31
## 33 MI 14150362 / R: 1365 ->1544 N:3 W W B B
## 35 11 CAMERON WILLIAM MC LEMAN 4.5 D 38 W 56 W 6 L 7
## 36 MI 12581589 / R: 1712 ->1696 N:3 B W B W
## 38 12 KENNETH J TACK 4.5 W 42 W 33 D 5 W 38
## 39 MI 12681257 / R: 1663 ->1670 N:3 W B W B
## 41 13 TORRANCE HENRY JR 4.5 W 36 W 27 L 7 D 5
## 42 MI 15082995 / R: 1666 ->1662 N:3 B W B B
## 44 14 BRADLEY SHAW 4.5 W 54 W 44 W 8 L 1
## 45 MI 10131499 / R: 1610 ->1618 N:3 W B W W
## 47 15 ZACHARY JAMES HOUGHTON 4.5 D 19 L 16 W 30 L 22
## 48 MI 15619130 / R: 1220P13->1416P20 N:3 B B W W
## 50 16 MIKE NIKITIN 4.0 D 10 W 15 H W 39
## 51 MI 10295068 / R: 1604 ->1613 N:3 B W B
## 53 17 RONALD GRZEGORCZYK 4.0 W 48 W 41 L 26 L 2
## 54 MI 10297702 / R: 1629 ->1610 N:3 W B W B
## 56 18 DAVID SUNDEEN 4.0 W 47 W 9 L 1 W 32
## 57 MI 11342094 / R: 1600 ->1600 N:3 B W B W
## 59 19 DIPANKAR ROY 4.0 D 15 W 10 W 52 D 28
## 60 MI 14862333 / R: 1564 ->1570 N:3 W B W B
## 62 20 JASON ZHENG 4.0 L 40 W 49 W 23 W 41
## 63 MI 14529060 / R: 1595 ->1569 N:4 W B W B
## 65 21 DINH DANG BUI 4.0 W 43 L 1 W 47 L 3
## 66 ON 15495066 / R: 1563P22->1562 N:3 B W B W
## 68 22 EUGENE L MCCLURE 4.0 W 64 D 52 L 28 W 15
## 69 MI 12405534 / R: 1555 ->1529 N:4 W B W B
## 71 23 ALAN BUI 4.0 L 4 W 43 L 20 W 58
## 72 ON 15030142 / R: 1363 ->1371 B W B W
## 74 24 MICHAEL R ALDRICH 4.0 L 28 L 47 W 43 L 25
## 75 MI 13469010 / R: 1229 ->1300 N:4 B W B B
## 77 25 LOREN SCHWIEBERT 3.5 L 9 W 53 L 3 W 24
## 78 MI 12486656 / R: 1745 ->1681 N:4 B W B W
## 80 26 MAX ZHU 3.5 W 49 W 40 W 17 L 4
## 81 ON 15131520 / R: 1579 ->1564 N:4 B W B W
## 83 27 GAURAV GIDWANI 3.5 W 51 L 13 W 46 W 37
## 84 MI 14476567 / R: 1552 ->1539 N:4 W B W B
## 86 28 SOFIA ADINA STANESCU-BELLU 3.5 W 24 D 4 W 22 D 19
## 87 MI 14882954 / R: 1507 ->1513 N:3 W W B W
## 89 29 CHIEDOZIE OKORIE 3.5 W 50 D 6 L 38 L 34
## 90 MI 15323285 / R: 1602P6 ->1508P12 N:4 B W B W
## 92 30 GEORGE AVERY JONES 3.5 L 52 D 64 L 15 W 55
## 93 ON 12577178 / R: 1522 ->1444 W B B W
## 95 31 RISHI SHETTY 3.5 L 58 D 55 W 64 L 10
## 96 MI 15131618 / R: 1494 ->1444 B W B W
## 98 32 JOSHUA PHILIP MATHEWS 3.5 W 61 L 8 W 44 L 18
## 99 ON 14073750 / R: 1441 ->1433 N:4 W B W B
## 101 33 JADE GE 3.5 W 60 L 12 W 50 D 36
## 102 MI 14691842 / R: 1449 ->1421 B W B W
## 104 34 MICHAEL JEFFERY THOMAS 3.5 L 6 W 60 L 37 W 29
## 105 MI 15051807 / R: 1399 ->1400 B W B B
## 107 35 JOSHUA DAVID LEE 3.5 L 46 L 38 W 56 L 6
## 108 MI 14601397 / R: 1438 ->1392 W W B W
## 110 36 SIDDHARTH JHA 3.5 L 13 W 57 W 51 D 33
## 111 MI 14773163 / R: 1355 ->1367 N:4 W B W B
## 113 37 AMIYATOSH PWNANANDAM 3.5 B L 5 W 34 L 27
## 114 MI 15489571 / R: 980P12->1077P17 B W W
## 116 38 BRIAN LIU 3.0 D 11 W 35 W 29 L 12
## 117 MI 15108523 / R: 1423 ->1439 N:4 W B W W
## 119 39 JOEL R HENDON 3.0 L 1 W 54 W 40 L 16
## 120 MI 12923035 / R: 1436P23->1413 N:4 B W B W
## 122 40 FOREST ZHANG 3.0 W 20 L 26 L 39 W 59
## 123 MI 14892710 / R: 1348 ->1346 B B W W
## 125 41 KYLE WILLIAM MURPHY 3.0 W 59 L 17 W 58 L 20
## 126 MI 15761443 / R: 1403P5 ->1341P9 B W B W
## 128 42 JARED GE 3.0 L 12 L 50 L 57 D 60
## 129 MI 14462326 / R: 1332 ->1256 B W B B
## 131 43 ROBERT GLEN VASEY 3.0 L 21 L 23 L 24 W 63
## 132 MI 14101068 / R: 1283 ->1244 W B W W
## 134 44 JUSTIN D SCHILLING 3.0 B L 14 L 32 W 53
## 135 MI 15323504 / R: 1199 ->1199 W B B
## 137 45 DEREK YAN 3.0 L 5 L 51 D 60 L 56
## 138 MI 15372807 / R: 1242 ->1191 W B W B
## 140 46 JACOB ALEXANDER LAVALLEY 3.0 W 35 L 7 L 27 L 50
## 141 MI 15490981 / R: 377P3 ->1076P10 B W B W
## 143 47 ERIC WRIGHT 2.5 L 18 W 24 L 21 W 61
## 144 MI 12533115 / R: 1362 ->1341 W B W B
## 146 48 DANIEL KHAIN 2.5 L 17 W 63 H D 52
## 147 MI 14369165 / R: 1382 ->1335 B W B
## 149 49 MICHAEL J MARTIN 2.5 L 26 L 20 D 63 D 64
## 150 MI 12531685 / R: 1291P12->1259P17 W W B W
## 152 50 SHIVAM JHA 2.5 L 29 W 42 L 33 W 46
## 153 MI 14773178 / R: 1056 ->1111 W B W B
## 155 51 TEJAS AYYAGARI 2.5 L 27 W 45 L 36 W 57
## 156 MI 15205474 / R: 1011 ->1097 B W B W
## 158 52 ETHAN GUO 2.5 W 30 D 22 L 19 D 48
## 159 MI 14918803 / R: 935 ->1092 N:4 B W B W
## 161 53 JOSE C YBARRA 2.0 H L 25 H L 44
## 162 MI 12578849 / R: 1393 ->1359 B W
## 164 54 LARRY HODGE 2.0 L 14 L 39 L 61 B
## 165 MI 12836773 / R: 1270 ->1200 B B W
## 167 55 ALEX KONG 2.0 L 62 D 31 L 10 L 30
## 168 MI 15412571 / R: 1186 ->1163 W B W B
## 170 56 MARISA RICCI 2.0 H L 11 L 35 W 45
## 171 MI 14679887 / R: 1153 ->1140 B W W
## 173 57 MICHAEL LU 2.0 L 7 L 36 W 42 L 51
## 174 MI 15113330 / R: 1092 ->1079 B W W B
## 176 58 VIRAJ MOHILE 2.0 W 31 L 2 L 41 L 23
## 177 MI 14700365 / R: 917 -> 941 W B W B
## 179 59 SEAN M MC CORMICK 2.0 L 41 B L 9 L 40
## 180 MI 12841036 / R: 853 -> 878 W B B
## 182 60 JULIA SHEN 1.5 L 33 L 34 D 45 D 42
## 183 MI 14579262 / R: 967 -> 984 W B B W
## 185 61 JEZZEL FARKAS 1.5 L 32 L 3 W 54 L 47
## 186 ON 15771592 / R: 955P11-> 979P18 B W B W
## 188 62 ASHWIN BALAJI 1.0 W 55 U U U
## 189 MI 15219542 / R: 1530 ->1535 B
## 191 63 THOMAS JOSEPH HOSMER 1.0 L 2 L 48 D 49 L 43
## 192 MI 15057092 / R: 1175 ->1125 W B W B
## 194 64 BEN LI 1.0 L 22 D 30 L 31 D 49
## 195 MI 15006561 / R: 1163 ->1112 B W W B
## V8 V9 V10 V11
## 2 Round Round Round NA
## 3 5 6 7 NA
## 5 W 7 D 12 D 4 NA
## 6 W B W NA
## 8 W 16 W 20 W 7 NA
## 9 B W B NA
## 11 W 11 W 13 W 12 NA
## 12 W B W NA
## 14 D 5 W 19 D 1 NA
## 15 W B B NA
## 17 D 4 W 14 W 17 NA
## 18 B W B NA
## 20 D 10 W 27 W 21 NA
## 21 B W B NA
## 23 L 1 W 9 L 2 NA
## 24 B W W NA
## 26 W 47 W 28 W 19 NA
## 27 B W W NA
## 29 W 26 L 7 W 20 NA
## 30 W B B NA
## 32 D 6 W 25 W 18 NA
## 33 W B W NA
## 35 L 3 W 34 W 26 NA
## 36 B W B NA
## 38 H D 1 L 3 NA
## 39 W B NA
## 41 W 33 L 3 W 32 NA
## 42 W W B NA
## 44 D 27 L 5 W 31 NA
## 45 B B W NA
## 47 W 54 W 33 W 38 NA
## 48 B B W NA
## 50 L 2 W 36 U NA
## 51 W B NA
## 53 W 23 W 22 L 5 NA
## 54 W B