library(stringr)## Warning: package 'stringr' was built under R version 3.2.5library(plyr)## Warning: package 'plyr' was built under R version 3.2.5working.dir <- "G:/OneDrive/Learning/_CUNY_SPS_MSDS/2018_Spring/DATA 607/Projects/Project 1"
setwd(working.dir)# Import data from text file.
chess.sourcefile <- paste0(working.dir, "/tournamentinfo.txt")header.rt <- read.table(chess.sourcefile, sep = "|", fill = T, nrows = 3, stringsAsFactors = F)
# Grab rows 2 and 3.
header1 <- header.rt[2,]
header2 <- header.rt[3,]
header.comb <- cbind(header1, header2)
# Clean up residual objects.
header.rt <- NULL
header1 <- NULL
header2 <- NULLheader.clean <- str_trim(header.comb)
# Clean up residual objects.
header.comb <- NULLheader.clean[1:3] <- c("Pairing Number", "Player\'s Name", "Tourney Points")
header.clean[4:10] <- c("Rnd1", "Rnd2", "Rnd3", "Rnd4", "Rnd5", "Rnd6", "Rnd7")
header.clean[12] <- c("Player's State")
header.clean[14:21] <- c("N:Value", "Rnd1 Outcome", "Rnd2 Outcome", "Rnd3 Outcome", "Rnd4 Outcome", "Rnd5 Outcome", "Rnd6 Outcome", "Rnd7 Outcome")chess.source <- read.table(chess.sourcefile, sep = "|", fill = T, skip = 4, header = F, stringsAsFactors = F)# Get count of rows for subsetting.
source.rows <- nrow(chess.source)
# Remove blank rows and concatenate the remaining by competitor.
seq1 <- seq(1, as.integer(source.rows), 3)
seq2 <- seq(2, as.integer(source.rows), 3)
row1 <- chess.source[seq1,]
row2 <- chess.source[seq2,]
chess.comb <- cbind(row1, row2)
# Clean up residual objects.
seq1 <- NULL
seq2 <- NULL
row1 <- NULL
row2 <- NULLchess.master <- rbind(header.clean, chess.comb)
# Create a temp file to prep the header names and read them in to the master table.
chess.master.temp <- chess.master
chess.master.temp[] <- lapply(chess.master, as.character)
colnames(chess.master) <- chess.master.temp[1,]
# Eliminate the first row and temp table.
chess.master <- chess.master[-1,]
chess.master.temp <- NULL# Would like to learn how to employ a for-loop here - there's no way this code is efficient.
chess.master$`Pairing Number`<- as.integer(str_trim(chess.master$`Pairing Number`))
chess.master$`Player's Name` <- str_trim(chess.master$`Player's Name`)
chess.master$`Tourney Points` <- as.numeric(chess.master$`Tourney Points`)
chess.master$`Player's State` <- str_trim(chess.master$`Player's State`)
chess.master$`Rnd1 Outcome` <- str_trim(chess.master$`Rnd1 Outcome`)
chess.master$`Rnd2 Outcome` <- str_trim(chess.master$`Rnd2 Outcome`)
chess.master$`Rnd3 Outcome` <- str_trim(chess.master$`Rnd3 Outcome`)
chess.master$`Rnd4 Outcome` <- str_trim(chess.master$`Rnd4 Outcome`)
chess.master$`Rnd5 Outcome` <- str_trim(chess.master$`Rnd5 Outcome`)
chess.master$`Rnd6 Outcome` <- str_trim(chess.master$`Rnd6 Outcome`)
chess.master$`Rnd7 Outcome` <- str_trim(chess.master$`Rnd7 Outcome`)# Extract column 13 from chess.master.
chess.ptrt.orig <- chess.master[,13]
# Extract data points, ensuring that every competitor receives a value for each (i.e. the new vectors line up cleanly with chess.master table row order).
chess.points <- unlist(str_extract_all(chess.ptrt.orig, "(?<=\\s)[0-9]+(?=\\s/)"))
chess.prertg <- unlist(str_extract_all(chess.ptrt.orig, "(?<=:\\s|\\s{2})[0-9]{3,4}"))
chess.pstrtg <- unlist(str_extract_all(chess.ptrt.orig, "(?<=>|>\\s)[0-9]{3,4}"))
# Append the columnar vectors back to the table as new separate columns.
chess.master <- cbind(chess.master,
chess.points,
chess.prertg,
chess.pstrtg)
# Label the new columns.
colnames(chess.master)[23] <- "Lifetime Points"
colnames(chess.master)[24] <- "Player\'s Pre-Tourney Rating"
colnames(chess.master)[25] <- "Player\'s Post-Tourney Rating"# Would like to learn how to employ a for-loop here - there's no way this code is efficient.
