library(data.table)
library(knitr)

Read in Data

dat = read.delim2('tournament.txt', sep = '\t')
vec.names = dat[1:2, ]
dat = dat[apply(dat, 1, function(x) grepl(x, "(\\-)+")),]


lst.dat = strsplit(dat, split = "\\s*\\|")
mat.dat = t(do.call(cbind, lst.dat))
dt.dat = data.table(mat.dat)
setnames(dt.dat, c("Pair", "Player.Name", "Total.Pts", "Round.1", "Round.2", "Round.3", "Round.4", "Round.5", "Round.6", "Round.7"))

dt.dat[, c("Player.Id", "Rating.Change"):= tstrsplit(Player.Name, "\\s*\\/\\s*")]

Participants and State

dt.participants = dt.dat[, .(Pair, Player.Id)]
dt.participants[grepl("\\d+", Player.Id), Id := Player.Id]
dt.participants[grepl("[a-z]+", Pair, ignore.case = T), State := Pair]
dt.participants[, New.Id := shift(Id, -1L)]
dt.participants[is.na(Id), Id := New.Id][, New.Id := NULL]

dt.participants[, New.State := shift(State, -1L)]
dt.participants[is.na(State), State := New.State][, New.State := NULL]
dt.participants = dt.participants[grepl( "[A-Z]+", Player.Id),]
kable(head(dt.participants))
Pair Player.Id Id State
1 GARY HUA 15445895 ON
2 DAKSHESH DARURI 14598900 MI
3 ADITYA BAJAJ 14959604 MI
4 PATRICK H SCHILLING 12616049 MI
5 HANSHI ZUO 14601533 MI
6 HANSEN SONG 15055204 OH

Total Points per Player

dt.participants.merged = merge(dt.dat[grepl("\\d+", Pair), .(Pair, Total.Pts)], dt.participants, by = "Pair")
kable(head(dt.participants.merged))
Pair Total.Pts Player.Id Id State
1 6.0 GARY HUA 15445895 ON
2 6.0 DAKSHESH DARURI 14598900 MI
3 6.0 ADITYA BAJAJ 14959604 MI
4 5.5 PATRICK H SCHILLING 12616049 MI
5 5.5 HANSHI ZUO 14601533 MI
6 5.0 HANSEN SONG 15055204 OH

Rating - Add Participants Rating

dt.ratings = dt.dat[!is.na(Rating.Change), .(Player.Id, Rating.Change)]
dt.ratings[, Rating.Change := gsub("R\\:", "", Rating.Change)]
dt.ratings[, c("Pre.Rating", "Post.Rating") := tstrsplit(Rating.Change, "\\s*\\->\\s*")]
dt.participants.merged = merge(dt.participants.merged, dt.ratings[,.(Player.Id, Pre.Rating)], by.x = "Id", by.y = "Player.Id")
dt.participants.merged[, Pair := as.integer(Pair)]

Opponents - Average Rating

dt.opponents = dt.dat[grepl("\\d+",Pair), .(Pair, Round.1, Round.2, Round.3, Round.4, Round.5, Round.6, Round.7)]
dt.opponents= as.data.table(apply(dt.opponents, 2, function(x) x= as.integer(gsub("[A-Z]\\s*(\\d+)", "\\1", x))))
mlt.opponents = melt(dt.opponents, id.vars = "Pair", value.name = "Opp.Pair", variable.name = "Round")
dt.opponents = merge(mlt.opponents, dt.participants.merged[, .(Pair, Pre.Rating)], by.x = "Opp.Pair", by.y = "Pair")
dt.opponents[grepl("P\\d+", Pre.Rating), Pre.Rating := gsub("P\\d+", "", Pre.Rating)]
dt.opponents[, Pre.Rating := as.integer(Pre.Rating)]
dt.opponents.avg.rating = dt.opponents[, mean(Pre.Rating, na.rm = T), by = "Pair"]
setnames(dt.opponents.avg.rating, "V1", "Avg.Rating")

