Read dataset from url on github
myurl <- "https://raw.githubusercontent.com/BanuB/Week4AssignmentDATA607/master/tournamentinfo.txt"
x = read.csv(file=myurl)
filename <- "tournamenttxtfile.txt"
downloader::download(myurl, filename)
df <- readLines(filename)## Warning in readLines(filename): incomplete final line found on
## 'tournamenttxtfile.txt'myconn <- file(filename,open="r")
myfiledata <- readLines(filename,warn = FALSE)
close(myconn)
head(myfiledata)## [1] "-----------------------------------------------------------------------------------------"
## [2] " Pair | Player Name |Total|Round|Round|Round|Round|Round|Round|Round| "
## [3] " Num | USCF ID / Rtg (Pre->Post) | Pts | 1 | 2 | 3 | 4 | 5 | 6 | 7 | "
## [4] "-----------------------------------------------------------------------------------------"
## [5] " 1 | GARY HUA |6.0 |W 39|W 21|W 18|W 14|W 7|D 12|D 4|"
## [6] " ON | 15445895 / R: 1794 ->1817 |N:2 |W |B |W |B |W |B |W |"ncol(myfiledata)## NULLFinal 2 Subsets to extract and separate and join back to get only 1 row for each player
#Final 2 Subsets to extract and separate and join back to get only 1 row for each player
#------------------------------------------------------------------------
subset1 <- myfiledata[str_detect(myfiledata,"([0-9]{1}[|])")]
subset1## [1] " 1 | GARY HUA |6.0 |W 39|W 21|W 18|W 14|W 7|D 12|D 4|"
## [2] " 2 | DAKSHESH DARURI |6.0 |W 63|W 58|L 4|W 17|W 16|W 20|W 7|"
## [3] " 3 | ADITYA BAJAJ |6.0 |L 8|W 61|W 25|W 21|W 11|W 13|W 12|"
## [4] " 4 | PATRICK H SCHILLING |5.5 |W 23|D 28|W 2|W 26|D 5|W 19|D 1|"
## [5] " 5 | HANSHI ZUO |5.5 |W 45|W 37|D 12|D 13|D 4|W 14|W 17|"
## [6] " 6 | HANSEN SONG |5.0 |W 34|D 29|L 11|W 35|D 10|W 27|W 21|"
## [7] " 7 | GARY DEE SWATHELL |5.0 |W 57|W 46|W 13|W 11|L 1|W 9|L 2|"
## [8] " 8 | EZEKIEL HOUGHTON |5.0 |W 3|W 32|L 14|L 9|W 47|W 28|W 19|"
## [9] " 9 | STEFANO LEE |5.0 |W 25|L 18|W 59|W 8|W 26|L 7|W 20|"
## [10] " 10 | ANVIT RAO |5.0 |D 16|L 19|W 55|W 31|D 6|W 25|W 18|"
## [11] " 11 | CAMERON WILLIAM MC LEMAN |4.5 |D 38|W 56|W 6|L 7|L 3|W 34|W 26|"
## [12] " 12 | KENNETH J TACK |4.5 |W 42|W 33|D 5|W 38|H |D 1|L 3|"
## [13] " 13 | TORRANCE HENRY JR |4.5 |W 36|W 27|L 7|D 5|W 33|L 3|W 32|"
## [14] " 14 | BRADLEY SHAW |4.5 |W 54|W 44|W 8|L 1|D 27|L 5|W 31|"
## [15] " 15 | ZACHARY JAMES HOUGHTON |4.5 |D 19|L 16|W 30|L 22|W 54|W 33|W 38|"
## [16] " 16 | MIKE NIKITIN |4.0 |D 10|W 15|H |W 39|L 2|W 36|U |"
## [17] " 17 | RONALD GRZEGORCZYK |4.0 |W 48|W 41|L 26|L 2|W 23|W 22|L 5|"
## [18] " 18 | DAVID SUNDEEN |4.0 |W 47|W 9|L 1|W 32|L 19|W 38|L 10|"
## [19] " 19 | DIPANKAR ROY |4.0 |D 15|W 10|W 52|D 28|W 18|L 4|L 8|"
## [20] " 20 | JASON ZHENG |4.0 |L 40|W 49|W 23|W 41|W 28|L 2|L 9|"
## [21] " 21 | DINH DANG BUI |4.0 |W 43|L 1|W 47|L 3|W 40|W 39|L 6|"
## [22] " 22 | EUGENE L MCCLURE |4.0 |W 64|D 52|L 28|W 15|H |L 17|W 40|"
## [23] " 23 | ALAN BUI |4.0 |L 4|W 43|L 20|W 58|L 17|W 37|W 46|"
## [24] " 24 | MICHAEL R ALDRICH |4.0 |L 28|L 47|W 43|L 25|W 60|W 44|W 39|"
## [25] " 25 | LOREN SCHWIEBERT |3.5 |L 9|W 53|L 3|W 24|D 34|L 10|W 47|"
## [26] " 26 | MAX ZHU |3.5 |W 49|W 40|W 17|L 4|L 9|D 32|L 11|"
## [27] " 27 | GAURAV GIDWANI |3.5 |W 51|L 13|W 46|W 37|D 14|L 6|U |"
## [28] " 28 | SOFIA ADINA STANESCU-BELLU |3.5 |W 24|D 4|W 22|D 19|L 20|L 8|D 36|"
## [29] " 29 | CHIEDOZIE OKORIE |3.5 |W 50|D 6|L 38|L 34|W 52|W 48|U |"
## [30] " 30 | GEORGE AVERY JONES |3.5 |L 52|D 64|L 15|W 55|L 31|W 61|W 50|"
## [31] " 31 | RISHI SHETTY |3.5 |L 58|D 55|W 64|L 10|W 30|W 50|L 14|"
## [32] " 32 | JOSHUA PHILIP MATHEWS |3.5 |W 61|L 8|W 44|L 18|W 51|D 26|L 13|"
## [33] " 33 | JADE GE |3.5 |W 60|L 12|W 50|D 36|L 13|L 15|W 51|"
## [34] " 34 | MICHAEL JEFFERY THOMAS |3.5 |L 6|W 60|L 37|W 29|D 25|L 11|W 52|"
## [35] " 35 | JOSHUA DAVID LEE |3.5 |L 46|L 38|W 56|L 6|W 57|D 52|W 48|"
## [36] " 36 | SIDDHARTH JHA |3.5 |L 13|W 57|W 51|D 33|H |L 16|D 28|"
## [37] " 37 | AMIYATOSH PWNANANDAM |3.5 |B |L 5|W 34|L 27|H |L 23|W 61|"
## [38] " 38 | BRIAN LIU |3.0 |D 11|W 35|W 29|L 12|H |L 18|L 15|"
## [39] " 39 | JOEL R HENDON |3.0 |L 1|W 54|W 40|L 16|W 44|L 21|L 24|"
## [40] " 40 | FOREST ZHANG |3.0 |W 20|L 26|L 39|W 59|L 21|W 56|L 22|"
## [41] " 41 | KYLE WILLIAM MURPHY |3.0 |W 59|L 17|W 58|L 20|X |U |U |"
## [42] " 42 | JARED GE |3.0 |L 12|L 50|L 57|D 60|D 61|W 64|W 56|"
## [43] " 43 | ROBERT GLEN VASEY |3.0 |L 21|L 23|L 24|W 63|W 59|L 46|W 55|"
## [44] " 44 | JUSTIN D SCHILLING |3.0 |B |L 14|L 32|W 53|L 39|L 24|W 59|"
## [45] " 45 | DEREK YAN |3.0 |L 5|L 51|D 60|L 56|W 63|D 55|W 58|"
## [46] " 46 | JACOB ALEXANDER LAVALLEY |3.0 |W 35|L 7|L 27|L 50|W 64|W 43|L 23|"
## [47] " 47 | ERIC WRIGHT |2.5 |L 18|W 24|L 21|W 61|L 8|D 51|L 25|"
## [48] " 48 | DANIEL KHAIN |2.5 |L 17|W 63|H |D 52|H |L 29|L 35|"
## [49] " 49 | MICHAEL J MARTIN |2.5 |L 26|L 20|D 63|D 64|W 58|H |U |"
## [50] " 50 | SHIVAM JHA |2.5 |L 29|W 42|L 33|W 46|H |L 31|L 30|"
## [51] " 51 | TEJAS AYYAGARI |2.5 |L 27|W 45|L 36|W 57|L 32|D 47|L 33|"
## [52] " 52 | ETHAN GUO |2.5 |W 30|D 22|L 19|D 48|L 29|D 35|L 34|"
## [53] " 53 | JOSE C YBARRA |2.0 |H |L 25|H |L 44|U |W 57|U |"
## [54] " 54 | LARRY HODGE |2.0 |L 14|L 39|L 61|B |L 15|L 59|W 64|"
## [55] " 55 | ALEX KONG |2.0 |L 62|D 31|L 10|L 30|B |D 45|L 43|"
## [56] " 56 | MARISA RICCI |2.0 |H |L 11|L 35|W 45|H |L 40|L 42|"
## [57] " 57 | MICHAEL LU |2.0 |L 7|L 36|W 42|L 51|L 35|L 53|B |"
## [58] " 58 | VIRAJ MOHILE |2.0 |W 31|L 2|L 41|L 23|L 49|B |L 45|"
## [59] " 59 | SEAN M MC CORMICK |2.0 |L 41|B |L 9|L 40|L 43|W 54|L 44|"
## [60] " 60 | JULIA SHEN |1.5 |L 33|L 34|D 45|D 42|L 24|H |U |"
## [61] " 61 | JEZZEL FARKAS |1.5 |L 32|L 3|W 54|L 47|D 42|L 30|L 37|"
## [62] " 62 | ASHWIN BALAJI |1.0 |W 55|U |U |U |U |U |U |"
## [63] " 63 | THOMAS JOSEPH HOSMER |1.0 |L 2|L 48|D 49|L 43|L 45|H |U |"
## [64] " 64 | BEN LI |1.0 |L 22|D 30|L 31|D 49|L 46|L 42|L 54|"subset2 <- myfiledata[str_detect(myfiledata,"([:]{1})")]
subset2## [1] " ON | 15445895 / R: 1794 ->1817 |N:2 |W |B |W |B |W |B |W |"
## [2] " MI | 14598900 / R: 1553 ->1663 |N:2 |B |W |B |W |B |W |B |"
## [3] " MI | 14959604 / R: 1384 ->1640 |N:2 |W |B |W |B |W |B |W |"
## [4] " MI | 12616049 / R: 1716 ->1744 |N:2 |W |B |W |B |W |B |B |"
## [5] " MI | 14601533 / R: 1655 ->1690 |N:2 |B |W |B |W |B |W |B |"
## [6] " OH | 15055204 / R: 1686 ->1687 |N:3 |W |B |W |B |B |W |B |"
## [7] " MI | 11146376 / R: 1649 ->1673 |N:3 |W |B |W |B |B |W |W |"
## [8] " MI | 15142253 / R: 1641P17->1657P24 |N:3 |B |W |B |W |B |W |W |"
## [9] " ON | 14954524 / R: 1411 ->1564 |N:2 |W |B |W |B |W |B |B |"
## [10] " MI | 14150362 / R: 1365 ->1544 |N:3 |W |W |B |B |W |B |W |"
## [11] " MI | 12581589 / R: 1712 ->1696 |N:3 |B |W |B |W |B |W |B |"
## [12] " MI | 12681257 / R: 1663 ->1670 |N:3 |W |B |W |B | |W |B |"
## [13] " MI | 15082995 / R: 1666 ->1662 |N:3 |B |W |B |B |W |W |B |"
## [14] " MI | 10131499 / R: 1610 ->1618 |N:3 |W |B |W |W |B |B |W |"
## [15] " MI | 15619130 / R: 1220P13->1416P20 |N:3 |B |B |W |W |B |B |W |"
## [16] " MI | 10295068 / R: 1604 ->1613 |N:3 |B |W | |B |W |B | |"
## [17] " MI | 10297702 / R: 1629 ->1610 |N:3 |W |B |W |B |W |B |W |"
## [18] " MI | 11342094 / R: 1600 ->1600 |N:3 |B |W |B |W |B |W |B |"
## [19] " MI | 14862333 / R: 1564 ->1570 |N:3 |W |B |W |B |W |W |B |"
## [20] " MI | 14529060 / R: 1595 ->1569 |N:4 |W |B |W |B |W |B |W |"
## [21] " ON | 15495066 / R: 1563P22->1562 |N:3 |B |W |B |W |W |B |W |"
## [22] " MI | 12405534 / R: 1555 ->1529 |N:4 |W |B |W |B | |W |B |"
## [23] " ON | 15030142 / R: 1363 ->1371 | |B |W |B |W |B |W |B |"
## [24] " MI | 13469010 / R: 1229 ->1300 |N:4 |B |W |B |B |W |W |B |"
## [25] " MI | 12486656 / R: 1745 ->1681 |N:4 |B |W |B |W |B |W |B |"
## [26] " ON | 15131520 / R: 1579 ->1564 |N:4 |B |W |B |W |B |W |W |"
## [27] " MI | 14476567 / R: 1552 ->1539 |N:4 |W |B |W |B |W |B | |"
## [28] " MI | 14882954 / R: 1507 ->1513 |N:3 |W |W |B |W |B |B |W |"
## [29] " MI | 15323285 / R: 1602P6 ->1508P12 |N:4 |B |W |B |W |W |B | |"
## [30] " ON | 12577178 / R: 1522 ->1444 | |W |B |B |W |W |B |B |"
## [31] " MI | 15131618 / R: 1494 ->1444 | |B |W |B |W |B |W |B |"
## [32] " ON | 14073750 / R: 1441 ->1433 |N:4 |W |B |W |B |W |B |W |"
## [33] " MI | 14691842 / R: 1449 ->1421 | |B |W |B |W |B |W |B |"
## [34] " MI | 15051807 / R: 1399 ->1400 | |B |W |B |B |W |B |W |"
## [35] " MI | 14601397 / R: 1438 ->1392 | |W |W |B |W |B |B |W |"
## [36] " MI | 14773163 / R: 1355 ->1367 |N:4 |W |B |W |B | |W |B |"
## [37] " MI | 15489571 / R: 980P12->1077P17 | | |B |W |W | |B |W |"
## [38] " MI | 15108523 / R: 1423 ->1439 |N:4 |W |B |W |W | |B |B |"
## [39] " MI | 12923035 / R: 1436P23->1413 |N:4 |B |W |B |W |B |W |W |"
## [40] " MI | 14892710 / R: 1348 ->1346 | |B |B |W |W |B |W |W |"
## [41] " MI | 15761443 / R: 1403P5 ->1341P9 | |B |W |B |W | | | |"
## [42] " MI | 14462326 / R: 1332 ->1256 | |B |W |B |B |W |W |B |"
## [43] " MI | 14101068 / R: 1283 ->1244 | |W |B |W |W |B |B |W |"
## [44] " MI | 15323504 / R: 1199 ->1199 | | |W |B |B |W |B |W |"
## [45] " MI | 15372807 / R: 1242 ->1191 | |W |B |W |B |W |B |W |"
## [46] " MI | 15490981 / R: 377P3 ->1076P10 | |B |W |B |W |B |W |W |"
## [47] " MI | 12533115 / R: 1362 ->1341 | |W |B |W |B |W |B |W |"
## [48] " MI | 14369165 / R: 1382 ->1335 | |B |W | |B | |W |B |"
## [49] " MI | 12531685 / R: 1291P12->1259P17 | |W |W |B |W |B | | |"
## [50] " MI | 14773178 / R: 1056 ->1111 | |W |B |W |B | |B |W |"
## [51] " MI | 15205474 / R: 1011 ->1097 | |B |W |B |W |B |W |W |"
## [52] " MI | 14918803 / R: 935 ->1092 |N:4 |B |W |B |W |B |W |B |"
## [53] " MI | 12578849 / R: 1393 ->1359 | | |B | |W | |W | |"
## [54] " MI | 12836773 / R: 1270 ->1200 | |B |B |W | |W |B |W |"
## [55] " MI | 15412571 / R: 1186 ->1163 | |W |B |W |B | |W |B |"
## [56] " MI | 14679887 / R: 1153 ->1140 | | |B |W |W | |B |W |"
## [57] " MI | 15113330 / R: 1092 ->1079 | |B |W |W |B |W |B | |"
## [58] " MI | 14700365 / R: 917 -> 941 | |W |B |W |B |W | |B |"
## [59] " MI | 12841036 / R: 853 -> 878 | |W | |B |B |W |W |B |"
## [60] " MI | 14579262 / R: 967 -> 984 | |W |B |B |W |B | | |"
## [61] " ON | 15771592 / R: 955P11-> 979P18 | |B |W |B |W |B |W |B |"
## [62] " MI | 15219542 / R: 1530 ->1535 | |B | | | | | | |"
## [63] " MI | 15057092 / R: 1175 ->1125 | |W |B |W |B |B | | |"
## [64] " MI | 15006561 / R: 1163 ->1112 | |B |W |W |B |W |B |B |"Start with this dataset which is a cbind of rows for each player in 1 row
#--------------------------------------------------------------------------