# Extract columns 4 through 10 from chess.master.
chess.rnd.orig <- chess.master[4:10]
# Extract data points, ensuring that every competitor receives a value for each (i.e. the new vectors line up cleanly with chess.master table row order).
chess.rnd1.opponent <- unlist(str_extract_all(chess.rnd.orig[,1], "\\d+|\\s$"))
chess.rnd1.result <- unlist(str_extract_all(chess.rnd.orig[,1], "^[[:upper:]]"))
chess.rnd2.opponent <- unlist(str_extract_all(chess.rnd.orig[,2], "\\d+|\\s$"))
chess.rnd2.result <- unlist(str_extract_all(chess.rnd.orig[,2], "^[[:upper:]]"))
chess.rnd3.opponent <- unlist(str_extract_all(chess.rnd.orig[,3], "\\d+|\\s$"))
chess.rnd3.result <- unlist(str_extract_all(chess.rnd.orig[,3], "^[[:upper:]]"))
chess.rnd4.opponent <- unlist(str_extract_all(chess.rnd.orig[,4], "\\d+|\\s$"))
chess.rnd4.result <- unlist(str_extract_all(chess.rnd.orig[,4], "^[[:upper:]]"))
chess.rnd5.opponent <- unlist(str_extract_all(chess.rnd.orig[,5], "\\d+|\\s$"))
chess.rnd5.result <- unlist(str_extract_all(chess.rnd.orig[,5], "^[[:upper:]]"))
chess.rnd6.opponent <- unlist(str_extract_all(chess.rnd.orig[,6], "\\d+|\\s$"))
chess.rnd6.result <- unlist(str_extract_all(chess.rnd.orig[,6], "^[[:upper:]]"))
chess.rnd7.opponent <- unlist(str_extract_all(chess.rnd.orig[,7], "\\d+|\\s$"))
chess.rnd7.result <- unlist(str_extract_all(chess.rnd.orig[,7], "^[[:upper:]]"))
# Append the columnar vectors back to the table as new separate columns: seven identifying the opponent in each round, seven identifying whether the result of the round (win, lose, draw)
chess.master <- cbind(chess.master,
chess.rnd1.result,
chess.rnd2.result,
chess.rnd3.result,
chess.rnd4.result,
chess.rnd5.result,
chess.rnd6.result,
chess.rnd7.result,
chess.rnd1.opponent,
chess.rnd2.opponent,
chess.rnd3.opponent,
chess.rnd4.opponent,
chess.rnd5.opponent,
chess.rnd6.opponent,
chess.rnd7.opponent)
# Label the new columns.
colnames(chess.master)[26:32] <- c("Rnd1 Result",
"Rnd2 Result",
"Rnd3 Result",
"Rnd4 Result",
"Rnd5 Result",
"Rnd6 Result",
"Rnd7 Result")
colnames(chess.master)[33:39] <- c("Rnd1 Opponent",
"Rnd2 Opponent",
"Rnd3 Opponent",
"Rnd4 Opponent",
"Rnd5 Opponent",
"Rnd6 Opponent",
"Rnd7 Opponent")# The aim is to extract opponents for each round and then look up their ratings. I could not get plyr or for-loops to play nice, and fell back on the approach below, which still did not work. The R_0 vector isn't getting correctly called for the R_R vector below, and the resulting player ratings are based on opponent pairing numbers not pre-tourney ratings. Sigh...
