In this project, you’re given a text file with chess tournament results where the information has some structure. Your job is to create an R Markdown file that generates a .CSV file (that could for example be imported into a SQL database) with the following information for all of the players:Player’s Name, Player’s State, Total Number of Points, Player’s Pre-Rating, and Average Pre Chess Rating of Opponents
library (stringr)
library (readr)
library (dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary (tidyr)
library (knitr)Read Raw text data into
TournamentRaw = readLines ("E:/tournamentinfo.txt")
#print(TournamentRaw[1:15],)Since there are seperators as ———, we used replace function to replace - into empty space
TournamentRaw2 = str_replace_all(TournamentRaw ,"-","")
#print(TournamentRaw2[1:15],)TournamentRaw3 = read.delim(textConnection(TournamentRaw2),header = F, sep = "|", stringsAsFactors = F)
print (TournamentRaw3)## V1 V2 V3 V4 V5 V6 V7
## 1 Pair Player Name Total Round Round Round Round
## 2 Num USCF ID / Rtg (Pre>Post) Pts 1 2 3 4
## 3 1 GARY HUA 6.0 W 39 W 21 W 18 W 14
## 4 ON 15445895 / R: 1794 >1817 N:2 W B W B
## 5 2 DAKSHESH DARURI 6.0 W 63 W 58 L 4 W 17
## 6 MI 14598900 / R: 1553 >1663 N:2 B W B W
## 7 3 ADITYA BAJAJ 6.0 L 8 W 61 W 25 W 21
## 8 MI 14959604 / R: 1384 >1640 N:2 W B W B
## 9 4 PATRICK H SCHILLING 5.5 W 23 D 28 W 2 W 26
## 10 MI 12616049 / R: 1716 >1744 N:2 W B W B
## 11 5 HANSHI ZUO 5.5 W 45 W 37 D 12 D 13
## 12 MI 14601533 / R: 1655 >1690 N:2 B W B W
## 13 6 HANSEN SONG 5.0 W 34 D 29 L 11 W 35
## 14 OH 15055204 / R: 1686 >1687 N:3 W B W B
## 15 7 GARY DEE SWATHELL 5.0 W 57 W 46 W 13 W 11
## 16 MI 11146376 / R: 1649 >1673 N:3 W B W B
## 17 8 EZEKIEL HOUGHTON 5.0 W 3 W 32 L 14 L 9
## 18 MI 15142253 / R: 1641P17>1657P24 N:3 B W B W
## 19 9 STEFANO LEE 5.0 W 25 L 18 W 59 W 8
## 20 ON 14954524 / R: 1411 >1564 N:2 W B W B
## 21 10 ANVIT RAO 5.0 D 16 L 19 W 55 W 31
## 22 MI 14150362 / R: 1365 >1544 N:3 W W B B
## 23 11 CAMERON WILLIAM MC LEMAN 4.5 D 38 W 56 W 6 L 7
## 24 MI 12581589 / R: 1712 >1696 N:3 B W B W
## 25 12 KENNETH J TACK 4.5 W 42 W 33 D 5 W 38
## 26 MI 12681257 / R: 1663 >1670 N:3 W B W B
## 27 13 TORRANCE HENRY JR 4.5 W 36 W 27 L 7 D 5
## 28 MI 15082995 / R: 1666 >1662 N:3 B W B B
## 29 14 BRADLEY SHAW 4.5 W 54 W 44 W 8 L 1
## 30 MI 10131499 / R: 1610 >1618 N:3 W B W W
## 31 15 ZACHARY JAMES HOUGHTON 4.5 D 