#Start with this dataset which is a cbind of rows for each player in 1 row
df1 <- data.frame(subset1)
df2 <- data.frame(subset2)
df3 <- cbind (df1, df2)
str(df3)## 'data.frame': 64 obs. of 2 variables:
## $ subset1: Factor w/ 64 levels " 1 | GARY HUA |6.0 |W 39|W 21|W 18|W 14|W 7|D 12|D 4|",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ subset2: Factor w/ 64 levels " MI | 10131499 / R: 1610 ->1618 |N:3 |W |B |W |W |B |B |W |",..: 62 25 37 12 27 56 4 45 59 19 ...df3## subset1
## 1 1 | GARY HUA |6.0 |W 39|W 21|W 18|W 14|W 7|D 12|D 4|
## 2 2 | DAKSHESH DARURI |6.0 |W 63|W 58|L 4|W 17|W 16|W 20|W 7|
## 3 3 | ADITYA BAJAJ |6.0 |L 8|W 61|W 25|W 21|W 11|W 13|W 12|
## 4 4 | PATRICK H SCHILLING |5.5 |W 23|D 28|W 2|W 26|D 5|W 19|D 1|
## 5 5 | HANSHI ZUO |5.5 |W 45|W 37|D 12|D 13|D 4|W 14|W 17|
## 6 6 | HANSEN SONG |5.0 |W 34|D 29|L 11|W 35|D 10|W 27|W 21|
## 7 7 | GARY DEE SWATHELL |5.0 |W 57|W 46|W 13|W 11|L 1|W 9|L 2|
## 8 8 | EZEKIEL HOUGHTON |5.0 |W 3|W 32|L 14|L 9|W 47|W 28|W 19|
## 9 9 | STEFANO LEE |5.0 |W 25|L 18|W 59|W 8|W 26|L 7|W 20|
## 10 10 | ANVIT RAO |5.0 |D 16|L 19|W 55|W 31|D 6|W 25|W 18|
## 11 11 | CAMERON WILLIAM MC LEMAN |4.5 |D 38|W 56|W 6|L 7|L 3|W 34|W 26|
## 12 12 | KENNETH J TACK |4.5 |W 42|W 33|D 5|W 38|H |D 1|L 3|
## 13 13 | TORRANCE HENRY JR |4.5 |W 36|W 27|L 7|D 5|W 33|L 3|W 32|
## 14 14 | BRADLEY SHAW |4.5 |W 54|W 44|W 8|L 1|D 27|L 5|W 31|
## 15 15 | ZACHARY JAMES HOUGHTON |4.5 |D 19|L 16|W 30|L 22|W 54|W 33|W 38|
## 16 16 | MIKE NIKITIN |4.0 |D 10|W 15|H |W 39|L 2|W 36|U |
## 17 17 | RONALD GRZEGORCZYK |4.0 |W 48|W 41|L 26|L 2|W 23|W 22|L 5|
## 18 18 | DAVID SUNDEEN |4.0 |W 47|W 9|L 1|W 32|L 19|W 38|L 10|
## 19 19 | DIPANKAR ROY |4.0 |D 15|W 10|W 52|D 28|W 18|L 4|L 8|
## 20 20 | JASON ZHENG |4.0 |L 40|W 49|W 23|W 41|W 28|L 2|L 9|
## 21 21 | DINH DANG BUI |4.0 |W 43|L 1|W 47|L 3|W 40|W 39|L 6|
## 22 22 | EUGENE L MCCLURE |4.0 |W 64|D 52|L 28|W 15|H |L 17|W 40|
## 23 23 | ALAN BUI |4.0 |L 4|W 43|L 20|W 58|L 17|W 37|W 46|
## 24 24 | MICHAEL R ALDRICH |4.0 |L 28|L 47|W 43|L 25|W 60|W 44|W 39|
## 25 25 | LOREN SCHWIEBERT |3.5 |L 9|W 53|L 3|W 24|D 34|L 10|W 47|
## 26 26 | MAX ZHU |3.5 |W 49|W 40|W 17|L 4|L 9|D 32|L 11|
## 27 27 | GAURAV GIDWANI |3.5 |W 51|L 13|W 46|W 37|D 14|L 6|U |
## 28 28 | SOFIA ADINA STANESCU-BELLU |3.5 |W 24|D 4|W 22|D 19|L 20|L 8|D 36|
## 29 29 | CHIEDOZIE OKORIE |3.5 |W 50|D 6|L 38|L 34|W 52|W 48|U |
## 30 30 | GEORGE AVERY JONES |3.5 |L 52|D 64|L 15|W 55|L 31|W 61|W 50|
## 31 31 | RISHI SHETTY |3.5 |L 58|D 55|W 64|L 10|W 30|W 50|L 14|
## 32 32 | JOSHUA PHILIP MATHEWS |3.5 |W 61|L 8|W 44|L 18|W 51|D 26|L 13|
## 33 33 | JADE GE |3.5 |W 60|L 12|W 50|D 36|L 13|L 15|W 51|
## 34 34 | MICHAEL JEFFERY THOMAS |3.5 |L 6|W 60|L 37|W 29|D 25|L 11|W 52|
## 35 35 | JOSHUA DAVID LEE |3.5 |L 46|L 38|W 56|L 6|W 57|D 52|W 48|
## 36 36 | SIDDHARTH JHA |3.5 |L 13|W 57|W 51|D 33|H |L 16|D 28|
## 37 37 | AMIYATOSH PWNANANDAM |3.5 |B |L 5|W 34|L 27|H |L 23|W 61|
## 38 38 | BRIAN LIU |3.0 |D 11|W 35|W 29|L 12|H |L 18|L 15|
## 39 39 | JOEL R HENDON |3.0 |L 1|W 54|W 40|L 16|W 44|L 21|L 24|
## 40 40 | FOREST ZHANG |3.0 |W 20|L 26|L 39|W 59|L 21|W 56|L 22|
## 41 41 | KYLE WILLIAM MURPHY |3.0 |W 59|L 17|W 58|L 20|X |U |U |
## 42 42 | JARED GE |3.0 |L 12|L 50|L 57|D 60|D 61|W 64|W 56|
## 43 43 | ROBERT GLEN VASEY |3.0 |L 21|L 23|L 24|W 63|W 59|L 46|W 55|
## 44 44 | JUSTIN D SCHILLING |3.0 |B |L 14|L 32|W 53|L 39|L 24|W 59|
## 45 45 | DEREK YAN |3.0 |L 5|L 51|D 60|L 56|W 63|D 55|W 58|
## 46 46 | JACOB ALEXANDER LAVALLEY |3.0 |W 35|L 7|L 27|L 50|W 64|W 43|L 23|
## 47 47 | ERIC WRIGHT |2.5 |L 18|W 24|L 21|W 61|L 8|D 51|L 25|
## 48 48 | DANIEL KHAIN |2.5 |L 17|W 63|H |D 52|H |L 29|L 35|
## 49 49 | MICHAEL J MARTIN |2.5 |L 26|L 20|D 63|D 64|W 58|H |U |
## 50 50 | SHIVAM JHA |2.5 |L 29|W 42|L 33|W 46|H |L 31|L 30|
## 51 51 | TEJAS AYYAGARI |2.5 |L 27|W 45|L 36|W 57|L 32|D 47|L 33|
## 52 52 | ETHAN GUO |2.5 |W 30|D 22|L 19|D 48|L 29|D 35|L 34|
## 53 53 | JOSE C YBARRA |2.0 |H |L 25|H |L 44|U |W 57|U |
## 54 54 | LARRY HODGE |2.0 |L 14|L 39|L 61|B |L 15|L 59|W 64|
## 55 55 | ALEX KONG |2.0 |L 62|D 31|L 10|L 30|B |D 45|L 43|
## 56 56 | MARISA RICCI |2.0 |H |L 11|L 35|W 45|H |L 40|L 42|
## 57 57 | MICHAEL LU |2.0 |L 7|L 36|W 42|L 51|L 35|L 53|B |
## 58 58 | VIRAJ MOHILE |2.0 |W 31|L 2|L 41|L 23|L 49|B |L 45|
## 59 59 | SEAN M MC CORMICK |2.0 |L 41|B |L 9|L 40|L 43|W 54|L 44|
## 60 60 | JULIA SHEN |1.5 |L 33|L 34|D 45|D 42|L 24|H |U |
## 61 61 | JEZZEL FARKAS |1.5 |L 32|L 3|W 54|L 47|D 42|L 30|L 37|
## 62 62 | ASHWIN BALAJI |1.0 |W 55|U |U |U |U |U |U |
## 63 63 | THOMAS JOSEPH HOSMER |1.0 |L 2|L 48|D 49|L 43|L 45|H |U |
## 64 64 | BEN LI |1.0 |L 22|D 30|L 31|D 49|L 46|L 42|L 54|
## subset2
## 1 ON | 15445895 / R: 1794 ->1817 |N:2 |W |B |W |B |W |B |W |
## 2 MI | 14598900 / R: 1553 ->1663 |N:2 |B |W |B |W |B |W |B |
## 3 MI | 14959604 / R: 1384 ->1640 |N:2 |W |B |W |B |W |B |W |
## 4 MI | 12616049 / R: 1716 ->1744 |N:2 |W |B |W |B |W |B |B |
## 5 MI | 14601533 / R: 1655 ->1690 |N:2 |B |W |B |W |B |W |B |
## 6 OH | 15055204 / R: 1686 ->1687 |N:3 |W |B |W |B |B |W |B |
## 7 MI | 11146376 / R: 1649 ->1673 |N:3 |W |B |W |B |B |W |W |
## 8 MI | 15142253 / R: 1641P17->1657P24 |N:3 |B |W |B |W |B |W |W |
## 9 ON | 14954524 / R: 1411 ->1564 |N:2 |W |B |W |B |W |B |B |
## 10 MI | 14150362 / R: 1365 ->1544 |N:3 |W |W |B |B |W |B |W |
## 11 MI | 12581589 / R: 1712 ->1696 |N:3 |B |W |B |W |B |W |B |
## 12 MI | 12681257 / R: 1663 ->1670 |N:3 |W |B |W |B | |W |B |
## 13 MI | 15082995 / R: 1666 ->1662 |N:3 |B |W |B |B |W |W |B |
## 14 MI | 10131499 / R: 1610 ->1618 |N:3 |W |B |W |W |B |B |W |
## 15 MI | 15619130 / R: 1220P13->1416P20 |N:3 |B |B |W |W |B |B |W |
## 16 MI | 10295068 / R: 1604 ->1613 |N:3 |B |W | |B |W |B | |
## 17 MI | 10297702 / R: 1629 ->1610 |N:3 |W |B |W |B |W |B |W |
## 18 MI | 11342094 / R: 1600 ->1600 |N:3 |B |W |B |W |B |W |B |
## 19 MI | 14862333 / R: 1564 ->1570 |N:3 |W |B |W |B |W |W |B |
## 20 MI | 14529060 / R: 1595 ->1569 |N:4 |W |B |W |B |W |B |W |
## 21 ON | 15495066 / R: 1563P22->1562 |N:3 |B |W |B |W |W |B |W |
## 22 MI | 12405534 / R: 1555 ->1529 |N:4 |W |B |W |B | |W |B |
## 23 ON | 15030142 / R: 1363 ->1371 | |B |W |B |W |B |W |B |
## 24 MI | 13469010 / R: 1229 ->1300 |N:4 |B |W |B |B |W |W |B |
## 25 MI | 12486656 / R: 1745 ->1681 |N:4 |B |W |B |W |B |W |B |
## 26 ON | 15131520 / R: 1579 ->1564 |N:4 |B |W |B |W |B |W |W |
## 27 MI | 14476567 / R: 1552 ->1539 |N:4 |W |B |W |B |W |B | |
## 28 MI | 14882954 / R: 1507 ->1513 |N:3 |W |W |B |W |B |B |W |
## 29 MI | 15323285 / R: 1602P6 ->1508P12 |N:4 |B |W |B |W |W |B | |
## 30 ON | 12577178 / R: 1522 ->1444 | |W |B |B |W |W |B |B |
## 31 MI | 15131618 / R: 1494 ->1444 | |B |W |B |W |B |W |B |
## 32 ON | 14073750 / R: 1441 ->1433 |N:4 |W |B |W |B |W |B |W |
## 33 MI | 14691842 / R: 1449 ->1421 | |B |W |B |W |B |W |B |
## 34 MI | 15051807 / R: 1399 ->1400 | |B |W |B |B |W |B |W |
## 35 MI | 14601397 / R: 1438 ->1392 | |W |W |B |W |B |B |W |
## 36 MI | 14773163 / R: 1355 ->1367 |N:4 |W |B |W |B | |W |B |
## 37 MI | 15489571 / R: 980P12->1077P17 | | |B |W |W | |B |W |
## 38 MI | 15108523 / R: 1423 ->1439 |N:4 |W |B |W |W | |B |B |
## 39 MI | 12923035 / R: 1436P23->1413 |N:4 |B |W |B |W |B |W |W |
## 40 MI | 14892710 / R: 1348 ->1346 | |B |B |W |W |B |W |W |
## 41 MI | 15761443 / R: 1403P5 ->1341P9 | |B |W |B |W | | | |
## 42 MI | 14462326 / R: 1332 ->1256 | |B |W |B |B |W |W |B |
## 43 MI | 14101068 / R: 1283 ->1244 | |W |B |W |W |B |B |W |
## 44 MI | 15323504 / R: 1199 ->1199 | | |W |B |B |W |B |W |
## 45 MI | 15372807 / R: 1242 ->1191 | |W |B |W |B |W |B |W |
## 46 MI | 15490981 / R: 377P3 ->1076P10 | |B |W |B |W |B |W |W |
## 47 MI | 12533115 / R: 1362 ->1341 | |W |B |W |B |W |B |W |
## 48 MI | 14369165 / R: 1382 ->1335 | |B |W | |B | |W |B |
## 49 MI | 12531685 / R: 1291P12->1259P17 | |W |W |B |W |B | | |
## 50 MI | 14773178 / R: 1056 ->1111 | |W |B |W |B | |B |W |
## 51 MI | 15205474 / R: 1011 ->1097 | |B |W |B |W |B |W |W |
## 52 MI | 14918803 / R: 935 ->1092 |N:4 |B |W |B |W |B |W |B |
## 53 MI | 12578849 / R: 1393 ->1359 | | |B | |W | |W | |
## 54 MI | 12836773 / R: 1270 ->1200 | |B |B |W | |W |B |W |
## 55 MI | 15412571 / R: 1186 ->1163 | |W |B |W |B | |W |B |
## 56 MI | 14679887 / R: 1153 ->1140 | | |B |W |W | |B |W |
## 57 MI | 15113330 / R: 1092 ->1079 | |B |W |W |B |W |B | |
## 58 MI | 14700365 / R: 917 -> 941 | |W |B |W |B |W | |B |
## 59 MI | 12841036 / R: 853 -> 878 | |W | |B |B |W |W |B |
## 60 MI | 14579262 / R: 967 -> 984 | |W |B |B |W |B | | |
## 61 ON | 15771592 / R: 955P11-> 979P18 | |B |W |B |W |B |W |B |
## 62 MI | 15219542 / R: 1530 ->1535 | |B | | | | | | |
## 63 MI | 15057092 / R: 1175 ->1125 | |W |B |W |B |B | | |
## 64 MI | 15006561 / R: 1163 ->1112 | |B |W |W |B |W |B |B |String locate to locate positions to extract our subtring to build the final players dataframe. Using only 1st row since we only need that to determine positions for certain field and rest of the rows will follow the same positions to extract from
str_locate_all(df3$subset1[1],"\\b[:alnum:]+\\b")## [[1]]
## start end
## [1,] 5 5
## [2,] 9 12
## [3,] 14 16
## [4,] 42 42
## [5,] 44 44
## [6,] 48 48
## [7,] 51 52
## [8,] 54 54
## [9,] 57 58
## [10,] 60 60
## [11,] 63 64
## [12,] 66 66
## [13,] 69 70
## [14,] 72 72
## [15,] 76 76
## [16,] 78 78
## [17,] 81 82
## [18,] 84 84
## [19,] 88 88str_locate_all(df3$subset2[1],"\\b[:alnum:]+\\b")## [[1]]
## start end
## [1,] 4 5
## [2,] 9 16
## [3,] 20 20
## [4,] 23 26
## [5,] 32 35
## [6,] 42 42
## [7,] 44 44
## [8,] 48 48
## [9,] 54 54
## [10,] 60 60
## [11,] 66 66
## [12,] 72 72
## [13,] 78 78
## [14,] 84 84#------------------------------------------------------------------------------------------------------------------------
#First Main chess dataframe to build final columns to display. Convert player_num and player_prerating, total_points to decimal
startnew <- data.frame(player_num = type.convert((substr(df3$subset1,4,5)),na.strings = "NA", as.is = FALSE, dec = "."),
player_name = paste(c(str_extract(df3$subset1,"\\b([A-Z]+)\\s*([A-Z]+)\\s*([A-Z]+)\\s*([A-Z]+)\\b"))),
State = substr(df3$subset2,4,5),
# total_points = paste(c(str_extract(df3$subset1,"(\\d\\.)[0-9]"))),
total_points = type.convert(str_extract(df3$subset1,"(\\d\\.)[0-9]"), na.strings = "NA", as.is = FALSE, dec= "."),
player_prerating = type.convert((substr(df3$subset2,23,26)),na.strings = "NA", as.is = FALSE, dec = "."))