# Would like to learn how to employ a for-loop here - there's no way this code is efficient.
R1O <- (chess.master$`Rnd1 Opponent`)
R1R <- as.integer(chess.master$`Player's Pre-Tourney Rating`[R1O])
R2O <- (chess.master$`Rnd2 Opponent`)
R2R <- as.integer(chess.master$`Player's Pre-Tourney Rating`[R2O])
R3O <- (chess.master$`Rnd3 Opponent`)
R3R <- as.integer(chess.master$`Player's Pre-Tourney Rating`[R3O])
R4O <- (chess.master$`Rnd4 Opponent`)
R4R <- as.integer(chess.master$`Player's Pre-Tourney Rating`[R4O])
R5O <- (chess.master$`Rnd5 Opponent`)
R5R <- as.integer(chess.master$`Player's Pre-Tourney Rating`[R5O])
R6O <- (chess.master$`Rnd6 Opponent`)
R6R <- as.integer(chess.master$`Player's Pre-Tourney Rating`[R6O])
R7O <- (chess.master$`Rnd7 Opponent`)
R7R <- as.integer(chess.master$`Player's Pre-Tourney Rating`[R7O])
# Collect the opponents ratings and sum them across rounds (i.e. by rows). This will serve as the numerator when calculating average opponent rating.
opponents.ratings <- cbind(R1R, R2R, R3R, R4R, R5R, R6R, R7R)
opponents.ratings.cum <- apply(opponents.ratings, MARGIN = 1, FUN = sum)
# Collect the result of each round - win (W), lose (L), or draw (D).
matches.played <- cbind(as.character(chess.master$`Rnd1 Result`),
as.character(chess.master$`Rnd2 Result`),
as.character(chess.master$`Rnd3 Result`),
as.character(chess.master$`Rnd4 Result`),
as.character(chess.master$`Rnd5 Result`),
as.character(chess.master$`Rnd6 Result`),
as.character(chess.master$`Rnd7 Result`))
# Calculate the total number of games played (i.e. sum of wins, losses, and draws). This will serve as the denominator when calculating the average opponent rating.
matches.played.wld <- rowSums(matches.played == "W") + rowSums(matches.played == "L") + rowSums(matches.played == "D")
# Calculate the average opponent rating.
opponents.rating.avg <- opponents.ratings.cum / matches.played.wld
# Append the columnar vector back to the table a new separate column.
chess.master <- cbind(chess.master, opponents.rating.avg)
# Label the new column - would that it were correctly computed...
colnames(chess.master)[40] <- "Avg Pre-Tourney Opponent Rating"chess.final.cols <- c("Pairing Number", "Player\'s Name", "Lifetime Points", "Player\'s State", "N:Value", "Tourney Points", "Avg Pre-Tourney Opponent Rating", "Player\'s Pre-Tourney Rating", "Player\'s Post-Tourney Rating", "Rnd1 Opponent", "Rnd1 Outcome", "Rnd1 Result", "Rnd2 Opponent", "Rnd2 Outcome", "Rnd2 Result", "Rnd3 Opponent", "Rnd3 Outcome", "Rnd3 Result", "Rnd4 Opponent", "Rnd4 Outcome", "Rnd4 Result", "Rnd5 Opponent", "Rnd5 Outcome", "Rnd5 Result", "Rnd6 Opponent", "Rnd6 Outcome", "Rnd6 Result", "Rnd7 Opponent", "Rnd7 Outcome", "Rnd7 Result")