W NA
## 56 L 19 W 38 L 10 NA
## 57 B W B NA
## 59 W 18 L 4 L 8 NA
## 60 W W B NA
## 62 W 28 L 2 L 9 NA
## 63 W B W NA
## 65 W 40 W 39 L 6 NA
## 66 W B W NA
## 68 H L 17 W 40 NA
## 69 W B NA
## 71 L 17 W 37 W 46 NA
## 72 B W B NA
## 74 W 60 W 44 W 39 NA
## 75 W W B NA
## 77 D 34 L 10 W 47 NA
## 78 B W B NA
## 80 L 9 D 32 L 11 NA
## 81 B W W NA
## 83 D 14 L 6 U NA
## 84 W B NA
## 86 L 20 L 8 D 36 NA
## 87 B B W NA
## 89 W 52 W 48 U NA
## 90 W B NA
## 92 L 31 W 61 W 50 NA
## 93 W B B NA
## 95 W 30 W 50 L 14 NA
## 96 B W B NA
## 98 W 51 D 26 L 13 NA
## 99 W B W NA
## 101 L 13 L 15 W 51 NA
## 102 B W B NA
## 104 D 25 L 11 W 52 NA
## 105 W B W NA
## 107 W 57 D 52 W 48 NA
## 108 B B W NA
## 110 H L 16 D 28 NA
## 111 W B NA
## 113 H L 23 W 61 NA
## 114 B W NA
## 116 H L 18 L 15 NA
## 117 B B NA
## 119 W 44 L 21 L 24 NA
## 120 B W W NA
## 122 L 21 W 56 L 22 NA
## 123 B W W NA
## 125 X U U NA
## 126 NA
## 128 D 61 W 64 W 56 NA
## 129 W W B NA
## 131 W 59 L 46 W 55 NA
## 132 B B W NA
## 134 L 39 L 24 W 59 NA
## 135 W B W NA
## 137 W 63 D 55 W 58 NA
## 138 W B W NA
## 140 W 64 W 43 L 23 NA
## 141 B W W NA
## 143 L 8 D 51 L 25 NA
## 144 W B W NA
## 146 H L 29 L 35 NA
## 147 W B NA
## 149 W 58 H U NA
## 150 B NA
## 152 H L 31 L 30 NA
## 153 B W NA
## 155 L 32 D 47 L 33 NA
## 156 B W W NA
## 158 L 29 D 35 L 34 NA
## 159 B W B NA
## 161 U W 57 U NA
## 162 W NA
## 164 L 15 L 59 W 64 NA
## 165 W B W NA
## 167 B D 45 L 43 NA
## 168 W B NA
## 170 H L 40 L 42 NA
## 171 B W NA
## 173 L 35 L 53 B NA
## 174 W B NA
## 176 L 49 B L 45 NA
## 177 W B NA
## 179 L 43 W 54 L 44 NA
## 180 W W B NA
## 182 L 24 H U NA
## 183 B NA
## 185 D 42 L 30 L 37 NA
## 186 B W B NA
## 188 U U U NA
## 189 NA
## 191 L 45 H U NA
## 192 B NA
## 194 L 46 L 42 L 54 NA
## 195 W B B NAtournament_data <- tournament_data[-c(seq(1, 2, by=1)),]
tournament_data## V1 V2 V3 V4 V5 V6 V7
## 5 1 GARY HUA 6.0 W 39 W 21 W 18 W 14
## 6 ON 15445895 / R: 1794 ->1817 N:2 W B W B
## 8 2 DAKSHESH DARURI 6.0 W 63 W 58 L 4 W 17
## 9 MI 14598900 / R: 1553 ->1663 N:2 B W B W
## 11 3 ADITYA BAJAJ 6.0 L 8 W 61 W 25 W 21
## 12 MI 14959604 / R: 1384 ->1640 N:2 W B W B
## 14 4 PATRICK H SCHILLING 5.5 W 23 D 28 W 2 W 26
## 15 MI 12616049 / R: 1716 ->1744 N:2 W B W B
## 17 5 HANSHI ZUO 5.5 W 45 W 37 D 12 D 13
## 18 MI 14601533 / R: 1655 ->1690 N:2 B W B W
## 20 6 HANSEN SONG 5.0 W 34 D 29 L 11 W 35
## 21 OH 15055204 / R: 1686 ->1687 N:3 W B W B
## 23 7 GARY DEE SWATHELL 5.0 W 57 W 46 W 13 W 11
## 24 MI 11146376 / R: 1649 ->1673 N:3 W B W B
## 26 8 EZEKIEL HOUGHTON 5.0 W 3 W 32 L 14 L 9
## 27 MI 15142253 / R: 1641P17->1657P24 N:3 B W B W
## 29 9 STEFANO LEE 5.0 W 25 L 18 W 59 W 8
## 30 ON 14954524 / R: 1411 ->1564 N:2 W B W B
## 32 10 ANVIT RAO 5.0 D 16 L 19 W 55 W 31
## 33 MI 14150362 / R: 1365 ->1544 N:3 W W B B
## 35 11 CAMERON WILLIAM MC LEMAN 4.5 D 38 W 56 W 6 L 7
## 36 MI 12581589 / R: 1712 ->1696 N:3 B W B W
## 38 12 KENNETH J TACK 4.5 W 42 W 33 D 5 W 38
## 39 MI 12681257 / R: 1663 ->1670 N:3 W B W B
## 41 13 TORRANCE HENRY JR 4.5 W 36 W 27 L 7 D 5
## 42 MI 15082995 / R: 1666 ->1662 N:3 B W B B
## 44 14 BRADLEY SHAW 4.5 W 54 W 44 W 8 L 1
## 45 MI 10131499 / R: 1610 ->1618 N:3 W B W W
## 47 15 ZACHARY JAMES HOUGHTON 4.5 D 19 L 16 W 30 L 22
## 48 MI 15619130 / R: 1220P13->1416P20 N:3 B B W W
## 50 16 MIKE NIKITIN 4.0 D 10 W 15 H W 39
## 51 MI 10295068 / R: 1604 ->1613 N:3 B W B
## 53 17 RONALD GRZEGORCZYK 4.0 W 48 W 41 L 26 L 2
## 54 MI 10297702 / R: 1629 ->1610 N:3 W B W B
## 56 18 DAVID SUNDEEN 4.0 W 47 W 9 L 1 W 32
## 57 MI 11342094 / R: 1600 ->1600 N:3 B W B W
## 59 19 DIPANKAR ROY 4.0 D 15 W 10 W 52 D 28
## 60 MI 14862333 / R: 1564 ->1570 N:3 W B W B
## 62 20 JASON ZHENG 4.0 L 40 W 49 W 23 W 41
## 63 MI 14529060 / R: 1595 ->1569 N:4 W B W B
## 65 21 DINH DANG BUI 4.0 W 43 L 1 W 47 L 3
## 66 ON 15495066 / R: 1563P22->1562 N:3 B W B W
## 68 22 EUGENE L MCCLURE 4.0 W 64 D 52 L 28 W 15
## 69 MI 12405534 / R: 1555 ->1529 N:4 W B W B
## 71 23 ALAN BUI 4.0 L 4 W 43 L 20 W 58
## 72 ON 15030142 / R: 1363 ->1371 B W B W
## 74 24 MICHAEL R ALDRICH 4.0 L 28 L 47 W 43 L 25
## 75 MI 13469010 / R: 1229 ->1300 N:4 B W B B
## 77 25 LOREN SCHWIEBERT 3.5 L 9 W 53 L 3 W 24
## 78 MI 12486656 / R: 1745 ->1681 N:4 B W B W
## 80 26 MAX ZHU 3.5 W 49 W 40 W 17 L 4
## 81 ON 15131520 / R: 1579 ->1564 N:4 B W B W
## 83 27 GAURAV GIDWANI 3.5 W 51 L 13 W 46 W 37
## 84 MI 14476567 / R: 1552 ->1539 N:4 W B W B
## 86 28 SOFIA ADINA STANESCU-BELLU 3.5 W 24 D 4 W 22 D 19
## 87 MI 14882954 / R: 1507 ->1513 N:3 W W B W
## 89 29 CHIEDOZIE OKORIE 3.5 W 50 D 6 L 38 L 34
## 90 MI 15323285 / R: 1602P6 ->1508P12 N:4 B W B W
## 92 30 GEORGE AVERY JONES 3.5 L 52 D 64 L 15 W 55
## 93 ON 12577178 / R: 1522 ->1444 W B B W
## 95 31 RISHI SHETTY 3.5 L 58 D 55 W 64 L 10
## 96 MI 15131618 / R: 1494 ->1444 B W B W
## 98 32 JOSHUA PHILIP MATHEWS 3.5 W 61 L 8 W 44 L 18
## 99 ON 14073750 / R: 1441 ->1433 N:4 W B W B
## 101 33 JADE GE 3.5 W 60 L 12 W 50 D 36
## 102 MI 14691842 / R: 1449 ->1421 B W B W
## 104 34 MICHAEL JEFFERY THOMAS 3.5 L 6 W 60 L 37 W 29
## 105 MI 15051807 / R: 1399 ->1400 B W B B
## 107 35 JOSHUA DAVID LEE 3.5 L 46 L 38 W 56 L 6
## 108 MI 14601397 / R: 1438 ->1392 W W B W
## 110 36 SIDDHARTH JHA 3.5 L 13 W 57 W 51 D 33
## 111 MI 14773163 / R: 1355 ->1367 N:4 W B W B
## 113 37 AMIYATOSH PWNANANDAM 3.5 B L 5 W 34 L 27
## 114 MI 15489571 / R: 980P12->1077P17 B W W
## 116 38 BRIAN LIU 3.0 D 11 W 35 W 29 L 12
## 117 MI 15108523 / R: 1423 ->1439 N:4 W B W W
## 119 39 JOEL R HENDON 3.0 L 1 W 54 W 40 L 16
## 120 MI 12923035 / R: 1436P23->1413 N:4 B W B W
## 122 40 FOREST ZHANG 3.0 W 20 L 26 L 39 W 59
## 123 MI 14892710 / R: 1348 ->1346 B B W W
## 125 41 KYLE WILLIAM MURPHY 3.0 W 59 L 17 W 58 L 20
## 126 MI 15761443 / R: 1403P5 ->1341P9 B W B W
## 128 42 JARED GE 