startnew## player_num player_name State total_points player_prerating
## 1 1 GARY HUA ON 6.0 1794
## 2 2 DAKSHESH DARURI MI 6.0 1553
## 3 3 ADITYA BAJAJ MI 6.0 1384
## 4 4 PATRICK H SCHILLING MI 5.5 1716
## 5 5 HANSHI ZUO MI 5.5 1655
## 6 6 HANSEN SONG OH 5.0 1686
## 7 7 GARY DEE SWATHELL MI 5.0 1649
## 8 8 EZEKIEL HOUGHTON MI 5.0 1641
## 9 9 STEFANO LEE ON 5.0 1411
## 10 10 ANVIT RAO MI 5.0 1365
## 11 11 CAMERON WILLIAM MC LEMAN MI 4.5 1712
## 12 12 KENNETH J TACK MI 4.5 1663
## 13 13 TORRANCE HENRY JR MI 4.5 1666
## 14 14 BRADLEY SHAW MI 4.5 1610
## 15 15 ZACHARY JAMES HOUGHTON MI 4.5 1220
## 16 16 MIKE NIKITIN MI 4.0 1604
## 17 17 RONALD GRZEGORCZYK MI 4.0 1629
## 18 18 DAVID SUNDEEN MI 4.0 1600
## 19 19 DIPANKAR ROY MI 4.0 1564
## 20 20 JASON ZHENG MI 4.0 1595
## 21 21 DINH DANG BUI ON 4.0 1563
## 22 22 EUGENE L MCCLURE MI 4.0 1555
## 23 23 ALAN BUI ON 4.0 1363
## 24 24 MICHAEL R ALDRICH MI 4.0 1229
## 25 25 LOREN SCHWIEBERT MI 3.5 1745
## 26 26 MAX ZHU ON 3.5 1579
## 27 27 GAURAV GIDWANI MI 3.5 1552
## 28 28 SOFIA ADINA STANESCU MI 3.5 1507
## 29 29 CHIEDOZIE OKORIE MI 3.5 1602
## 30 30 GEORGE AVERY JONES ON 3.5 1522
## 31 31 RISHI SHETTY MI 3.5 1494
## 32 32 JOSHUA PHILIP MATHEWS ON 3.5 1441
## 33 33 JADE GE MI 3.5 1449
## 34 34 MICHAEL JEFFERY THOMAS MI 3.5 1399
## 35 35 JOSHUA DAVID LEE MI 3.5 1438
## 36 36 SIDDHARTH JHA MI 3.5 1355
## 37 37 AMIYATOSH PWNANANDAM MI 3.5 980
## 38 38 BRIAN LIU MI 3.0 1423
## 39 39 JOEL R HENDON MI 3.0 1436
## 40 40 FOREST ZHANG MI 3.0 1348
## 41 41 KYLE WILLIAM MURPHY MI 3.0 1403
## 42 42 JARED GE MI 3.0 1332
## 43 43 ROBERT GLEN VASEY MI 3.0 1283
## 44 44 JUSTIN D SCHILLING MI 3.0 1199
## 45 45 DEREK YAN MI 3.0 1242
## 46 46 JACOB ALEXANDER LAVALLEY MI 3.0 377
## 47 47 ERIC WRIGHT MI 2.5 1362
## 48 48 DANIEL KHAIN MI 2.5 1382
## 49 49 MICHAEL J MARTIN MI 2.5 1291
## 50 50 SHIVAM JHA MI 2.5 1056
## 51 51 TEJAS AYYAGARI MI 2.5 1011
## 52 52 ETHAN GUO MI 2.5 935
## 53 53 JOSE C YBARRA MI 2.0 1393
## 54 54 LARRY HODGE MI 2.0 1270
## 55 55 ALEX KONG MI 2.0 1186
## 56 56 MARISA RICCI MI 2.0 1153
## 57 57 MICHAEL LU MI 2.0 1092
## 58 58 VIRAJ MOHILE MI 2.0 917
## 59 59 SEAN M MC CORMICK MI 2.0 853
## 60 60 JULIA SHEN MI 1.5 967
## 61 61 JEZZEL FARKAS ON 1.5 955
## 62 62 ASHWIN BALAJI MI 1.0 1530
## 63 63 THOMAS JOSEPH HOSMER MI 1.0 1175
## 64 64 BEN LI MI 1.0 1163str(startnew)## 'data.frame': 64 obs. of 5 variables:
## $ player_num : int 1 2 3 4 5 6 7 8 9 10 ...
## $ player_name : Factor w/ 64 levels "ADITYA BAJAJ",..: 24 12 1 51 28 27 23 21 59 5 ...
## $ State : Factor w/ 3 levels "MI","OH","ON": 3 1 1 1 1 2 1 1 3 1 ...
## $ total_points : num 6 6 6 5.5 5.5 5 5 5 5 5 ...
## $ player_prerating: int 1794 1553 1384 1716 1655 1686 1649 1641 1411 1365 ...#------------------------------------------------------------------------------------------------------------------------
#melt list with opponentnumber to get player with the opponentnumber and put that in a dataframe
opponentnum <- type.convert(str_extract_all(df3$subset1,"\\b\\s+([0-9]+)\\b"),na.strings = "NA", as.is = FALSE, dec = ".")
opponentnum## [[1]]
## [1] 39 21 18 14 7 12 4
##
## [[2]]
## [1] 63 58 4 17 16 20 7
##
## [[3]]
## [1] 8 61 25 21 11 13 12
##
## [[4]]
## [1] 23 28 2 26 5 19 1
##
## [[5]]
## [1] 45 37 12 13 4 14 17
##
## [[6]]
## [1] 34 29 11 35 10 27 21
##
## [[7]]
## [1] 57 46 13 11 1 9 2
##
## [[8]]
## [1] 3 32 14 9 47 28 19
##
## [[9]]
## [1] 25 18 59 8 26 7 20
##
## [[10]]
## [1] 16 19 55 31 6 25 18
##
## [[11]]
## [1] 38 56 6 7 3 34 26
##
## [[12]]
## [1] 42 33 5 38 1 3
##
## [[13]]
## [1] 36 27 7 5 33 3 32
##
## [[14]]
## [1] 54 44 8 1 27 5 31
##
## [[15]]
## [1] 19 16 30 22 54 33 38
##
## [[16]]
## [1] 10 15 39 2 36
##
## [[17]]
## [1] 48 41 26 2 23 22 5
##
## [[18]]
## [1] 47 9 1 32 19 38 10
##
## [[19]]
## [1] 15 10 52 28 18 4 8
##
## [[20]]
## [1] 40 49 23 41 28 2 9
##
## [[21]]
## [1] 43 1 47 3 40 39 6
##
## [[22]]
## [1] 64 52 28 15 17 40
##
## [[23]]
## [1] 4 43 20 58 17 37 46
##
## [[24]]
## [1] 28 47 43 25 60 44 39
##
## [[25]]
## [1] 9 53 3 24 34 10 47
##
## [[26]]
## [1] 49 40 17 4 9 32 11
##
## [[27]]
## [1] 51 13 46 37 14 6
##
## [[28]]
## [1] 24 4 22 19 20 8 36
##
## [[29]]
## [1] 50 6 38 34 52 48
##
## [[30]]
## [1] 52 64 15 55 31 61 50
##
## [[31]]
## [1] 58 55 64 10 30 50 14
##
## [[32]]
## [1] 61 8 44 18 51 26 13
##
## [[33]]
## [1] 60 12 50 36 13 15 51
##
## [[34]]
## [1] 6 60 37 29 25 11 52
##
## [[35]]
## [1] 46 38 56 6 57 52 48
##
## [[36]]
## [1] 13 57 51 33 16 28
##
## [[37]]
## [1] 5 34 27 23 61
##
## [[38]]
## [1] 11 35 29 12 18 15
##
## [[39]]
## [1] 1 54 40 16 44 21 24
##
## [[40]]
## [1] 20 26 39 59 21 56 22
##
## [[41]]
## [1] 59 17 58 20
##
## [[42]]
## [1] 12 50 57 60 61 64 56
##
## [[43]]
## [1] 21 23 24 63 59 46 55
##
## [[44]]
## [1] 14 32 53 39 24 59
##
## [[45]]
## [1] 5 51 60 56 63 55 58
##
## [[46]]
## [1] 35 7 27 50 64 43 23
##
## [[47]]
## [1] 18 24 21 61 8 51 25
##
## [[48]]
## [1] 17 63 52 29 35
##
## [[49]]
## [1] 26 20 63 64 58
##
## [[50]]
## [1] 29 42 33 46 31 30
##
## [[51]]
## [1] 27 45 36 57 32 47 33
##
## [[52]]
## [1] 30 22 19 48 29 35 34
##
## [[53]]
## [1] 25 44 57
##
## [[54]]
## [1] 14 39 61 15 59 64
##
## [[55]]
## [1] 62 31 10 30 45 43
##
## [[56]]
## [1] 11 35 45 40 42
##
## [[57]]
## [1] 7 36 42 51 35 53
##
## [[58]]
## [1] 31 2 41 23 49 45
##
## [[59]]
## [1] 41 9 40 43 54 44
##
## [[60]]
## [1] 33 34 45 42 24
##
## [[61]]
## [1] 32 3 54 47 42 30 37
##
## [[62]]
## [1] 55
##
## [[63]]
## [1] 2 48 49 43 45
##
## [[64]]
## [1] 22 30 31 49 46 42 54str(opponentnum)## List of 64
## $ : int [1:7] 39 21 18 14 7 12 4
## $ : int [1:7] 63 58 4 17 16 20 7
## $ : int [1:7] 8 61 25 21 11 13 12
## $ : int [1:7] 23 28 2 26 5 19 1
## $ : int [1:7] 45 37 12 13 4 14 17
## $ : int [1:7] 34 29 11 35 10 27 21
## $ : int [1:7] 57 46 13 11 1 9 2
## $ : int [1:7] 3 32 14 9 47 28 19
## $ : int [1:7] 25 18 59 8 26 7 20
## $ : int [1:7] 16 19 55 31 6 25 18
## $ : int [1:7] 38 56 6 7 3 34 26
## $ : int [1:6] 42 33 5 38 1 3
## $ : int [1:7] 36 27 7 5 33 3 32
## $ : int [1:7] 54 44 8 1 27 5 31
## $ : int [1:7] 19 16 30 22 54 33 38
## $ : int [1:5] 10 15 39 2 36
## $ : int [1:7] 48 41 26 2 23 22 5
## $ : int [1:7] 47 9 1 32 19 38 10
## $ : int [1:7] 15 10 52 28 18 4 8
## $ : int [1:7] 40 49 23 41 28 2 9
## $ : int [1:7] 43 1 47 3 40 39 6
## $ : int [1:6] 64 52 28 15 17 40
## $ : int [1:7] 4 43 20 58 17 37 46
## $ : int [1:7] 28 47 43 25 60 44 39
## $ : int [1:7] 9 53 3 24 34 10 47
## $ : int [1:7] 49 40 17 4 9 32 11
## $ : int [1:6] 51 13 46 37 14 6
## $ : int [1:7] 24 4 22 19 20 8 36
## $ : int [1:6] 50 6 38 34 52 48
## $ : int [1:7] 52 64 15 55 31 61 50
## $ : int [1:7] 58 55 64 10 30 50 14
## $ : int [1:7] 61 8 44 18 51 26 13
## $ : int [1:7] 60 12 50 36 13 15 51
## $ : int [1:7] 6 60 37 29 25 11 52
## $ : int [1:7] 46 38 56 6 57 52 48
## $ : int [1:6] 13 57 51 33 16 28
## $ : int [1:5] 5 34 27 23 61
## $ : int [1:6] 11 35 29 12 18 15
## $ : int [1:7] 1 54 40 16 44 21 24
## $ : int [1:7] 20 26 39 59 21 56 22
## $ : int [1:4] 59 17 58 20
## $ : int [1:7] 12 50 57 60 61 64 56
## $ : int [1:7] 21 23 24 63 59 46 55
## $ : int [1:6] 14 32 53 39 24 59
## $ : int [1:7] 5 51 60 56 63 55 58
## $ : int [1:7] 35 7 27 50 64 43 23
## $ : int [1:7] 18 24 21 61 8 51 25
## $ : int [1:5] 17 63 52 29 35
## $ : int [1:5] 26 20 63 64 58
## $ : int [1:6] 29 42 33 46 31 30
## $ : int [1:7] 27 45 36 57 32 47 33
## $ : int [1:7] 30 22 19 48 29 35 34
## $ : int [1:3] 25 44 57
## $ : int [1:6] 14 39 61 15 59 64
## $ : int [1:6] 62 31 10 30 45 43
## $ : int [1:5] 11 35 45 40 42
## $ : int [1:6] 7 36 42 51 35 53
## $ : int [1:6] 31 2 41 23 49 45
## $ : int [1:6] 41 9 40 43 54 44
## $ : int [1:5] 33 34 45 42 24
## $ : int [1:7] 32 3 54 47 42 30 37
## $ : int 55
## $ : int [1:5] 2 48 49 43 45
## $ : int [1:7] 22 30 31 49 46 42 54df <- melt(opponentnum)#--------------------------------------------------------------------------------------------------------------------------------
#view melted dataframe and reassign column names to a different name
df <- setNames(df,c("oppo_num","player_num"))
str(df)## 'data.frame': 408 obs. of 2 variables:
## $ oppo_num : int 39 21 18 14 7 12 4 63 58 4 ...
## $ player_num: int 1 1 1 1 1 1 1 2 2 2 ...df## oppo_num player_num
## 1 39 1
## 2 21 1
## 3 18 1
## 4 14 1
## 5 7 1
## 6 12 1
## 7 4 1
## 8 63 2
## 9 58 2
## 10 4 2
## 11 17 2
## 12 16 2
## 13 20 2
## 14 7 2
## 15 8 3
## 16 61 3
## 17 25 3
## 18 21 3
## 19 11 3
## 20 13 3
## 21 12 3
## 22 23 4
## 23 28 4
## 24 2 4
## 25 26 4
## 26 5 4
## 27 19 4
## 28 1 4
## 29 45 5
## 30 37 5
## 31 12 5
## 32 13 5
## 33 4 5
## 34 14 5
## 35 17 5
## 36 34 6
## 37 29 6
## 38 11 6
## 39 35 6
## 40 10 6
## 41 27 6
## 42 21 6
## 43 57 7
## 44 46 7
## 45 13 7
## 46 11 7
## 47 1 7
## 48 9 7
## 49 2 7
## 50 3 8
## 51 32 8
## 52 14 8
## 53 9 8
## 54 47 8
## 55 28 8
## 56 19 8
## 57 25 9
## 58 18 9
## 59 59 9
## 60 8 9
## 61 26 9
## 62 7 9
## 63 20 9
## 64 16 10
## 65 19 10
## 66 55 10
## 67 31 10
## 68 6 10
## 69 25 10
## 70 18 10
## 71 38 11
## 72 56 11
## 73 6 11
## 74 7 11
## 75 3 11
## 76 34 11
## 77 26 11
## 78 42 12
## 79 33 12
## 80 5 12
## 81 38 12
## 82 1 12
## 83 3 12
## 84 36 13
## 85 27 13
## 86 7 13
## 87 5 13
## 88 33 13
## 89 3 13
## 90 32 13
## 91 54 14
## 92 44 14
## 93 8 14
## 94 1 14
## 95 27 14
## 96 5 14
## 97 31 14
## 98 19 15
## 99 16 15
## 100 30 15
## 101 22 15
## 102 54 15
## 103 33 15
## 104 38 15
## 105 10 16
## 106 15 16
## 107 39 16
## 108 2 16
## 109 36 16
## 110 48 17
## 111 41 17
## 112 26 17
## 113 2 17
## 114 23 17
## 115 22 17
## 116 5 17
## 117 47 18
## 118 9 18
## 119 1 18
## 120 32 18
## 121 19 18
## 122 38 18
## 123 10 18
## 124 15 19
## 125 10 19
## 126 52 19
## 127 28 19
## 128 18 19
## 129 4 19
## 130 8 19
## 131 40 20
## 132 49 20
## 133 23 20
## 134 41 20
## 135 28 20
## 136 2 20
## 137 9 20
## 138 43 21
## 139 1 21
## 140 47 21
## 141 3 21
## 142 40 21
## 143 39 21
## 144 6 21
## 145 64 22
## 146 52 22
## 147 28 22
## 148 15 22
## 149 17 22
## 150 40 22
## 151 4 23
## 152 43 23
## 153 20 23
## 154 58 23
## 155 17 23
## 156 37 23
## 157 46 23
## 158 28 24
## 159 47 24
## 160 43 24
## 161 25 24
## 162 60 24
## 163 44 24
## 164 39 24
## 165 9 25
## 166 53 25
## 167 3 25
## 168 24 25
## 169 34 25
## 170 10 25
## 171 47 25
## 172 49 26
## 173 40 26
## 174 17 26
## 175 4 26
## 176 9 26
## 177 32 26
## 178 11 26
## 179 51 27
## 180 13 27
## 181 46 27
## 182 37 27
## 183 14 27
## 184 6 27
## 185 24 28
## 186 4 28
## 187 22 28
## 188 19 28
## 189 20 28
## 190 8 28
## 191 36 28
## 192 50 29
## 193 6 29
## 194 38 29
## 195 34 29
## 196 52 29
## 197 48 29
## 198 52 30
## 199 64 30
## 200 15 30
## 201 55 30
## 202 31 30
## 203 61 30
## 204 50 30
## 205 58 31
## 206 55 31
## 207 64 31
## 208 10 31
## 209 30 31
## 210 50 31
## 211 14 31
## 212 61 32
## 213 8 32
## 214 44 32
## 215 18 32
## 216 51 32
## 217 26 32
## 218 13 32
## 219 60 33
## 220 12 33
## 221 50 33
## 222 36 33
## 223 13 33
## 224 15 33
## 225 51 33
## 226 6 34
## 227 60 34
## 228 37 34
## 229 29 34
## 230 25 34
## 231 11 34
## 232 52 34
## 233 46 35
## 234 38 35
## 235 56 35
## 236 6 35
## 237 57 35
## 238 52 35
## 239 48 35
## 240 13 36
## 241 57 36
## 242 51 36
## 243 33 36
## 244 16 36
## 245 28 36
## 246 5 37
## 247 34 37
## 248 27 37
## 249 23 37
## 250 61 37
## 251 11 38
## 252 35 38
## 253 29 38
## 254 12 38
## 255 18 38
## 256 15 38
## 257 1 39
## 258 54 39
## 259 40 39
## 260 16 39
## 261 44 39
## 262 21 39
## 263 24 39
## 264 20 40
## 265 26 40
## 266 39 40
## 267 59 40
## 268 21 40
## 269 56 40
## 270 22 40
## 271 59 41
## 272 17 41
## 273 58 41
## 274 20 41
## 275 12 42
## 276 50 42
## 277 57 42
## 278 60 42
## 279 61 42
## 280 64 42
## 281 56 42
## 282 21 43
## 283 23 43
## 284 24 43
## 285 63 43
## 286 59 43
## 287 46 43
## 288 55 43
## 289 14 44
## 290 32 44
## 291 53 44
## 292 39 44
## 293 24 44
## 294 59 44
## 295 5 45
## 296 51 45
## 297 60 45
## 298 56 45
## 299 63 45
## 300 55 45
## 301 58 45
## 302 35 46
## 303 7 46
## 304 27 46
## 305 50 46
## 306 64 46
## 307 43 46
## 308 23 46
## 309 18 47
## 310 24 47
## 311 21 47
## 312 61 47
## 313 8 47
## 314 51 47
## 315 25 47
## 316 17 48
## 317 63 48
## 318 52 48
## 319 29 48
## 320 35 48
## 321 26 49
## 322 20 49
## 323 63 49
## 324 64 49
## 325 58 49
## 326 29 50
## 327 42 50
## 328 33 50
## 329 46 50
## 330 31 50
## 331 30 50
## 332 27 51
## 333 45 51
## 334 36 51
## 335 57 51
## 336 32 51
## 337 47 51
## 338 33 51
## 339 30 52
## 340 22 52
## 341 19 52
## 342 48 52
## 343 29 52
## 344 35 52
## 345 34 52
## 346 25 53
## 347 44 53
## 348 57 53
## 349 14 54
## 350 39 54
## 351 61 54
## 352 15 54
## 353 59 54
## 354 64 54
## 355 62 55
## 356 31 55
## 357 10 55
## 358 30 55
## 359 45 55
## 360 43 55
## 361 11 56
## 362 35 56
## 363 45 56
## 364 40 56
## 365 42 56
## 366 7 57
## 367 36 57
## 368 42 57
## 369 51 57
## 370 35 57
## 371 53 57
## 372 31 58
## 373 2 58
## 374 41 58
## 375 23 58
## 376 49 58
## 377 45 58
## 378 41 59
## 379 9 59
## 380 40 59
## 381 43 59
## 382 54 59
## 383 44 59
## 384 33 60
## 385 34 60
## 386 45 60
## 387 42 60
## 388 24 60
## 389 32 61
## 390 3 61
## 391 54 61
## 392 47 61
## 393 42 61
## 394 30 61
## 395 37 61
## 396 55 62
## 397 2 63
## 398 48 63
## 399 49 63
## 400 43 63
## 401 45 63
## 402 22 64
## 403 30 64
## 404 31 64
## 405 49 64
## 406 46 64
## 407 42 64
## 408 54 64#--------------------------------------------------------------------------------------------------------------------------------
#Merge melted dataframe with dataframe below by player_num to get rows of player with opponent number. Here any games with
#missing opponent number is not kept. For instance the Melt only reads in the 5 games that were played for player #16 since the other 2 games don't have a opponent # listed in the
#string that was read in for this player
df2 <- data.frame(player_num = type.convert(substr(df3$subset1,4,5),na.strings = "NA", as.is = FALSE, dec = "."),
player_name = paste(c(str_extract(df3$subset1,"\\b([A-Z]+)\\s*([A-Z]+)\\s*([A-Z]+)\\s*([A-Z]+)\\b"))),
player_prerating = type.convert((substr(df3$subset2,23,26)),na.strings = "NA", as.is = FALSE, dec = "."))
str(df2)## 'data.frame': 64 obs. of 3 variables:
## $ player_num : int 1 2 3 4 5 6 7 8 9 10 ...
## $ player_name : Factor w/ 64 levels "ADITYA BAJAJ",..: 24 12 1 51 28 27 23 21 59 5 ...
## $ player_prerating: int 1794 1553 1384 1716 1655 1686 1649 1641 1411 1365 ...df2## player_num player_name player_prerating
## 1 1 GARY HUA 1794
## 2 2 DAKSHESH DARURI 1553
## 3 3 ADITYA BAJAJ 1384
## 4 4 PATRICK H SCHILLING 1716
## 5 5 HANSHI ZUO 1655
## 6 6 HANSEN SONG 1686
## 7 7 GARY DEE SWATHELL 1649
## 8 8 EZEKIEL HOUGHTON 1641
## 9 9 STEFANO LEE 1411
## 10 10 ANVIT RAO 1365
## 11 11 CAMERON WILLIAM MC LEMAN 1712
## 12 12 KENNETH J TACK 1663
## 13 13 TORRANCE HENRY JR 1666
## 14 14 BRADLEY SHAW 1610
## 15 15 ZACHARY JAMES HOUGHTON 1220
## 16 16 MIKE NIKITIN 1604
## 17 17 RONALD GRZEGORCZYK 1629
## 18 18 DAVID SUNDEEN 1600
## 19 19 DIPANKAR ROY 1564
## 20 20 JASON ZHENG 1595
## 21 21 DINH DANG BUI 1563
## 22 22 EUGENE L MCCLURE 1555
## 23 23 ALAN BUI 1363
## 24 24 MICHAEL R ALDRICH 1229
## 25 25 LOREN SCHWIEBERT 1745
## 26 26 MAX ZHU 1579
## 27 27 GAURAV GIDWANI 1552
## 28 28 SOFIA ADINA STANESCU 1507
## 29 29 CHIEDOZIE OKORIE 1602
## 30 30 GEORGE AVERY JONES 1522
## 31 31 RISHI SHETTY 1494
## 32 32 JOSHUA PHILIP MATHEWS 1441
## 33 33 JADE GE 1449
## 34 34 MICHAEL JEFFERY THOMAS 1399
## 35 35 JOSHUA DAVID LEE 1438
## 36 36 SIDDHARTH JHA 1355
## 37 37 AMIYATOSH PWNANANDAM 980
## 38 38 BRIAN LIU 1423
## 39 39 JOEL R HENDON 1436
## 40 40 FOREST ZHANG 1348
## 41 41 KYLE WILLIAM MURPHY 1403
## 42 42 JARED GE 1332
## 43 43 ROBERT GLEN VASEY 1283
## 44 44 JUSTIN D SCHILLING 1199
## 45 45 DEREK YAN 1242
## 46 46 JACOB ALEXANDER LAVALLEY 377
## 47 47 ERIC WRIGHT 1362
## 48 48 DANIEL KHAIN 1382
## 49 49 MICHAEL J MARTIN 1291
## 50 50 SHIVAM JHA 1056
## 51 51 TEJAS AYYAGARI 1011
## 52 52 ETHAN GUO 935
## 53 53 JOSE C YBARRA 1393
## 54 54 LARRY HODGE 1270
## 55 55 ALEX KONG 1186
## 56 56 MARISA RICCI 1153
## 57 57 MICHAEL LU 1092
## 58 58 VIRAJ MOHILE 917
## 59 59 SEAN M MC CORMICK 853
## 60 60 JULIA SHEN 967
## 61 61 JEZZEL FARKAS 955
## 62 62 ASHWIN BALAJI 1530
## 63 63 THOMAS JOSEPH HOSMER 1175
## 64 64 BEN LI 1163df4 <- merge(df2,df,by ="player_num")
head(df4)## player_num player_name player_prerating oppo_num
## 1 1 GARY HUA 1794 39
## 2 1 GARY HUA 1794 21
## 3 1 GARY HUA 1794 18
## 4 1 GARY HUA 1794 14
## 5 1 GARY HUA 1794 7
## 6 1 GARY HUA 1794 12str(df4)## 'data.frame': 408 obs. of 4 variables:
## $ player_num : int 1 1 1 1 1 1 1 2 2 2 ...
## $ player_name : Factor w/ 64 levels "ADITYA BAJAJ",..: 24 24 24 24 24 24 24 12 12 12 ...
## $ player_prerating: int 1794 1794 1794 1794 1794 1794 1794 1553 1553 1553 ...
## $ oppo_num : int 39 21 18 14 7 12 4 63 58 4 ...#--------------------------------------------------------------------------------------------------------------------------------
#new dataset to reassign just player prerating and player number as opponent prerating and opponent playernumber
df6 <- data.frame(oppo_num = df2$player_num, oppo_prerating = df2$player_prerating)
df6 ## oppo_num oppo_prerating
## 1 1 1794
## 2 2 1553
## 3 3 1384
## 4 4 1716
## 5 5 1655
## 6 6 1686
## 7 7 1649
## 8 8 1641
## 9 9 1411
## 10 10 1365
## 11 11 1712
## 12 12 1663
## 13 13 1666
## 14 14 1610
## 15 15 1220
## 16 16 1604
## 17 17 1629
## 18 18 1600
## 19 19 1564
## 20 20 1595
## 21 21 1563
## 22 22 1555
## 23 23 1363
## 24 24 1229
## 25 25 1745
## 26 26 1579
## 27 27 1552
## 28 28 1507
## 29 29 1602
## 30 30 1522
## 31 31 1494
## 32 32 1441
## 33 33 1449
## 34 34 1399
## 35 35 1438
## 36 36 1355
## 37 37 980
## 38 38 1423
## 39 39 1436
## 40 40 1348
## 41 41 1403
## 42 42 1332
## 43 43 1283
## 44 44 1199
## 45 45 1242
## 46 46 377
## 47 47 1362
## 48 48 1382
## 49 49 1291
## 50 50 1056
## 51 51 1011
## 52 52 935
## 53 53 1393
## 54 54 1270
## 55 55 1186
## 56 56 1153
## 57 57 1092
## 58 58 917
## 59 59 853
## 60 60 967
## 61 61 955
## 62 62 1530
## 63 63 1175
## 64 64 1163#--------------------------------------------------------------------------------------------------------------------------------
#merge to get oppo prerating
df7 <- merge(df4, df6, by = "oppo_num")
df7## oppo_num player_num player_name player_prerating