chess.final <- chess.master[chess.final.cols]
chess.final## Pairing Number Player's Name Lifetime Points
## 11 1 GARY HUA 15445895
## 4 2 DAKSHESH DARURI 14598900
## 7 3 ADITYA BAJAJ 14959604
## 10 4 PATRICK H SCHILLING 12616049
## 13 5 HANSHI ZUO 14601533
## 16 6 HANSEN SONG 15055204
## 19 7 GARY DEE SWATHELL 11146376
## 22 8 EZEKIEL HOUGHTON 15142253
## 25 9 STEFANO LEE 14954524
## 28 10 ANVIT RAO 14150362
## 31 11 CAMERON WILLIAM MC LEMAN 12581589
## 34 12 KENNETH J TACK 12681257
## 37 13 TORRANCE HENRY JR 15082995
## 40 14 BRADLEY SHAW 10131499
## 43 15 ZACHARY JAMES HOUGHTON 15619130
## 46 16 MIKE NIKITIN 10295068
## 49 17 RONALD GRZEGORCZYK 10297702
## 52 18 DAVID SUNDEEN 11342094
## 55 19 DIPANKAR ROY 14862333
## 58 20 JASON ZHENG 14529060
## 61 21 DINH DANG BUI 15495066
## 64 22 EUGENE L MCCLURE 12405534
## 67 23 ALAN BUI 15030142
## 70 24 MICHAEL R ALDRICH 13469010
## 73 25 LOREN SCHWIEBERT 12486656
## 76 26 MAX ZHU 15131520
## 79 27 GAURAV GIDWANI 14476567
## 82 28 SOFIA ADINA STANESCU-BELLU 14882954
## 85 29 CHIEDOZIE OKORIE 15323285
## 88 30 GEORGE AVERY JONES 12577178
## 91 31 RISHI SHETTY 15131618
## 94 32 JOSHUA PHILIP MATHEWS 14073750
## 97 33 JADE GE 14691842
## 100 34 MICHAEL JEFFERY THOMAS 15051807
## 103 35 JOSHUA DAVID LEE 14601397
## 106 36 SIDDHARTH JHA 14773163
## 109 37 AMIYATOSH PWNANANDAM 15489571
## 112 38 BRIAN LIU 15108523
## 115 39 JOEL R HENDON 12923035
## 118 40 FOREST ZHANG 14892710
## 121 41 KYLE WILLIAM MURPHY 15761443
## 124 42 JARED GE 14462326
## 127 43 ROBERT GLEN VASEY 14101068
## 130 44 JUSTIN D SCHILLING 15323504
## 133 45 DEREK YAN 15372807
## 136 46 JACOB ALEXANDER LAVALLEY 15490981
## 139 47 ERIC WRIGHT 12533115
## 142 48 DANIEL KHAIN 14369165
## 145 49 MICHAEL J MARTIN 12531685
## 148 50 SHIVAM JHA 14773178
## 151 51 TEJAS AYYAGARI 15205474
## 154 52 ETHAN GUO 14918803
## 157 53 JOSE C YBARRA 12578849
## 160 54 LARRY HODGE 12836773
## 163 55 ALEX KONG 15412571
## 166 56 MARISA RICCI 14679887
## 169 57 MICHAEL LU 15113330
## 172 58 VIRAJ MOHILE 14700365
## 175 59 SEAN M MC CORMICK 12841036
## 178 60 JULIA SHEN 14579262
## 181 61 JEZZEL FARKAS 15771592
## 184 62 ASHWIN BALAJI 15219542
## 187 63 THOMAS JOSEPH HOSMER 15057092
## 190 64 BEN LI 15006561
## Player's State N:Value Tourney Points Avg Pre-Tourney Opponent Rating
## 11 ON N:2 6.0 27.14286
## 4 MI N:2 6.0 24.42857
## 7 MI N:2 6.0 47.57143
## 10 MI N:2 5.5 42.00000
## 13 MI N:2 5.5 38.57143
## 16 OH N:3 5.0 37.00000
## 19 MI N:3 5.0 38.85714
## 22 MI N:3 