19 L 16 W 30 L 22
## 32 MI 15619130 / R: 1220P13>1416P20 N:3 B B W W
## 33 16 MIKE NIKITIN 4.0 D 10 W 15 H W 39
## 34 MI 10295068 / R: 1604 >1613 N:3 B W B
## 35 17 RONALD GRZEGORCZYK 4.0 W 48 W 41 L 26 L 2
## 36 MI 10297702 / R: 1629 >1610 N:3 W B W B
## 37 18 DAVID SUNDEEN 4.0 W 47 W 9 L 1 W 32
## 38 MI 11342094 / R: 1600 >1600 N:3 B W B W
## 39 19 DIPANKAR ROY 4.0 D 15 W 10 W 52 D 28
## 40 MI 14862333 / R: 1564 >1570 N:3 W B W B
## 41 20 JASON ZHENG 4.0 L 40 W 49 W 23 W 41
## 42 MI 14529060 / R: 1595 >1569 N:4 W B W B
## 43 21 DINH DANG BUI 4.0 W 43 L 1 W 47 L 3
## 44 ON 15495066 / R: 1563P22>1562 N:3 B W B W
## 45 22 EUGENE L MCCLURE 4.0 W 64 D 52 L 28 W 15
## 46 MI 12405534 / R: 1555 >1529 N:4 W B W B
## 47 23 ALAN BUI 4.0 L 4 W 43 L 20 W 58
## 48 ON 15030142 / R: 1363 >1371 B W B W
## 49 24 MICHAEL R ALDRICH 4.0 L 28 L 47 W 43 L 25
## 50 MI 13469010 / R: 1229 >1300 N:4 B W B B
## 51 25 LOREN SCHWIEBERT 3.5 L 9 W 53 L 3 W 24
## 52 MI 12486656 / R: 1745 >1681 N:4 B W B W
## 53 26 MAX ZHU 3.5 W 49 W 40 W 17 L 4
## 54 ON 15131520 / R: 1579 >1564 N:4 B W B W
## 55 27 GAURAV GIDWANI 3.5 W 51 L 13 W 46 W 37
## 56 MI 14476567 / R: 1552 >1539 N:4 W B W B
## 57 28 SOFIA ADINA STANESCUBELLU 3.5 W 24 D 4 W 22 D 19
## 58 MI 14882954 / R: 1507 >1513 N:3 W W B W
## 59 29 CHIEDOZIE OKORIE 3.5 W 50 D 6 L 38 L 34
## 60 MI 15323285 / R: 1602P6 >1508P12 N:4 B W B W
## 61 30 GEORGE AVERY JONES 3.5 L 52 D 64 L 15 W 55
## 62 ON 12577178 / R: 1522 >1444 W B B W
## 63 31 RISHI SHETTY 3.5 L 58 D 55 W 64 L 10
## 64 MI 15131618 / R: 1494 >1444 B W B W
## 65 32 JOSHUA PHILIP MATHEWS 3.5 W 61 L 8 W 44 L 18
## 66 ON 14073750 / R: 1441 >1433 N:4 W B W B
## 67 33 JADE GE 3.5 W 60 L 12 W 50 D 36
## 68 MI 14691842 / R: 1449 >1421 B W B W
## 69 34 MICHAEL JEFFERY THOMAS 3.5 L 6 W 60 L 37 W 29
## 70 MI 15051807 / R: 1399 >1400 B W B B
## 71 35 JOSHUA DAVID LEE 3.5 L 46 L 38 W 56 L 6
## 72 MI 14601397 / R: 1438 >1392 W W B W
## 73 36 SIDDHARTH JHA 3.5 L 13 W 57 W 51 D 33
## 74 MI 14773163 / R: 1355 >1367 N:4 W B W B
## 75 37 AMIYATOSH PWNANANDAM 3.5 B L 5 W 34 L 27
## 76 MI 15489571 / R: 980P12>1077P17 B W W
## 77 38 BRIAN LIU 3.0 D 11 W 35 W 29 L 12
## 78 MI 15108523 / R: 1423 >1439 N:4 W B W W
## 79 39 JOEL R HENDON 3.0 L 1 W 54 W 40 L 16
## 80 MI 12923035 / R: 1436P23>1413 N:4 B W B W
## 81 40 FOREST ZHANG 3.0 W 20 L 26 L 39 W 59
## 82 MI 14892710 / R: 1348 >1346 B B W W
## 83 