3.0 L 12 L 50 L 57 D 60
## 129 MI 14462326 / R: 1332 ->1256 B W B B
## 131 43 ROBERT GLEN VASEY 3.0 L 21 L 23 L 24 W 63
## 132 MI 14101068 / R: 1283 ->1244 W B W W
## 134 44 JUSTIN D SCHILLING 3.0 B L 14 L 32 W 53
## 135 MI 15323504 / R: 1199 ->1199 W B B
## 137 45 DEREK YAN 3.0 L 5 L 51 D 60 L 56
## 138 MI 15372807 / R: 1242 ->1191 W B W B
## 140 46 JACOB ALEXANDER LAVALLEY 3.0 W 35 L 7 L 27 L 50
## 141 MI 15490981 / R: 377P3 ->1076P10 B W B W
## 143 47 ERIC WRIGHT 2.5 L 18 W 24 L 21 W 61
## 144 MI 12533115 / R: 1362 ->1341 W B W B
## 146 48 DANIEL KHAIN 2.5 L 17 W 63 H D 52
## 147 MI 14369165 / R: 1382 ->1335 B W B
## 149 49 MICHAEL J MARTIN 2.5 L 26 L 20 D 63 D 64
## 150 MI 12531685 / R: 1291P12->1259P17 W W B W
## 152 50 SHIVAM JHA 2.5 L 29 W 42 L 33 W 46
## 153 MI 14773178 / R: 1056 ->1111 W B W B
## 155 51 TEJAS AYYAGARI 2.5 L 27 W 45 L 36 W 57
## 156 MI 15205474 / R: 1011 ->1097 B W B W
## 158 52 ETHAN GUO 2.5 W 30 D 22 L 19 D 48
## 159 MI 14918803 / R: 935 ->1092 N:4 B W B W
## 161 53 JOSE C YBARRA 2.0 H L 25 H L 44
## 162 MI 12578849 / R: 1393 ->1359 B W
## 164 54 LARRY HODGE 2.0 L 14 L 39 L 61 B
## 165 MI 12836773 / R: 1270 ->1200 B B W
## 167 55 ALEX KONG 2.0 L 62 D 31 L 10 L 30
## 168 MI 15412571 / R: 1186 ->1163 W B W B
## 170 56 MARISA RICCI 2.0 H L 11 L 35 W 45
## 171 MI 14679887 / R: 1153 ->1140 B W W
## 173 57 MICHAEL LU 2.0 L 7 L 36 W 42 L 51
## 174 MI 15113330 / R: 1092 ->1079 B W W B
## 176 58 VIRAJ MOHILE 2.0 W 31 L 2 L 41 L 23
## 177 MI 14700365 / R: 917 -> 941 W B W B
## 179 59 SEAN M MC CORMICK 2.0 L 41 B L 9 L 40
## 180 MI 12841036 / R: 853 -> 878 W B B
## 182 60 JULIA SHEN 1.5 L 33 L 34 D 45 D 42
## 183 MI 14579262 / R: 967 -> 984 W B B W
## 185 61 JEZZEL FARKAS 1.5 L 32 L 3 W 54 L 47
## 186 ON 15771592 / R: 955P11-> 979P18 B W B W
## 188 62 ASHWIN BALAJI 1.0 W 55 U U U
## 189 MI 15219542 / R: 1530 ->1535 B
## 191 63 THOMAS JOSEPH HOSMER 1.0 L 2 L 48 D 49 L 43
## 192 MI 15057092 / R: 1175 ->1125 W B W B
## 194 64 BEN LI 1.0 L 22 D 30 L 31 D 49
## 195 MI 15006561 / R: 1163 ->1112 B W W B
## V8 V9 V10 V11
## 5 W 7 D 12 D 4 NA
## 6 W B W NA
## 8 W 16 W 20 W 7 NA
## 9 B W B NA
## 11 W 11 W 13 W 12 NA
## 12 W B W NA
## 14 D 5 W 19 D 1 NA
## 15 W B B NA
## 17 D 4 W 14 W 17 NA
## 18 B W B NA
## 20 D 10 W 27 W 21 NA
## 21 B W B NA
## 23 L 1 W 9 L 2 NA
## 24 B W W NA
## 26 W 47 W 28 W 19 NA
## 27 B W W NA
## 29 W 26 L 7 W 20 NA
## 30 W B B NA
## 32 D 6 W 25 W 18 NA
## 33 W B W NA
## 35 L 3 W 34 W 26 NA
## 36 B W B NA
## 38 H D 1 L 3 NA
## 39 W B NA
## 41 W 33 L 3 W 32 NA
## 42 W W B NA
## 44 D 27 L 5 W 31 NA
## 45 B B W NA
## 47 W 54 W 33 W 38 NA
## 48 B B W NA
## 50 L 2 W 36 U NA
## 51 W B NA
## 53 W 23 W 22 L 5 NA
## 54 W B W NA
## 56 L 19 W 38 L 10 NA
## 57 B W B NA
## 59 W 18 L 4 L 8 NA
## 60 W W B NA
## 62 W 28 L 2 L 9 NA
## 63 W B W NA
## 65 W 40 W 39 L 6 NA
## 66 W B W NA
## 68 H L 17 W 40 NA
## 69 W B NA
## 71 L 17 W 37 W 46 NA
## 72 B W B NA
## 74 W 60 W 44 W 39 NA
## 75 W W B NA
## 77 D 34 L 10 W 47 NA
## 78 B W B NA
## 80 L 9 D 32 L 11 NA
## 81 B W W NA
## 83 D 14 L 6 U NA
## 84 W B NA
## 86 L 20 L 8 D 36 NA
## 87 B B W NA
## 89 W 52 W 48 U NA
## 90 W B NA
## 92 L 31 W 61 W 50 NA
## 93 W B B NA
## 95 W 30 W 50 L 14 NA
## 96 B W B NA
## 98 W 51 D 26 L 13 NA
## 99 W B W NA
## 101 L 13 L 15 W 51 NA
## 102 B W B NA
## 104 D 25 L 11 W 52 NA
## 105 W B W NA
## 107 W 57 D 52 W 48 NA
## 108 B B W NA
## 110 H L 16 D 28 NA
## 111 W B NA
## 113 H L 23 W 61 NA
## 114 B W NA
## 116 H L 18 L 15 NA
## 117 B B NA
## 119 W 44 L 21 L 24 NA
## 120 B W W NA
## 122 L 21 W 56 L 22 NA
## 123 B W W NA
## 125 X U U NA
## 126 NA
## 128 D 61 W 64 W 56 NA
## 129 W W B NA
## 131 W 59 L 46 W 55 NA
## 132 B B W NA
## 134 L 39 L 24 W 59 NA
## 135 W B W NA
## 137 W 63 D 55 W 58 NA
## 138 W B W NA
## 140 W 64 W 43 L 23 NA
## 141 B W W NA
## 143 L 8 D 51 L 25 NA
## 144 W B W NA
## 146 H L 29 L 35 NA
## 147 W B NA
## 149 W 58 H U NA
## 150 B NA
## 152 H L 31 L 30 NA
## 153 B W NA
## 155 L 32 D 47 L 33 NA
## 156 B W W NA
## 158 L 29 D 35 L 34 NA
## 159 B W B NA
## 161 U W 57 U NA
## 162 W NA
## 164 L 15 L 59 W 64 NA
## 165 W B W NA
## 167 B D 45 L 43 NA
## 168 W B NA
## 170 H L 40 L 42 NA
## 171 B W NA
## 173 L 35 L 53 B NA
## 174 W B NA
## 176 L 49 B L 45 NA
## 177 W B NA
## 179 L 43 W 54 L 44 NA
## 180 W W B NA
## 182 L 24 H U NA
## 183 B NA
## 185 D 42 L 30 L 37 NA
## 186 B W B NA
## 188 U U U NA
## 189 NA
## 191 L 45 H U NA
## 192 B NA
## 194 L 46 L 42 L 54 NA
## 195 W B B NA# First filtering only states and score rows from the dataset
temp_data <- tournament_data[-c(seq(1, row_count, by=2)),]
temp_data## V1 V2 V3 V4 V5 V6 V7
## 6 ON 15445895 / R: 1794 ->1817 N:2 W B W B
## 9 MI 14598900 / R: 1553 ->1663 N:2 B W B W
## 12 MI 14959604 / R: 1384 ->1640 N:2 W B W B
## 15 MI 12616049 / R: 1716 ->1744 N:2 W B W B
## 18 MI 14601533 / R: 1655 ->1690 N:2 B W B W
## 21 OH 15055204 / R: 1686 ->1687 N:3 W B W B
## 24 MI 11146376 / R: 1649 ->1673 N:3 W B W B
## 27 MI 15142253 / R: 1641P17->1657P24 N:3 B W B W
## 30 ON 14954524 / R: 1411 ->1564 N:2 W B W B
## 33 MI 14150362 / R: 1365 ->1544 N:3 W W B B
## 36 MI 12581589 / R: 1712 ->1696 N:3 B W B W
## 39 MI 12681257 / R: 1663 ->1670 N:3 W B W B
## 42 MI 15082995 / R: 1666 ->1662 N:3 B W B B
## 45 MI 10131499 / R: 1610 ->1618 N:3 W B W W
## 48 MI 15619130 / R: 1220P13->1416P20 N:3 B B W W
## 51 MI 10295068 / R: 1604 ->1613 N:3 B W B
## 54 MI 10297702 / R: 1629 ->1610 N:3 W B W B
## 57 MI 11342094 / R: 1600 ->1600 N:3 B W B W
## 60 MI 14862333 / R: 1564 ->1570 N:3 W B W B
## 63 MI 14529060 / R: 1595 ->1569 N:4 W B W B
## 66 ON 15495066 / R: 1563P22->1562 N:3 B W B W
## 69 MI 12405534 / R: 1555 ->1529 N:4 W B W B
## 72 ON 15030142 / R: 1363 ->1371 B W B W
## 75 MI 13469010 / R: 1229 ->1300 N:4 B W B B
## 78 MI 