options(warn=-1)
library(XML)
library(httr)
library(curl)##
## Attaching package: 'curl'## The following object is masked from 'package:httr':
##
## handle_resetlibrary(RCurl)## Loading required package: bitopslibrary(stringr)
library(tm)## Loading required package: NLP##
## Attaching package: 'NLP'## The following object is masked from 'package:httr':
##
## contentlibrary(sm)## Package 'sm', version 2.2-5.5: type help(sm) for summary informationtxt<- character()
txt <- c(txt, readLines('tournamentinfo.txt'))
txt <- str_c(txt, collapse = "\n")
txt_parsed <-unlist(str_split(txt, "-----------------------------------------------------------------------------------------"))
txt_parsed<-str_replace(txt_parsed,pattern="^\n","")
txt_parsed<-str_replace(txt_parsed,pattern="\n$","")
txt_parsed<-str_replace(txt_parsed," \n Num ","Num")
txt_parsed<-str_replace(txt_parsed," / R:","|")
txt_parsed<-str_replace(txt_parsed,"USCF ID / Rtg ", "USCF ID|Rtg")
txt_parsed<-str_replace(txt_parsed,"->","|")
txt_parsed<-str_replace(txt_parsed,"Rtg\\(Pre|Post\\)", "Rating Prior|Rating Post")
txt_parsed<-str_replace(txt_parsed,"Post\\)", "")
txt_parsed<-str_replace(txt_parsed,"\n","")
txt_parsed<-txt_parsed[txt_parsed != ""]txt_parsed[2]## [1] " 1 | GARY HUA |6.0 |W 39|W 21|W 18|W 14|W 7|D 12|D 4| ON | 15445895| 1794 |1817 |N:2 |W |B |W |B |W |B |W |"str_locate_all(txt_parsed[2], "\\|")## [[1]]
## start end
## [1,] 7 7
## [2,] 41 41
## [3,] 47 47
## [4,] 53 53
## [5,] 59 59
## [6,] 65 65
## [7,] 71 71
## [8,] 77 77
## [9,] 83 83
## [10,] 89 89
## [11,] 96 96
## [12,] 106 106
## [13,] 115 115
## [14,] 125 125
## [15,] 131 131
## [16,] 137 137
## [17,] 143 143
## [18,] 149 149
## [19,] 155 155
## [20,] 161 161
## [21,] 167 167
## [22,] 173 173player_id <- trimws(str_sub(txt_parsed[2:65],0,6))
player_name<- trimws(str_sub(txt_parsed[2:65],8,40))
player_state<- trimws(str_sub(txt_parsed[2:65],90,95))
total_no_of_points<-trimws(str_sub(txt_parsed[2:65],42,46))
player_pre_rating<-str_replace(trimws(str_sub(txt_parsed[2:65],107,114)),"P.*$","")
player_opp<-trimws(str_sub(txt_parsed[2:65],48,88))
black_white <- trimws(str_sub(txt_parsed[2:65],132,168))player_id<-as.numeric(player_id)
player_id## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
## [24] 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46
## [47] 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64player_name## [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"player_state## [1] "ON" "MI" "MI" "MI" "MI" "OH" "MI" "MI" "ON" "MI" "MI" "MI" "MI" "MI"
## [15] "MI" "MI" "MI" "MI" "MI" "MI" "ON" "MI" "ON" "MI" "MI" "ON" "MI" "MI"
## [29] "MI" "ON" "MI" "ON" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI"
## [43] "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI"
## [57] "MI" "MI" "MI" "MI" "ON" "MI" "MI" "MI"total_no_of_points<-as.numeric(total_no_of_points)
total_no_of_points## [1] 6.0 6.0 6.0 5.5 5.5 5.0 5.0 5.0 5.0 5.0 4.5 4.5 4.5 4.5 4.5 4.0 4.0
## [18] 4.0 4.0 4.0 4.0 4.0 4.0 4.0 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5
## [35] 3.5 3.5 3.5 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 2.5 2.5 2.5 2.5 2.5
## [52] 2.5 2.0 2.0 2.0 2.0 2.0 2.0 2.0 1.5 1.5 1.0 1.0 1.0player_pre_rating<-as.numeric(player_pre_rating)
player_pre_rating## [1] 1794 1553 1384 1716 1655 1686 1649 1641 1411 1365 1712 1663 1666 1610
## [15] 1220 1604 1629 1600 1564 1595 1563 1555 1363 1229 1745 1579 1552 1507
## [29] 1602 1522 1494 1441 1449 1399 1438 1355 980 1423 1436 1348 1403 1332
## [43] 1283 1199 1242 377 1362 1382 1291 1056 1011 935 1393 1270 1186 1153
## [57] 1092 917 853 967 955 1530 1175 1163df = data.frame(player_id,player_name,player_state,total_no_of_points,player_pre_rating)
df## player_id player_name player_state total_no_of_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_pre_rating
## 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 1163test<- unlist(str_extract_all(player_opp[1],"[:digit:]{1,2}"))
test <- as.numeric(test)
temp<- df[df$player_id %in% test,]
mean(temp$player_pre_rating)## [1] 1605.2865a. With a successful test, I created a for loop which calculates the Average Pre Chess Rating of Opponents for all of the playerrs, and then adding that vector to the data frame.