5.0 36.71429
## 25 ON N:2 5.0 35.71429
## 28 MI N:3 5.0 35.71429
## 31 MI N:3 4.5 35.57143
## 34 MI N:3 4.5 44.83333
## 37 MI N:3 4.5 35.57143
## 40 MI N:3 4.5 29.14286
## 43 MI N:3 4.5 30.57143
## 46 MI N:3 4.0 58.20000
## 49 MI N:3 4.0 35.71429
## 52 MI N:3 4.0 30.14286
## 55 MI N:3 4.0 26.71429
## 58 MI N:4 4.0 35.71429
## 61 ON N:3 4.0 21.00000
## 64 MI N:4 4.0 44.83333
## 67 ON 4.0 28.42857
## 70 MI N:4 4.0 28.00000
## 73 MI N:4 3.5 30.28571
## 76 ON N:4 3.5 28.42857
## 79 MI N:4 3.5 48.00000
## 82 MI N:3 3.5 39.57143
## 85 MI N:4 3.5 30.66667
## 88 ON 3.5 23.14286
## 91 MI 3.5 31.85714
## 94 ON N:4 3.5 33.57143
## 97 MI 3.5 30.85714
## 100 MI 3.5 26.71429
## 103 MI 3.5 14.28571
## 106 MI N:4 3.5 46.83333
## 109 MI 3.5 58.40000
## 112 MI N:4 3.0 56.00000
## 115 MI N:4 3.0 24.71429
## 118 MI 3.0 26.00000
## 121 MI 3.0 89.75000
## 124 MI 3.0 16.42857
## 127 MI 3.0 36.57143
## 130 MI 3.0 41.66667
## 133 MI 3.0 19.14286
## 136 MI 3.0 32.85714
## 139 MI 2.5 52.14286
## 142 MI 2.5 52.40000
## 145 MI 2.5 55.20000
## 148 MI 2.5 38.83333
## 151 MI 2.5 35.42857
## 154 MI N:4 2.5 39.57143
## 157 MI 2.0 115.00000
## 160 MI 2.0 44.50000
## 163 MI 2.0 37.50000
## 166 MI 2.0 55.80000
## 169 MI 2.0 47.83333
## 172 MI 2.0 50.83333
## 175 MI 2.0 35.83333
## 178 MI 1.5 55.80000
## 181 ON 1.5 30.00000
## 184 MI 1.0 356.00000
## 187 MI 1.0 48.80000
## 190 MI 1.0 37.14286
## Player's Pre-Tourney Rating Player's Post-Tourney Rating Rnd1 Opponent
## 11 1794 1817 39
## 4 1553 1663 63
## 7 1384 1640 8
## 10 1716 1744 23
## 13 1655 1690 45
## 16 1686 1687 34
## 19 1649 1673 57
## 22 1641 1657 3
## 25 1411 1564 25
## 28 1365 1544 16
## 31 1712 1696 38
## 34 1663 1670 42
## 37 1666 1662 36
## 40 1610 1618 54
## 43 1220 1416 19
## 46 1604 1613 10
## 49 1629 1610 48
## 52 1600 1600 47
## 55 1564 1570 15
## 58 1595 1569 40
## 61 1563 1562 43
## 64 1555 1529 64
## 67 1363 1371 4
## 70 1229 1300 28
## 73 1745 1681 9
## 76 1579 1564 49
## 79 1552 1539 51
## 82 1507 1513 24
## 85 1602 1508 50
## 88 1522 1444 52
## 91 1494 1444 58
## 94 1441 1433 61
## 97 1449 1421 60
## 100 1399 1400 6
## 103 1438 1392 46
## 106 1355 1367 13
## 109 980 1077
## 112 1423 1439 11
## 115 1436 1413 1
## 118 1348 1346 20
## 121 1403 1341 59
## 124 1332 1256 12
## 127 1283 1244 21
## 130 1199 1199
## 133 1242 1191 5
## 136 377 1076 35
## 139 1362 1341 18
## 142 1382 1335 17
## 145 1291 1259 26
## 148 1056 1111 29
## 151 1011 1097 27
## 154 935 1092 30
## 157 1393 1359
## 160 1270 1200 14
## 163 1186 1163 62
## 166 1153 1140
## 169 1092 1079 7
## 172 917 941 31
## 175 853 878 41
## 178 967 984 33
## 181 955 979 