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
Load required packages
library("stringr")tournamentDf<-read.csv("https://raw.githubusercontent.com/rathish-ps/Data607-Assignment/main/tournamentinfo.txt",header=FALSE)
tournamentData <- matrix(unlist(tournamentDf), byrow=TRUE)dotLineSeq <- seq(1,length(tournamentData),3)
#Remove all --- line
linn1<-tournamentData[-dotLineSeq]
# Read all the alternate lines
#Read player names
playerInfo <- linn1[seq(3,length(linn1),2)]
# Read state and pre-tournament ratings
playerDetails <- linn1[seq(4,length(linn1),2)]
#combile the player information and state information into single vector
playerInfo<-paste0(playerInfo,playerDetails)
playerInfo## [1] " 1 | GARY HUA |6.0 |W 39|W 21|W 18|W 14|W 7|D 12|D 4| ON | 15445895 / R: 1794 ->1817 |N:2 |W |B |W |B |W |B |W |"
## [2] " 2 | DAKSHESH DARURI |6.0 |W 63|W 58|L 4|W 17|W 16|W 20|W 7| MI | 14598900 / R: 1553 ->1663 |N:2 |B |W |B |W |B |W |B |"
## [3] " 3 | ADITYA BAJAJ |6.0 |L 8|W 61|W 25|W 21|W 11|W 13|W 12| MI | 14959604 / R: 1384 ->1640 |N:2 |W |B |W |B |W |B |W |"
## [4] " 4 | PATRICK H SCHILLING |5.5 |W 23|D 28|W 2|W 26|D 5|W 19|D 1| MI | 12616049 / R: 1716 ->1744 |N:2 |W |B |W |B |W |B |B |"
## [5] " 5 | HANSHI ZUO |5.5 |W 45|W 37|D 12|D 13|D 4|W 14|W 17| MI | 14601533 / R: 1655 ->1690 |N:2 |B |W |B |W |B |W |B |"
## [6] " 6 | HANSEN SONG |5.0 |W 34|D 29|L 11|W 35|D 10|W 27|W 21| OH | 15055204 / R: 1686 ->1687 |N:3 |W |B |W |B |B |W |B |"
## [7] " 7 | GARY DEE SWATHELL |5.0 |W 57|W 46|W 13|W 11|L 1|W 9|L 2| MI | 11146376 / R: 1649 ->1673 |N:3 |W |B |W |B |B |W |W |"
## [8] " 8 | EZEKIEL HOUGHTON |5.0 |W 3|W 32|L 14|L 9|W 47|W 28|W 19| MI | 15142253 / R: 1641P17->1657P24 |N:3 |B |W |B |W |B |W |W |"
## [9] " 9 | STEFANO LEE |5.0 |W 25|L 18|W 59|W 8|W 26|L 7|W 20| ON | 14954524 / R: 1411 ->1564 |N:2 |W |B |W |B |W |B |B |"
## [10] " 10 | ANVIT RAO |5.0 |D 16|L 19|W 55|W 31|D 6|W 25|W 18| MI | 14150362 / R: 1365 ->1544 |N:3 |W |W |B |B |W |B |W |"
## [11] " 11 | CAMERON WILLIAM MC LEMAN |4.5 |D 38|W 56|W 6|L 7|L 3|W 34|W 26| MI | 12581589 / R: 1712 ->1696 |N:3 |B |W |B |W |B |W |B |"
## [12] " 12 | KENNETH J TACK |4.5 |W 42|W 33|D 5|W 38|H |D 1|L 3| MI | 12681257 / R: 1663 ->1670 |N:3 |W |B |W |B | |W |B |"
## [13] " 13 | TORRANCE HENRY JR |4.5 |W 36|W 27|L 7|D 5|W 33|L 3|W 32| MI | 15082995 / R: 1666 ->1662 |N:3 |B |W |B |B |W |W |B |"
## [14] " 14 | BRADLEY SHAW |4.5 |W 54|W 44|W 8|L 1|D 27|L 5|W 31| MI | 10131499 / R: 1610 ->1618 |N:3 |W |B |W |W |B |B |W |"
## [15] " 15 | ZACHARY JAMES HOUGHTON |4.5 |D 19|L 16|W 30|L 22|W 54|W 33|W 38| MI | 15619130 / R: 1220P13->1416P20 |N:3 |B |B |W |W |B |B |W |"
## [16] " 16 | MIKE NIKITIN |4.0 |D 10|W 15|H |W 39|L 2|W 36|U | MI | 10295068 / R: 1604 ->1613 |N:3 |B |W | |B |W |B | |"
## [17] " 17 | RONALD GRZEGORCZYK |4.0 |W 48|W 41|L 26|L 2|W 23|W 22|L 5| MI | 10297702 / R: 1629 ->1610 |N:3 |W |B |W |B |W |B |W |"
## [18] " 18 | DAVID SUNDEEN |4.0 |W 47|W 9|L 1|W 32|L 19|W 38|L 10| MI | 11342094 / R: 1600 ->1600 |N:3 |B |W |B |W |B |W |B |"
## [19] " 19 | DIPANKAR ROY |4.0 |D 15|W 10|W 52|D 28|W 18|L 4|L 8| MI | 14862333 / R: 1564 ->1570 |N:3 |W |B |W |B |W |W |B |"
## [20] " 20 | JASON ZHENG |4.0 |L 40|W 49|W 23|W 41|W 28|L 2|L 9| MI | 14529060 / R: 1595 ->1569 |N:4 |W |B |W |B |W |B |W |"
## [21] " 21 | DINH DANG BUI |4.0 |W 43|L 1|W 47|L 3|W 40|W 39|L 6| ON | 15495066 / R: 1563P22->1562 |N:3 |B |W |B |W |W |B |W |"
## [22] " 22 | EUGENE L MCCLURE |4.0 |W 64|D 52|L 28|W 15|H |L 17|W 40| MI | 12405534 / R: 1555 ->1529 |N:4 |W |B |W |B | |W |B |"
## [23] " 23 | ALAN BUI |4.0 |L 4|W 43|L 20|W 58|L 17|W 37|W 46| ON | 15030142 / R: 1363 ->1371 | |B |W |B |W |B |W |B |"
## [24] " 24 | MICHAEL R ALDRICH |4.0 |L 28|L 47|W 43|L 25|W 60|W 44|W 39| MI | 13469010 / R: 1229 ->1300 |N:4 |B |W |B |B |W |W |B |"
## [25] " 25 | LOREN SCHWIEBERT |3.5 |L 9|W 53|L 3|W 24|D 34|L 10|W 47| MI | 12486656 / R: 1745 ->1681 |N:4 |B |W |B |W |B |W |B |"
## [26] " 26 | MAX ZHU |3.5 |W 49|W 40|W 17|L 4|L 9|D 32|L 11| ON | 15131520 / R: 1579 ->1564 |N:4 |B |W |B |W |B |W |W |"
## [27] " 27 | GAURAV GIDWANI |3.5 |W 51|L 13|W 46|W 37|D 14|L 6|U | MI | 14476567 / R: 1552 ->1539 |N:4 |W |B |W |B |W |B | |"
## [28] " 28 | SOFIA ADINA STANESCU-BELLU |3.5 |W 24|D 4|W 22|D 19|L 20|L 8|D 36| MI | 14882954 / R: 1507 ->1513 |N:3 |W |W |B |W |B |B |W |"
## [29] " 29 | CHIEDOZIE OKORIE |3.5 |W 50|D 6|L 38|L 34|W 52|W 48|U | MI | 15323285 / R: 1602P6 ->1508P12 |N:4 |B |W |B |W |W |B | |"
## [30] " 30 | GEORGE AVERY JONES |3.5 |L 52|D 64|L 15|W 55|L 31|W 61|W 50| ON | 12577178 / R: 1522 ->1444 | |W |B |B |W |W |B |B |"
## [31] " 31 | RISHI SHETTY |3.5 |L 58|D 55|W 64|L 10|W 30|W 50|L 14| MI | 15131618 / R: 1494 ->1444 | |B |W |B |W |B |W |B |"
## [32] " 32 | JOSHUA PHILIP MATHEWS |3.5 |W 61|L 8|W 44|L 18|W 51|D 26|L 13| ON | 14073750 / R: 1441 ->1433 |N:4 |W |B |W |B |W |B |W |"
## [33] " 33 | JADE GE |3.5 |W 60|L 12|W 50|D 36|L 13|L 15|W 51| MI | 14691842 / R: 1449 ->1421 | |B |W |B |W |B |W |B |"
## [34] " 34 | MICHAEL JEFFERY THOMAS |3.5 |L 6|W 60|L 37|W 29|D 25|L 11|W 52| MI | 15051807 / R: 1399 ->1400 | |B |W |B |B |W |B |W |"
## [35] " 35 | JOSHUA DAVID LEE |3.5 |L 46|L 38|W 56|L 6|W 57|D 52|W 48| MI | 14601397 / R: 1438 ->1392 | |W |W |B |W |B |B |W |"
## [36] " 36 | SIDDHARTH JHA |3.5 |L 13|W 57|W 51|D 33|H |L 16|D 28| MI | 14773163 / R: 1355 ->1367 |N:4 |W |B |W |B | |W |B |"
## [37] " 37 | AMIYATOSH PWNANANDAM |3.5 |B |L 5|W 34|L 27|H |L 23|W 61| MI | 15489571 / R: 980P12->1077P17 | | |B |W |W | |B |W |"
## [38] " 38 | BRIAN LIU |3.0 |D 11|W 35|W 29|L 12|H |L 18|L 15| MI | 15108523 / R: 1423 ->1439 |N:4 |W |B |W |W | |B |B |"
## [39] " 39 | JOEL R HENDON |3.0 |L 1|W 54|W 40|L 16|W 44|L 21|L 24| MI | 12923035 / R: 1436P23->1413 |N:4 |B |W |B |W |B |W |W |"
## [40] " 40 | FOREST ZHANG |3.0 |W 20|L 26|L 39|W 59|L 21|W 56|L 22| MI | 14892710 / R: 1348 ->1346 | |B |B |W |W |B |W |W |"
## [41] " 41 | KYLE WILLIAM MURPHY |3.0 |W 59|L 17|W 58|L 20|X |U |U | MI | 15761443 / R: 1403P5 ->1341P9 | |B |W |B |W | | | |"
## [42] " 42 | JARED GE |3.0 |L 12|L 50|L 57|D 60|D 61|W 64|W 56| MI | 14462326 / R: 1332 ->1256 | |B |W |B |B |W |W |B |"
## [43] " 43 | ROBERT GLEN VASEY |3.0 |L 21|L 23|L 24|W 63|W 59|L 46|W 55| MI | 14101068 / R: 1283 ->1244 | |W |B |W |W |B |B |W |"
## [44] " 44 | JUSTIN D SCHILLING |3.0 |B |L 14|L 32|W 53|L 39|L 24|W 59| MI | 15323504 / R: 1199 ->1199 | | |W |B |B |W |B |W |"
## [45] " 45 | DEREK YAN |3.0 |L 5|L 51|D 60|L 56|W 63|D 55|W 58| MI | 15372807 / R: 1242 ->1191 | |W |B |W |B |W |B |W |"
## [46] " 46 | JACOB ALEXANDER LAVALLEY |3.0 |W 35|L 7|L 27|L 50|W 64|W 43|L 23| MI | 15490981 / R: 377P3 ->1076P10 | |B |W |B |W |B |W |W |"
## [47] " 47 | ERIC WRIGHT |2.5 |L 18|W 24|L 21|W 61|L 8|D 51|L 25| MI | 12533115 / R: 1362 ->1341 | |W |B |W |B |W |B |W |"
## [48] " 48 | DANIEL KHAIN |2.5 |L 17|W 63|H |D 52|H |L 29|L 35| MI | 14369165 / R: 1382 ->1335 | |B |W | |B | |W |B |"
## [49] " 49 | MICHAEL J MARTIN |2.5 |L 26|L 20|D 63|D 64|W 58|H |U | MI | 12531685 / R: 