In this project, I used regular expressions to extract player names, their state, total points and prerating. Also, I extracted and computed the average prerating of the individual player’s opponents and write the output to a csv file called tournament.csv
Load the required libraries
library(tidyverse)
library(stringr)Read the file
url = "https://raw.githubusercontent.com/chinedu2301/DATA607-Data-Acquisition-and-Management/main/tournamentinfo.txt"
text <- read.table(url, sep = ",", skip = 4)
text <- text$V1
head(text, n = 11)## [1] " 1 | GARY HUA |6.0 |W 39|W 21|W 18|W 14|W 7|D 12|D 4|"
## [2] " ON | 15445895 / R: 1794 ->1817 |N:2 |W |B |W |B |W |B |W |"
## [3] "-----------------------------------------------------------------------------------------"
## [4] " 2 | DAKSHESH DARURI |6.0 |W 63|W 58|L 4|W 17|W 16|W 20|W 7|"
## [5] " MI | 14598900 / R: 1553 ->1663 |N:2 |B |W |B |W |B |W |B |"
## [6] "-----------------------------------------------------------------------------------------"
## [7] " 3 | ADITYA BAJAJ |6.0 |L 8|W 61|W 25|W 21|W 11|W 13|W 12|"
## [8] " MI | 14959604 / R: 1384 ->1640 |N:2 |W |B |W |B |W |B |W |"
## [9] "-----------------------------------------------------------------------------------------"
## [10] " 4 | PATRICK H SCHILLING |5.5 |W 23|D 28|W 2|W 26|D 5|W 19|D 1|"
## [11] " MI | 12616049 / R: 1716 ->1744 |N:2 |W |B |W |B |W |B |B |"Remove all dashes
text <- str_remove_all(text, pattern = "-")
head(text, n = 11)## [1] " 1 | GARY HUA |6.0 |W 39|W 21|W 18|W 14|W 7|D 12|D 4|"
## [2] " ON | 15445895 / R: 1794 >1817 |N:2 |W |B |W |B |W |B |W |"
## [3] ""
## [4] " 2 | DAKSHESH DARURI |6.0 |W 63|W 58|L 4|W 17|W 16|W 20|W 7|"
## [5] " MI | 14598900 / R: 1553 >1663 |N:2 |B |W |B |W |B |W |B |"
## [6] ""
## [7] " 3 | ADITYA BAJAJ |6.0 |L 8|W 61|W 25|W 21|W 11|W 13|W 12|"
## [8] " MI | 14959604 / R: 1384 >1640 |N:2 |W |B |W |B |W |B |W |"
## [9] ""
## [10] " 4 | PATRICK H SCHILLING |5.5 |W 23|D 28|W 2|W 26|D 5|W 19|D 1|"
## [11] " MI | 12616049 / R: 1716 >1744 |N:2 |W |B |W |B |W |B |B |"Remove all empty spaces
text <- str_replace_all(text, pattern = "\\s+", replacement = " " )
head(text, n = 11)## [1] " 1 | GARY HUA |6.0 |W 39|W 21|W 18|W 14|W 7|D 12|D 4|"
## [2] " ON | 15445895 / R: 1794 >1817 |N:2 |W |B |W |B |W |B |W |"
## [3] ""
## [4] " 2 | DAKSHESH DARURI |6.0 |W 63|W 58|L 4|W 17|W 16|W 20|W 7|"
## [5] " MI | 14598900 / R: 1553 >1663 |N:2 |B |W |B |W |B |W |B |"
## [6] ""
## [7] " 3 | ADITYA BAJAJ |6.0 |L 8|W 61|W 25|W 21|W 11|W 13|W 12|"
## [8] " MI | 14959604 / R: 1384 >1640 |N:2 |W |B |W |B |W |B |W |"
## [9] ""
## [10] " 4 | PATRICK H SCHILLING |5.5 |W 23|D 28|W 2|W 26|D 5|W 19|D 1|"
## [11] " MI | 12616049 / R: 1716 >1744 |N:2 |W |B |W |B |W |B |B |"Extract the Player Names
p_name <- unlist(str_extract_all(text, pattern = "\\| [:alpha:]+\\s*[:alpha:]+ (\\w+)?\\s*(\\w+)?"))