## 1 1 12 KENNETH J TACK 1663
## 2 1 21 DINH DANG BUI 1563
## 3 1 4 PATRICK H SCHILLING 1716
## 4 1 39 JOEL R HENDON 1436
## 5 1 7 GARY DEE SWATHELL 1649
## 6 1 18 DAVID SUNDEEN 1600
## 7 1 14 BRADLEY SHAW 1610
## 8 2 58 VIRAJ MOHILE 917
## 9 2 63 THOMAS JOSEPH HOSMER 1175
## 10 2 17 RONALD GRZEGORCZYK 1629
## 11 2 20 JASON ZHENG 1595
## 12 2 16 MIKE NIKITIN 1604
## 13 2 4 PATRICK H SCHILLING 1716
## 14 2 7 GARY DEE SWATHELL 1649
## 15 3 25 LOREN SCHWIEBERT 1745
## 16 3 21 DINH DANG BUI 1563
## 17 3 61 JEZZEL FARKAS 955
## 18 3 11 CAMERON WILLIAM MC LEMAN 1712
## 19 3 8 EZEKIEL HOUGHTON 1641
## 20 3 12 KENNETH J TACK 1663
## 21 3 13 TORRANCE HENRY JR 1666
## 22 4 28 SOFIA ADINA STANESCU 1507
## 23 4 5 HANSHI ZUO 1655
## 24 4 19 DIPANKAR ROY 1564
## 25 4 23 ALAN BUI 1363
## 26 4 2 DAKSHESH DARURI 1553
## 27 4 26 MAX ZHU 1579
## 28 4 1 GARY HUA 1794
## 29 5 17 RONALD GRZEGORCZYK 1629
## 30 5 45 DEREK YAN 1242
## 31 5 4 PATRICK H SCHILLING 1716
## 32 5 13 TORRANCE HENRY JR 1666
## 33 5 14 BRADLEY SHAW 1610
## 34 5 37 AMIYATOSH PWNANANDAM 980
## 35 5 12 KENNETH J TACK 1663
## 36 6 34 MICHAEL JEFFERY THOMAS 1399
## 37 6 35 JOSHUA DAVID LEE 1438
## 38 6 21 DINH DANG BUI 1563
## 39 6 10 ANVIT RAO 1365
## 40 6 11 CAMERON WILLIAM MC LEMAN 1712
## 41 6 27 GAURAV GIDWANI 1552
## 42 6 29 CHIEDOZIE OKORIE 1602
## 43 7 13 TORRANCE HENRY JR 1666
## 44 7 2 DAKSHESH DARURI 1553
## 45 7 1 GARY HUA 1794
## 46 7 57 MICHAEL LU 1092
## 47 7 11 CAMERON WILLIAM MC LEMAN 1712
## 48 7 9 STEFANO LEE 1411
## 49 7 46 JACOB ALEXANDER LAVALLEY 377
## 50 8 32 JOSHUA PHILIP MATHEWS 1441
## 51 8 14 BRADLEY SHAW 1610
## 52 8 19 DIPANKAR ROY 1564
## 53 8 47 ERIC WRIGHT 1362
## 54 8 9 STEFANO LEE 1411
## 55 8 3 ADITYA BAJAJ 1384
## 56 8 28 SOFIA ADINA STANESCU 1507
## 57 9 7 GARY DEE SWATHELL 1649
## 58 9 25 LOREN SCHWIEBERT 1745
## 59 9 59 SEAN M MC CORMICK 853
## 60 9 20 JASON ZHENG 1595
## 61 9 26 MAX ZHU 1579
## 62 9 8 EZEKIEL HOUGHTON 1641
## 63 9 18 DAVID SUNDEEN 1600
## 64 10 19 DIPANKAR ROY 1564
## 65 10 16 MIKE NIKITIN 1604
## 66 10 18 DAVID SUNDEEN 1600
## 67 10 31 RISHI SHETTY 1494
## 68 10 55 ALEX KONG 1186
## 69 10 25 LOREN SCHWIEBERT 1745
## 70 10 6 HANSEN SONG 1686
## 71 11 26 MAX ZHU 1579
## 72 11 34 MICHAEL JEFFERY THOMAS 1399
## 73 11 3 ADITYA BAJAJ 1384
## 74 11 56 MARISA RICCI 1153
## 75 11 38 BRIAN LIU 1423
## 76 11 6 HANSEN SONG 1686
## 77 11 7 GARY DEE SWATHELL 1649
## 78 12 42 JARED GE 1332
## 79 12 3 ADITYA BAJAJ 1384
## 80 12 38 BRIAN LIU 1423
## 81 12 33 JADE GE 1449
## 82 12 1 GARY HUA 1794
## 83 12 5 HANSHI ZUO 1655
## 84 13 33 JADE GE 1449
## 85 13 5 HANSHI ZUO 1655
## 86 13 3 ADITYA BAJAJ 1384
## 87 13 7 GARY DEE SWATHELL 1649
## 88 13 36 SIDDHARTH JHA 1355
## 89 13 27 GAURAV GIDWANI 1552
## 90 13 32 JOSHUA PHILIP MATHEWS 1441
## 91 14 1 GARY HUA 1794
## 92 14 27 GAURAV GIDWANI 1552
## 93 14 5 HANSHI ZUO 1655
## 94 14 44 JUSTIN D SCHILLING 1199
## 95 14 54 LARRY HODGE 1270
## 96 14 8 EZEKIEL HOUGHTON 1641
## 97 14 31 RISHI SHETTY 1494
## 98 15 38 BRIAN LIU 1423
## 99 15 33 JADE GE 1449
## 100 15 30 GEORGE AVERY JONES 1522
## 101 15 16 MIKE NIKITIN 1604
## 102 15 54 LARRY HODGE 1270
## 103 15 19 DIPANKAR ROY 1564
## 104 15 22 EUGENE L MCCLURE 1555
## 105 16 15 ZACHARY JAMES HOUGHTON 1220
## 106 16 39 JOEL R HENDON 1436
## 107 16 10 ANVIT RAO 1365
## 108 16 2 DAKSHESH DARURI 1553
## 109 16 36 SIDDHARTH JHA 1355
## 110 17 48 DANIEL KHAIN 1382
## 111 17 2 DAKSHESH DARURI 1553
## 112 17 22 EUGENE L MCCLURE 1555
## 113 17 26 MAX ZHU 1579
## 114 17 23 ALAN BUI 1363
## 115 17 41 KYLE WILLIAM MURPHY 1403
## 116 17 5 HANSHI ZUO 1655
## 117 18 10 ANVIT RAO 1365
## 118 18 19 DIPANKAR ROY 1564
## 119 18 9 STEFANO LEE 1411
## 120 18 47 ERIC WRIGHT 1362
## 121 18 38 BRIAN LIU 1423
## 122 18 32 JOSHUA PHILIP MATHEWS 1441
## 123 18 1 GARY HUA 1794
## 124 19 15 ZACHARY JAMES HOUGHTON 1220
## 125 19 8 EZEKIEL HOUGHTON 1641
## 126 19 10 ANVIT RAO 1365
## 127 19 52 ETHAN GUO 935
## 128 19 4 PATRICK H SCHILLING 1716
## 129 19 28 SOFIA ADINA STANESCU 1507
## 130 19 18 DAVID SUNDEEN 1600
## 131 20 2 DAKSHESH DARURI 1553
## 132 20 40 FOREST ZHANG 1348
## 133 20 41 KYLE WILLIAM MURPHY 1403
## 134 20 9 STEFANO LEE 1411
## 135 20 49 MICHAEL J MARTIN 1291
## 136 20 23 ALAN BUI 1363
## 137 20 28 SOFIA ADINA STANESCU 1507
## 138 21 47 ERIC WRIGHT 1362
## 139 21 3 ADITYA BAJAJ 1384
## 140 21 39 JOEL R HENDON 1436
## 141 21 40 FOREST ZHANG 1348
## 142 21 1 GARY HUA 1794
## 143 21 6 HANSEN SONG 1686
## 144 21 43 ROBERT GLEN VASEY 1283
## 145 22 28 SOFIA ADINA STANESCU 1507
## 146 22 52 ETHAN GUO 935
## 147 22 17 RONALD GRZEGORCZYK 1629
## 148 22 64 BEN LI 1163
## 149 22 15 ZACHARY JAMES HOUGHTON 1220
## 150 22 40 FOREST ZHANG 1348
## 151 23 46 JACOB ALEXANDER LAVALLEY 377
## 152 23 4 PATRICK H SCHILLING 1716
## 153 23 43 ROBERT GLEN VASEY 1283
## 154 23 20 JASON ZHENG 1595
## 155 23 37 AMIYATOSH PWNANANDAM 980
## 156 23 17 RONALD GRZEGORCZYK 1629
## 157 23 58 VIRAJ MOHILE 917
## 158 24 44 JUSTIN D SCHILLING 1199
## 159 24 47 ERIC WRIGHT 1362
## 160 24 25 LOREN SCHWIEBERT 1745
## 161 24 28 SOFIA ADINA STANESCU 1507
## 162 24 39 JOEL R HENDON 1436
## 163 24 60 JULIA SHEN 967
## 164 24 43 ROBERT GLEN VASEY 1283
## 165 25 3 ADITYA BAJAJ 1384
## 166 25 47 ERIC WRIGHT 1362
## 167 25 53 JOSE C YBARRA 1393
## 168 25 34 MICHAEL JEFFERY THOMAS 1399
## 169 25 10 ANVIT RAO 1365
## 170 25 24 MICHAEL R ALDRICH 1229
## 171 25 9 STEFANO LEE 1411
## 172 26 4 PATRICK H SCHILLING 1716
## 173 26 40 FOREST ZHANG 1348
## 174 26 32 JOSHUA PHILIP MATHEWS 1441
## 175 26 17 RONALD GRZEGORCZYK 1629
## 176 26 49 MICHAEL J MARTIN 1291
## 177 26 11 CAMERON WILLIAM MC LEMAN 1712
## 178 26 9 STEFANO LEE 1411
## 179 27 13 TORRANCE HENRY JR 1666
## 180 27 6 HANSEN SONG 1686
## 181 27 51 TEJAS AYYAGARI 1011
## 182 27 14 BRADLEY SHAW 1610
## 183 27 37 AMIYATOSH PWNANANDAM 980
## 184 27 46 JACOB ALEXANDER LAVALLEY 377
## 185 28 20 JASON ZHENG 1595
## 186 28 24 MICHAEL R ALDRICH 1229
## 187 28 4 PATRICK H SCHILLING 1716
## 188 28 19 DIPANKAR ROY 1564
## 189 28 8 EZEKIEL HOUGHTON 1641
## 190 28 22 EUGENE L MCCLURE 1555
## 191 28 36 SIDDHARTH JHA 1355
## 192 29 38 BRIAN LIU 1423
## 193 29 6 HANSEN SONG 1686
## 194 29 48 DANIEL KHAIN 1382
## 195 29 52 ETHAN GUO 935
## 196 29 50 SHIVAM JHA 1056
## 197 29 34 MICHAEL JEFFERY THOMAS 1399
## 198 30 55 ALEX KONG 1186
## 199 30 61 JEZZEL FARKAS 955
## 200 30 52 ETHAN GUO 935
## 201 30 50 SHIVAM JHA 1056
## 202 30 31 RISHI SHETTY 1494
## 203 30 15 ZACHARY JAMES HOUGHTON 1220
## 204 30 64 BEN LI 1163
## 205 31 58 VIRAJ MOHILE 917
## 206 31 50 SHIVAM JHA 1056
## 207 31 14 BRADLEY SHAW 1610
## 208 31 10 ANVIT RAO 1365
## 209 31 30 GEORGE AVERY JONES 1522
## 210 31 64 BEN LI 1163
## 211 31 55 ALEX KONG 1186
## 212 32 8 EZEKIEL HOUGHTON 1641
## 213 32 13 TORRANCE HENRY JR 1666
## 214 32 61 JEZZEL FARKAS 955
## 215 32 44 JUSTIN D SCHILLING 1199
## 216 32 51 TEJAS AYYAGARI 1011
## 217 32 18 DAVID SUNDEEN 1600
## 218 32 26 MAX ZHU 1579
## 219 33 15 ZACHARY JAMES HOUGHTON 1220
## 220 33 13 TORRANCE HENRY JR 1666
## 221 33 60 JULIA SHEN 967
## 222 33 51 TEJAS AYYAGARI 1011
## 223 33 36 SIDDHARTH JHA 1355
## 224 33 12 KENNETH J TACK 1663
## 225 33 50 SHIVAM JHA 1056
## 226 34 25 LOREN SCHWIEBERT 1745
## 227 34 52 ETHAN GUO 935
## 228 34 29 CHIEDOZIE OKORIE 1602
## 229 34 37 AMIYATOSH PWNANANDAM 980
## 230 34 11 CAMERON WILLIAM MC LEMAN 1712
## 231 34 6 HANSEN SONG 1686
## 232 34 60 JULIA SHEN 967
## 233 35 56 MARISA RICCI 1153
## 234 35 6 HANSEN SONG 1686
## 235 35 46 JACOB ALEXANDER LAVALLEY 377
## 236 35 52 ETHAN GUO 935
## 237 35 38 BRIAN LIU 1423
## 238 35 48 DANIEL KHAIN 1382
## 239 35 57 MICHAEL LU 1092
## 240 36 51 TEJAS AYYAGARI 1011
## 241 36 16 MIKE NIKITIN 1604
## 242 36 57 MICHAEL LU 1092
## 243 36 28 SOFIA ADINA STANESCU 1507
## 244 36 33 JADE GE 1449
## 245 36 13 TORRANCE HENRY JR 1666
## 246 37 5 HANSHI ZUO 1655
## 247 37 23 ALAN BUI 1363
## 248 37 27 GAURAV GIDWANI 1552
## 249 37 61 JEZZEL FARKAS 955
## 250 37 34 MICHAEL JEFFERY THOMAS 1399
## 251 38 15 ZACHARY JAMES HOUGHTON 1220
## 252 38 11 CAMERON WILLIAM MC LEMAN 1712
## 253 38 18 DAVID SUNDEEN 1600
## 254 38 35 JOSHUA DAVID LEE 1438
## 255 38 12 KENNETH J TACK 1663
## 256 38 29 CHIEDOZIE OKORIE 1602
## 257 39 1 GARY HUA 1794
## 258 39 54 LARRY HODGE 1270
## 259 39 24 MICHAEL R ALDRICH 1229
## 260 39 21 DINH DANG BUI 1563
## 261 39 44 JUSTIN D SCHILLING 1199
## 262 39 40 FOREST ZHANG 1348
## 263 39 16 MIKE NIKITIN 1604
## 264 40 20 JASON ZHENG 1595
## 265 40 26 MAX ZHU 1579
## 266 40 59 SEAN M MC CORMICK 853
## 267 40 56 MARISA RICCI 1153
## 268 40 21 DINH DANG BUI 1563
## 269 40 39 JOEL R HENDON 1436
## 270 40 22 EUGENE L MCCLURE 1555
## 271 41 17 RONALD GRZEGORCZYK 1629
## 272 41 58 VIRAJ MOHILE 917
## 273 41 20 JASON ZHENG 1595
## 274 41 59 SEAN M MC CORMICK 853
## 275 42 50 SHIVAM JHA 1056
## 276 42 57 MICHAEL LU 1092
## 277 42 56 MARISA RICCI 1153
## 278 42 60 JULIA SHEN 967
## 279 42 12 KENNETH J TACK 1663
## 280 42 64 BEN LI 1163
## 281 42 61 JEZZEL FARKAS 955
## 282 43 63 THOMAS JOSEPH HOSMER 1175
## 283 43 59 SEAN M MC CORMICK 853
## 284 43 55 ALEX KONG 1186
## 285 43 24 MICHAEL R ALDRICH 1229
## 286 43 21 DINH DANG BUI 1563
## 287 43 46 JACOB ALEXANDER LAVALLEY 377
## 288 43 23 ALAN BUI 1363
## 289 44 53 JOSE C YBARRA 1393
## 290 44 59 SEAN M MC CORMICK 853
## 291 44 14 BRADLEY SHAW 1610
## 292 44 24 MICHAEL R ALDRICH 1229
## 293 44 39 JOEL R HENDON 1436
## 294 44 32 JOSHUA PHILIP MATHEWS 1441
## 295 45 63 THOMAS JOSEPH HOSMER 1175
## 296 45 60 JULIA SHEN 967
## 297 45 55 ALEX KONG 1186
## 298 45 56 MARISA RICCI 1153
## 299 45 5 HANSHI ZUO 1655
## 300 45 51 TEJAS AYYAGARI 1011
## 301 45 58 VIRAJ MOHILE 917
## 302 46 35 JOSHUA DAVID LEE 1438
## 303 46 64 BEN LI 1163
## 304 46 7 GARY DEE SWATHELL 1649
## 305 46 27 GAURAV GIDWANI 1552
## 306 46 43 ROBERT GLEN VASEY 1283
## 307 46 50 SHIVAM JHA 1056
## 308 46 23 ALAN BUI 1363
## 309 47 21 DINH DANG BUI 1563
## 310 47 25 LOREN SCHWIEBERT 1745
## 311 47 24 MICHAEL R ALDRICH 1229
## 312 47 8 EZEKIEL HOUGHTON 1641
## 313 47 18 DAVID SUNDEEN 1600
## 314 47 61 JEZZEL FARKAS 955
## 315 47 51 TEJAS AYYAGARI 1011
## 316 48 35 JOSHUA DAVID LEE 1438
## 317 48 29 CHIEDOZIE OKORIE 1602
## 318 48 63 THOMAS JOSEPH HOSMER 1175
## 319 48 17 RONALD GRZEGORCZYK 1629
## 320 48 52 ETHAN GUO 935
## 321 49 63 THOMAS JOSEPH HOSMER 1175
## 322 49 20 JASON ZHENG 1595
## 323 49 26 MAX ZHU 1579
## 324 49 64 BEN LI 1163
## 325 49 58 VIRAJ MOHILE 917
## 326 50 42 JARED GE 1332
## 327 50 46 JACOB ALEXANDER LAVALLEY 377
## 328 50 31 RISHI SHETTY 1494
## 329 50 30 GEORGE AVERY JONES 1522
## 330 50 29 CHIEDOZIE OKORIE 1602
## 331 50 33 JADE GE 1449
## 332 51 57 MICHAEL LU 1092
## 333 51 32 JOSHUA PHILIP MATHEWS 1441
## 334 51 33 JADE GE 1449
## 335 51 27 GAURAV GIDWANI 1552
## 336 51 45 DEREK YAN 1242
## 337 51 36 SIDDHARTH JHA 1355
## 338 51 47 ERIC WRIGHT 1362
## 339 52 35 JOSHUA DAVID LEE 1438
## 340 52 30 GEORGE AVERY JONES 1522
## 341 52 48 DANIEL KHAIN 1382
## 342 52 29 CHIEDOZIE OKORIE 1602
## 343 52 22 EUGENE L MCCLURE 1555
## 344 52 34 MICHAEL JEFFERY THOMAS 1399
## 345 52 19 DIPANKAR ROY 1564
## 346 53 25 LOREN SCHWIEBERT 1745
## 347 53 44 JUSTIN D SCHILLING 1199
## 348 53 57 MICHAEL LU 1092
## 349 54 15 ZACHARY JAMES HOUGHTON 1220
## 350 54 14 BRADLEY SHAW 1610
## 351 54 64 BEN LI 1163
## 352 54 61 JEZZEL FARKAS 955
## 353 54 59 SEAN M MC CORMICK 853
## 354 54 39 JOEL R HENDON 1436
## 355 55 10 ANVIT RAO 1365
## 356 55 31 RISHI SHETTY 1494
## 357 55 45 DEREK YAN 1242
## 358 55 30 GEORGE AVERY JONES 1522
## 359 55 43 ROBERT GLEN VASEY 1283
## 360 55 62 ASHWIN BALAJI 1530
## 361 56 11 CAMERON WILLIAM MC LEMAN 1712
## 362 56 42 JARED GE 1332
## 363 56 35 JOSHUA DAVID LEE 1438
## 364 56 45 DEREK YAN 1242
## 365 56 40 FOREST ZHANG 1348
## 366 57 7 GARY DEE SWATHELL 1649
## 367 57 35 JOSHUA DAVID LEE 1438
## 368 57 36 SIDDHARTH JHA 1355
## 369 57 42 JARED GE 1332
## 370 57 53 JOSE C YBARRA 1393
## 371 57 51 TEJAS AYYAGARI 1011
## 372 58 31 RISHI SHETTY 1494
## 373 58 45 DEREK YAN 1242
## 374 58 41 KYLE WILLIAM MURPHY 1403
## 375 58 23 ALAN BUI 1363
## 376 58 2 DAKSHESH DARURI 1553
## 377 58 49 MICHAEL J MARTIN 1291
## 378 59 54 LARRY HODGE 1270
## 379 59 43 ROBERT GLEN VASEY 1283
## 380 59 44 JUSTIN D SCHILLING 1199
## 381 59 41 KYLE WILLIAM MURPHY 1403
## 382 59 9 STEFANO LEE 1411
## 383 59 40 FOREST ZHANG 1348
## 384 60 42 JARED GE 1332
## 385 60 45 DEREK YAN 1242
## 386 60 33 JADE GE 1449
## 387 60 24 MICHAEL R ALDRICH 1229
## 388 60 34 MICHAEL JEFFERY THOMAS 1399
## 389 61 42 JARED GE 1332
## 390 61 54 LARRY HODGE 1270
## 391 61 37 AMIYATOSH PWNANANDAM 980
## 392 61 32 JOSHUA PHILIP MATHEWS 1441
## 393 61 30 GEORGE AVERY JONES 1522
## 394 61 3 ADITYA BAJAJ 1384
## 395 61 47 ERIC WRIGHT 1362
## 396 62 55 ALEX KONG 1186
## 397 63 43 ROBERT GLEN VASEY 1283
## 398 63 49 MICHAEL J MARTIN 1291
## 399 63 45 DEREK YAN 1242
## 400 63 2 DAKSHESH DARURI 1553
## 401 63 48 DANIEL KHAIN 1382
## 402 64 31 RISHI SHETTY 1494
## 403 64 49 MICHAEL J MARTIN 1291
## 404 64 46 JACOB ALEXANDER LAVALLEY 377
## 405 64 54 LARRY HODGE 1270
## 406 64 22 EUGENE L MCCLURE 1555
## 407 64 42 JARED GE 1332
## 408 64 30 GEORGE AVERY JONES 1522
## oppo_prerating
## 1 1794
## 2 1794
## 3 1794
## 4 1794
## 5 1794
## 6 1794
## 7 1794
## 8 1553
## 9 1553
## 10 1553
## 11 1553
## 