1291P12->1259P17 | |W |W |B |W |B | | |"
## [50] " 50 | SHIVAM JHA |2.5 |L 29|W 42|L 33|W 46|H |L 31|L 30| MI | 14773178 / R: 1056 ->1111 | |W |B |W |B | |B |W |"
## [51] " 51 | TEJAS AYYAGARI |2.5 |L 27|W 45|L 36|W 57|L 32|D 47|L 33| MI | 15205474 / R: 1011 ->1097 | |B |W |B |W |B |W |W |"
## [52] " 52 | ETHAN GUO |2.5 |W 30|D 22|L 19|D 48|L 29|D 35|L 34| MI | 14918803 / R: 935 ->1092 |N:4 |B |W |B |W |B |W |B |"
## [53] " 53 | JOSE C YBARRA |2.0 |H |L 25|H |L 44|U |W 57|U | MI | 12578849 / R: 1393 ->1359 | | |B | |W | |W | |"
## [54] " 54 | LARRY HODGE |2.0 |L 14|L 39|L 61|B |L 15|L 59|W 64| MI | 12836773 / R: 1270 ->1200 | |B |B |W | |W |B |W |"
## [55] " 55 | ALEX KONG |2.0 |L 62|D 31|L 10|L 30|B |D 45|L 43| MI | 15412571 / R: 1186 ->1163 | |W |B |W |B | |W |B |"
## [56] " 56 | MARISA RICCI |2.0 |H |L 11|L 35|W 45|H |L 40|L 42| MI | 14679887 / R: 1153 ->1140 | | |B |W |W | |B |W |"
## [57] " 57 | MICHAEL LU |2.0 |L 7|L 36|W 42|L 51|L 35|L 53|B | MI | 15113330 / R: 1092 ->1079 | |B |W |W |B |W |B | |"
## [58] " 58 | VIRAJ MOHILE |2.0 |W 31|L 2|L 41|L 23|L 49|B |L 45| MI | 14700365 / R: 917 -> 941 | |W |B |W |B |W | |B |"
## [59] " 59 | SEAN M MC CORMICK |2.0 |L 41|B |L 9|L 40|L 43|W 54|L 44| MI | 12841036 / R: 853 -> 878 | |W | |B |B |W |W |B |"
## [60] " 60 | JULIA SHEN |1.5 |L 33|L 34|D 45|D 42|L 24|H |U | MI | 14579262 / R: 967 -> 984 | |W |B |B |W |B | | |"
## [61] " 61 | JEZZEL FARKAS |1.5 |L 32|L 3|W 54|L 47|D 42|L 30|L 37| ON | 15771592 / R: 955P11-> 979P18 | |B |W |B |W |B |W |B |"
## [62] " 62 | ASHWIN BALAJI |1.0 |W 55|U |U |U |U |U |U | MI | 15219542 / R: 1530 ->1535 | |B | | | | | | |"
## [63] " 63 | THOMAS JOSEPH HOSMER |1.0 |L 2|L 48|D 49|L 43|L 45|H |U | MI | 15057092 / R: 1175 ->1125 | |W |B |W |B |B | | |"
## [64] " 64 | BEN LI |1.0 |L 22|D 30|L 31|D 49|L 46|L 42|L 54| MI | 15006561 / R: 1163 ->1112 | |B |W |W |B |W |B |B |"# Create an Empty data frame
playerInfoDf <- data.frame(data1 = character(0),Name = character(0),data3= character(0),Round1= character(0),Round2= character(0),Round3= character(0),Round4= character(0),Round5= character(0),Round6= character(0),Round7= character(0),state= character(0),data12= character(0))
# Split the string by | and populate records in data frame (only first 12 columns)
i<- 1
while (i <= length(playerInfo)){