41 KYLE WILLIAM MURPHY 3.0 W 59 L 17 W 58 L 20
## 84 MI 15761443 / R: 1403P5 >1341P9 B W B W
## 85 42 JARED GE 3.0 L 12 L 50 L 57 D 60
## 86 MI 14462326 / R: 1332 >1256 B W B B
## 87 43 ROBERT GLEN VASEY 3.0 L 21 L 23 L 24 W 63
## 88 MI 14101068 / R: 1283 >1244 W B W W
## 89 44 JUSTIN D SCHILLING 3.0 B L 14 L 32 W 53
## 90 MI 15323504 / R: 1199 >1199 W B B
## 91 45 DEREK YAN 3.0 L 5 L 51 D 60 L 56
## 92 MI 15372807 / R: 1242 >1191 W B W B
## 93 46 JACOB ALEXANDER LAVALLEY 3.0 W 35 L 7 L 27 L 50
## 94 MI 15490981 / R: 377P3 >1076P10 B W B W
## 95 47 ERIC WRIGHT 2.5 L 18 W 24 L 21 W 61
## 96 MI 12533115 / R: 1362 >1341 W B W B
## 97 48 DANIEL KHAIN 2.5 L 17 W 63 H D 52
## 98 MI 14369165 / R: 1382 >1335 B W B
## 99 49 MICHAEL J MARTIN 2.5 L 26 L 20 D 63 D 64
## 100 MI 12531685 / R: 1291P12>1259P17 W W B W
## 101 50 SHIVAM JHA 2.5 L 29 W 42 L 33 W 46
## 102 MI 14773178 / R: 1056 >1111 W B W B
## 103 51 TEJAS AYYAGARI 2.5 L 27 W 45 L 36 W 57
## 104 MI 15205474 / R: 1011 >1097 B W B W
## 105 52 ETHAN GUO 2.5 W 30 D 22 L 19 D 48
## 106 MI 14918803 / R: 935 >1092 N:4 B W B W
## 107 53 JOSE C YBARRA 2.0 H L 25 H L 44
## 108 MI 12578849 / R: 1393 >1359 B W
## 109 54 LARRY HODGE 2.0 L 14 L 39 L 61 B
## 110 MI 12836773 / R: 1270 >1200 B B W
## 111 55 ALEX KONG 2.0 L 62 D 31 L 10 L 30
## 112 MI 15412571 / R: 1186 >1163 W B W B
## 113 56 MARISA RICCI 2.0 H L 11 L 35 W 45
## 114 MI 14679887 / R: 1153 >1140 B W W
## 115 57 MICHAEL LU 2.0 L 7 L 36 W 42 L 51
## 116 MI 15113330 / R: 1092 >1079 B W W B
## 117 58 VIRAJ MOHILE 2.0 W 31 L 2 L 41 L 23
## 118 MI 14700365 / R: 917 > 941 W B W B
## 119 59 SEAN M MC CORMICK 2.0 L 41 B L 9 L 40
## 120 MI 12841036 / R: 853 > 878 W B B
## 121 60 JULIA SHEN 1.5 L 33 L 34 D 45 D 42
## 122 MI 14579262 / R: 967 > 984 W B B W
## 123 61 JEZZEL FARKAS 1.5 L 32 L 3 W 54 L 47
## 124 ON 15771592 / R: 955P11> 979P18 B W B W
## 125 62 ASHWIN BALAJI 1.0 W 55 U U U
## 126 MI 15219542 / R: 1530 >1535 B
## 127 63 THOMAS JOSEPH HOSMER 1.0 L 2 L 48 D 49 L 43
## 128 MI 15057092 / R: 1175 >1125 W B W B
## 129 64 BEN LI 1.0 L 22 D 30 L 31 D 49
## 130 MI 15006561 / R: 1163 >1112 B W W B
## V8 V9 V10 V11
## 1 Round Round Round NA
## 2 5 6 7 NA
## 3 W 7 D 12 D 4 NA
## 4 W B W NA
## 5 W 16 W 20 W 7 NA
## 6 B W B NA
## 7 W 11 W 13 W 12 NA
## 8 W B W NA
## 9 D 5 W 19 D 1 NA
## 10 W B B NA
## 11 D 4 W 14 W 17 NA
## 12 B W B NA
## 13 D 10 W 27 W 21 NA
## 14 B