12 1553
## 13 1553
## 14 1553
## 15 1384
## 16 1384
## 17 1384
## 18 1384
## 19 1384
## 20 1384
## 21 1384
## 22 1716
## 23 1716
## 24 1716
## 25 1716
## 26 1716
## 27 1716
## 28 1716
## 29 1655
## 30 1655
## 31 1655
## 32 1655
## 33 1655
## 34 1655
## 35 1655
## 36 1686
## 37 1686
## 38 1686
## 39 1686
## 40 1686
## 41 1686
## 42 1686
## 43 1649
## 44 1649
## 45 1649
## 46 1649
## 47 1649
## 48 1649
## 49 1649
## 50 1641
## 51 1641
## 52 1641
## 53 1641
## 54 1641
## 55 1641
## 56 1641
## 57 1411
## 58 1411
## 59 1411
## 60 1411
## 61 1411
## 62 1411
## 63 1411
## 64 1365
## 65 1365
## 66 1365
## 67 1365
## 68 1365
## 69 1365
## 70 1365
## 71 1712
## 72 1712
## 73 1712
## 74 1712
## 75 1712
## 76 1712
## 77 1712
## 78 1663
## 79 1663
## 80 1663
## 81 1663
## 82 1663
## 83 1663
## 84 1666
## 85 1666
## 86 1666
## 87 1666
## 88 1666
## 89 1666
## 90 1666
## 91 1610
## 92 1610
## 93 1610
## 94 1610
## 95 1610
## 96 1610
## 97 1610
## 98 1220
## 99 1220
## 100 1220
## 101 1220
## 102 1220
## 103 1220
## 104 1220
## 105 1604
## 106 1604
## 107 1604
## 108 1604
## 109 1604
## 110 1629
## 111 1629
## 112 1629
## 113 1629
## 114 1629
## 115 1629
## 116 1629
## 117 1600
## 118 1600
## 119 1600
## 120 1600
## 121 1600
## 122 1600
## 123 1600
## 124 1564
## 125 1564
## 126 1564
## 127 1564
## 128 1564
## 129 1564
## 130 1564
## 131 1595
## 132 1595
## 133 1595
## 134 1595
## 135 1595
## 136 1595
## 137 1595
## 138 1563
## 139 1563
## 140 1563
## 141 1563
## 142 1563
## 143 1563
## 144 1563
## 145 1555
## 146 1555
## 147 1555
## 148 1555
## 149 1555
## 150 1555
## 151 1363
## 152 1363
## 153 1363
## 154 1363
## 155 1363
## 156 1363
## 157 1363
## 158 1229
## 159 1229
## 160 1229
## 161 1229
## 162 1229
## 163 1229
## 164 1229
## 165 1745
## 166 1745
## 167 1745
## 168 1745
## 169 1745
## 170 1745
## 171 1745
## 172 1579
## 173 1579
## 174 1579
## 175 1579
## 176 1579
## 177 1579
## 178 1579
## 179 1552
## 180 1552
## 181 1552
## 182 1552
## 183 1552
## 184 1552
## 185 1507
## 186 1507
## 187 1507
## 188 1507
## 189 1507
## 190 1507
## 191 1507
## 192 1602
## 193 1602
## 194 1602
## 195 1602
## 196 1602
## 197 1602
## 198 1522
## 199 1522
## 200 1522
## 201 1522
## 202 1522
## 203 1522
## 204 1522
## 205 1494
## 206 1494
## 207 1494
## 208 1494
## 209 1494
## 210 1494
## 211 1494
## 212 1441
## 213 1441
## 214 1441
## 215 1441
## 216 1441
## 217 1441
## 218 1441
## 219 1449
## 220 1449
## 221 1449
## 222 1449
## 223 1449
## 224 1449
## 225 1449
## 226 1399
## 227 1399
## 228 1399
## 229 1399
## 230 1399
## 231 1399
## 232 1399
## 233 1438
## 234 1438
## 235 1438
## 236 1438
## 237 1438
## 238 1438
## 239 1438
## 240 1355
## 241 1355
## 242 1355
## 243 1355
## 244 1355
## 245 1355
## 246 980
## 247 980
## 248 980
## 249 980
## 250 980
## 251 1423
## 252 1423
## 253 1423
## 254 1423
## 255 1423
## 256 1423
## 257 1436
## 258 1436
## 259 1436
## 260 1436
## 261 1436
## 262 1436
## 263 1436
## 264 1348
## 265 1348
## 266 1348
## 267 1348
## 268 1348
## 269 1348
## 270 1348
## 271 1403
## 272 1403
## 273 1403
## 274 1403
## 275 1332
## 276 1332
## 277 1332
## 278 1332
## 279 1332
## 280 1332
## 281 1332
## 282 1283
## 283 1283
## 284 1283
## 285 1283
## 286 1283
## 287 1283
## 288 1283
## 289 1199
## 290 1199
## 291 1199
## 292 1199
## 293 1199
## 294 1199
## 295 1242
## 296 1242
## 297 1242
## 298 1242
## 299 1242
## 300 1242
## 301 1242
## 302 377
## 303 377
## 304 377
## 305 377
## 306 377
## 307 377
## 308 377
## 309 1362
## 310 1362
## 311 1362
## 312 1362
## 313 1362
## 314 1362
## 315 1362
## 316 1382
## 317 1382
## 318 1382
## 319 1382
## 320 1382
## 321 1291
## 322 1291
## 323 1291
## 324 1291
## 325 1291
## 326 1056
## 327 1056
## 328 1056
## 329 1056
## 330 1056
## 331 1056
## 332 1011
## 333 1011
## 334 1011
## 335 1011
## 336 1011
## 337 1011
## 338 1011
## 339 935
## 340 935
## 341 935
## 342 935
## 343 935
## 344 935
## 345 935
## 346 1393
## 347 1393
## 348 1393
## 349 1270
## 350 1270
## 351 1270
## 352 1270
## 353 1270
## 354 1270
## 355 1186
## 356 1186
## 357 1186
## 358 1186
## 359 1186
## 360 1186
## 361 1153
## 362 1153
## 363 1153
## 364 1153
## 365 1153
## 366 1092
## 367 1092
## 368 1092
## 369 1092
## 370 1092
## 371 1092
## 372 917
## 373 917
## 374 917
## 375 917
## 376 917
## 377 917
## 378 853
## 379 853
## 380 853
## 381 853
## 382 853
## 383 853
## 384 967
## 385 967
## 386 967
## 387 967
## 388 967
## 389 955
## 390 955
## 391 955
## 392 955
## 393 955
## 394 955
## 395 955
## 396 1530
## 397 1175
## 398 1175
## 399 1175
## 400 1175
## 401 1175
## 402 1163
## 403 1163
## 404 1163
## 405 1163
## 406 1163
## 407 1163
## 408 1163str(df7)## 'data.frame': 408 obs. of 5 variables:
## $ oppo_num : int 1 1 1 1 1 1 1 2 2 2 ...
## $ player_num : int 12 21 4 39 7 18 14 58 63 17 ...
## $ player_name : Factor w/ 64 levels "ADITYA BAJAJ",..: 40 16 51 34 23 14 8 63 61 54 ...
## $ player_prerating: int 1663 1563 1716 1436 1649 1600 1610 917 1175 1629 ...
## $ oppo_prerating : int 1794 1794 1794 1794 1794 1794 1794 1553 1553 1553 ...#--------------------------------------------------------------------------------------------------------------------------------
#order the df8 dataset by player# so we can group to find average oppponent prechess rating
df8 <- df7[
order( df7[,2] ),
]
df8## oppo_num player_num player_name player_prerating
## 28 4 1 GARY HUA 1794
## 45 7 1 GARY HUA 1794
## 82 12 1 GARY HUA 1794
## 91 14 1 GARY HUA 1794
## 123 18 1 GARY HUA 1794
## 142 21 1 GARY HUA 1794
## 257 39 1 GARY HUA 1794
## 26 4 2 DAKSHESH DARURI 1553
## 44 7 2 DAKSHESH DARURI 1553
## 108 16 2 DAKSHESH DARURI 1553
## 111 17 2 DAKSHESH DARURI 1553
## 131 20 2 DAKSHESH DARURI 1553
## 376 58 2 DAKSHESH DARURI 1553
## 400 63 2 DAKSHESH DARURI 1553
## 55 8 3 ADITYA BAJAJ 1384
## 73 11 3 ADITYA BAJAJ 1384
## 79 12 3 ADITYA BAJAJ 1384
## 86 13 3 ADITYA BAJAJ 1384
## 139 21 3 ADITYA BAJAJ 1384
## 165 25 3 ADITYA BAJAJ 1384
## 394 61 3 ADITYA BAJAJ 1384
## 3 1 4 PATRICK H SCHILLING 1716
## 13 2 4 PATRICK H SCHILLING 1716
## 31 5 4 PATRICK H SCHILLING 1716
## 128 19 4 PATRICK H SCHILLING 1716
## 152 23 4 PATRICK H SCHILLING 1716
## 172 26 4 PATRICK H SCHILLING 1716
## 187 28 4 PATRICK H SCHILLING 1716
## 23 4 5 HANSHI ZUO 1655
## 83 12 5 HANSHI ZUO 1655
## 85 13 5 HANSHI ZUO 1655
## 93 14 5 HANSHI ZUO 1655
## 116 17 5 HANSHI ZUO 1655
## 246 37 5 HANSHI ZUO 1655
## 299 45 5 HANSHI ZUO 1655
## 70 10 6 HANSEN SONG 1686
## 76 11 6 HANSEN SONG 1686
## 143 21 6 HANSEN SONG 1686
## 180 27 6 HANSEN SONG 1686
## 193 29 6 HANSEN SONG 1686
## 231 34 6 HANSEN SONG 1686
## 234 35 6 HANSEN SONG 1686
## 5 1 7 GARY DEE SWATHELL 1649
## 14 2 7 GARY DEE SWATHELL 1649
## 57 9 7 GARY DEE SWATHELL 1649
## 77 11 7 GARY DEE SWATHELL 1649
## 87 13 7 GARY DEE SWATHELL 1649
## 304 46 7 GARY DEE SWATHELL 1649
## 366 57 7 GARY DEE SWATHELL 1649
## 19 3 8 EZEKIEL HOUGHTON 1641
## 62 9 8 EZEKIEL HOUGHTON 1641
## 96 14 8 EZEKIEL HOUGHTON 1641
## 125 19 8 EZEKIEL HOUGHTON 1641
## 189 28 8 EZEKIEL HOUGHTON 1641
## 212 32 8 EZEKIEL HOUGHTON 1641
## 312 47 8 EZEKIEL HOUGHTON 1641
## 48 7 9 STEFANO LEE 1411
## 54 8 9 STEFANO LEE 1411
## 119 18 9 STEFANO LEE 1411
## 134 20 9 STEFANO LEE 1411
## 171 25 9 STEFANO LEE 1411
## 178 26 9 STEFANO LEE 1411
## 382 59 9 STEFANO LEE 1411
## 39 6 10 ANVIT RAO 1365
## 107 16 10 ANVIT RAO 1365
## 117 18 10 ANVIT RAO 1365
## 126 19 10 ANVIT RAO 1365
## 169 25 10 ANVIT RAO 1365
## 208 31 10 ANVIT RAO 1365
## 355 55 10 ANVIT RAO 1365
## 18 3 11 CAMERON WILLIAM MC LEMAN 1712
## 40 6 11 CAMERON WILLIAM MC LEMAN 1712
## 47 7 11 CAMERON WILLIAM MC LEMAN 1712
## 177 26 11 CAMERON WILLIAM MC LEMAN 1712
## 230 34 11 CAMERON WILLIAM MC LEMAN 1712
## 252 38 11 CAMERON WILLIAM MC LEMAN 1712
## 361 56 11 CAMERON WILLIAM MC LEMAN 1712
## 1 1 12 KENNETH J TACK 1663
## 20 3 12 KENNETH J TACK 1663
## 35 5 12 KENNETH J TACK 1663
## 224 33 12 KENNETH J TACK 1663
## 255 38 12 KENNETH J TACK 1663
## 279 42 12 KENNETH J TACK 1663
## 21 3 13 TORRANCE HENRY JR 1666
## 32 5 13 TORRANCE HENRY JR 1666
## 43 7 13 TORRANCE HENRY JR 1666
## 179 27 13 TORRANCE HENRY JR 1666
## 213 32 13 TORRANCE HENRY JR 1666
## 220 33 13 TORRANCE HENRY JR 1666
## 245 36 13 TORRANCE HENRY JR 1666
## 7 1 14 BRADLEY SHAW 1610
## 33 5 14 BRADLEY SHAW 1610
## 51 8 14 BRADLEY SHAW 1610
## 182 27 14 BRADLEY SHAW 1610
## 207 31 14 BRADLEY SHAW 1610
## 291 44 14 BRADLEY SHAW 1610
## 350 54 14 BRADLEY SHAW 1610
## 105 16 15 ZACHARY JAMES HOUGHTON 1220
## 124 19 15 ZACHARY JAMES HOUGHTON 1220
## 149 22 15 ZACHARY JAMES HOUGHTON 1220
## 203 30 15 ZACHARY JAMES HOUGHTON 1220
## 219 33 15 ZACHARY JAMES HOUGHTON 1220
## 251 38 15 ZACHARY JAMES HOUGHTON 1220
## 349 54 15 ZACHARY JAMES HOUGHTON 1220
## 12 2 16 MIKE NIKITIN 1604
## 65 10 16 MIKE NIKITIN 1604
## 101 15 16 MIKE NIKITIN 1604
## 241 36 16 MIKE NIKITIN 1604
## 263 39 16 MIKE NIKITIN 1604
## 10 2 17 RONALD GRZEGORCZYK 1629
## 29 5 17 RONALD GRZEGORCZYK 1629
## 147 22 17 RONALD GRZEGORCZYK 1629
## 156 23 17 RONALD GRZEGORCZYK 1629
## 175 26 17 RONALD GRZEGORCZYK 1629
## 271 41 17 RONALD GRZEGORCZYK 1629
## 319 48 17 RONALD GRZEGORCZYK 1629
## 6 1 18 DAVID SUNDEEN 1600
## 63 9 18 DAVID SUNDEEN 1600
## 66 10 18 DAVID SUNDEEN 1600
## 130 19 18 DAVID SUNDEEN 1600
## 217 32 18 DAVID SUNDEEN 1600
## 253 38 18 DAVID SUNDEEN 1600
## 313 47 18 DAVID SUNDEEN 1600
## 24 4 19 DIPANKAR ROY 1564
## 52 8 19 DIPANKAR ROY 1564
## 64 10 19 DIPANKAR ROY 1564
## 103 15 19 DIPANKAR ROY 1564
## 118 18 19 DIPANKAR ROY 1564
## 188 28 19 DIPANKAR ROY 1564
## 345 52 19 DIPANKAR ROY 1564
## 11 2 20 JASON ZHENG 1595
## 60 9 20 JASON ZHENG 1595
## 154 23 20 JASON ZHENG 1595
## 185 28 20 JASON ZHENG 1595
## 264 40 20 JASON ZHENG 1595
## 273 41 20 JASON ZHENG 1595
## 322 49 20 JASON ZHENG 1595
## 2 1 21 DINH DANG BUI 1563
## 16 3 21 DINH DANG BUI 1563
## 38 6 21 DINH DANG BUI 1563
## 260 39 21 DINH DANG BUI 1563
## 268 40 21 DINH DANG BUI 1563
## 286 43 21 DINH DANG BUI 1563
## 309 47 21 DINH DANG BUI 1563
## 104 15 22 EUGENE L MCCLURE 1555
## 112 17 22 EUGENE L MCCLURE 1555
## 190 28 22 EUGENE L MCCLURE 1555
## 270 40 22 EUGENE L MCCLURE 1555
## 343 52 22 EUGENE L MCCLURE 1555
## 406 64 22 EUGENE L MCCLURE 1555
## 25 4 23 ALAN BUI 1363
## 114 17 23 ALAN BUI 1363
## 136 20 23 ALAN BUI 1363
## 247 37 23 ALAN BUI 1363
## 288 43 23 ALAN BUI 1363
## 308 46 23 ALAN BUI 1363
## 375 58 23 ALAN BUI 1363
## 170 25 24 MICHAEL R ALDRICH 1229
## 186 28 24 MICHAEL R ALDRICH 1229
## 259 39 24 MICHAEL R ALDRICH 1229
## 285 43 24 MICHAEL R ALDRICH 1229
## 292 44 24 MICHAEL R ALDRICH 1229
## 311 47 24 MICHAEL R ALDRICH 1229
## 387 60 24 MICHAEL R ALDRICH 1229
## 15 3 25 LOREN SCHWIEBERT 1745
## 58 9 25 LOREN SCHWIEBERT 1745
## 69 10 25 LOREN SCHWIEBERT 1745
## 160 24 25 LOREN SCHWIEBERT 1745
## 226 34 25 LOREN SCHWIEBERT 1745
## 310 47 25 LOREN SCHWIEBERT 1745
## 346 53 25 LOREN SCHWIEBERT 1745
## 27 4 26 MAX ZHU 1579
## 61 9 26 MAX ZHU 1579
## 71 11 26 MAX ZHU 1579
## 113 17 26 MAX ZHU 1579
## 218 32 26 MAX ZHU 1579
## 265 40 26 MAX ZHU 1579
## 323 49 26 MAX ZHU 1579
## 41 6 27 GAURAV GIDWANI 1552
## 89 13 27 GAURAV GIDWANI 1552
## 92 14 27 GAURAV GIDWANI 1552
## 248 37 27 GAURAV GIDWANI 1552
## 305 46 27 GAURAV GIDWANI 1552
## 335 51 27 GAURAV GIDWANI 1552
## 22 4 28 SOFIA ADINA STANESCU 1507
## 56 8 28 SOFIA ADINA STANESCU 1507
## 129 19 28 SOFIA ADINA STANESCU 1507
## 137 20 28 SOFIA ADINA STANESCU 1507
## 145 22 28 SOFIA ADINA STANESCU 1507
## 161 24 28 SOFIA ADINA STANESCU 1507
## 243 36 28 SOFIA ADINA STANESCU 1507
## 42 6 29 CHIEDOZIE OKORIE 1602
## 228 34 29 CHIEDOZIE OKORIE 1602
## 256 38 29 CHIEDOZIE OKORIE 1602
## 317 48 29 CHIEDOZIE OKORIE 1602
## 330 50 29 CHIEDOZIE OKORIE 1602
## 342 52 29 CHIEDOZIE OKORIE 1602
## 100 15 30 GEORGE AVERY