Merge

dt.FINAL.TABLE = dt.opponents.avg.rating[dt.participants.merged, on = "Pair"]
dt.FINAL.TABLE = dt.FINAL.TABLE[order(Pair), .(Player.Name = Player.Id, State, Total.Pts, Pre.Rating, Avg.Rating)]

write.csv(dt.FINAL.TABLE, 'TOURNAMENT_FINAL.CSV', row.names = F)
kable(dt.FINAL.TABLE)
Player.Name State Total.Pts Pre.Rating Avg.Rating
GARY HUA ON 6.0 1794 1605.286
DAKSHESH DARURI MI 6.0 1553 1469.286
ADITYA BAJAJ MI 6.0 1384 1563.571
PATRICK H SCHILLING MI 5.5 1716 1573.571
HANSHI ZUO MI 5.5 1655 1500.857
HANSEN SONG OH 5.0 1686 1518.714
GARY DEE SWATHELL MI 5.0 1649 1372.143
EZEKIEL HOUGHTON MI 5.0 1641P17 1468.429
STEFANO LEE ON 5.0 1411 1523.143
ANVIT RAO MI 5.0 1365 1554.143
CAMERON WILLIAM MC LEMAN MI 4.5 1712 1467.571
KENNETH J TACK MI 4.5 1663 1506.167
TORRANCE HENRY JR MI 4.5 1666 1497.857
BRADLEY SHAW MI 4.5 1610 1515.000
ZACHARY JAMES HOUGHTON MI 4.5 1220P13 1483.857
MIKE NIKITIN MI 4.0 1604 1385.800
RONALD GRZEGORCZYK MI 4.0 1629 1498.571
DAVID SUNDEEN MI 4.0 1600 1480.000
DIPANKAR ROY MI 4.0 1564 1426.286
JASON ZHENG MI 4.0 1595 1410.857
DINH DANG BUI ON 4.0 1563P22 1470.429
EUGENE L MCCLURE MI 4.0 1555 1300.333
ALAN BUI ON 4.0 1363 1213.857
MICHAEL R ALDRICH MI 4.0 1229 1357.000
LOREN SCHWIEBERT MI 3.5 1745 1363.286
MAX ZHU ON 3.5 1579 1506.857
GAURAV GIDWANI MI 3.5 1552 1221.667
SOFIA ADINA STANESCU-BELLU MI 3.5 1507 1522.143
CHIEDOZIE OKORIE MI 3.5 1602P6 1313.500
GEORGE AVERY JONES ON 3.5 1522 1144.143
RISHI SHETTY MI 3.5 1494 1259.857
JOSHUA PHILIP MATHEWS ON 3.5 1441 1378.714
JADE GE MI 3.5 1449 1276.857
MICHAEL JEFFERY THOMAS MI 3.5 1399 1375.286
JOSHUA DAVID LEE MI 3.5 1438 1149.714
SIDDHARTH JHA MI 3.5 1355 1388.167
AMIYATOSH PWNANANDAM MI 3.5 980P12 1384.800
BRIAN LIU MI 3.0 1423 1539.167
JOEL R HENDON MI 3.0 1436P23 1429.571
FOREST ZHANG MI 3.0 1348 1390.571
KYLE WILLIAM MURPHY MI 3.0 1403P5 1248.500
JARED GE MI 3.0 1332 1149.857
ROBERT GLEN VASEY MI 3.0 1283 1106.571
JUSTIN D SCHILLING MI 3.0 1199 1327.000
DEREK YAN MI 3.0 1242 1152.000
JACOB ALEXANDER LAVALLEY MI 3.0 377P3 1357.714
ERIC WRIGHT MI 2.5 1362 1392.000
DANIEL KHAIN MI 2.5 1382 1355.800
MICHAEL J MARTIN MI 2.5 1291P12 1285.800
SHIVAM JHA MI 2.5 1056 1296.000
TEJAS AYYAGARI MI 2.5 1011 1356.143
ETHAN GUO MI 2.5 935 1494.571
JOSE C YBARRA MI 2.0 1393 1345.333
LARRY HODGE MI 2.0 1270 1206.167
ALEX KONG MI 2.0 1186 1406.000
MARISA RICCI MI 2.0 1153 1414.400
MICHAEL LU MI 2.0 1092 1363.000
VIRAJ MOHILE MI 2.0 917 1391.000
SEAN M MC CORMICK MI 2.0 853 1319.000
JULIA SHEN MI 1.5 967 1330.200
JEZZEL FARKAS ON 1.5 955P11 1327.286
ASHWIN BALAJI MI 1.0 1530 1186.000
THOMAS JOSEPH HOSMER MI 1.0 1175 1350.200
BEN LI MI 1.0 1163 1263.000