playerInfoDf[nrow(playerInfoDf) + 1, ] <- str_trim(strsplit(playerInfo[i],"|",fixed=TRUE)[[1]])[1:12]
i <- i+1
}
head(playerInfoDf)## data1 Name data3 Round1 Round2 Round3 Round4 Round5 Round6
## 1 1 GARY HUA 6.0 W 39 W 21 W 18 W 14 W 7 D 12
## 2 2 DAKSHESH DARURI 6.0 W 63 W 58 L 4 W 17 W 16 W 20
## 3 3 ADITYA BAJAJ 6.0 L 8 W 61 W 25 W 21 W 11 W 13
## 4 4 PATRICK H SCHILLING 5.5 W 23 D 28 W 2 W 26 D 5 W 19
## 5 5 HANSHI ZUO 5.5 W 45 W 37 D 12 D 13 D 4 W 14
## 6 6 HANSEN SONG 5.0 W 34 D 29 L 11 W 35 D 10 W 27
## Round7 state data12
## 1 D 4 ON 15445895 / R: 1794 ->1817
## 2 W 7 MI 14598900 / R: 1553 ->1663
## 3 W 12 MI 14959604 / R: 1384 ->1640
## 4 D 1 MI 12616049 / R: 1716 ->1744
## 5 W 17 MI 14601533 / R: 1655 ->1690
## 6 W 21 OH 15055204 / R: 1686 ->1687#Player’s Name, Player’s State, Total Number of Points, Player’s Pre-Rating, and Average Pre Chess Rating of Opponents
finalResultCSVFrame <- data.frame(Name = character(0),State = character(0),TotalPoints= character(0),PreRating= character(0),AvgRatingOfOpp= character(0))
#iterate each record
for(i in 1:nrow(playerInfoDf)){
value3 <- 0
cnt <-0
# extract opponent id for each round (1 to 7 ) -from column 4,10
for (j in 4:10){
#split each round column by space
roundVals <- strsplit(playerInfoDf[i,j], "\\s+")
if(roundVals[[1]][1]== "W" || roundVals[[1]][1]== "L"|| roundVals[[1]][1]== "D"){
#using the row id ,find opponent Pre-rating column
value2 <- playerInfoDf[roundVals[[1]][2],12]
#find the 2nd number ,ie number after R: ,and sum it
value3 <- value3 + as.numeric(regmatches(value2 , gregexpr("[[:digit:]]+", value2 ))[[1]][2])
cnt <- cnt +1
}
}
#find the mean value and round it
meanValue <- round(value3/cnt)
#find the player pre-rating info
playerPreValue <- as.numeric(regmatches(playerInfoDf[i,12] , gregexpr("[[:digit:]]+", playerInfoDf[i,12] ))[[1]][2])
finalResultCSVFrame[nrow(finalResultCSVFrame) + 1, ] <-c(playerInfoDf[i,'Name'],playerInfoDf[i,'state'],playerInfoDf[i,'data3'],playerPreValue,meanValue)
}
finalResultCSVFrame## Name State TotalPoints PreRating AvgRatingOfOpp