JONES 1522
## 209 31 30 GEORGE AVERY JONES 1522
## 329 50 30 GEORGE AVERY JONES 1522
## 340 52 30 GEORGE AVERY JONES 1522
## 358 55 30 GEORGE AVERY JONES 1522
## 393 61 30 GEORGE AVERY JONES 1522
## 408 64 30 GEORGE AVERY JONES 1522
## 67 10 31 RISHI SHETTY 1494
## 97 14 31 RISHI SHETTY 1494
## 202 30 31 RISHI SHETTY 1494
## 328 50 31 RISHI SHETTY 1494
## 356 55 31 RISHI SHETTY 1494
## 372 58 31 RISHI SHETTY 1494
## 402 64 31 RISHI SHETTY 1494
## 50 8 32 JOSHUA PHILIP MATHEWS 1441
## 90 13 32 JOSHUA PHILIP MATHEWS 1441
## 122 18 32 JOSHUA PHILIP MATHEWS 1441
## 174 26 32 JOSHUA PHILIP MATHEWS 1441
## 294 44 32 JOSHUA PHILIP MATHEWS 1441
## 333 51 32 JOSHUA PHILIP MATHEWS 1441
## 392 61 32 JOSHUA PHILIP MATHEWS 1441
## 81 12 33 JADE GE 1449
## 84 13 33 JADE GE 1449
## 99 15 33 JADE GE 1449
## 244 36 33 JADE GE 1449
## 331 50 33 JADE GE 1449
## 334 51 33 JADE GE 1449
## 386 60 33 JADE GE 1449
## 36 6 34 MICHAEL JEFFERY THOMAS 1399
## 72 11 34 MICHAEL JEFFERY THOMAS 1399
## 168 25 34 MICHAEL JEFFERY THOMAS 1399
## 197 29 34 MICHAEL JEFFERY THOMAS 1399
## 250 37 34 MICHAEL JEFFERY THOMAS 1399
## 344 52 34 MICHAEL JEFFERY THOMAS 1399
## 388 60 34 MICHAEL JEFFERY THOMAS 1399
## 37 6 35 JOSHUA DAVID LEE 1438
## 254 38 35 JOSHUA DAVID LEE 1438
## 302 46 35 JOSHUA DAVID LEE 1438
## 316 48 35 JOSHUA DAVID LEE 1438
## 339 52 35 JOSHUA DAVID LEE 1438
## 363 56 35 JOSHUA DAVID LEE 1438
## 367 57 35 JOSHUA DAVID LEE 1438
## 88 13 36 SIDDHARTH JHA 1355
## 109 16 36 SIDDHARTH JHA 1355
## 191 28 36 SIDDHARTH JHA 1355
## 223 33 36 SIDDHARTH JHA 1355
## 337 51 36 SIDDHARTH JHA 1355
## 368 57 36 SIDDHARTH JHA 1355
## 34 5 37 AMIYATOSH PWNANANDAM 980
## 155 23 37 AMIYATOSH PWNANANDAM 980
## 183 27 37 AMIYATOSH PWNANANDAM 980
## 229 34 37 AMIYATOSH PWNANANDAM 980
## 391 61 37 AMIYATOSH PWNANANDAM 980
## 75 11 38 BRIAN LIU 1423
## 80 12 38 BRIAN LIU 1423
## 98 15 38 BRIAN LIU 1423
## 121 18 38 BRIAN LIU 1423
## 192 29 38 BRIAN LIU 1423
## 237 35 38 BRIAN LIU 1423
## 4 1 39 JOEL R HENDON 1436
## 106 16 39 JOEL R HENDON 1436
## 140 21 39 JOEL R HENDON 1436
## 162 24 39 JOEL R HENDON 1436
## 269 40 39 JOEL R HENDON 1436
## 293 44 39 JOEL R HENDON 1436
## 354 54 39 JOEL R HENDON 1436
## 132 20 40 FOREST ZHANG 1348
## 141 21 40 FOREST ZHANG 1348
## 150 22 40 FOREST ZHANG 1348
## 173 26 40 FOREST ZHANG 1348
## 262 39 40 FOREST ZHANG 1348
## 365 56 40 FOREST ZHANG 1348
## 383 59 40 FOREST ZHANG 1348
## 115 17 41 KYLE WILLIAM MURPHY 1403
## 133 20 41 KYLE WILLIAM MURPHY 1403
## 374 58 41 KYLE WILLIAM MURPHY 1403
## 381 59 41 KYLE WILLIAM MURPHY 1403
## 78 12 42 JARED GE 1332
## 326 50 42 JARED GE 1332
## 362 56 42 JARED GE 1332
## 369 57 42 JARED GE 1332
## 384 60 42 JARED GE 1332
## 389 61 42 JARED GE 1332
## 407 64 42 JARED GE 1332
## 144 21 43 ROBERT GLEN VASEY 1283
## 153 23 43 ROBERT GLEN VASEY 1283
## 164 24 43 ROBERT GLEN VASEY 1283
## 306 46 43 ROBERT GLEN VASEY 1283
## 359 55 43 ROBERT GLEN VASEY 1283
## 379 59 43 ROBERT GLEN VASEY 1283
## 397 63 43 ROBERT GLEN VASEY 1283
## 94 14 44 JUSTIN D SCHILLING 1199
## 158 24 44 JUSTIN D SCHILLING 1199
## 215 32 44 JUSTIN D SCHILLING 1199
## 261 39 44 JUSTIN D SCHILLING 1199
## 347 53 44 JUSTIN D SCHILLING 1199
## 380 59 44 JUSTIN D SCHILLING 1199
## 30 5 45 DEREK YAN 1242
## 336 51 45 DEREK YAN 1242
## 357 55 45 DEREK YAN 1242
## 364 56 45 DEREK YAN 1242
## 373 58 45 DEREK YAN 1242
## 385 60 45 DEREK YAN 1242
## 399 63 45 DEREK YAN 1242
## 49 7 46 JACOB ALEXANDER LAVALLEY 377
## 151 23 46 JACOB ALEXANDER LAVALLEY 377
## 184 27 46 JACOB ALEXANDER LAVALLEY 377
## 235 35 46 JACOB ALEXANDER LAVALLEY 377
## 287 43 46 JACOB ALEXANDER LAVALLEY 377
## 327 50 46 JACOB ALEXANDER LAVALLEY 377
## 404 64 46 JACOB ALEXANDER LAVALLEY 377
## 53 8 47 ERIC WRIGHT 1362
## 120 18 47 ERIC WRIGHT 1362
## 138 21 47 ERIC WRIGHT 1362
## 159 24 47 ERIC WRIGHT 1362
## 166 25 47 ERIC WRIGHT 1362
## 338 51 47 ERIC WRIGHT 1362
## 395 61 47 ERIC WRIGHT 1362
## 110 17 48 DANIEL KHAIN 1382
## 194 29 48 DANIEL KHAIN 1382
## 238 35 48 DANIEL KHAIN 1382
## 341 52 48 DANIEL KHAIN 1382
## 401 63 48 DANIEL KHAIN 1382
## 135 20 49 MICHAEL J MARTIN 1291
## 176 26 49 MICHAEL J MARTIN 1291
## 377 58 49 MICHAEL J MARTIN 1291
## 398 63 49 MICHAEL J MARTIN 1291
## 403 64 49 MICHAEL J MARTIN 1291
## 196 29 50 SHIVAM JHA 1056
## 201 30 50 SHIVAM JHA 1056
## 206 31 50 SHIVAM JHA 1056
## 225 33 50 SHIVAM JHA 1056
## 275 42 50 SHIVAM JHA 1056
## 307 46 50 SHIVAM JHA 1056
## 181 27 51 TEJAS AYYAGARI 1011
## 216 32 51 TEJAS AYYAGARI 1011
## 222 33 51 TEJAS AYYAGARI 1011
## 240 36 51 TEJAS AYYAGARI 1011
## 300 45 51 TEJAS AYYAGARI 1011
## 315 47 51 TEJAS AYYAGARI 1011
## 371 57 51 TEJAS AYYAGARI 1011
## 127 19 52 ETHAN GUO 935
## 146 22 52 ETHAN GUO 935
## 195 29 52 ETHAN GUO 935
## 200 30 52 ETHAN GUO 935
## 227 34 52 ETHAN GUO 935
## 236 35 52 ETHAN GUO 935
## 320 48 52 ETHAN GUO 935
## 167 25 53 JOSE C YBARRA 1393
## 289 44 53 JOSE C YBARRA 1393
## 370 57 53 JOSE C YBARRA 1393
## 95 14 54 LARRY HODGE 1270
## 102 15 54 LARRY HODGE 1270
## 258 39 54 LARRY HODGE 1270
## 378 59 54 LARRY HODGE 1270
## 390 61 54 LARRY HODGE 1270
## 405 64 54 LARRY HODGE 1270
## 68 10 55 ALEX KONG 1186
## 198 30 55 ALEX KONG 1186
## 211 31 55 ALEX KONG 1186
## 284 43 55 ALEX KONG 1186
## 297 45 55 ALEX KONG 1186
## 396 62 55 ALEX KONG 1186
## 74 11 56 MARISA RICCI 1153
## 233 35 56 MARISA RICCI 1153
## 267 40 56 MARISA RICCI 1153
## 277 42 56 MARISA RICCI 1153
## 298 45 56 MARISA RICCI 1153
## 46 7 57 MICHAEL LU 1092
## 239 35 57 MICHAEL LU 1092
## 242 36 57 MICHAEL LU 1092
## 276 42 57 MICHAEL LU 1092
## 332 51 57 MICHAEL LU 1092
## 348 53 57 MICHAEL LU 1092
## 8 2 58 VIRAJ MOHILE 917
## 157 23 58 VIRAJ MOHILE 917
## 205 31 58 VIRAJ MOHILE 917
## 272 41 58 VIRAJ MOHILE 917
## 301 45 58 VIRAJ MOHILE 917
## 325 49 58 VIRAJ MOHILE 917
## 59 9 59 SEAN M MC CORMICK 853
## 266 40 59 SEAN M MC CORMICK 853
## 274 41 59 SEAN M MC CORMICK 853
## 283 43 59 SEAN M MC CORMICK 853
## 290 44 59 SEAN M MC CORMICK 853
## 353 54 59 SEAN M MC CORMICK 853
## 163 24 60 JULIA SHEN 967
## 221 33 60 JULIA SHEN 967
## 232 34 60 JULIA SHEN 967
## 278 42 60 JULIA SHEN 967
## 296 45 60 JULIA SHEN 967
## 17 3 61 JEZZEL FARKAS 955
## 199 30 61 JEZZEL FARKAS 955
## 214 32 61 JEZZEL FARKAS 955
## 249 37 61 JEZZEL FARKAS 955
## 281 42 61 JEZZEL FARKAS 955
## 314 47 61 JEZZEL FARKAS 955
## 352 54 61 JEZZEL FARKAS 955
## 360 55 62 ASHWIN BALAJI 1530
## 9 2 63 THOMAS JOSEPH HOSMER 1175
## 282 43 63 THOMAS JOSEPH HOSMER 1175
## 295 45 63 THOMAS JOSEPH HOSMER 1175
## 318 48 63 THOMAS JOSEPH HOSMER 1175
## 321 49 63 THOMAS JOSEPH HOSMER 1175
## 148 22 64 BEN LI 1163
## 204 30 64 BEN LI 1163
## 210 31 64 BEN LI 1163
## 280 42 64 BEN LI 1163
## 303 46 64 BEN LI 1163
## 324 49 64 BEN LI 1163
## 351 54 64 BEN LI 1163
## oppo_prerating
## 28 1716
## 45 1649
## 82 1663
## 91 1610
## 123 1600
## 142 1563
## 257 1436
## 26 1716
## 44 1649
## 108 1604
## 111 1629
## 131 1595
## 376 917
## 400 1175
## 55 1641
## 73 1712
## 79 1663
## 86 1666
## 139 1563
## 165 1745
## 394 955
## 3 1794
## 13 1553
## 31 1655
## 128 1564
## 152 1363
## 172 1579
## 187 1507
## 23 1716
## 83 1663
## 85 1666
## 93 1610
## 116 1629
## 246 980
## 299 1242
## 70 1365
## 76 1712
## 143 1563
## 180 1552
## 193 1602
## 231 1399
## 234 1438
## 5 1794
## 14 1553
## 57 1411
## 77 1712
## 87 1666
## 304 377
## 366 1092
## 19 1384
## 62 1411
## 96 1610
## 125 1564
## 189 1507
## 212 1441
## 312 1362
## 48 1649
## 54 1641
## 119 1600
## 134 1595
## 171 1745
## 178 1579
## 382 853
## 39 1686
## 107 1604
## 117 1600
## 126 1564
## 169 1745
## 208 1494
## 355 1186
## 18 1384
## 40 1686
## 47 1649
## 177 1579
## 230 1399
## 252 1423
## 361 1153
## 1 1794
## 20 1384
## 35 1655
## 224 1449
## 255 1423
## 279 1332
## 21 1384
## 32 1655
## 43 1649
## 179 1552
## 213 1441
## 220 1449
## 245 1355
## 7 1794
## 33 1655
## 51 1641
## 182 1552
## 207 1494
## 291 1199
## 350 1270
## 105 1604
## 124 1564
## 149 1555
## 203 1522
## 219 1449
## 251 1423
## 349 1270
## 12 1553
## 65 1365
## 101 1220
## 241 1355
## 263 1436
## 10 1553
## 29 1655
## 147 1555
## 156 1363
## 175 1579
## 271 1403
## 319 1382
## 6 1794
## 63 1411
## 66 1365
## 130 1564
## 217 1441
## 253 1423
## 313 1362
## 24 1716
## 52 1641
## 64 1365
## 103 1220
## 118 1600
## 188 1507
## 345 935
## 11 1553
## 60 1411
## 154 1363
## 185 1507
## 264 1348
## 273 1403
## 322 1291
## 2 1794
## 16 1384
## 38 1686
## 260 1436
## 268 1348
## 286 1283
## 309 1362
## 104 1220
## 112 1629
## 190 1507
## 270 1348
## 343 935
## 406 1163
## 25 1716
## 114 1629
## 136 1595
## 247 980
## 288 1283
## 308 377
## 375 917
## 170 1745
## 186 1507
## 259 1436
## 285 1283
## 292 1199
## 311 1362
## 387 967
## 15 1384
## 58 1411
## 69 1365
## 160 1229
## 226 1399
## 310 1362
## 346 1393
## 27 1716
## 61 1411
## 71 1712
## 113 1629
## 218 1441
## 265 1348
## 323 1291
## 41 1686
## 89 1666
## 92 1610
## 248 980
## 305 377
## 335 1011
## 22 1716
## 56 1641
## 129 1564
## 137 1595
## 145 1555
## 161 1229
## 243 1355
## 42 1686
## 228 1399
## 256 1423
## 317 1382
## 330 1056
## 342 935
## 100 1220
## 209 1494
## 329 1056
## 340 935
## 358 1186
## 393 955
## 408 1163
## 67 1365
## 97 1610
## 202 1522
## 328 1056
## 356 1186
## 372 917
## 402 1163
## 50 1641
## 90 1666
## 122 1600
## 174 1579
## 294 1199
## 333 1011
## 392 955
## 81 1663
## 84 1666
## 99 1220
## 244 1355
## 331 1056
## 334 1011
## 386 967
## 36 1686
## 72 1712
## 168 1745
## 197 1602
## 250 980
## 344 935
## 388 967
## 37 1686
## 254 1423
## 302 377
## 316 1382
## 339 935
## 363 1153
## 367 1092
## 88 1666
## 109 1604
## 191 1507
## 223 1449
## 337 1011
## 368 1092
## 34 1655
## 155 1363
## 183 1552
## 229 1399
## 391 955
## 75 1712
## 80 1663
## 98 1220
## 121 1600
## 192 1602
## 237 1438
## 4 1794
## 106 1604
## 140 1563
## 162 1229
## 269 1348
## 293 1199
## 354 1270
## 132 1595
## 141 1563
## 150 1555
## 173 1579
## 262 1436
## 365 1153
## 383 853
## 115 1629
## 133 1595
## 374 917
## 381 853
## 78 1663
## 326 1056
## 362 1153
## 369 1092
## 384 967
## 389 955
## 407 1163
## 144 1563
## 153 1363
## 164 1229
## 306 377
## 359 1186
## 379 853
## 397 1175
## 94 1610
## 158 1229
## 215 1441
## 261 1436
## 347 1393
## 380 853
## 30 1655
## 336 1011
## 357 1186
## 364 1153
## 373 917
## 385 967
## 399 1175
## 49 1649
## 151 1363
## 184 1552
## 235 1438
## 287 1283
## 327 1056
## 404 1163
## 53 1641
## 120 1600
## 138 1563
## 159 1229
## 166 1745
## 338 1011
## 395 955
## 110 1629
## 194 1602
## 238 1438
## 341 935
## 401 1175
## 135 1595
## 176 1579
## 377 917
## 398 1175
## 403 1163
## 196 1602
## 201 1522
## 206 1494
## 225 1449
## 275 1332
## 307 377
## 181 1552
## 216 1441
## 222 1449
## 240 1355
## 300 1242
## 315 1362
## 371 1092
## 127 1564
## 146 1555
## 195 1602
## 200 1522
## 227 1399
## 236 1438
## 320 1382
## 167 1745
## 289 1199
## 370 1092
## 95 1610
## 102 1220
## 258 1436
## 378 853
## 390 955
## 405 1163
## 68 1365
## 198 1522
## 211 1494
## 284 1283
## 297 1242
## 396 1530
## 74 1712
## 233 1438
## 267 1348
## 277 1332
## 298 1242
## 46 1649
## 239 1438
## 242 1355
## 276 1332
## 332 1011
## 348 1393
## 8 1553
## 157 1363
## 205 1494
## 272 1403
## 301 1242
## 325 1291
## 59 1411
## 266 1348
## 274 1403
## 283 1283
## 290 1199
## 353 1270
## 163 1229
## 221 1449
## 232 1399
## 278 1332
## 296 1242
## 17 1384
## 199 1522
## 214 1441
## 249 980
## 281 1332
## 314 1362
## 352 1270
## 360 1186
## 9 1553
## 282 1283
## 295 1242
## 318 1382
## 321 1291
## 148 1555
## 204 1522
## 210 1494
## 280 1332
## 303 377
## 324 1291
## 351 1270chessrating_df <- df8 %>% group_by(player_num) %>% summarize(avg_prechess_rating = mean(oppo_prerating))
str(chessrating_df)## Classes 'tbl_df', 'tbl' and 'data.frame': 64 obs. of 2 variables:
## $ player_num : int 1 2 3 4 5 6 7 8 9 10 ...
## $ avg_prechess_rating: num 1605 1469 1564 1574 1501 ...chessrating_df## # A tibble: 64 x 2
## player_num avg_prechess_rating
## <int> <dbl>
## 1 1 1605.
## 2 2 1469.
## 3 3 1564.
## 4 4 1574.
## 5 5 1501.
## 6 6 1519.
## 7 7 1372.
## 8 8 1468.
## 9 9 1523.
## 10 10 1554.
## # ... with 54 more rows#merge back to main dataset to get the finaldataset by player_number with all the fields
finaldf <- merge(startnew, chessrating_df, by = "player_num")
finaldf## player_num player_name State total_points player_prerating
## 1 1 GARY HUA ON 6.0 1794
## 2 2 DAKSHESH DARURI MI 6.0 1553
## 3 3 ADITYA BAJAJ MI 6.0 1384
## 4 4 PATRICK H SCHILLING MI 5.5 1716
## 5 5 HANSHI ZUO MI 5.5 1655
## 6 6 HANSEN SONG OH 5.0 1686
## 7 7 GARY DEE SWATHELL MI 5.0 1649
## 8 8 EZEKIEL HOUGHTON MI 5.0 1641
## 9 9 STEFANO LEE ON 5.0 1411
## 10 10 ANVIT RAO MI 5.0 1365
## 11 11 CAMERON WILLIAM MC LEMAN MI 4.5 1712
## 12 12 KENNETH J TACK MI 4.5 1663
## 13 13 TORRANCE HENRY JR MI 4.5 1666
## 14 14 BRADLEY SHAW MI 4.5 1610
## 15 15 ZACHARY JAMES HOUGHTON MI 4.5 1220
## 16 16 MIKE NIKITIN MI 4.0 1604
## 17 17 RONALD GRZEGORCZYK MI 4.0 1629
## 18 18 DAVID SUNDEEN MI 4.0 1600
## 19 19 DIPANKAR ROY MI 4.0 1564
## 20 20 JASON ZHENG MI 4.0 1595
## 21 21 DINH DANG BUI ON 4.0 1563
## 22 22 EUGENE L MCCLURE MI 4.0 1555
## 23 23 ALAN BUI ON 4.0 1363
## 24 24 MICHAEL R ALDRICH MI 4.0 1229
## 25 25 LOREN SCHWIEBERT MI 3.5 1745
## 26 26 MAX ZHU ON 3.5 1579
## 27 27 GAURAV GIDWANI MI 3.5 1552
## 28 28 SOFIA ADINA STANESCU MI 3.5 1507
## 29 29 CHIEDOZIE OKORIE MI 3.5 1602
## 30 30 GEORGE AVERY JONES ON 3.5 1522
## 31 31 RISHI SHETTY MI 3.5 1494
## 32 32 JOSHUA PHILIP MATHEWS ON 3.5 1441
## 33 33 JADE GE MI 3.5 1449
## 34 34 MICHAEL JEFFERY THOMAS MI 3.5 1399
## 35 35 JOSHUA DAVID LEE MI 3.5 1438
## 36 36 SIDDHARTH JHA MI 3.5 1355
## 37 37 AMIYATOSH PWNANANDAM MI 3.5 980
## 38 38 BRIAN LIU MI 3.0 1423
## 39 39 JOEL R HENDON MI 3.0 1436
## 40 40 FOREST ZHANG MI 3.0 1348
## 41 41 KYLE WILLIAM MURPHY MI 3.0 1403
## 42 42 JARED GE MI 3.0 1332
## 43 43 ROBERT GLEN VASEY MI 3.0 1283
## 44 44 JUSTIN D SCHILLING MI 3.0 1199
## 45 45 DEREK YAN MI 3.0 1242
## 46 46 JACOB ALEXANDER LAVALLEY MI 3.0 377
## 47 47 ERIC WRIGHT MI 2.5 1362
## 48 48 DANIEL KHAIN MI 2.5 1382
## 49 49 MICHAEL J MARTIN MI 2.5 1291
## 50 50 SHIVAM JHA MI 2.5 1056
## 51 51 TEJAS AYYAGARI MI 2.5 1011
## 52 52 ETHAN GUO MI 2.5 935
## 53 53 JOSE C YBARRA MI 2.0 1393
## 54 54 LARRY HODGE MI 2.0 1270
## 55 55 ALEX KONG MI 2.0 1186
## 56 56 MARISA RICCI MI 2.0 1153
## 57 57 MICHAEL LU MI 2.0 1092
## 58 58 VIRAJ MOHILE MI 2.0 917
## 59 59 SEAN M MC CORMICK MI 2.0 853
## 60 60 JULIA SHEN MI 1.5 967
## 61 61 JEZZEL FARKAS ON 1.5 955
## 62 62 ASHWIN BALAJI MI 1.0 1530
## 63 63 THOMAS JOSEPH HOSMER MI 1.0 1175
## 64 64 BEN LI MI 1.0 1163
## avg_prechess_rating
## 1 1605.286
## 2 1469.286
## 3 1563.571
## 4 1573.571
## 5 1500.857
## 6 1518.714
## 7 1372.143
## 8 1468.429
## 9 1523.143
## 10 1554.143
## 11 1467.571
## 12 1506.167
## 13 1497.857
## 14 1515.000
## 15 1483.857
## 16 1385.800
## 17 1498.571
## 18 1480.000
## 19 1426.286
## 20 1410.857
## 21 1470.429
## 22 1300.333
## 23 1213.857
## 24 1357.000
## 25 1363.286
## 26 1506.857
## 27 1221.667
## 28 1522.143
## 29 1313.500
## 30 1144.143
## 31 1259.857
## 32 1378.714
## 33 1276.857
## 34 1375.286
## 35 1149.714
## 36 1388.167
## 37 1384.800
## 38 1539.167
## 39 1429.571
## 40 1390.571
## 41 1248.500
## 42 1149.857
## 43 1106.571
## 44 1327.000
## 45 1152.000
## 46 1357.714
## 47 1392.000
## 48 1355.800
## 49 1285.800
## 50 1296.000
## 51 1356.143
## 52 1494.571
## 53 1345.333
## 54 1206.167
## 55 1406.000
## 56 1414.400
## 57 1363.000
## 58 1391.000
## 59 1319.000
## 60 1330.200
## 61 1327.286
## 62 1186.000
## 63 1350.200
## 64 1263.000finaldf %>% kable() %>% kable_styling()| player_num | player_name | State | total_points | player_prerating | avg_prechess_rating |
|---|---|---|---|---|---|
| 1 | GARY HUA | ON | 6.0 | 1794 | 1605.286 |
| 2 | DAKSHESH DARURI | MI | 6.0 | 1553 | 1469.286 |
| 3 | ADITYA BAJAJ | MI | 6.0 | 1384 | 1563.571 |
| 4 | PATRICK H SCHILLING | MI | 5.5 | 1716 | 1573.571 |
| 5 | HANSHI ZUO | MI | 5.5 | 1655 | 1500.857 |
| 6 | HANSEN SONG | OH | 5.0 | 1686 | 1518.714 |
| 7 | GARY DEE SWATHELL | MI | 5.0 | 1649 | 1372.143 |
| 8 | EZEKIEL HOUGHTON | MI | 5.0 | 1641 | 1468.429 |
| 9 | STEFANO LEE | ON | 5.0 | 1411 | 1523.143 |
| 10 | ANVIT RAO | MI | 5.0 | 1365 | 1554.143 |
| 11 | CAMERON WILLIAM MC LEMAN | MI | 4.5 | 1712 | 1467.571 |
| 12 | KENNETH J TACK | MI | 4.5 | 1663 | 1506.167 |
| 13 | TORRANCE HENRY JR | MI | 4.5 | 1666 | 1497.857 |
| 14 | BRADLEY SHAW | MI | 4.5 | 1610 | 1515.000 |
| 15 | ZACHARY JAMES HOUGHTON | MI | 4.5 | 1220 | 1483.857 |
| 16 | MIKE NIKITIN | MI | 4.0 | 1604 | 1385.800 |
| 17 | RONALD GRZEGORCZYK | MI | 4.0 | 1629 | 1498.571 |
| 18 | DAVID SUNDEEN | MI | 4.0 | 1600 | 1480.000 |
| 19 | DIPANKAR ROY | MI | 4.0 | 1564 | 1426.286 |
| 20 | JASON ZHENG | MI | 4.0 | 1595 | 1410.857 |
| 21 | DINH DANG BUI | ON | 4.0 | 1563 | 1470.429 |
| 22 | EUGENE L MCCLURE | MI | 4.0 | 1555 | 1300.333 |
| 23 | ALAN BUI | ON | 4.0 | 1363 | 1213.857 |
| 24 | MICHAEL R ALDRICH | MI | 4.0 | 1229 | 1357.000 |
| 25 | LOREN SCHWIEBERT | MI | 3.5 | 1745 | 1363.286 |
| 26 | MAX ZHU | ON | 3.5 | 1579 | 1506.857 |
| 27 | GAURAV GIDWANI | MI | 3.5 | 1552 | 1221.667 |
| 28 | SOFIA ADINA STANESCU | MI | 3.5 | 1507 | 1522.143 |
| 29 | CHIEDOZIE OKORIE | MI | 3.5 | 1602 | 1313.500 |
| 30 | GEORGE AVERY JONES | ON | 3.5 | 1522 | 1144.143 |
| 31 | RISHI SHETTY | MI | 3.5 | 1494 | 1259.857 |
| 32 | JOSHUA PHILIP MATHEWS | ON | 3.5 | 1441 | 1378.714 |
| 33 | JADE GE | MI | 3.5 | 1449 | 1276.857 |
| 34 | MICHAEL JEFFERY THOMAS | MI | 3.5 | 1399 | 1375.286 |
| 35 | JOSHUA DAVID LEE | MI | 3.5 | 1438 | 1149.714 |
| 36 | SIDDHARTH JHA | MI | 3.5 | 1355 | 1388.167 |
| 37 | AMIYATOSH PWNANANDAM | MI | 3.5 | 980 | 1384.800 |
| 38 | BRIAN LIU | MI | 3.0 | 1423 | 1539.167 |
| 39 | JOEL R HENDON | MI | 3.0 | 1436 | 1429.571 |
| 40 | FOREST ZHANG | MI | 3.0 | 1348 | 1390.571 |
| 41 | KYLE WILLIAM MURPHY | MI | 3.0 | 1403 | 1248.500 |
| 42 | JARED GE | MI | 3.0 | 1332 | 1149.857 |
| 43 | ROBERT GLEN VASEY | MI | 3.0 | 1283 | 1106.571 |
| 44 | JUSTIN D SCHILLING | MI | 3.0 | 1199 | 1327.000 |
| 45 | DEREK YAN | MI | 3.0 | 1242 | 1152.000 |
| 46 | JACOB ALEXANDER LAVALLEY | MI | 3.0 | 377 | 1357.714 |
| 47 | ERIC WRIGHT | MI | 2.5 | 1362 | 1392.000 |
| 48 | DANIEL KHAIN | MI | 2.5 | 1382 | 1355.800 |
| 49 | MICHAEL J MARTIN | MI | 2.5 | 1291 | 1285.800 |
| 50 | SHIVAM JHA | MI | 2.5 | 1056 | 1296.000 |
| 51 | TEJAS AYYAGARI | MI | 2.5 | 1011 | 1356.143 |
| 52 | ETHAN GUO | MI | 2.5 | 935 | 1494.571 |
| 53 | JOSE C YBARRA | MI | 2.0 | 1393 | 1345.333 |
| 54 | LARRY HODGE | MI | 2.0 | 1270 | 1206.167 |
| 55 | ALEX KONG | MI | 2.0 | 1186 | 1406.000 |
| 56 | MARISA RICCI | MI | 2.0 | 1153 | 1414.400 |
| 57 | MICHAEL LU | MI | 2.0 | 1092 | 1363.000 |
| 58 | VIRAJ MOHILE | MI | 2.0 | 917 | 1391.000 |
| 59 | SEAN M MC CORMICK | MI | 2.0 | 853 | 1319.000 |
| 60 | JULIA SHEN | MI | 1.5 | 967 | 1330.200 |
| 61 | JEZZEL FARKAS | ON | 1.5 | 955 | 1327.286 |
| 62 | ASHWIN BALAJI | MI | 1.0 | 1530 | 1186.000 |
| 63 | THOMAS JOSEPH HOSMER | MI | 1.0 | 1175 | 1350.200 |
| 64 | BEN LI | MI | 1.0 | 1163 | 1263.000 |
#Write file to a CSV file
write.csv(finaldf, "Banufinalchessratingdataset.csv", row.names = FALSE)
write.csv(df8, "Banuplayerrowswithopponentrating.csv", row.names = FALSE)