12486656 / R: 1745 ->1681 N:4 B W B W
## 81 ON 15131520 / R: 1579 ->1564 N:4 B W B W
## 84 MI 14476567 / R: 1552 ->1539 N:4 W B W B
## 87 MI 14882954 / R: 1507 ->1513 N:3 W W B W
## 90 MI 15323285 / R: 1602P6 ->1508P12 N:4 B W B W
## 93 ON 12577178 / R: 1522 ->1444 W B B W
## 96 MI 15131618 / R: 1494 ->1444 B W B W
## 99 ON 14073750 / R: 1441 ->1433 N:4 W B W B
## 102 MI 14691842 / R: 1449 ->1421 B W B W
## 105 MI 15051807 / R: 1399 ->1400 B W B B
## 108 MI 14601397 / R: 1438 ->1392 W W B W
## 111 MI 14773163 / R: 1355 ->1367 N:4 W B W B
## 114 MI 15489571 / R: 980P12->1077P17 B W W
## 117 MI 15108523 / R: 1423 ->1439 N:4 W B W W
## 120 MI 12923035 / R: 1436P23->1413 N:4 B W B W
## 123 MI 14892710 / R: 1348 ->1346 B B W W
## 126 MI 15761443 / R: 1403P5 ->1341P9 B W B W
## 129 MI 14462326 / R: 1332 ->1256 B W B B
## 132 MI 14101068 / R: 1283 ->1244 W B W W
## 135 MI 15323504 / R: 1199 ->1199 W B B
## 138 MI 15372807 / R: 1242 ->1191 W B W B
## 141 MI 15490981 / R: 377P3 ->1076P10 B W B W
## 144 MI 12533115 / R: 1362 ->1341 W B W B
## 147 MI 14369165 / R: 1382 ->1335 B W B
## 150 MI 12531685 / R: 1291P12->1259P17 W W B W
## 153 MI 14773178 / R: 1056 ->1111 W B W B
## 156 MI 15205474 / R: 1011 ->1097 B W B W
## 159 MI 14918803 / R: 935 ->1092 N:4 B W B W
## 162 MI 12578849 / R: 1393 ->1359 B W
## 165 MI 12836773 / R: 1270 ->1200 B B W
## 168 MI 15412571 / R: 1186 ->1163 W B W B
## 171 MI 14679887 / R: 1153 ->1140 B W W
## 174 MI 15113330 / R: 1092 ->1079 B W W B
## 177 MI 14700365 / R: 917 -> 941 W B W B
## 180 MI 12841036 / R: 853 -> 878 W B B
## 183 MI 14579262 / R: 967 -> 984 W B B W
## 186 ON 15771592 / R: 955P11-> 979P18 B W B W
## 189 MI 15219542 / R: 1530 ->1535 B
## 192 MI 15057092 / R: 1175 ->1125 W B W B
## 195 MI 15006561 / R: 1163 ->1112 B W W B
## V8 V9 V10 V11
## 6 W B W NA
## 9 B W B NA
## 12 W B W NA
## 15 W B B NA
## 18 B W B NA
## 21 B W B NA
## 24 B W W NA
## 27 B W W NA
## 30 W B B NA
## 33 W B W NA
## 36 B W B NA
## 39 W B NA
## 42 W W B NA
## 45 B B W NA
## 48 B B W NA
## 51 W B NA
## 54 W B W NA
## 57 B W B NA
## 60 W W B NA
## 63 W B W NA
## 66 W B W NA
## 69 W B NA
## 72 B W B NA
## 75 W W B NA
## 78 B W B NA
## 81 B W W NA
## 84 W B NA
## 87 B B W NA
## 90 W B NA
## 93 W B B NA
## 96 B W B NA
## 99 W B W NA
## 102 B W B NA
## 105 W B W NA
## 108 B B W NA
## 111 W B NA
## 114 B W NA
## 117 B B NA
## 120 B W W NA
## 123 B W W NA
## 126 NA
## 129 W W B NA
## 132 B B W NA
## 135 W B W NA
## 138 W B W NA
## 141 B W W NA
## 144 W B W NA
## 147 W B NA
## 150 B NA
## 153 B W NA
## 156 B W W NA
## 159 B W B NA
## 162 W NA
## 165 W B W NA
## 168 W B NA
## 171 B W NA
## 174 W B NA
## 177 W B NA
## 180 W W B NA
## 183 B NA
## 186 B W B NA
## 189 NA
## 192 B NA
## 195 W B B NAtemp_data$V1 <- trimws(temp_data$V1, which = c("both"))
temp_data$V2 <- trimws(temp_data$V2, which = c("both"))
temp_data## V1 V2 V3 V4 V5 V6 V7 V8 V9
## 6 ON 15445895 / R: 1794 ->1817 N:2 W B W B W B
## 9 MI 14598900 / R: 1553 ->1663 N:2 B W B W B W
## 12 MI 14959604 / R: 1384 ->1640 N:2 W B W B W B
## 15 MI 12616049 / R: 1716 ->1744 N:2 W B W B W B
## 18 MI 14601533 / R: 1655 ->1690 N:2 B W B W B W
## 21 OH 15055204 / R: 1686 ->1687 N:3 W B W B B W
## 24 MI 11146376 / R: 1649 ->1673 N:3 W B W B B W
## 27 MI 15142253 / R: 1641P17->1657P24 N:3 B W B W B W
## 30 ON 14954524 / R: 1411 ->1564 N:2 W B W B W B
## 33 MI 14150362 / R: 1365 ->1544 N:3 W W B B W B
## 36 MI 12581589 / R: 1712 ->1696 N:3 B W B W B W
## 39 MI 12681257 / R: 1663 ->1670 N:3 W B W B W
## 42 MI 15082995 / R: 1666 ->1662 N:3 B W B B W W
## 45 MI 10131499 / R: 1610 ->1618 N:3 W B W W B B
## 48 MI 15619130 / R: 1220P13->1416P20 N:3 B B W W B B
## 51 MI 10295068 / R: 1604 ->1613 N:3 B W B W B
## 54 MI 10297702 / R: 1629 ->1610 N:3 W B W B W B
## 57 MI 11342094 / R: 1600 ->1600 N:3 B W B W B W
## 60 MI 14862333 / R: 1564 ->1570 N:3 W B W B W W
## 63 MI 14529060 / R: 1595 ->1569 N:4 W B W B W B
## 66 ON 15495066 / R: 1563P22->1562 N:3 B W B W W B
## 69 MI 12405534 / R: 1555 ->1529 N:4 W B W B W
## 72 ON 15030142 / R: 1363 ->1371 B W B W B W
## 75 MI 13469010 / R: 1229 ->1300 N:4 B W B B W W
## 78 MI 12486656 / R: 1745 ->1681 N:4 B W B W B W
## 81 ON 15131520 / R: 1579 ->1564 N:4 B W B W B W
## 84 MI 14476567 / R: 1552 ->1539 N:4 W B W B W B
## 87 MI 14882954 / R: 1507 ->1513 N:3 W W B W B B
## 90 MI 15323285 / R: 1602P6 ->1508P12 N:4 B W B W W B
## 93 ON 12577178 / R: 1522 ->1444 W B B W W B
## 96 MI 15131618 / R: 1494 ->1444 B W B W B W
## 99 ON 14073750 / R: 1441 ->1433 N:4 W B W B W B
## 102 MI 14691842 / R: 1449 ->1421 B W B W B W
## 105 MI 15051807 / R: 1399 ->1400 B W B B W B
## 108 MI 14601397 / R: 1438 ->1392 W W B W B B
## 111 MI 14773163 / R: 1355 ->1367 N:4 W B W B W
## 114 MI 15489571 / R: 980P12->1077P17 B W W B
## 117 MI 15108523 / R: 1423 ->1439 N:4 W B W W B
## 120 MI 12923035 / R: 1436P23->1413 N:4 B W B W B W
## 123 MI 14892710 / R: 1348 ->1346 B B W W B W
## 126 MI 15761443 / R: 1403P5 ->1341P9 B W B W
## 129 MI 14462326 / R: 1332 ->1256 B W B B W W
## 132 MI 14101068 / R: 1283 ->1244 W B W W B B
## 135 MI 15323504 / R: 1199 ->1199 W B B W B
## 138 MI 15372807 / R: 1242 ->1191 W B W B W B
## 141 MI 15490981 / R: 377P3 ->1076P10 B W B W B W
## 144 MI 12533115 / R: 1362 ->1341 W B W B W B
## 147 MI 14369165 / R: 1382 ->1335 B W B W
## 150 MI 12531685 / R: 1291P12->1259P17 W W B W B
## 153 MI 14773178 / R: 1056 ->1111 W B W B B
## 156 MI 15205474 / R: 1011 ->1097 B W B W B W
## 159 MI 14918803 / R: 935 ->1092 N:4 B W B W B W
## 162 MI 12578849 / R: 1393 ->1359 B W W
## 165 MI 12836773 / R: 1270 ->1200 B B W W B
## 168 MI 15412571 / R: 1186 ->1163 W B W B W
## 171 MI 14679887 / R: 1153 ->1140 B W W B
## 174 MI 15113330 / R: 1092 ->1079 B W W B W B
## 177 MI 14700365 / R: 917 -> 941 W B W B