W B NA
## 15 L 1 W 9 L 2 NA
## 16 B W W NA
## 17 W 47 W 28 W 19 NA
## 18 B W W NA
## 19 W 26 L 7 W 20 NA
## 20 W B B NA
## 21 D 6 W 25 W 18 NA
## 22 W B W NA
## 23 L 3 W 34 W 26 NA
## 24 B W B NA
## 25 H D 1 L 3 NA
## 26 W B NA
## 27 W 33 L 3 W 32 NA
## 28 W W B NA
## 29 D 27 L 5 W 31 NA
## 30 B B W NA
## 31 W 54 W 33 W 38 NA
## 32 B B W NA
## 33 L 2 W 36 U NA
## 34 W B NA
## 35 W 23 W 22 L 5 NA
## 36 W B W NA
## 37 L 19 W 38 L 10 NA
## 38 B W B NA
## 39 W 18 L 4 L 8 NA
## 40 W W B NA
## 41 W 28 L 2 L 9 NA
## 42 W B W NA
## 43 W 40 W 39 L 6 NA
## 44 W B W NA
## 45 H L 17 W 40 NA
## 46 W B NA
## 47 L 17 W 37 W 46 NA
## 48 B W B NA
## 49 W 60 W 44 W 39 NA
## 50 W W B NA
## 51 D 34 L 10 W 47 NA
## 52 B W B NA
## 53 L 9 D 32 L 11 NA
## 54 B W W NA
## 55 D 14 L 6 U NA
## 56 W B NA
## 57 L 20 L 8 D 36 NA
## 58 B B W NA
## 59 W 52 W 48 U NA
## 60 W B NA
## 61 L 31 W 61 W 50 NA
## 62 W B B NA
## 63 W 30 W 50 L 14 NA
## 64 B W B NA
## 65 W 51 D 26 L 13 NA
## 66 W B W NA
## 67 L 13 L 15 W 51 NA
## 68 B W B NA
## 69 D 25 L 11 W 52 NA
## 70 W B W NA
## 71 W 57 D 52 W 48 NA
## 72 B B W NA
## 73 H L 16 D 28 NA
## 74 W B NA
## 75 H L 23 W 61 NA
## 76 B W NA
## 77 H L 18 L 15 NA
## 78 B B NA
## 79 W 44 L 21 L 24 NA
## 80 B W W NA
## 81 L 21 W 56 L 22 NA
## 82 B W W NA
## 83 X U U NA
## 84 NA
## 85 D 61 W 64 W 56 NA
## 86 W W B NA
## 87 W 59 L 46 W 55 NA
## 88 B B W NA
## 89 L 39 L 24 W 59 NA
## 90 W B W NA
## 91 W 63 D 55 W 58 NA
## 92 W B W NA
## 93 W 64 W 43 L 23 NA
## 94 B W W NA
## 95 L 8 D 51 L 25 NA
## 96 W B W NA
## 97 H L 29 L 35 NA
## 98 W B NA
## 99 W 58 H U NA
## 100 B NA
## 101 H L 31 L 30 NA
## 102 B W NA
## 103 L 32 D 47 L 33 NA
## 104 B W W NA
## 105 L 29 D 35 L 34 NA
## 106 B W B NA
## 107 U W 57 U NA
## 108 W NA
## 109 L 15 L 59 W 64 NA
## 110 W B W NA
## 111 B D 45 L 43 NA
## 112 W B NA
## 113 H L 40 L 42 NA
## 114 B W NA
## 115 L 35 L 53 B NA
## 116 W B NA
## 117 L 49 B L 45 NA
## 118 W B NA
## 119 L 43 W 54 L 44 NA
## 120 W W B NA
## 121 L 24 H U NA
## 122 B NA
## 123 D 42 L 30 L 37 NA
## 124 B W B NA
## 125 U U U NA
## 126 NA
## 127 L 45 H U NA
## 128 B NA
## 129 L 46 L 42 L 54 NA
## 130 W B B NA#write_csv(TournamentRaw3, "E:/TournamentRaw3.csv")As we can see, dol11 is all NA, this column is not needed
Tournament4 = TournamentRaw3[,-11]
print (Tournament4 [1:10], )## V1 V2 V3 V4 V5 V6 V7