p_name## [1] "| GARY HUA " "| DAKSHESH DARURI "
## [3] "| ADITYA BAJAJ " "| PATRICK H SCHILLING "
## [5] "| HANSHI ZUO " "| HANSEN SONG "
## [7] "| GARY DEE SWATHELL " "| EZEKIEL HOUGHTON "
## [9] "| STEFANO LEE " "| ANVIT RAO "
## [11] "| CAMERON WILLIAM MC LEMAN" "| KENNETH J TACK "
## [13] "| TORRANCE HENRY JR " "| BRADLEY SHAW "
## [15] "| ZACHARY JAMES HOUGHTON " "| MIKE NIKITIN "
## [17] "| RONALD GRZEGORCZYK " "| DAVID SUNDEEN "
## [19] "| DIPANKAR ROY " "| JASON ZHENG "
## [21] "| DINH DANG BUI " "| EUGENE L MCCLURE "
## [23] "| ALAN BUI " "| MICHAEL R ALDRICH "
## [25] "| LOREN SCHWIEBERT " "| MAX ZHU "
## [27] "| GAURAV GIDWANI " "| SOFIA ADINA STANESCUBELLU "
## [29] "| CHIEDOZIE OKORIE " "| GEORGE AVERY JONES "
## [31] "| RISHI SHETTY " "| JOSHUA PHILIP MATHEWS "
## [33] "| JADE GE " "| MICHAEL JEFFERY THOMAS "
## [35] "| JOSHUA DAVID LEE " "| SIDDHARTH JHA "
## [37] "| AMIYATOSH PWNANANDAM " "| BRIAN LIU "
## [39] "| JOEL R HENDON " "| FOREST ZHANG "
## [41] "| KYLE WILLIAM MURPHY " "| JARED GE "
## [43] "| ROBERT GLEN VASEY " "| JUSTIN D SCHILLING "
## [45] "| DEREK YAN " "| JACOB ALEXANDER LAVALLEY "
## [47] "| ERIC WRIGHT " "| DANIEL KHAIN "
## [49] "| MICHAEL J MARTIN " "| SHIVAM JHA "
## [51] "| TEJAS AYYAGARI " "| ETHAN GUO "
## [53] "| JOSE C YBARRA " "| LARRY HODGE "
## [55] "| ALEX KONG " "| MARISA RICCI "
## [57] "| MICHAEL LU " "| VIRAJ MOHILE "
## [59] "| SEAN M MC CORMICK" "| JULIA SHEN "
## [61] "| JEZZEL FARKAS " "| ASHWIN BALAJI "
## [63] "| THOMAS JOSEPH HOSMER " "| BEN LI "Remove the pipe “|” and the space before the names
pl_name <- unlist(str_remove_all(p_name, pattern = "\\| "))
pl_name## [1] "GARY HUA " "DAKSHESH DARURI "
## [3] "ADITYA BAJAJ " "PATRICK H SCHILLING "
## [5] "HANSHI ZUO " "HANSEN SONG "
## [7] "GARY DEE SWATHELL " "EZEKIEL HOUGHTON "
## [9] "STEFANO LEE " "ANVIT RAO "
## [11] "CAMERON WILLIAM MC LEMAN" "KENNETH J TACK "
## [13] "TORRANCE HENRY JR " "BRADLEY SHAW "
## [15] "ZACHARY JAMES HOUGHTON " "MIKE NIKITIN "
## [17] "RONALD GRZEGORCZYK " "DAVID SUNDEEN "
## [19] "DIPANKAR ROY " "JASON ZHENG "
## [21] "DINH DANG BUI " "EUGENE L MCCLURE "
## [23] "ALAN BUI " "MICHAEL R ALDRICH "
## [25] "LOREN SCHWIEBERT " "MAX ZHU "
## [27] "GAURAV GIDWANI " "SOFIA ADINA STANESCUBELLU "
## [29] "CHIEDOZIE OKORIE " "GEORGE AVERY JONES "
## [31] "RISHI SHETTY " "JOSHUA PHILIP MATHEWS "
## [33] "JADE GE " "MICHAEL JEFFERY THOMAS "
## [35] "JOSHUA DAVID LEE " "SIDDHARTH JHA "
## [37] "AMIYATOSH PWNANANDAM " "BRIAN LIU "
## [39] "JOEL R HENDON " "FOREST ZHANG "
## [41] "KYLE WILLIAM MURPHY " "JARED GE "
## [43] "ROBERT GLEN VASEY " "JUSTIN D SCHILLING "
## [45] "DEREK YAN " "JACOB ALEXANDER LAVALLEY "
## [47] "ERIC WRIGHT " "DANIEL KHAIN "
## [49] "MICHAEL J MARTIN " "SHIVAM JHA "
## [51] "TEJAS AYYAGARI " "ETHAN GUO "
## [53] "JOSE C YBARRA " "LARRY HODGE "
## [55] "ALEX KONG " "MARISA RICCI "
## [57] "MICHAEL LU " "VIRAJ MOHILE "
## [59] "SEAN M MC CORMICK" "JULIA SHEN "
## [61] "JEZZEL FARKAS " "ASHWIN BALAJI "
## [63] "THOMAS JOSEPH HOSMER " "BEN LI "Extract Player’s State
player_state <- unlist(str_extract_all(text, pattern = " [:alpha:]{2}\\s\\| "))
player_state## [1] " ON | " " MI | " " MI | " " MI | " " MI | " " OH | " " MI | " " MI | "
## [9] " ON | " " MI | " " MI | " " MI | " " MI | " " MI | " " MI | " " MI | "
## [17] " MI | " " MI | " " MI | " " MI | " " ON | " " MI | " " ON | " " MI | "
## [25] " MI | " " ON | " " MI | " " MI | " " MI | " " ON | " " MI | " " ON | "
## [33] " MI | " " MI | " " MI | " " MI | " " MI | " " MI | " " MI | " " MI | "
## [41] " MI | " " MI | " " MI | " " MI | " " MI | " " MI | " " MI | " " MI | "
## [49] " MI | " " MI | " " MI | " " MI | " " MI | " " MI | " " MI | " " MI | "
## [57] " MI | " " MI | " " MI | " " MI | " " ON | " " MI | " " MI | " " MI | "Remove the “|” and spaces
player_state <- unlist(str_remove_all(player_state, pattern = "\\s\\| "))
player_state <- unlist(str_remove_all(player_state, pattern = " "))
player_state## [1] "ON" "MI" "MI" "MI" "MI" "OH" "MI" "MI" "ON" "MI" "MI" "MI" "MI" "MI" "MI"
## [16] "MI" "MI" "MI" "MI" "MI" "ON" "MI" "ON" "MI" "MI" "ON" "MI" "MI" "MI" "ON"
## [31] "MI" "ON" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI"
## [46] "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI"
## [61] "ON" "MI" "MI" "MI"Extract Player’s Total Number of Points
total_points <- unlist(str_extract_all(text, pattern = "[:digit:]\\.[:digit:]"))
total_points## [1] "6.0" "6.0" "6.0" "5.5" "5.5" "5.0" "5.0" "5.0" "5.0" "5.0" "4.5" "4.5"
## [13] "4.5" "4.5" "4.5" "4.0" "4.0" "4.0" "4.0" "4.0" "4.0" "4.0" "4.0" "4.0"
## [25] "3.5" "3.5" "3.5" "3.5" "3.5" "3.5" "3.5" "3.5" "3.5" "3.5" "3.5" "3.5"
## [37] "3.5" "3.0" "3.0" "3.0" "3.0" "3.0" "3.0" "3.0" "3.0" "3.0" "2.5" "2.5"
## [49] "2.5" "2.5" "2.5" "2.5" "2.0" "2.0" "2.0" "2.0" "2.0" "2.0" "2.0" "1.5"
## [61] "1.5" "1.0" "1.0" "1.0"Change the Player’s Total Points to numeric
player_total_points <- map_dbl(total_points, as.numeric)