W
## 180 MI 12841036 / R: 853 -> 878 W B B W W
## 183 MI 14579262 / R: 967 -> 984 W B B W B
## 186 ON 15771592 / R: 955P11-> 979P18 B W B W B W
## 189 MI 15219542 / R: 1530 ->1535 B
## 192 MI 15057092 / R: 1175 ->1125 W B W B B
## 195 MI 15006561 / R: 1163 ->1112 B W W B W B
## V10 V11
## 6 W NA
## 9 B NA
## 12 W NA
## 15 B NA
## 18 B NA
## 21 B NA
## 24 W NA
## 27 W NA
## 30 B NA
## 33 W NA
## 36 B NA
## 39 B NA
## 42 B NA
## 45 W NA
## 48 W NA
## 51 NA
## 54 W NA
## 57 B NA
## 60 B NA
## 63 W NA
## 66 W NA
## 69 B NA
## 72 B NA
## 75 B NA
## 78 B NA
## 81 W NA
## 84 NA
## 87 W NA
## 90 NA
## 93 B NA
## 96 B NA
## 99 W NA
## 102 B NA
## 105 W NA
## 108 W NA
## 111 B NA
## 114 W NA
## 117 B NA
## 120 W NA
## 123 W NA
## 126 NA
## 129 B NA
## 132 W NA
## 135 W NA
## 138 W NA
## 141 W NA
## 144 W NA
## 147 B NA
## 150 NA
## 153 W NA
## 156 W NA
## 159 B NA
## 162 NA
## 165 W NA
## 168 B NA
## 171 W NA
## 174 NA
## 177 B NA
## 180 B NA
## 183 NA
## 186 B NA
## 189 NA
## 192 NA
## 195 B NAstate_vector <- temp_data$V1
score_vector <- temp_data$V2
state_vector## [1] "ON" "MI" "MI" "MI" "MI" "OH" "MI" "MI" "ON" "MI" "MI" "MI" "MI" "MI" "MI"
## [16] "MI" "MI" "MI" "MI" "MI" "ON" "MI" "ON" "MI" "MI" "ON" "MI" "MI" "MI" "ON"
## [31] "MI" "ON" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI"
## [46] "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI"
## [61] "ON" "MI" "MI" "MI"length(state_vector)## [1] 64score_vector## [1] "15445895 / R: 1794 ->1817" "14598900 / R: 1553 ->1663"
## [3] "14959604 / R: 1384 ->1640" "12616049 / R: 1716 ->1744"
## [5] "14601533 / R: 1655 ->1690" "15055204 / R: 1686 ->1687"
## [7] "11146376 / R: 1649 ->1673" "15142253 / R: 1641P17->1657P24"
## [9] "14954524 / R: 1411 ->1564" "14150362 / R: 1365 ->1544"
## [11] "12581589 / R: 1712 ->1696" "12681257 / R: 1663 ->1670"
## [13] "15082995 / R: 1666 ->1662" "10131499 / R: 1610 ->1618"
## [15] "15619130 / R: 1220P13->1416P20" "10295068 / R: 1604 ->1613"
## [17] "10297702 / R: 1629 ->1610" "11342094 / R: 1600 ->1600"
## [19] "14862333 / R: 1564 ->1570" "14529060 / R: 1595 ->1569"
## [21] "15495066 / R: 1563P22->1562" "12405534 / R: 1555 ->1529"
## [23] "15030142 / R: 1363 ->1371" "13469010 / R: 1229 ->1300"
## [25] "12486656 / R: 1745 ->1681" "15131520 / R: 1579 ->1564"
## [27] "14476567 / R: 1552 ->1539" "14882954 / R: 1507 ->1513"
## [29] "15323285 / R: 1602P6 ->1508P12" "12577178 / R: 1522 ->1444"
## [31] "15131618 / R: 1494 ->1444" "14073750 / R: 1441 ->1433"
## [33] "14691842 / R: 1449 ->1421" "15051807 / R: 1399 ->1400"
## [35] "14601397 / R: 1438 ->1392" "14773163 / R: 1355 ->1367"
## [37] "15489571 / R: 980P12->1077P17" "15108523 / R: 1423 ->1439"
## [39] "12923035 / R: 1436P23->1413" "14892710 / R: 1348 ->1346"
## [41] "15761443 / R: 1403P5 ->1341P9" "14462326 / R: 1332 ->1256"
## [43] "14101068 / R: 1283 ->1244" "15323504 / R: 1199 ->1199"
## [45] "15372807 / R: 1242 ->1191" "15490981 / R: 377P3 ->1076P10"
## [47] "12533115 / R: 1362 ->1341" "14369165 / R: 1382 ->1335"
## [49] "12531685 / R: 1291P12->1259P17" "14773178 / R: 1056 ->1111"
## [51] "15205474 / R: 1011 ->1097" "14918803 / R: 935 ->1092"
## [53] "12578849 / R: 1393 ->1359" "12836773 / R: 1270 ->1200"
## [55] "15412571 / R: 1186 ->1163" "14679887 / R: 1153 ->1140"
## [57] "15113330 / R: 1092 ->1079" "14700365 / R: 917 -> 941"
## [59] "12841036 / R: 853 -> 878" "14579262 / R: 967 -> 984"
## [61] "15771592 / R: 955P11-> 979P18" "15219542 / R: 1530 ->1535"
## [63] "15057092 / R: 1175 ->1125" "15006561 / R: 1163 ->1112"str_locate( "15445895 / R: 1794", "1794")## start end
## [1,] 15 18score_vector <- str_sub(score_vector, (str_locate( "15445895 / R: 1794", "1794")))
score_vector## [1] "1794" "1553" "1384" "1716" "1655" "1686" "1649" "1641" "1411" "1365"
## [11] "1712" "1663" "1666" "1610" "1220" "1604" "1629" "1600" "1564" "1595"
## [21] "1563" "1555" "1363" "1229" "1745" "1579" "1552" "1507" "1602" "1522"
## [31] "1494" "1441" "1449" "1399" "1438" "1355" " 980" "1423" "1436" "1348"
## [41] "1403" "1332" "1283" "1199" "1242" " 377" "1362" "1382" "1291" "1056"
## [51] "1011" " 935" "1393" "1270" "1186" "1153" "1092" " 917" " 853" " 967"
## [61] " 955" "1530" "1175" "1163"state_vector <- trimws(state_vector, which = c("both"))
score_vector <- as.numeric(trimws(score_vector, which = c("both")))
score_vector## [1] 1794 1553 1384 1716 1655 1686 1649 1641 1411 1365 1712 1663 1666 1610 1220
## [16] 1604 1629 1600 1564 1595 1563 1555 1363 1229 1745 1579 1552 1507 1602 1522
## [31] 1494 1441 1449 1399 1438 1355 980 1423 1436 1348 1403 1332 1283 1199 1242
## [46] 377 1362 1382 1291 1056 1011 935 1393 1270 1186 1153 1092 917 853 967
## [61] 955 1530 1175 1163# Now fetching only names and score rows in the dataset
temp_data <- tournament_data[-c(seq(2, row_count, by=2)),]
temp_data## V1 V2 V3 V4 V5 V6 V7
## 5 1 GARY HUA 6.0 W 39 W 21 W 18 W 14
## 8 2 DAKSHESH DARURI 6.0 W 63 W 58 L 4 W 17
## 11 3 ADITYA BAJAJ 6.0 L 8 W 61 W 25 W 21
## 14 4 PATRICK H SCHILLING 5.5 W 23 D 28 W 2 W 26
## 17 5 HANSHI ZUO 5.5 W 45 W 37 D 12 D 13
## 20 6 HANSEN SONG 5.0 W 34 D 29 L 11 W 35
## 23 7 GARY DEE SWATHELL 5.0 W 57 W 46 W 13 W 11
## 26 8 EZEKIEL HOUGHTON 5.0 W 3 W 32 L 14 L 9
## 29 9 STEFANO LEE 5.0 W 25 L 18 W 59 W 8
## 32 10 ANVIT RAO 5.0 D 16 L 19 W 55 W 31
## 35 11 CAMERON WILLIAM MC LEMAN 4.5 D 38 W 56 W 6 L 7
## 38 12 KENNETH J TACK 4.5 W 42 W 33 D 5 W 38
## 41 13 TORRANCE HENRY JR 4.5 W 36 W 27 L 7 D 5
## 44 14 BRADLEY SHAW 4.5 W 54 W 44 W 8 L 1
## 47 15 ZACHARY JAMES HOUGHTON 4.5 D 19 L 16 W 30 L 22
## 50 16 MIKE NIKITIN 4.0 D 10 W 15 H W 39
## 53 17 RONALD GRZEGORCZYK 4.0 W 48 W 41 L 26 L 2
## 56 18 DAVID SUNDEEN 4.0 W 47 W 9 L 1 W 32
## 59 19 DIPANKAR ROY 4.0 D 15 W 10 W 52 D 28
## 62 20 JASON ZHENG 4.0 L 40 W 49 W 23 W 41
## 