## 1 GARY HUA ON 6.0 1794 1605
## 2 DAKSHESH DARURI MI 6.0 1553 1469
## 3 ADITYA BAJAJ MI 6.0 1384 1564
## 4 PATRICK H SCHILLING MI 5.5 1716 1574
## 5 HANSHI ZUO MI 5.5 1655 1501
## 6 HANSEN SONG OH 5.0 1686 1519
## 7 GARY DEE SWATHELL MI 5.0 1649 1372
## 8 EZEKIEL HOUGHTON MI 5.0 1641 1468
## 9 STEFANO LEE ON 5.0 1411 1523
## 10 ANVIT RAO MI 5.0 1365 1554
## 11 CAMERON WILLIAM MC LEMAN MI 4.5 1712 1468
## 12 KENNETH J TACK MI 4.5 1663 1506
## 13 TORRANCE HENRY JR MI 4.5 1666 1498
## 14 BRADLEY SHAW MI 4.5 1610 1515
## 15 ZACHARY JAMES HOUGHTON MI 4.5 1220 1484
## 16 MIKE NIKITIN MI 4.0 1604 1386
## 17 RONALD GRZEGORCZYK MI 4.0 1629 1499
## 18 DAVID SUNDEEN MI 4.0 1600 1480
## 19 DIPANKAR ROY MI 4.0 1564 1426
## 20 JASON ZHENG MI 4.0 1595 1411
## 21 DINH DANG BUI ON 4.0 1563 1470
## 22 EUGENE L MCCLURE MI 4.0 1555 1300
## 23 ALAN BUI ON 4.0 1363 1214
## 24 MICHAEL R ALDRICH MI 4.0 1229 1357
## 25 LOREN SCHWIEBERT MI 3.5 1745 1363
## 26 MAX ZHU ON 3.5 1579 1507
## 27 GAURAV GIDWANI MI 3.5 1552 1222
## 28 SOFIA ADINA STANESCU-BELLU MI 3.5 1507 1522
## 29 CHIEDOZIE OKORIE MI 3.5 1602 1314
## 30 GEORGE AVERY JONES ON 3.5 1522 1144
## 31 RISHI SHETTY MI 3.5 1494 1260
## 32 JOSHUA PHILIP MATHEWS ON 3.5 1441 1379
## 33 JADE GE MI 3.5 1449 1277
## 34 MICHAEL JEFFERY THOMAS MI 3.5 1399 1375
## 35 JOSHUA DAVID LEE MI 3.5 1438 1150
## 36 SIDDHARTH JHA MI 3.5 1355 1388
## 37 AMIYATOSH PWNANANDAM MI 3.5 980 1385
## 38 BRIAN LIU MI 3.0 1423 1539
## 39 JOEL R HENDON MI 3.0 1436 1430
## 40 FOREST ZHANG MI 3.0 1348 1391
## 41 KYLE WILLIAM MURPHY MI 3.0 1403 1248
## 42 JARED GE MI 3.0 1332 1150
## 43 ROBERT GLEN VASEY MI 3.0 1283 1107
## 44 JUSTIN D SCHILLING MI 3.0 1199 1327
## 45 DEREK YAN MI 3.0 1242 1152
## 46 JACOB ALEXANDER LAVALLEY MI 3.0 377 1358
## 47 ERIC WRIGHT MI 2.5 1362 1392
## 48 DANIEL KHAIN MI 2.5 1382 1356
## 49 MICHAEL J MARTIN MI 2.5 1291 1286
## 50 SHIVAM JHA MI 2.5 1056 1296
## 51 TEJAS AYYAGARI MI 2.5 1011 1356
## 52 ETHAN GUO MI 2.5 935 1495
## 53 JOSE C YBARRA MI 2.0 1393 1345
## 54 LARRY HODGE MI 2.0 1270 1206
## 55 ALEX KONG MI 2.0 1186 1406
## 56 MARISA RICCI MI 2.0 1153 1414
## 57 MICHAEL LU MI 2.0 1092 1363
## 58 VIRAJ MOHILE MI 2.0 917 1391
## 59 SEAN M MC CORMICK MI 2.0 853 1319
## 60 JULIA SHEN MI 1.5 967 1330
## 61 JEZZEL FARKAS ON 1.5 955 1327
## 62 ASHWIN BALAJI MI 1.0 1530 1186
## 63 THOMAS JOSEPH HOSMER MI 1.0 1175 1350
## 64 BEN LI MI 1.0 1163 1263#write the data into CSV file
write.csv(finalResultCSVFrame,"playerInfo.csv")The application read the text input file, transform the data into required output format and finally write the data frame into a CSV file (playerInfo.csv)