## 1 Pair Player Name Total Round Round Round Round
## 2 Num USCF ID / Rtg (Pre>Post) Pts 1 2 3 4
## 3 1 GARY HUA 6.0 W 39 W 21 W 18 W 14
## 4 ON 15445895 / R: 1794 >1817 N:2 W B W B
## 5 2 DAKSHESH DARURI 6.0 W 63 W 58 L 4 W 17
## 6 MI 14598900 / R: 1553 >1663 N:2 B W B W
## 7 3 ADITYA BAJAJ 6.0 L 8 W 61 W 25 W 21
## 8 MI 14959604 / R: 1384 >1640 N:2 W B W B
## 9 4 PATRICK H SCHILLING 5.5 W 23 D 28 W 2 W 26
## 10 MI 12616049 / R: 1716 >1744 N:2 W B W B
## 11 5 HANSHI ZUO 5.5 W 45 W 37 D 12 D 13
## 12 MI 14601533 / R: 1655 >1690 N:2 B W B W
## 13 6 HANSEN SONG 5.0 W 34 D 29 L 11 W 35
## 14 OH 15055204 / R: 1686 >1687 N:3 W B W B
## 15 7 GARY DEE SWATHELL 5.0 W 57 W 46 W 13 W 11
## 16 MI 11146376 / R: 1649 >1673 N:3 W B W B
## 17 8 EZEKIEL HOUGHTON 5.0 W 3 W 32 L 14 L 9
## 18 MI 15142253 / R: 1641P17>1657P24 N:3 B W B W
## 19 9 STEFANO LEE 5.0 W 25 L 18 W 59 W 8
## 20 ON 14954524 / R: 1411 >1564 N:2 W B W B
## 21 10 ANVIT RAO 5.0 D 16 L 19 W 55 W 31
## 22 MI 14150362 / R: 1365 >1544 N:3 W W B B
## 23 11 CAMERON WILLIAM MC LEMAN 4.5 D 38 W 56 W 6 L 7
## 24 MI 12581589 / R: 1712 >1696 N:3 B W B W
## 25 12 KENNETH J TACK 4.5 W 42 W 33 D 5 W 38
## 26 MI 12681257 / R: 1663 >1670 N:3 W B W B
## 27 13 TORRANCE HENRY JR 4.5 W 36 W 27 L 7 D 5
## 28 MI 15082995 / R: 1666 >1662 N:3 B W B B
## 29 14 BRADLEY SHAW 4.5 W 54 W 44 W 8 L 1
## 30 MI 10131499 / R: 1610 >1618 N:3 W B W W
## 31 15 ZACHARY JAMES HOUGHTON 4.5 D 19 L 16 W 30 L 22
## 32 MI 15619130 / R: 1220P13>1416P20 N:3 B B W W
## 33 16 MIKE NIKITIN 4.0 D 10 W 15 H W 39
## 34 MI 10295068 / R: 1604 >1613 N:3 B W B
## 35 17 RONALD GRZEGORCZYK 4.0 W 48 W 41 L 26 L 2
## 36 MI 10297702 / R: 1629 >1610 N:3 W B W B
## 37 18 DAVID SUNDEEN 4.0 W 47 W 9 L 1 W 32
## 38 MI 11342094 / R: 1600 >1600 N:3 B W B W
## 39 19 DIPANKAR ROY 4.0 D 15 W 10 W 52 D 28
## 40 MI 14862333 / R: 1564 >1570 N:3 W B W B
## 41 20 JASON ZHENG 4.0 L 40 W 49 W 23 W 41
## 42 MI 14529060 / R: 1595 >1569 N:4 W B W B
## 43 21 DINH DANG BUI 4.0 W 43 L 1 W 47 L 3
## 44 ON 15495066 / R: 1563P22>1562 N:3 B W B W
## 45 22 EUGENE L MCCLURE 4.0 W 64 D 52 L 28 W 15
## 46 MI 12405534 / R: 1555 >1529 N:4 W B W B
## 47 23 ALAN BUI 4.0 L 4 W 43 L 20 W 58
## 48 ON 15030142 / R: 1363 >1371 B W B W
## 49 24 MICHAEL R ALDRICH 4.0 L 28 L 47 W 43 L 25
## 50 MI 13469010 / R: 1229 >1300 N:4 B W B B
## 51 25 LOREN SCHWIEBERT 3.5 L 9 W 53 L 3 W 24
## 52 MI 12486656 / R: 1745 >1681 N:4 B W B W
## 53 26 MAX ZHU 3.5 W 49 W 40 