65 21 DINH DANG BUI 4.0 W 43 L 1 W 47 L 3
## 68 22 EUGENE L MCCLURE 4.0 W 64 D 52 L 28 W 15
## 71 23 ALAN BUI 4.0 L 4 W 43 L 20 W 58
## 74 24 MICHAEL R ALDRICH 4.0 L 28 L 47 W 43 L 25
## 77 25 LOREN SCHWIEBERT 3.5 L 9 W 53 L 3 W 24
## 80 26 MAX ZHU 3.5 W 49 W 40 W 17 L 4
## 83 27 GAURAV GIDWANI 3.5 W 51 L 13 W 46 W 37
## 86 28 SOFIA ADINA STANESCU-BELLU 3.5 W 24 D 4 W 22 D 19
## 89 29 CHIEDOZIE OKORIE 3.5 W 50 D 6 L 38 L 34
## 92 30 GEORGE AVERY JONES 3.5 L 52 D 64 L 15 W 55
## 95 31 RISHI SHETTY 3.5 L 58 D 55 W 64 L 10
## 98 32 JOSHUA PHILIP MATHEWS 3.5 W 61 L 8 W 44 L 18
## 101 33 JADE GE 3.5 W 60 L 12 W 50 D 36
## 104 34 MICHAEL JEFFERY THOMAS 3.5 L 6 W 60 L 37 W 29
## 107 35 JOSHUA DAVID LEE 3.5 L 46 L 38 W 56 L 6
## 110 36 SIDDHARTH JHA 3.5 L 13 W 57 W 51 D 33
## 113 37 AMIYATOSH PWNANANDAM 3.5 B L 5 W 34 L 27
## 116 38 BRIAN LIU 3.0 D 11 W 35 W 29 L 12
## 119 39 JOEL R HENDON 3.0 L 1 W 54 W 40 L 16
## 122 40 FOREST ZHANG 3.0 W 20 L 26 L 39 W 59
## 125 41 KYLE WILLIAM MURPHY 3.0 W 59 L 17 W 58 L 20
## 128 42 JARED GE 3.0 L 12 L 50 L 57 D 60
## 131 43 ROBERT GLEN VASEY 3.0 L 21 L 23 L 24 W 63
## 134 44 JUSTIN D SCHILLING 3.0 B L 14 L 32 W 53
## 137 45 DEREK YAN 3.0 L 5 L 51 D 60 L 56
## 140 46 JACOB ALEXANDER LAVALLEY 3.0 W 35 L 7 L 27 L 50
## 143 47 ERIC WRIGHT 2.5 L 18 W 24 L 21 W 61
## 146 48 DANIEL KHAIN 2.5 L 17 W 63 H D 52
## 149 49 MICHAEL J MARTIN 2.5 L 26 L 20 D 63 D 64
## 152 50 SHIVAM JHA 2.5 L 29 W 42 L 33 W 46
## 155 51 TEJAS AYYAGARI 2.5 L 27 W 45 L 36 W 57
## 158 52 ETHAN GUO 2.5 W 30 D 22 L 19 D 48
## 161 53 JOSE C YBARRA 2.0 H L 25 H L 44
## 164 54 LARRY HODGE 2.0 L 14 L 39 L 61 B
## 167 55 ALEX KONG 2.0 L 62 D 31 L 10 L 30
## 170 56 MARISA RICCI 2.0 H L 11 L 35 W 45
## 173 57 MICHAEL LU 2.0 L 7 L 36 W 42 L 51
## 176 58 VIRAJ MOHILE 2.0 W 31 L 2 L 41 L 23
## 179 59 SEAN M MC CORMICK 2.0 L 41 B L 9 L 40
## 182 60 JULIA SHEN 1.5 L 33 L 34 D 45 D 42
## 185 61 JEZZEL FARKAS 1.5 L 32 L 3 W 54 L 47
## 188 62 ASHWIN BALAJI 1.0 W 55 U U U
## 191 63 THOMAS JOSEPH HOSMER 1.0 L 2 L 48 D 49 L 43
## 194 64 BEN LI 1.0 L 22 D 30 L 31 D 49
## V8 V9 V10 V11
## 5 W 7 D 12 D 4 NA
## 8 W 16 W 20 W 7 NA
## 11 W 11 W 13 W 12 NA
## 14 D 5 W 19 D 1 NA
## 17 D 4 W 14 W 17 NA
## 20 D 10 W 27 W 21 NA
## 23 L 1 W 9 L 2 NA
## 26 W 47 W 28 W 19 NA
## 29 W 26 L 7 W 20 NA
## 32 D 6 W 25 W 18 NA
## 35 L 3 W 34 W 26 NA
## 38 H D 1 L 3 NA
## 41 W 33 L 3 W 32 NA
## 44 D 27 L 5 W 31 NA
## 47 W 54 W 33 W 38 NA
## 50 L 2 W 36 U NA
## 53 W 23 W 22 L 5 NA
## 56 L 19 W 38 L 10 NA
## 59 W 18 L 4 L 8 NA
## 62 W 28 L 2 L 9 NA
## 65 W 40 W 39 L 6 NA
## 68 H L 17 W 40 NA
## 71 L 17 W 37 W 46 NA
## 74 W 60 W 44 W 39 NA
## 77 D 34 L 10 W 47 NA
## 80 L 9 D 32 L 11 NA
## 83 D 14 L 6 U NA
## 86 L 20 L 8 D 36 NA
## 89 W 52 W 48 U NA
## 92 L 31 W 61 W 50 NA
## 95 W 30 W 50 L 14 NA
## 98 W 51 D 26 L 13 NA
## 101 L 13 L 15 W 51 NA
## 104 D 25 L 11 W 52 NA
## 107 W 57 D 52 W 48 NA
## 110 H L 16 D 28 NA
## 113 H L 23 W 61 NA
## 116 H L 18 L 15 NA
## 119 W 44 L 21 L 24 NA
## 122 L 21 W 56 L 22 NA
## 125 X U U NA
## 128 D 61 W 64 W 56 NA
## 131 W 59 L 46 W 55 NA
## 134 L 39 L 24 W 59 NA
## 137 W 63 D 55 W 58 NA
## 140 W 64 W 43 L 23 NA
## 143 L 8 D 51 L 25 NA
## 146 H L 29 L 35 NA
## 149 W 58 H U NA
## 152 H L 31 L 30 NA
## 155 L 32 D 47 L 33 NA
## 158 L 29 D 35 L 34 NA
## 161 U W 57 U NA
## 164 L 15 L 59 W 64 NA
## 167 B D 45 L 43 NA
## 170 H L 40 L 42 NA
## 173 L 35 L 53 B NA
## 176 L 49 B L 45 NA
## 179 L 43 W 54 L 44 NA
## 182 L 24 H U NA
## 185 D 42 L 30 L 37 NA
## 188 U U U NA
## 191 L 45 H U NA
## 194 L 46 L 42 L 54 NAnumber_vector <- temp_data$V1
name_vector <- temp_data$V2
points_vector <- temp_data$V3
name_vector## [1] " GARY HUA " " DAKSHESH DARURI "
## [3] " ADITYA BAJAJ " " PATRICK H SCHILLING "
## [5] " HANSHI ZUO " " HANSEN SONG "
## [7] " GARY DEE SWATHELL " " EZEKIEL HOUGHTON "
## [9] " STEFANO LEE " " ANVIT RAO "
## [11] " CAMERON WILLIAM MC LEMAN " " KENNETH J TACK "
## [13] " TORRANCE HENRY JR " " BRADLEY SHAW "
## [15] " ZACHARY JAMES HOUGHTON " " MIKE NIKITIN "
## [17] " RONALD GRZEGORCZYK " " DAVID SUNDEEN "
## [19] " DIPANKAR ROY " " JASON ZHENG "
## [21] " DINH DANG BUI " " EUGENE L MCCLURE "
## [23] " ALAN BUI " " MICHAEL R ALDRICH "
## [25] " LOREN SCHWIEBERT " " MAX ZHU "
## [27] " GAURAV GIDWANI " " SOFIA ADINA STANESCU-BELLU "
## [29] " CHIEDOZIE OKORIE " " GEORGE AVERY JONES "
## [31] " RISHI SHETTY " " JOSHUA PHILIP MATHEWS "
## [33] " JADE GE " " MICHAEL JEFFERY THOMAS "
## [35] " JOSHUA DAVID LEE " " SIDDHARTH JHA "
## [37] " AMIYATOSH PWNANANDAM " " BRIAN LIU "
## [39] " JOEL R HENDON " " FOREST ZHANG "
## [41] " KYLE WILLIAM MURPHY " " JARED GE "
## [43] " ROBERT GLEN VASEY " " JUSTIN D SCHILLING "
## [45] " DEREK YAN " " JACOB ALEXANDER LAVALLEY "
## [47] " ERIC WRIGHT " " DANIEL KHAIN "
## [49] " MICHAEL J MARTIN " " SHIVAM JHA "
## [51] " TEJAS AYYAGARI " " ETHAN GUO "
## [53] " JOSE C YBARRA " " LARRY HODGE "
## [55] " ALEX KONG " " MARISA RICCI "
## [57] " MICHAEL LU " " VIRAJ MOHILE "
## [59] " SEAN M MC CORMICK " " JULIA SHEN "
## [61] " JEZZEL FARKAS " " ASHWIN BALAJI "
## [63] " THOMAS JOSEPH HOSMER " " BEN LI "number_vector <- as.numeric(trimws(number_vector, which = c("both")))
name_vector <- trimws(name_vector, which = c("both"))
points_vector <- as.numeric(trimws(points_vector, which = c("both")))
name_vector## [1] "GARY HUA" "DAKSHESH DARURI"