opponent_avg <- vector(mode="numeric")
for (i in seq(1,length(player_opp)))
{
test<- unlist(str_extract_all(player_opp[i],"[:digit:]{1,2}"))
test <- as.numeric(test)
temp<- df[df$player_id %in% test,]
opponent_avg<-append(opponent_avg, mean(temp$player_pre_rating))
}
opponent_avg## [1] 1605.286 1469.286 1563.571 1573.571 1500.857 1518.714 1372.143
## [8] 1468.429 1523.143 1554.143 1467.571 1506.167 1497.857 1515.000
## [15] 1483.857 1385.800 1498.571 1480.000 1426.286 1410.857 1470.429
## [22] 1300.333 1213.857 1357.000 1363.286 1506.857 1221.667 1522.143
## [29] 1313.500 1144.143 1259.857 1378.714 1276.857 1375.286 1149.714
## [36] 1388.167 1384.800 1539.167 1429.571 1390.571 1248.500 1149.857
## [43] 1106.571 1327.000 1152.000 1357.714 1392.000 1355.800 1285.800
## [50] 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
## [64] 1263.000df$avg_pre_chess_rating_of_opponents<-c(opponent_avg)df## player_id player_name player_state total_no_of_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_pre_rating avg_pre_chess_rating_of_opponents
## 1 1794 1605.286
## 2 1553 1469.286
## 3 1384 1563.571
## 4 1716 1573.571
## 5 1655 1500.857
## 6 1686 1518.714
## 7 1649 1372.143
## 8 1641 1468.429
## 9 1411 1523.143
## 10 1365 1554.143
## 11 1712 1467.571
## 12 1663 1506.167
## 13 1666 1497.857
## 14 1610 1515.000
## 15 1220 1483.857
## 16 1604 1385.800
## 17 1629 1498.571
## 18 1600 1480.000
## 19 1564 1426.286
## 20 1595 1410.857
## 21 1563 1470.429
## 22 1555 1300.333
## 23 1363 1213.857
## 24 1229 1357.000
## 25 1745 1363.286
## 26 1579 1506.857
## 27 1552 1221.667
## 28 1507 1522.143
## 29 1602 1313.500
## 30 1522 1144.143
## 31 1494 1259.857
## 32 1441 1378.714
## 33 1449 1276.857
## 34 1399 1375.286
## 35 1438 1149.714
## 36 1355 1388.167
## 37 980 1384.800
## 38 1423 1539.167
## 39 1436 1429.571
## 40 1348 1390.571
## 41 1403 1248.500
## 42 1332 1149.857
## 43 1283 1106.571
## 44 1199 1327.000
## 45 1242 1152.000
## 46 377 1357.714
## 47 1362 1392.000
## 48 1382 1355.800
## 49 1291 1285.800
## 50 1056 1296.000
## 51 1011 1356.143
## 52 935 1494.571
## 53 1393 1345.333
## 54 1270 1206.167
## 55 1186 1406.000
## 56 1153 1414.400
## 57 1092 1363.000
## 58 917 1391.000
## 59 853 1319.000
## 60 967 1330.200
## 61 955 1327.286
## 62 1530 1186.000
## 63 1175 1350.200
## 64 1163 1263.000countofWins<- vector(mode="numeric")
countofLosses<-vector(mode="numeric")
countofDraws <-vector(mode="numeric")
countofU <-vector(mode="numeric")
countofH <-vector(mode="numeric")
