Chess Tournament

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. For the first player, the information would be: Gary Hua, ON, 6.0, 1794, 1605 1605 was calculated by using the pre-tournament opponents’ ratings of 1436, 1563, 1600, 1610, 1649, 1663, 1716, and dividing by the total number of games played.

chess<- readLines("C:\\Users\\ambra\\Desktop\\Data 607\\W4\\tournamentinfo.txt")
## Warning in readLines("C:\\Users\\ambra\\Desktop\\Data 607\\W4\
## \tournamentinfo.txt"): incomplete final line found on 'C:\Users\ambra
## \Desktop\Data 607\W4\tournamentinfo.txt'
class(chess)
## [1] "character"
library(stringr)

##Remove headings 

chess<- chess[c(-1:-4)]


##Extract player's name

NAME<-str_trim(unlist(str_extract_all(chess, "[[:upper:]]{2,}\\s\\w+(\\s\\w+)?\\s?\\w+?\\s?[[:punct:]]?\\w*")))  


##Extract state

state<- unlist(str_extract_all(chess, "(ON|MI|OH)\\s\\|"))
STATE<- str_trim(unlist(str_replace_all(state, "\\|", "")))

##Extract total number of points

points<- unlist(str_extract_all(chess, "\\|\\d\\.\\d\\s"))

POINTS<- str_trim(unlist(str_replace_all(points,"\\|", "")))

##Extract pre-rating

prerating<- unlist(str_extract_all(chess,"R:\\s?\\s\\d{3,}P?"))
prerating2<-str_trim(str_replace_all(prerating, "R:\\s?\\s", ""))
PRE_RATING2<-str_trim(str_replace_all(prerating2, "P$", ""))

PRE_RATING<- sapply(PRE_RATING2, as.numeric)

y<- as.data.frame(chess)
##Extract opponents for each player


opponents<- apply (y, 1, function(x) (unlist(str_extract_all(x,"(W|D|L)( )+\\d{1,3}"))))

##Remove 0 characters from vector

opponents1<- opponents[lapply(opponents,length)>0]

##remove W|D|L

opponentsindex<- str_extract_all(opponents1,"\\d{1,3}")
library(plyr)
opponentsdf<- ldply(opponentsindex, rbind)
opponentsdf1<- sapply(opponentsdf, function(x) as.numeric(as.character(x)))


chessdf<- data.frame(cbind(NAME, STATE, POINTS, PRE_RATING))

chessdf["X1"]<-opponentsdf1[,1]
chessdf["X2"]<-opponentsdf1[,2]
chessdf["X3"]<-opponentsdf1[,3]
chessdf["X4"]<-opponentsdf1[,4]
chessdf["X5"]<-opponentsdf1[,5]
chessdf["X6"]<-opponentsdf1[,6]
chessdf["X7"]<-opponentsdf1[,7]

rownames(chessdf)<- seq_len(nrow(chessdf))


for (i in 1:nrow(chessdf)){
  
        chessdf$X1[i]<-as.numeric(as.character(chessdf$PRE_RATING[chessdf$X1[i]]))
        chessdf$X2[i]<-as.numeric(as.character(chessdf$PRE_RATING[chessdf$X2[i]]))
        chessdf$X3[i]<-as.numeric(as.character(chessdf$PRE_RATING[chessdf$X3[i]]))
        chessdf$X4[i]<-as.numeric(as.character(chessdf$PRE_RATING[chessdf$X4[i]]))
        chessdf$X5[i]<-as.numeric(as.character(chessdf$PRE_RATING[chessdf$X5[i]]))
        chessdf$X6[i]<-as.numeric(as.character(chessdf$PRE_RATING[chessdf$X6[i]]))
        chessdf$X7[i]<-as.numeric(as.character(chessdf$PRE_RATING[chessdf$X7[i]]))
        
}

chessdf["AVERAGE_PRERATING"]<-  round(rowMeans(chessdf[, 5:11], na.rm = TRUE ), digits = 0)

library(htmlTable)