W 17 L 4
## 54 ON 15131520 / R: 1579 >1564 N:4 B W B W
## 55 27 GAURAV GIDWANI 3.5 W 51 L 13 W 46 W 37
## 56 MI 14476567 / R: 1552 >1539 N:4 W B W B
## 57 28 SOFIA ADINA STANESCUBELLU 3.5 W 24 D 4 W 22 D 19
## 58 MI 14882954 / R: 1507 >1513 N:3 W W B W
## 59 29 CHIEDOZIE OKORIE 3.5 W 50 D 6 L 38 L 34
## 60 MI 15323285 / R: 1602P6 >1508P12 N:4 B W B W
## 61 30 GEORGE AVERY JONES 3.5 L 52 D 64 L 15 W 55
## 62 ON 12577178 / R: 1522 >1444 W B B W
## 63 31 RISHI SHETTY 3.5 L 58 D 55 W 64 L 10
## 64 MI 15131618 / R: 1494 >1444 B W B W
## 65 32 JOSHUA PHILIP MATHEWS 3.5 W 61 L 8 W 44 L 18
## 66 ON 14073750 / R: 1441 >1433 N:4 W B W B
## 67 33 JADE GE 3.5 W 60 L 12 W 50 D 36
## 68 MI 14691842 / R: 1449 >1421 B W B W
## 69 34 MICHAEL JEFFERY THOMAS 3.5 L 6 W 60 L 37 W 29
## 70 MI 15051807 / R: 1399 >1400 B W B B
## 71 35 JOSHUA DAVID LEE 3.5 L 46 L 38 W 56 L 6
## 72 MI 14601397 / R: 1438 >1392 W W B W
## 73 36 SIDDHARTH JHA 3.5 L 13 W 57 W 51 D 33
## 74 MI 14773163 / R: 1355 >1367 N:4 W B W B
## 75 37 AMIYATOSH PWNANANDAM 3.5 B L 5 W 34 L 27
## 76 MI 15489571 / R: 980P12>1077P17 B W W
## 77 38 BRIAN LIU 3.0 D 11 W 35 W 29 L 12
## 78 MI 15108523 / R: 1423 >1439 N:4 W B W W
## 79 39 JOEL R HENDON 3.0 L 1 W 54 W 40 L 16
## 80 MI 12923035 / R: 1436P23>1413 N:4 B W B W
## 81 40 FOREST ZHANG 3.0 W 20 L 26 L 39 W 59
## 82 MI 14892710 / R: 1348 >1346 B B W W
## 83 41 KYLE WILLIAM MURPHY 3.0 W 59 L 17 W 58 L 20
## 84 MI 15761443 / R: 1403P5 >1341P9 B W B W
## 85 42 JARED GE 3.0 L 12 L 50 L 57 D 60
## 86 MI 14462326 / R: 1332 >1256 B W B B
## 87 43 ROBERT GLEN VASEY 3.0 L 21 L 23 L 24 W 63
## 88 MI 14101068 / R: 1283 >1244 W B W W
## 89 44 JUSTIN D SCHILLING 3.0 B L 14 L 32 W 53
## 90 MI 15323504 / R: 1199 >1199 W B B
## 91 45 DEREK YAN 3.0 L 5 L 51 D 60 L 56
## 92 MI 15372807 / R: 1242 >1191 W B W B
## 93 46 JACOB ALEXANDER LAVALLEY 3.0 W 35 L 7 L 27 L 50
## 94 MI 15490981 / R: 377P3 >1076P10 B W B W
## 95 47 ERIC WRIGHT 2.5 L 18 W 24 L 21 W 61
## 96 MI 12533115 / R: 1362 >1341 W B W B
## 97 48 DANIEL KHAIN 2.5 L 17 W 63 H D 52
## 98 MI 14369165 / R: 1382 >1335 B W B
## 99 49 MICHAEL J MARTIN 2.5 L 26 L 20 D 63 D 64
## 100 MI 12531685 / R: 1291P12>1259P17 W W B W
## 101 50 SHIVAM JHA 2.5 L 29 W 42 L 33 W 46
## 102 MI 14773178 / R: 1056 >1111 W B W B
## 103 51 TEJAS AYYAGARI 2.5 L 27 W 45 L 36 W 57
## 104 MI 15205474 / R: 1011 >1097 B W B W
## 105 52 ETHAN GUO 2.5 W 30 