32
## 184 1530 1535 55
## 187 1175 1125 2
## 190 1163 1112 22
## Rnd1 Outcome Rnd1 Result Rnd2 Opponent Rnd2 Outcome Rnd2 Result
## 11 W W 21 B W
## 4 B W 58 W W
## 7 W L 61 B W
## 10 W W 28 B D
## 13 B W 37 W W
## 16 W W 29 B D
## 19 W W 46 B W
## 22 B W 32 W W
## 25 W W 18 B L
## 28 W D 19 W L
## 31 B D 56 W W
## 34 W W 33 B W
## 37 B W 27 W W
## 40 W W 44 B W
## 43 B D 16 B L
## 46 B D 15 W W
## 49 W W 41 B W
## 52 B W 9 W W
## 55 W D 10 B W
## 58 W L 49 B W
## 61 B W 1 W L
## 64 W W 52 B D
## 67 B L 43 W W
## 70 B L 47 W L
## 73 B L 53 W W
## 76 B W 40 W W
## 79 W W 13 B L
## 82 W W 4 W D
## 85 B W 6 W D
## 88 W L 64 B D
## 91 B L 55 W D
## 94 W W 8 B L
## 97 B W 12 W L
## 100 B L 60 W W
## 103 W L 38 W L
## 106 W L 57 B W
## 109 B 5 B L
## 112 W D 35 B W
## 115 B L 54 W W
## 118 B W 26 B L
## 121 B W 17 W L
## 124 B L 50 W L
## 127 W L 23 B L
## 130 B 14 W L
## 133 W L 51 B L
## 136 B W 7 W L
## 139 W L 24 B W
## 142 B L 63 W W
## 145 W L 20 W L
## 148 W L 42 B W
## 151 B L 45 W W
## 154 B W 22 W D
## 157 H 25 B L
## 160 B L 39 B L
## 163 W L 31 B D
## 166 H 11 B L
## 169 B L 36 W L
## 172 W W 2 B L
## 175 W L B
## 178 W L 34 B L
## 181 B L 3 W L
## 184 B W U
## 187 W L 48 B L
## 190 B L 30 W D
## Rnd3 Opponent Rnd3 Outcome Rnd3 Result Rnd4 Opponent Rnd4 Outcome
## 11 18 W W 14 B
## 4 4 B L 17 W
## 7 25 W W 21 B
## 10 2 W W 26 B
## 13 12 B D 13 W
## 16 11 W L 35 B
## 19 13 W W 11 B
## 22 14 B L 9 W
## 25 59 W W 8 B
## 28 55 B W 31 B
## 31 6 B W 7 W
## 34 5 W D 38 B
## 37 7 B L 5 B
## 40 8 W W 1 W
## 43 30 W W 22 W
## 46 H 39 B
## 49 26 W L 2 B
## 52 1 B L 32 W
## 55 52 W W 28 B
## 58 23 W W 41 B
## 61 47 B W 3 W
## 64 28 W L 15 B
## 67 20 B L 58 W
## 70 43 B W 25 B
## 73 3 B L 24 W
## 76 17 B W 4 W
## 79 46 W W 37 B
## 82 22 B W 19 W
## 85 38 B L 34 W
## 88 15 B L 55 W
## 91 64 B W 10 W
## 94 44 W W 18 B
## 97 50 B W 36 W
## 100 37 B L 29 B
## 103 56 B W 6 W
## 106 51 W W 33 B
## 109 34 W W 27 W
## 112 29 W W 12 W
## 115 40 B W 16 W
## 118 39 W L 59 W
## 121 58 B W 20 W
## 124 57 B L 60 B
## 127 24 W L 63 W
## 130 32 B L 53 B
## 133 60 W D 56 B
## 136 27 B L 50 W
## 139 21 W L 61 B
## 142 H 52 B
## 145 63 B D 64 W
## 148 33 W L 46 B
## 151 36 B L 57 W
## 154 19 B L 48 W
## 157 H 44 W
## 160 61 W L
## 163 10 W L 30 B
## 166 35 W L 45 W
## 169 42 W W 51 B
## 172 41 W L 23 B
## 175 9 B L 40 B
## 178 45 B D 42 W
## 181 54 B W 47 W
## 184 U
## 187 49 W D 43 B
## 190 31 W L 49 B
## Rnd4 Result Rnd5 Opponent Rnd5 Outcome Rnd5 Result Rnd6 Opponent
## 11 W 7 W W 12
## 4 W 16 B W 20
## 7 W 11 W W 13
## 10 W 5 W D 19
## 13 D 4 B D 14
## 16 W 10 B D 27
## 19 W 1 B L 