htmlTable(chessdf)
NAME STATE POINTS PRE_RATING X1 X2 X3 X4 X5 X6 X7 AVERAGE_PRERATING
1 GARY HUA ON 6.0 1794 1436 1563 1600 1610 1649 1663 1716 1605
2 DAKSHESH DARURI MI 6.0 1553 1175 917 1716 1629 1604 1595 1649 1469
3 ADITYA BAJAJ MI 6.0 1384 1641 955 1745 1563 1712 1666 1663 1564
4 PATRICK H SCHILLING MI 5.5 1716 1363 1507 1553 1579 1655 1564 1794 1574
5 HANSHI ZUO MI 5.5 1655 1242 980 1663 1666 1716 1610 1629 1501
6 HANSEN SONG OH 5.0 1686 1399 1602 1712 1438 1365 1552 1563 1519
7 GARY DEE SWATHELL MI 5.0 1649 1092 377 1666 1712 1794 1411 1553 1372
8 EZEKIEL HOUGHTON MI 5.0 1641 1384 1441 1610 1411 1362 1507 1564 1468
9 STEFANO LEE ON 5.0 1411 1745 1600 853 1641 1579 1649 1595 1523
10 ANVIT RAO MI 5.0 1365 1604 1564 1186 1494 1686 1745 1600 1554
11 CAMERON WILLIAM MC LEMAN MI 4.5 1712 1423 1153 1686 1649 1384 1399 1579 1468
12 KENNETH J TACK MI 4.5 1663 1332 1449 1655 1423 1794 1384 1506
13 TORRANCE HENRY JR MI 4.5 1666 1355 1552 1649 1655 1449 1384 1441 1498
14 BRADLEY SHAW MI 4.5 1610 1270 1199 1641 1794 1552 1655 1494 1515
15 ZACHARY JAMES HOUGHTON MI 4.5 1220 1564 1604 1522 1555 1270 1449 1423 1484
16 MIKE NIKITIN MI 4.0 1604 1365 1220 1436 1553 1355 1386
17 RONALD GRZEGORCZYK MI 4.0 1629 1382 1403 1579 1553 1363 1555 1655 1499
18 DAVID SUNDEEN MI 4.0 1600 1362 1411 1794 1441 1564 1423 1365 1480
19 DIPANKAR ROY MI 4.0 1564 1220 1365 935 1507 1600 1716 1641 1426
20 JASON ZHENG MI 4.0 1595 1348 1291 1363 1403 1507 1553 1411 1411
21 DINH DANG BUI ON 4.0 1563 1283 1794 1362 1384 1348 1436 1686 1470
22 EUGENE L MCCLURE MI 4.0 1555 1163 935 1507 1220 1629 1348 1300
23 ALAN BUI ON 4.0 1363 1716 1283 1595 917 1629 980 377 1214
24 MICHAEL R ALDRICH MI 4.0 1229 1507 1362 1283 1745 967 1199 1436 1357
25 LOREN SCHWIEBERT MI 3.5 1745 1411 1393 1384 1229 1399 1365 1362 1363
26 MAX ZHU ON 3.5 1579 1291 1348 1629 1716 1411 1441 1712 1507
27 GAURAV GIDWANI MI 3.5 1552 1011 1666 377 980 1610 1686 1222
28 SOFIA ADINA STANESCU-BELLU MI 3.5 1507 1229 1716 1555 1564 1595 1641 1355 1522
29 CHIEDOZIE OKORIE MI 3.5 1602 1056 1686 1423 1399 935 1382 1314
30 GEORGE AVERY JONES ON 3.5 1522 935 1163 1220 1186 1494 955 1056 1144
31 RISHI SHETTY MI 3.5 1494 917 1186 1163 1365 1522 1056 1610 1260
32 JOSHUA PHILIP MATHEWS ON 3.5 1441 955 1641 1199 1600 1011 1579 1666 1379
33 JADE GE MI 3.5 1449 967 1663 1056 1355 1666 1220 1011 1277
34 MICHAEL JEFFERY THOMAS MI 3.5 1399 1686 967 980 1602 1745 1712 935 1375
35 JOSHUA DAVID LEE MI 3.5 1438 377 1423 1153 1686 1092 935 1382 1150
36 SIDDHARTH JHA MI 3.5 1355 1666 1092 1011 1449 1604 1507 1388
37 AMIYATOSH PWNANANDAM MI 3.5 980 1655 1399 1552 1363 955 1385
38 BRIAN LIU MI 3.0 1423 1712 1438 1602 1663 1600 1220 1539
39 JOEL R HENDON MI 3.0 1436 1794 1270 1348 1604 1199 1563 1229 1430
40 FOREST ZHANG MI 3.0 1348 1595 1579 1436 853 1563 1153 1555 1391
41 KYLE WILLIAM MURPHY MI 3.0 1403 853 1629 917 1595 1248
42 JARED GE MI 3.0 1332 1663 1056 1092 967 955 1163 1153 1150
43 ROBERT GLEN VASEY MI 3.0 1283 1563 1363 1229 1175 853 377 1186 1107
44 JUSTIN D SCHILLING MI 3.0 1199 1610 1441 1393 1436 1229 853 1327
45 DEREK YAN MI 3.0 1242 1655 1011 967 1153 1175 1186 917 1152
46 JACOB ALEXANDER LAVALLEY MI 3.0 377 1438 1649 1552 1056 1163 1283 1363 1358
47 ERIC WRIGHT MI 2.5 1362 1600 1229 1563 955 1641 1011 1745 1392
48 DANIEL KHAIN MI 2.5 1382 1629 1175 935 1602 1438 1356
49 MICHAEL J MARTIN MI 2.5 1291 1579 1595 1175 1163 917 1286
50 SHIVAM JHA MI 2.5 1056 1602 1332 1449 377 1494 1522 1296
51 TEJAS AYYAGARI MI 2.5 1011 1552 1242 1355 1092 1441 1362 1449 1356
52 ETHAN GUO MI 2.5 935 1522 1555 1564 1382 1602 1438 1399 1495
53 JOSE C YBARRA MI 2.0 1393 1745 1199 1092 1345
54 LARRY HODGE MI 2.0 1270 1610 1436 955 1220 853 1163 1206
55 ALEX KONG MI 2.0 1186 1530 1494 1365 1522 1242 1283 1406
56 MARISA RICCI MI 2.0 1153 1712 1438 1242 1348 1332 1414
57 MICHAEL LU MI 2.0 1092 1649 1355 1332 1011 1438 1393 1363
58 VIRAJ MOHILE MI 2.0 917 1494 1553 1403 1363 1291 1242 1391
59 SEAN M MC CORMICK MI 2.0 853 1403 1411 1348 1283 1270 1199 1319
60 JULIA SHEN MI 1.5 967 1449 1399 1242 1332 1229 1330
61 JEZZEL FARKAS ON 1.5 955 1441 1384 1270 1362 1332 1522 980 1327
62 ASHWIN BALAJI MI 1.0 1530 1186 1186
63 THOMAS JOSEPH HOSMER MI 1.0 1175 1553 1382 1291 1283 1242 1350
64 BEN LI MI 1.0 1163 1555 1522 1494 1291 377 1332 1270 1263 
finalchessdf<- chessdf[-(5:11)]

write.csv(finalchessdf, file="C:/Users/ambra/Desktop/Data 607/W4/TournamentInfo.csv")