D 22 L 19 D 48
## 106 MI 14918803 / R: 935 >1092 N:4 B W B W
## 107 53 JOSE C YBARRA 2.0 H L 25 H L 44
## 108 MI 12578849 / R: 1393 >1359 B W
## 109 54 LARRY HODGE 2.0 L 14 L 39 L 61 B
## 110 MI 12836773 / R: 1270 >1200 B B W
## 111 55 ALEX KONG 2.0 L 62 D 31 L 10 L 30
## 112 MI 15412571 / R: 1186 >1163 W B W B
## 113 56 MARISA RICCI 2.0 H L 11 L 35 W 45
## 114 MI 14679887 / R: 1153 >1140 B W W
## 115 57 MICHAEL LU 2.0 L 7 L 36 W 42 L 51
## 116 MI 15113330 / R: 1092 >1079 B W W B
## 117 58 VIRAJ MOHILE 2.0 W 31 L 2 L 41 L 23
## 118 MI 14700365 / R: 917 > 941 W B W B
## 119 59 SEAN M MC CORMICK 2.0 L 41 B L 9 L 40
## 120 MI 12841036 / R: 853 > 878 W B B
## 121 60 JULIA SHEN 1.5 L 33 L 34 D 45 D 42
## 122 MI 14579262 / R: 967 > 984 W B B W
## 123 61 JEZZEL FARKAS 1.5 L 32 L 3 W 54 L 47
## 124 ON 15771592 / R: 955P11> 979P18 B W B W
## 125 62 ASHWIN BALAJI 1.0 W 55 U U U
## 126 MI 15219542 / R: 1530 >1535 B
## 127 63 THOMAS JOSEPH HOSMER 1.0 L 2 L 48 D 49 L 43
## 128 MI 15057092 / R: 1175 >1125 W B W B
## 129 64 BEN LI 1.0 L 22 D 30 L 31 D 49
## 130 MI 15006561 / R: 1163 >1112 B W W B
## V8 V9 V10
## 1 Round Round Round
## 2 5 6 7
## 3 W 7 D 12 D 4
## 4 W B W
## 5 W 16 W 20 W 7
## 6 B W B
## 7 W 11 W 13 W 12
## 8 W B W
## 9 D 5 W 19 D 1
## 10 W B B
## 11 D 4 W 14 W 17
## 12 B W B
## 13 D 10 W 27 W 21
## 14 B W B
## 15 L 1 W 9 L 2
## 16 B W W
## 17 W 47 W 28 W 19
## 18 B W W
## 19 W 26 L 7 W 20
## 20 W B B
## 21 D 6 W 25 W 18
## 22 W B W
## 23 L 3 W 34 W 26
## 24 B W B
## 25 H D 1 L 3
## 26 W B
## 27 W 33 L 3 W 32
## 28 W W B
## 29 D 27 L 5 W 31
## 30 B B W
## 31 W 54 W 33 W 38
## 32 B B W
## 33 L 2 W 36 U
## 34 W B
## 35 W 23 W 22 L 5
## 36 W B W
## 37 L 19 W 38 L 10
## 38 B W B
## 39 W 18 L 4 L 8
## 40 W W B
## 41 W 28 L 2 L 9
## 42 W B W
## 43 W 40 W 39 L 6
## 44 W B W
## 45 H L 17 W 40
## 46 W B
## 47 L 17 W 37 W 46
## 48 B W B
## 49 W 60 W 44 W 39
## 50 W W B
## 51 D 34 L 10 W 47
## 52 B W B
## 53 L 9 D 32 L 11
## 54 B W W
## 55 D 14 L 6 U
## 56 W B
## 57 L 20 L 8 D 36
## 58 B B W
## 59 W 52 W 48 U
## 60 W B
## 61 L 31 W 61 W 50
## 62 W B B
## 63 W 30 W 50 L 14
## 64 B W B
## 65 W 51 D 26 L 13
## 66 W B W
## 67 L 13 L 15 W 51
## 68 B W B
## 69 D 25 L 11 W 52
## 70 W B W
## 71 W 57 D 52 W 48
## 72 B B W
## 73 H L 16 D 28
## 74 W B
## 75 H L 23 W 61
## 76 B W
## 77 H L 18 L 15
## 78 B B
## 79 W 44 L 21 L 24
## 80 B W W
## 81 L 21 W 56 L 22
## 82 B W