countofWhite <-vector(mode="numeric")
countofBlack <- vector(mode="numeric")
for (i in seq(1,length(player_opp)))
{
wins<- unlist(str_extract_all(player_opp[i],"[W]"))
countofWins<-append(countofWins,length(wins))
losses <- unlist(str_extract_all(player_opp[i],"[L]"))
countofLosses<-append(countofLosses,length(losses))
draws <- unlist(str_extract_all(player_opp[i],"[D]"))
countofDraws<-append(countofDraws,length(draws))
U <- unlist(str_extract_all(player_opp[i],"[U]"))
countofU<-append(countofU,length(U))
H<-unlist(str_extract_all(player_opp[i],"[H]"))
countofH<-append(countofH,length(H))
WHite<-unlist(str_extract_all(black_white[i],"[W]"))
countofWhite<-append(countofWhite,length(WHite))
Black<-unlist(str_extract_all(black_white[i],"[B]"))
countofBlack<-append(countofBlack,length(Black))
}df$Wins <- countofWins
df$Losses <- countofLosses
df$Draws <- countofDraws
df$U <- countofU
df$H <- countofH
df$White<- countofWhite
df$Black<-countofBlack
df## player_id player_name player_state total_no_of_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_pre_rating avg_pre_chess_rating_of_opponents Wins Losses Draws U
## 1 1794 1605.286 5 0 2 0
## 2 1553 1469.286 6 1 0 0
## 3 1384 1563.571 6 1 0 0
## 4 1716 1573.571 4 0 3 0
## 5 1655 1500.857 4 0 3 0
## 6 1686 1518.714 4 1 2 0
## 7 1649 1372.143 5 2 0 0
## 8 1641 1468.429 5 2 0 0
## 9 1411 1523.143 5 2 0 0
## 10 1365 1554.143 4 1 2 0
## 11 1712 1467.571 4 2 1 0
## 12 1663 1506.167 3 1 2 0
## 13 1666 1497.857 4 2 1 0
## 14 1610 1515.000 4 2 1 0
## 15 1220 1483.857 4 2 1 0
## 16 1604 1385.800 3 1 1 1
## 17 1629 1498.571 4 3 0 0
## 18 1600 1480.000 4 3 0 0
## 19 1564 1426.286 3 2 2 0
## 20 1595 1410.857 4 3 0 0
## 21 1563 1470.429 4 3 0 0
## 22 1555 1300.333 3 2 1 0
## 23 1363 1213.857 4 3 0 0
## 24 1229 1357.000 4 3 0 0
## 25 1745 1363.286 3 3 1 0
## 26 1579 1506.857 3 3 1 0
## 27 1552 1221.667 3 2 1 1
## 28 1507 1522.143 2 2 3 0
## 29 1602 1313.500 3 2 1 1
## 30 1522 1144.143 3 3 1 0
## 31 1494 1259.857 3 3 1 0
## 32 1441 1378.714 3 3 1 0
## 33 1449 1276.857 3 3 1 0
## 34 1399 1375.286 3 3 1 0
## 35 1438 1149.714 3 3 1 0
## 36 1355 1388.167 2 2 2 0
## 37 980 1384.800 2 3 0 0
## 38 1423 1539.167 2 3 1 0
## 39 1436 1429.571 3 4 0 0
## 40 1348 1390.571 3 4 0 0
## 41 1403 1248.500 2 2 0 2
## 42 1332 1149.857 2 3 2 0
## 43 1283 1106.571 3 4 0 0
## 44 1199 1327.000 2 4 0 0
## 45 1242 1152.000 2 3 2 0
## 46 377 1357.714 3 4 0 0
## 47 1362 1392.000 2 4 1 0
## 48 1382 1355.800 1 3 1 0
## 49 1291 1285.800 1 2 2 1
## 50 