9
## 22 L 47 B W 28
## 25 W 26 W W 7
## 28 W 6 W D 25
## 31 L 3 B L 34
## 34 W H 1
## 37 D 33 W W 3
## 40 L 27 B D 5
## 43 L 54 B W 33
## 46 W 2 W L 36
## 49 L 23 W W 22
## 52 W 19 B L 38
## 55 D 18 W W 4
## 58 W 28 W W 2
## 61 L 40 W W 39
## 64 W H 17
## 67 W 17 B L 37
## 70 L 60 W W 44
## 73 W 34 B D 10
## 76 L 9 B L 32
## 79 W 14 W D 6
## 82 D 20 B L 8
## 85 L 52 W W 48
## 88 W 31 W L 61
## 91 L 30 B W 50
## 94 L 51 W W 26
## 97 D 13 B L 15
## 100 W 25 W D 11
## 103 L 57 B W 52
## 106 D H 16
## 109 L H 23
## 112 L H 18
## 115 L 44 B W 21
## 118 W 21 B L 56
## 121 L X
## 124 D 61 W D 64
## 127 W 59 B W 46
## 130 W 39 W L 24
## 133 L 63 W W 55
## 136 L 64 B W 43
## 139 W 8 W L 51
## 142 D H 29
## 145 D 58 B W
## 148 W H 31
## 151 W 32 B L 47
## 154 D 29 B L 35
## 157 L U 57
## 160 B 15 W L 59
## 163 L B 45
## 166 W H 40
## 169 L 35 W L 53
## 172 L 49 W L
## 175 L 43 W L 54
## 178 D 24 B L
## 181 L 42 B D 30
## 184 U U
## 187 L 45 B L
## 190 D 46 W L 42
## Rnd6 Outcome Rnd6 Result Rnd7 Opponent Rnd7 Outcome Rnd7 Result
## 11 B D 4 W D
## 4 W W 7 B W
## 7 B W 12 W W
## 10 B W 1 B D
## 13 W W 17 B W
## 16 W W 21 B W
## 19 W W 2 W L
## 22 W W 19 W W
## 25 B L 20 B W
## 28 B W 18 W W
## 31 W W 26 B W
## 34 W D 3 B L
## 37 W L 32 B W
## 40 B L 31 W W
## 43 B W 38 W W
## 46 B W U
## 49 B W 5 W L
## 52 W W 10 B L
## 55 W L 8 B L
## 58 B L 9 W L
## 61 B W 6 W L
## 64 W L 40 B W
## 67 W W 46 B W
## 70 W W 39 B W
## 73 W L 47 B W
## 76 W D 11 W L
## 79 B L U
## 82 B L 36 W D
## 85 B W U
## 88 B W 50 B W
## 91 W W 14 B L
## 94 B D 13 W L
## 97 W L 51 B W
## 100 B L 52 W W
## 103 B D 48 W W
## 106 W L 28 B D
## 109 B L 61 W W
## 112 B L 15 B L
## 115 W L 24 W L
## 118 W W 22 W L
## 121 U U
## 124 W W 56 B W
## 127 B L 55 W W
## 130 B L 59 W W
## 133 B D 58 W W
## 136 W W 23 W L
## 139 B D 25 W L
## 142 W L 35 B L
## 145 H U
## 148 B L 30 W L
## 151 W D 33 W L
## 154 W D 34 B L
## 157 W W U
## 160 B L 64 W W
## 163 W D 43 B L
## 166 B L 42 W L
## 169 B L B
## 172 B 45 B L
## 175 W W 44 B L
## 178 H U
## 181 W L 37 B L
## 184 U U
## 187 H U
## 190 B L 54 B Lchess.output.cols <- c("Player\'s Name", "Player\'s State", "Tourney Points", "Player\'s Pre-Tourney Rating", "Avg Pre-Tourney Opponent Rating")
output.table <- chess.final[chess.output.cols]
output.table[5] <- as.integer(output.table$`Avg Pre-Tourney Opponent Rating`)
output.table## Player's Name Player's State Tourney Points