player_total_points## [1] 6.0 6.0 6.0 5.5 5.5 5.0 5.0 5.0 5.0 5.0 4.5 4.5 4.5 4.5 4.5 4.0 4.0 4.0 4.0
## [20] 4.0 4.0 4.0 4.0 4.0 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.0
## [39] 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 2.5 2.5 2.5 2.5 2.5 2.5 2.0 2.0 2.0 2.0 2.0
## [58] 2.0 2.0 1.5 1.5 1.0 1.0 1.0Extract Player’s Pre-Rating
players_prerating <- unlist(str_extract_all(text, pattern = "R:\\s+[:digit:]+"))
players_prerating## [1] "R: 1794" "R: 1553" "R: 1384" "R: 1716" "R: 1655" "R: 1686" "R: 1649"
## [8] "R: 1641" "R: 1411" "R: 1365" "R: 1712" "R: 1663" "R: 1666" "R: 1610"
## [15] "R: 1220" "R: 1604" "R: 1629" "R: 1600" "R: 1564" "R: 1595" "R: 1563"
## [22] "R: 1555" "R: 1363" "R: 1229" "R: 1745" "R: 1579" "R: 1552" "R: 1507"
## [29] "R: 1602" "R: 1522" "R: 1494" "R: 1441" "R: 1449" "R: 1399" "R: 1438"
## [36] "R: 1355" "R: 980" "R: 1423" "R: 1436" "R: 1348" "R: 1403" "R: 1332"
## [43] "R: 1283" "R: 1199" "R: 1242" "R: 377" "R: 1362" "R: 1382" "R: 1291"
## [50] "R: 1056" "R: 1011" "R: 935" "R: 1393" "R: 1270" "R: 1186" "R: 1153"
## [57] "R: 1092" "R: 917" "R: 853" "R: 967" "R: 955" "R: 1530" "R: 1175"
## [64] "R: 1163"Remove the “R:”
players_prerating <- unlist(str_remove_all(players_prerating, pattern = "R: "))
players_prerating## [1] "1794" "1553" "1384" "1716" "1655" "1686" "1649" "1641" "1411" "1365"
## [11] "1712" "1663" "1666" "1610" "1220" "1604" "1629" "1600" "1564" "1595"
## [21] "1563" "1555" "1363" "1229" "1745" "1579" "1552" "1507" "1602" "1522"
## [31] "1494" "1441" "1449" "1399" "1438" "1355" "980" "1423" "1436" "1348"
## [41] "1403" "1332" "1283" "1199" "1242" "377" "1362" "1382" "1291" "1056"
## [51] "1011" "935" "1393" "1270" "1186" "1153" "1092" "917" "853" "967"
## [61] "955" "1530" "1175" "1163"Change players_prerating type to numeric
player_prerating <- map_dbl(players_prerating, as.numeric)
player_prerating## [1] 1794 1553 1384 1716 1655 1686 1649 1641 1411 1365 1712 1663 1666 1610 1220
## [16] 1604 1629 1600 1564 1595 1563 1555 1363 1229 1745 1579 1552 1507 1602 1522
## [31] 1494 1441 1449 1399 1438 1355 980 1423 1436 1348 1403 1332 1283 1199 1242
## [46] 377 1362 1382 1291 1056 1011 935 1393 1270 1186 1153 1092 917 853 967
## [61] 955 1530 1175 1163Extract Average Opponent Pre-Rating
opponent <- unlist(str_extract_all(text, pattern = "\\|[0-9].*"))