## [3] "ADITYA BAJAJ" "PATRICK H SCHILLING"
## [5] "HANSHI ZUO" "HANSEN SONG"
## [7] "GARY DEE SWATHELL" "EZEKIEL HOUGHTON"
## [9] "STEFANO LEE" "ANVIT RAO"
## [11] "CAMERON WILLIAM MC LEMAN" "KENNETH J TACK"
## [13] "TORRANCE HENRY JR" "BRADLEY SHAW"
## [15] "ZACHARY JAMES HOUGHTON" "MIKE NIKITIN"
## [17] "RONALD GRZEGORCZYK" "DAVID SUNDEEN"
## [19] "DIPANKAR ROY" "JASON ZHENG"
## [21] "DINH DANG BUI" "EUGENE L MCCLURE"
## [23] "ALAN BUI" "MICHAEL R ALDRICH"
## [25] "LOREN SCHWIEBERT" "MAX ZHU"
## [27] "GAURAV GIDWANI" "SOFIA ADINA STANESCU-BELLU"
## [29] "CHIEDOZIE OKORIE" "GEORGE AVERY JONES"
## [31] "RISHI SHETTY" "JOSHUA PHILIP MATHEWS"
## [33] "JADE GE" "MICHAEL JEFFERY THOMAS"
## [35] "JOSHUA DAVID LEE" "SIDDHARTH JHA"
## [37] "AMIYATOSH PWNANANDAM" "BRIAN LIU"
## [39] "JOEL R HENDON" "FOREST ZHANG"
## [41] "KYLE WILLIAM MURPHY" "JARED GE"
## [43] "ROBERT GLEN VASEY" "JUSTIN D SCHILLING"
## [45] "DEREK YAN" "JACOB ALEXANDER LAVALLEY"
## [47] "ERIC WRIGHT" "DANIEL KHAIN"
## [49] "MICHAEL J MARTIN" "SHIVAM JHA"
## [51] "TEJAS AYYAGARI" "ETHAN GUO"
## [53] "JOSE C YBARRA" "LARRY HODGE"
## [55] "ALEX KONG" "MARISA RICCI"
## [57] "MICHAEL LU" "VIRAJ MOHILE"
## [59] "SEAN M MC CORMICK" "JULIA SHEN"
## [61] "JEZZEL FARKAS" "ASHWIN BALAJI"
## [63] "THOMAS JOSEPH HOSMER" "BEN LI"new_df = data.frame(player_number = number_vector,
player_name= name_vector,
player_state= state_vector,
player_points = points_vector,
player_score = score_vector
)
new_df## player_number player_name player_state player_points
## 1 1 GARY HUA ON 6.0
## 2 2 DAKSHESH DARURI MI 6.0
## 3 3 ADITYA BAJAJ MI 6.0
## 4 4 PATRICK H SCHILLING MI 5.5
## 5 5 HANSHI ZUO MI 5.5
## 6 6 HANSEN SONG OH 5.0
## 7 7 GARY DEE SWATHELL MI 5.0
## 8 8 EZEKIEL HOUGHTON MI 5.0
## 9 9 STEFANO LEE ON 5.0
## 10 10 ANVIT RAO MI 5.0
## 11 11 CAMERON WILLIAM MC LEMAN MI 4.5
## 12 12 KENNETH J TACK MI 4.5
## 13 13 TORRANCE HENRY JR MI 4.5
## 14 14 BRADLEY SHAW MI 4.5
## 15 15 ZACHARY JAMES HOUGHTON MI 4.5
## 16 16 MIKE NIKITIN MI 4.0
## 17 17 RONALD GRZEGORCZYK MI 4.0
## 18 18 DAVID SUNDEEN MI 4.0
## 19 19 DIPANKAR ROY MI 4.0
## 20 20 JASON ZHENG MI 4.0
## 21 21 DINH DANG BUI ON 4.0
## 22 22 EUGENE L MCCLURE MI 4.0
## 23 23 ALAN BUI ON 4.0
## 24 24 MICHAEL R ALDRICH MI 4.0
## 25 25 LOREN SCHWIEBERT MI 3.5
## 26 26 MAX ZHU ON 3.5
## 27 27 GAURAV GIDWANI MI 3.5
## 28 28 SOFIA ADINA STANESCU-BELLU MI 3.5
## 29 29 CHIEDOZIE OKORIE MI 3.5
## 30 30 GEORGE AVERY JONES ON 3.5
## 31 31 RISHI SHETTY MI 3.5
## 32 32 JOSHUA PHILIP MATHEWS ON 3.5
## 33 33 JADE GE MI 3.5
## 34 34 MICHAEL JEFFERY THOMAS MI 3.5
## 35 35 JOSHUA DAVID LEE MI 3.5
## 36 36 SIDDHARTH JHA MI 3.5
## 37 37 AMIYATOSH PWNANANDAM MI 3.5
## 38 38 BRIAN LIU MI 3.0
## 39 39 JOEL R HENDON MI 3.0
## 40 40 FOREST ZHANG MI 3.0
## 41 41 KYLE WILLIAM MURPHY MI 3.0
## 42 42 JARED GE MI 3.0
## 43 43 ROBERT GLEN VASEY MI 3.0
## 44 44 JUSTIN D SCHILLING MI 3.0
## 45 45 DEREK YAN MI 3.0
## 46 46 JACOB ALEXANDER LAVALLEY MI 3.0
## 47 47 ERIC WRIGHT MI 2.5
## 48 48 DANIEL KHAIN MI 2.5
## 49 49 MICHAEL J MARTIN MI 2.5
## 50 50 SHIVAM JHA MI 2.5
## 51 51 TEJAS AYYAGARI MI 2.5
## 52 52 ETHAN GUO MI 2.5
## 53 53 JOSE C YBARRA MI 2.0
## 54 54 LARRY HODGE MI 2.0
## 55 55 ALEX KONG MI 2.0
## 56 56 MARISA RICCI MI 2.0
## 57 57 MICHAEL LU MI 2.0
## 58 58 VIRAJ MOHILE MI 2.0
## 59 59 SEAN M MC CORMICK MI 2.0
## 60 60 JULIA SHEN MI 1.5
## 61 61 JEZZEL FARKAS ON 1.5
## 62 62 ASHWIN BALAJI MI 1.0
## 63 63 THOMAS JOSEPH HOSMER MI 1.0
## 64 64 BEN LI MI 1.0
## player_score
## 1 1794
## 2 1553
## 3 1384
## 4 1716
## 5 1655
## 6 1686
## 7 1649
## 8 1641
## 9 1411
## 10 1365
## 11 1712
## 12 1663
## 13 1666
## 14 1610
## 15 1220
## 16 1604
## 17 1629
## 18 1600
## 19 1564
## 20 1595
## 21 1563
## 22 1555
## 23 1363
## 24 1229
## 25 1745
## 26 1579
## 27 1552
## 28 1507
## 29 1602
## 30 1522
## 31 1494
## 32 1441
## 33 1449
## 34 1399
## 35 1438
## 36 1355
## 37 980
## 38 1423
## 39 1436
## 40 1348
## 41 1403
## 42 1332
## 43 1283
## 44 1199
## 45 1242
## 46 377
## 47 1362
## 48 1382
## 49 1291
## 50 1056
## 51 1011
## 52 935
## 53 1393
## 54 1270
## 55 1186
## 56 1153
## 57 1092
## 58 917
## 59 853
## 60 967
## 61 955
## 62 1530
## 63 1175
## 64 1163temp_data <- select(temp_data, V4:V10)
temp_data## V4 V5 V6 V7 V8 V9 V10
## 5 W 39 W 21 W 18 W 14 W 7 D 12 D 4
## 8 W 63 W 58 L 4 W 17 W 16 W 20 W 7
## 11 L 8 W 61 W 25 W 21 W 11 W 13 W 12
## 14 W 23 D 28 W 2 W 26 D 5 W 19 D 1
## 17 W 45 W 37 D 12 D 13 D 4 W 14 W 17
## 20 W 34 D 29 L 11 W 35 D 10 W 27 W 21
## 23 W 57 W 46 W 13 W 11 L 1 W 9 L 2
## 26 W 3 W 32 L 14 L 9 W 47 W 28 W 19
## 29 W 25 L 18 W 59 W 8 W 26 L 7 W 20
## 32 D 16 L 19 W 55 W 31 D 6 W 25 W 18
## 35 D 38 W 56 W 6 L 7 L 3 W 34 W 26
## 38 W 42 W 33 D 5 W 38 H D 1 L 3
## 41 W 36 W 27 L 7 D 5 W 33 L 3 W 32
## 44 W 54 W 44 W 8 L 1 D 27 L 5 W 31
## 47 D 19 L 16 W 30 L 22 W 54 W 33 W 38
## 50 D 10 W 15 H W 39 L 2 W 36 U
## 53 W 48 W 41 L 26 L 2 W 23 W 22 L 5
## 56 W 47 W 9 L 1 W 32 L 19 W 38 L 10
## 59 D 15 W 10 W 52 D 28 W 18 L 4 L 8
## 62 L 40 W 49 W 23 W 41 W 28 L 2 L 9
## 65 W 43 L 1 W 47 L 3 W 40 W 39 L 6
## 68 W 64 D 52 L 28 W 15 H L 17 W 40
## 71 L 4 W 43 L 20 W 58 L 17 W 37 W 46
## 74 L 28 L 47 W 43 L 25 W 60 W 44 W 39
## 77 L 9 W 53 L 3 W 24 D 34 L 10 W 47
## 80 W 49 W 40 W 17 L 4 L 9 D 32 L 11
## 83 W 51 L 13 W 46 W 37 D 14 L 6 U
## 86 W 24 D 4 W 22 D 19 L 20 L 8 D 36
## 89 W 50 D 6 L 38 L 34 W 52 W 48 U
## 92 L 52 D 64 L 15 W 55 L 31 W 61 W 50
## 95 L 58 D 55 W 64 L 10 W 30 W 50 L 14
## 98 W 61 L 8 W 44 L 18 W 51 D 26 L 13
## 101 W 60 L 12 W 50 D 36 L 13 L 15 W 51
## 104 L 6 W 60 L 37 W 29 D 25 L 11 W 52
## 107 L 46 L 38 W 56 L 6 W 57 D 52 W 48
## 110 L 13 W 57 W 51 D 33 H L 16 D 28
## 113 B L 5 W 34 L 27 H L 23 W 61
## 116 D 11 W 35 W 29 L 12 H L 18 L 15
## 119 L 1 W 54 W 40 L 16 W 44 L 21 L 24
## 122 W 20 L 26 L 39 W 59 L 21 W 56 L 22
## 125 W 59 L 17 W 58 L 20 X U U
## 128 L 12 L 50 L 57 D 60 D 61 W 64 W 56
## 131 L 21 L 23 L 24 W 63 W 59 L 46 W 55
## 134 B L 14 L 32 W 53 L 39 L 24 W 59
## 137 L 5 L 51 D 60 L 56 W 63 D 55 W 58
## 140 W 35 L 7 L 27 L 50 W 64 W 43 L 23
## 143 L 18 W 24 L 21 W 61 L 8 D 51 L 25
## 146 L 17 W 63 H D 52 H L 29 L 35
## 149 L 26 L 20 D 63 D 64 W 58 H U
## 152 L 29 W 42 L 33 W 46 H L 31 L 30
## 155 L 27 W 45 L 36 W 57 L 32 D 47 L 33
## 158 W 30 D 22 L 19 D 48 L 29 D 35 L 34
## 161 H L 25 H L 44 U W 57 U
## 164 L 14 L 39 L 61 B L 15 L 59 W 64
## 167 L 62 D 31 L 10 L 30 B D 45 L 43
## 170 H L 11 L 35 W 45 H L 40 L 42
## 173 L 7 L 36 W 42 L 51 L 35 L 53 B
## 176 W 31 L 2 L 41 L 23 L 49 B L 45
## 179 L 41 B L 9 L 40 L 43 W 54 L 44
## 182 L 33 L 34 D 45 D 42 L 24 H U
## 185 L 32 L 3 W 54 L 47 D 42 L 30 L 37
## 188 W 55 U U U U U U
## 191 L 2 L 48 D 49 L 43 L 45 H U
## 194 L 22 D 30 L 31 D 49 L 46 L 42 L 54# Declaring a new empty vector
opponent_pre_rating_average <- vector(mode="numeric")