W
## 83 X U U
## 84
## 85 D 61 W 64 W 56
## 86 W W B
## 87 W 59 L 46 W 55
## 88 B B W
## 89 L 39 L 24 W 59
## 90 W B W
## 91 W 63 D 55 W 58
## 92 W B W
## 93 W 64 W 43 L 23
## 94 B W W
## 95 L 8 D 51 L 25
## 96 W B W
## 97 H L 29 L 35
## 98 W B
## 99 W 58 H U
## 100 B
## 101 H L 31 L 30
## 102 B W
## 103 L 32 D 47 L 33
## 104 B W W
## 105 L 29 D 35 L 34
## 106 B W B
## 107 U W 57 U
## 108 W
## 109 L 15 L 59 W 64
## 110 W B W
## 111 B D 45 L 43
## 112 W B
## 113 H L 40 L 42
## 114 B W
## 115 L 35 L 53 B
## 116 W B
## 117 L 49 B L 45
## 118 W B
## 119 L 43 W 54 L 44
## 120 W W B
## 121 L 24 H U
## 122 B
## 123 D 42 L 30 L 37
## 124 B W B
## 125 U U U
## 126
## 127 L 45 H U
## 128 B
## 129 L 46 L 42 L 54
## 130 W B BRemove the white space within some of the data elements, such as W 39 will become W39
TournamentRow1 = trimws(as.character(Tournament4[1,] ) )
TournamentRow1## [1] "Pair" "Player Name" "Total" "Round" "Round"
## [6] "Round" "Round" "Round" "Round" "Round"TournamentRow2 = trimws(as.character(Tournament4[2,]) )
TournamentRow2## [1] "Num" "USCF ID / Rtg (Pre>Post)"
## [3] "Pts" "1"
## [5] "2" "3"
## [7] "4" "5"
## [9] "6" "7"Put row1 and row2 together, because they are technically the herder
HeaderT = paste(TournamentRow1, TournamentRow2)
HeaderT## [1] "Pair Num"
## [2] "Player Name USCF ID / Rtg (Pre>Post)"
## [3] "Total Pts"
## [4] "Round 1"
## [5] "Round 2"
## [6] "Round 3"
## [7] "Round 4"
## [8] "Round 5"
## [9] "Round 6"
## [10] "Round 7"Tournament6 = as.tbl(Tournament4[-1:-2,])
names(Tournament6) = HeaderT
print (Tournament6)## # A tibble: 128 x 10
## `Pair Num` `Player Name US~ `Total Pts` `Round 1` `Round 2` `Round 3`
## * <chr> <chr> <chr> <chr> <chr> <chr>
## 1 " 1 " " GARY HUA ~ "6.0 " W 39 W 21 W 18
## 2 " ON " " 15445895 / R:~ "N:2 " "W " "B " "W "
## 3 " 2 " " DAKSHESH DARU~ "6.0 " W 63 W 58 L 4
## 4 " MI " " 14598900 / R:~ "N:2 " "B " "W " "B "
## 5 " 3 " " ADITYA BAJAJ ~ "6.0 " L 8 W 61 W 25
## 6 " MI " " 14959604 / R:~ "N:2 " "W " "B " "W "
## 7 " 4 " " PATRICK H SCH~ "5.5 " W 23 D 28 W 2
## 8 " MI " " 12616049 / R:~ "N:2 " "W " "B " "W "
## 9 " 5 " " HANSHI ZUO ~ "5.5 " W 45 W 37 D 12
## 10 " MI " " 14601533 / R:~ "N:2 " "B " "W " "B "
## # ... with 118 more rows, and 4 more variables: `Round 4` <chr>, `Round
## # 5` <chr>, `Round 6` <chr>, `Round 7` <chr># Tournament6 is a tibble
write_csv(Tournament6, "E:/Tournament6.csv")