1056 1296.000 2 4 0 0
## 51 1011 1356.143 2 4 1 0
## 52 935 1494.571 1 3 3 0
## 53 1393 1345.333 1 2 0 2
## 54 1270 1206.167 1 5 0 0
## 55 1186 1406.000 0 4 2 0
## 56 1153 1414.400 1 4 0 0
## 57 1092 1363.000 1 5 0 0
## 58 917 1391.000 1 5 0 0
## 59 853 1319.000 1 5 0 0
## 60 967 1330.200 0 3 2 1
## 61 955 1327.286 1 5 1 0
## 62 1530 1186.000 1 0 0 6
## 63 1175 1350.200 0 4 1 1
## 64 1163 1263.000 0 5 2 0
## H White Black
## 1 0 4 3
## 2 0 3 4
## 3 0 4 3
## 4 0 3 4
## 5 0 3 4
## 6 0 3 4
## 7 0 4 3
## 8 0 4 3
## 9 0 3 4
## 10 0 4 3
## 11 0 3 4
## 12 1 3 3
## 13 0 3 4
## 14 0 4 3
## 15 0 3 4
## 16 1 2 3
## 17 0 4 3
## 18 0 3 4
## 19 0 4 3
## 20 0 4 3
## 21 0 4 3
## 22 1 3 3
## 23 0 3 4
## 24 0 3 4
## 25 0 3 4
## 26 0 4 3
## 27 0 3 3
## 28 0 4 3
## 29 0 3 3
## 30 0 3 4
## 31 0 3 4
## 32 0 4 3
## 33 0 3 4
## 34 0 3 4
## 35 0 4 3
## 36 1 3 3
## 37 1 3 2
## 38 1 3 3
## 39 0 4 3
## 40 0 4 3
## 41 0 2 2
## 42 0 3 4
## 43 0 4 3
## 44 0 3 3
## 45 0 4 3
## 46 0 4 3
## 47 0 4 3
## 48 2 2 3
## 49 1 3 2
## 50 1 3 3
## 51 0 4 3
## 52 0 3 4
## 53 2 2 1
## 54 0 3 3
## 55 0 3 3
## 56 2 3 2
## 57 0 3 3
## 58 0 3 3
## 59 0 3 3
## 60 1 2 3
## 61 0 3 4
## 62 0 0 1
## 63 1 2 3
## 64 0 3 4summary(df)## player_id player_name player_state total_no_of_points
## Min. : 1.00 ADITYA BAJAJ : 1 MI:55 Min. :1.000
## 1st Qu.:16.75 ALAN BUI : 1 OH: 1 1st Qu.:2.500
## Median :32.50 ALEX KONG : 1 ON: 8 Median :3.500
## Mean :32.50 AMIYATOSH PWNANANDAM: 1 Mean :3.438
## 3rd Qu.:48.25 ANVIT RAO : 1 3rd Qu.:4.000
## Max. :64.00 ASHWIN BALAJI : 1 Max. :6.000
## (Other) :58
## player_pre_rating avg_pre_chess_rating_of_opponents Wins
## Min. : 377 Min. :1107 Min. :0.000
## 1st Qu.:1227 1st Qu.:1310 1st Qu.:2.000
## Median :1407 Median :1382 Median :3.000
## Mean :1378 Mean :1379 Mean :2.734
## 3rd Qu.:1583 3rd Qu.:1481 3rd Qu.:4.000
## Max. :1794 Max. :1605 Max. :6.000
##
## Losses Draws U H
## Min. :0.000 Min. :0.0000 Min. :0.00 Min. :0.00
## 1st Qu.:2.000 1st Qu.:0.0000 1st Qu.:0.00 1st Qu.:0.00
## Median :3.000 Median :1.0000 Median :0.00 Median :0.00
## Mean :2.734 Mean :0.9062 Mean :0.25 Mean :0.25
## 3rd Qu.:4.000 3rd Qu.:1.2500 3rd Qu.:0.00 3rd Qu.:0.00
## Max. :5.000 Max. :3.0000 Max. :6.00 Max. :2.00
##
## White Black
## Min. :0.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000
## Median :3.000 Median :3.000
## Mean :3.188 Mean :3.188
## 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :4.000 Max. :4.000
## write.csv(df, file = "Chess Tournament Results.csv", row.names = FALSE)