#Now fetch these specific opponent's rows and perform manipulation
for (i in (1:nrow(temp_data))){
opponent_vector <- temp_data[i, ]
opponent_vector <- str_extract(opponent_vector, "\\d+")
opponent_vector <- as.numeric(trimws(opponent_vector, which = c("both")))
# Replacing NA with 0 in a vector
opponent_vector[is.na(opponent_vector)] <- 0
# Saving opponent's score rows into a dataframe
opponent_df <- new_df[opponent_vector,]
#opponent_df
# Fetching the opponents score into a vector and calculating pre rating average
opponent_score_vector <- as.numeric(opponent_df$player_score)
opponent_average <- sum(opponent_score_vector)/length(opponent_score_vector)
#opponent_average
# Saving the average into a vector
opponent_pre_rating_average[i] <- opponent_average
#opponent_pre_rating_average[1]
#Now fetch these specific opponent's rows and perform manipulation
}
#opponent_vectoropponent_pre_rating_average## [1] 1605.286 1469.286 1563.571 1573.571 1500.857 1518.714 1372.143 1468.429
## [9] 1523.143 1554.143 1467.571 1506.167 1497.857 1515.000 1483.857 1385.800
## [17] 1498.571 1480.000 1426.286 1410.857 1470.429 1300.333 1213.857 1357.000
## [25] 1363.286 1506.857 1221.667 1522.143 1313.500 1144.143 1259.857 1378.714
## [33] 1276.857 1375.286 1149.714 1388.167 1384.800 1539.167 1429.571 1390.571
## [41] 1248.500 1149.857 1106.571 1327.000 1152.000 1357.714 1392.000 1355.800
## [49] 1285.800 1296.000 1356.143 1494.571 1345.333 1206.167 1406.000 1414.400
## [57] 1363.000 1391.000 1319.000 1330.200 1327.286 1186.000 1350.200 1263.000new_df$pre_rating_average <- round(opponent_pre_rating_average, 2)
new_df## player_number player_name player_state player_points
## 1 1 GARY HUA ON 6.0
## 2 2 DAKSHESH DARURI MI 6.0
## 3 3 ADITYA BAJAJ MI 6.0
## 4 4 PATRICK H SCHILLING MI 5.5
## 5 5 HANSHI ZUO MI 5.5
## 6 6 HANSEN SONG OH 5.0
## 7 7 GARY DEE SWATHELL MI 5.0
## 8 8 EZEKIEL HOUGHTON MI 5.0
## 9 9 STEFANO LEE ON 5.0
## 10 10 ANVIT RAO MI 5.0
## 11 11 CAMERON WILLIAM MC LEMAN MI 4.5
## 12 12 KENNETH J TACK MI 4.5
## 13 13 TORRANCE HENRY JR MI 4.5
## 14 14 BRADLEY SHAW MI 4.5
## 15 15 ZACHARY JAMES HOUGHTON MI 4.5
## 16 16 MIKE NIKITIN MI 4.0
## 17 17 RONALD GRZEGORCZYK MI 4.0
## 18 18 DAVID SUNDEEN MI 4.0
## 19 19 DIPANKAR ROY MI 4.0
## 20 20 JASON ZHENG MI 4.0
## 21 21 DINH DANG BUI ON 4.0
## 22 22 EUGENE L MCCLURE MI 4.0
## 23 23 ALAN BUI ON 4.0
## 24 24 MICHAEL R ALDRICH MI 4.0
## 25 25 LOREN SCHWIEBERT MI 3.5
## 26 26 MAX ZHU ON 3.5
## 27 27 GAURAV GIDWANI MI 3.5
## 28 28 SOFIA ADINA STANESCU-BELLU MI 3.5
## 29 29 CHIEDOZIE OKORIE MI 3.5
## 30 30 GEORGE AVERY JONES ON 3.5
## 31 31 RISHI SHETTY MI 3.5
## 32 32 JOSHUA PHILIP MATHEWS ON 3.5
## 33 33 JADE GE MI 3.5
## 34 34 MICHAEL JEFFERY THOMAS MI 3.5
## 35 35 JOSHUA DAVID LEE MI 3.5
## 36 36 SIDDHARTH JHA MI 3.5
## 37 37 AMIYATOSH PWNANANDAM MI 3.5
## 38 38 BRIAN LIU MI 3.0
## 39 39 JOEL R HENDON MI 3.0
## 40 40 FOREST ZHANG MI 3.0
## 41 41 KYLE WILLIAM MURPHY MI 3.0
## 42 42 JARED GE MI 3.0
## 43 43 ROBERT GLEN VASEY MI 3.0
## 44 44 JUSTIN D SCHILLING MI 3.0
## 45 45 DEREK YAN MI 3.0
## 46 46 JACOB ALEXANDER LAVALLEY MI 3.0
## 47 47 ERIC WRIGHT MI 2.5
## 48 48 DANIEL KHAIN MI 2.5
## 49 49 MICHAEL J MARTIN MI 2.5
## 50 50 SHIVAM JHA MI 2.5
## 51 51 TEJAS AYYAGARI MI 2.5
## 52 52 ETHAN GUO MI 2.5
## 53 53 JOSE C YBARRA MI 2.0
## 54 54 LARRY HODGE MI 2.0
## 55 55 ALEX KONG MI 2.0
## 56 56 MARISA RICCI MI 2.0
## 57 57 MICHAEL LU MI 2.0
## 58 58 VIRAJ MOHILE MI 2.0
## 59 59 SEAN M MC CORMICK MI 2.0
## 60 60 JULIA SHEN MI 1.5
## 61 61 JEZZEL FARKAS ON 1.5
## 62 62 ASHWIN BALAJI MI 1.0
## 63 63 THOMAS JOSEPH HOSMER MI 1.0
## 64 64 BEN LI MI 1.0
## player_score pre_rating_average
## 1 1794 1605.29
## 2 1553 1469.29
## 3 1384 1563.57
## 4 1716 1573.57
## 5 1655 1500.86
## 6 1686 1518.71
## 7 1649 1372.14
## 8 1641 1468.43
## 9 1411 1523.14
## 10 1365 1554.14
## 11 1712 1467.57
## 12 1663 1506.17
## 13 1666 1497.86
## 14 1610 1515.00
## 15 1220 1483.86
## 16 1604 1385.80
## 17 1629 1498.57
## 18 1600 1480.00
## 19 1564 1426.29
## 20 1595 1410.86
## 21 1563 1470.43
## 22 1555 1300.33
## 23 1363 1213.86
## 24 1229 1357.00
## 25 1745 1363.29
## 26 1579 1506.86
## 27 1552 1221.67
## 28 1507 1522.14
## 29 1602 1313.50
## 30 1522 1144.14
## 31 1494 1259.86
## 32 1441 1378.71
## 33 1449 1276.86
## 34 1399 1375.29
## 35 1438 1149.71
## 36 1355 1388.17
## 37 980 1384.80
## 38 1423 1539.17
## 39 1436 1429.57
## 40 1348 1390.57
## 41 1403 1248.50
## 42 1332 1149.86
## 43 1283 1106.57
## 44 1199 1327.00
## 45 1242 1152.00
## 46 377 1357.71
## 47 1362 1392.00
## 48 1382 1355.80
## 49 1291 1285.80
## 50 1056 1296.00
## 51 1011 1356.14
## 52 935 1494.57
## 53 1393 1345.33
## 54 1270 1206.17
## 55 1186 1406.00
## 56 1153 1414.40
## 57 1092 1363.00
## 58 917 1391.00
## 59 853 1319.00
## 60 967 1330.20
## 61 955 1327.29
## 62 1530 1186.00
## 63 1175 1350.20
## 64 1163 1263.00# Get working directory path
path <- getwd()
path## [1] "C:/Users/Uzma/CUNY_SPS_PROJECTS/Data_607_Project_1"# Export file as csv to working directory.
write.csv(new_df, file.path(path, "chess_cleaned_data.csv"))