## 11 GARY HUA ON 6.0
## 4 DAKSHESH DARURI MI 6.0
## 7 ADITYA BAJAJ MI 6.0
## 10 PATRICK H SCHILLING MI 5.5
## 13 HANSHI ZUO MI 5.5
## 16 HANSEN SONG OH 5.0
## 19 GARY DEE SWATHELL MI 5.0
## 22 EZEKIEL HOUGHTON MI 5.0
## 25 STEFANO LEE ON 5.0
## 28 ANVIT RAO MI 5.0
## 31 CAMERON WILLIAM MC LEMAN MI 4.5
## 34 KENNETH J TACK MI 4.5
## 37 TORRANCE HENRY JR MI 4.5
## 40 BRADLEY SHAW MI 4.5
## 43 ZACHARY JAMES HOUGHTON MI 4.5
## 46 MIKE NIKITIN MI 4.0
## 49 RONALD GRZEGORCZYK MI 4.0
## 52 DAVID SUNDEEN MI 4.0
## 55 DIPANKAR ROY MI 4.0
## 58 JASON ZHENG MI 4.0
## 61 DINH DANG BUI ON 4.0
## 64 EUGENE L MCCLURE MI 4.0
## 67 ALAN BUI ON 4.0
## 70 MICHAEL R ALDRICH MI 4.0
## 73 LOREN SCHWIEBERT MI 3.5
## 76 MAX ZHU ON 3.5
## 79 GAURAV GIDWANI MI 3.5
## 82 SOFIA ADINA STANESCU-BELLU MI 3.5
## 85 CHIEDOZIE OKORIE MI 3.5
## 88 GEORGE AVERY JONES ON 3.5
## 91 RISHI SHETTY MI 3.5
## 94 JOSHUA PHILIP MATHEWS ON 3.5
## 97 JADE GE MI 3.5
## 100 MICHAEL JEFFERY THOMAS MI 3.5
## 103 JOSHUA DAVID LEE MI 3.5
## 106 SIDDHARTH JHA MI 3.5
## 109 AMIYATOSH PWNANANDAM MI 3.5
## 112 BRIAN LIU MI 3.0
## 115 JOEL R HENDON MI 3.0
## 118 FOREST ZHANG MI 3.0
## 121 KYLE WILLIAM MURPHY MI 3.0
## 124 JARED GE MI 3.0
## 127 ROBERT GLEN VASEY MI 3.0
## 130 JUSTIN D SCHILLING MI 3.0
## 133 DEREK YAN MI 3.0
## 136 JACOB ALEXANDER LAVALLEY MI 3.0
## 139 ERIC WRIGHT MI 2.5
## 142 DANIEL KHAIN MI 2.5
## 145 MICHAEL J MARTIN MI 2.5
## 148 SHIVAM JHA MI 2.5
## 151 TEJAS AYYAGARI MI 2.5
## 154 ETHAN GUO MI 2.5
## 157 JOSE C YBARRA MI 2.0
## 160 LARRY HODGE MI 2.0
## 163 ALEX KONG MI 2.0
## 166 MARISA RICCI MI 2.0
## 169 MICHAEL LU MI 2.0
## 172 VIRAJ MOHILE MI 2.0
## 175 SEAN M MC CORMICK MI 2.0
## 178 JULIA SHEN MI 1.5
## 181 JEZZEL FARKAS ON 1.5
## 184 ASHWIN BALAJI MI 1.0
## 187 THOMAS JOSEPH HOSMER MI 1.0
## 190 BEN LI MI 1.0
## Player's Pre-Tourney Rating Avg Pre-Tourney Opponent Rating
## 11 1794 27
## 4 1553 24
## 7 1384 47
## 10 1716 42
## 13 1655 38
## 16 1686 37
## 19 1649 38
## 22 1641 36
## 25 1411 35
## 28 1365 35
## 31 1712 35
## 34 1663 44
## 37 1666 35
## 40 1610 29
## 43 1220 30
## 46 1604 58
## 49 1629 35
## 52 1600 30
## 55 1564 26
## 58 1595 35
## 61 1563 21
## 64 1555 44
## 67 1363 28
## 70 1229 28
## 73 1745 30
## 76 1579 28
## 79 1552 48
## 82 1507 39
## 85 1602 30
## 88 1522 23
## 91 1494 31
## 94 1441 33
## 97 1449 30
## 100 1399 26
## 103 1438 14
## 106 1355 46
## 109 980 58
## 112 1423 56
## 115 1436 24
## 118 1348 26
## 121 1403 89
## 124 1332 16
## 127 1283 36
## 130 1199 41
## 133 1242 19
## 136 377 32
## 139 1362 52
## 142 1382 52
## 145 1291 55
## 148 1056 38
## 151 1011 35
## 154 935 39
## 157 1393 115
## 160 1270 44
## 163 1186 37
## 166 1153 55
## 169 1092 47
## 172 917 50
## 175 853 35
## 178 967 55
## 181 955 30
## 184 1530 356
## 187 1175 48
## 190 1163 37