opponent## [1] "|6.0 |W 39|W 21|W 18|W 14|W 7|D 12|D 4|"
## [2] "|6.0 |W 63|W 58|L 4|W 17|W 16|W 20|W 7|"
## [3] "|6.0 |L 8|W 61|W 25|W 21|W 11|W 13|W 12|"
## [4] "|5.5 |W 23|D 28|W 2|W 26|D 5|W 19|D 1|"
## [5] "|5.5 |W 45|W 37|D 12|D 13|D 4|W 14|W 17|"
## [6] "|5.0 |W 34|D 29|L 11|W 35|D 10|W 27|W 21|"
## [7] "|5.0 |W 57|W 46|W 13|W 11|L 1|W 9|L 2|"
## [8] "|5.0 |W 3|W 32|L 14|L 9|W 47|W 28|W 19|"
## [9] "|5.0 |W 25|L 18|W 59|W 8|W 26|L 7|W 20|"
## [10] "|5.0 |D 16|L 19|W 55|W 31|D 6|W 25|W 18|"
## [11] "|4.5 |D 38|W 56|W 6|L 7|L 3|W 34|W 26|"
## [12] "|4.5 |W 42|W 33|D 5|W 38|H |D 1|L 3|"
## [13] "|4.5 |W 36|W 27|L 7|D 5|W 33|L 3|W 32|"
## [14] "|4.5 |W 54|W 44|W 8|L 1|D 27|L 5|W 31|"
## [15] "|4.5 |D 19|L 16|W 30|L 22|W 54|W 33|W 38|"
## [16] "|4.0 |D 10|W 15|H |W 39|L 2|W 36|U |"
## [17] "|4.0 |W 48|W 41|L 26|L 2|W 23|W 22|L 5|"
## [18] "|4.0 |W 47|W 9|L 1|W 32|L 19|W 38|L 10|"
## [19] "|4.0 |D 15|W 10|W 52|D 28|W 18|L 4|L 8|"
## [20] "|4.0 |L 40|W 49|W 23|W 41|W 28|L 2|L 9|"
## [21] "|4.0 |W 43|L 1|W 47|L 3|W 40|W 39|L 6|"
## [22] "|4.0 |W 64|D 52|L 28|W 15|H |L 17|W 40|"
## [23] "|4.0 |L 4|W 43|L 20|W 58|L 17|W 37|W 46|"
## [24] "|4.0 |L 28|L 47|W 43|L 25|W 60|W 44|W 39|"
## [25] "|3.5 |L 9|W 53|L 3|W 24|D 34|L 10|W 47|"
## [26] "|3.5 |W 49|W 40|W 17|L 4|L 9|D 32|L 11|"
## [27] "|3.5 |W 51|L 13|W 46|W 37|D 14|L 6|U |"
## [28] "|3.5 |W 24|D 4|W 22|D 19|L 20|L 8|D 36|"
## [29] "|3.5 |W 50|D 6|L 38|L 34|W 52|W 48|U |"
## [30] "|3.5 |L 52|D 64|L 15|W 55|L 31|W 61|W 50|"
## [31] "|3.5 |L 58|D 55|W 64|L 10|W 30|W 50|L 14|"
## [32] "|3.5 |W 61|L 8|W 44|L 18|W 51|D 26|L 13|"
## [33] "|3.5 |W 60|L 12|W 50|D 36|L 13|L 15|W 51|"
## [34] "|3.5 |L 6|W 60|L 37|W 29|D 25|L 11|W 52|"
## [35] "|3.5 |L 46|L 38|W 56|L 6|W 57|D 52|W 48|"
## [36] "|3.5 |L 13|W 57|W 51|D 33|H |L 16|D 28|"
## [37] "|3.5 |B |L 5|W 34|L 27|H |L 23|W 61|"
## [38] "|3.0 |D 11|W 35|W 29|L 12|H |L 18|L 15|"
## [39] "|3.0 |L 1|W 54|W 40|L 16|W 44|L 21|L 24|"
## [40] "|3.0 |W 20|L 26|L 39|W 59|L 21|W 56|L 22|"
## [41] "|3.0 |W 59|L 17|W 58|L 20|X |U |U |"
## [42] "|3.0 |L 12|L 50|L 57|D 60|D 61|W 64|W 56|"
## [43] "|3.0 |L 21|L 23|L 24|W 63|W 59|L 46|W 55|"
## [44] "|3.0 |B |L 14|L 32|W 53|L 39|L 24|W 59|"
## [45] "|3.0 |L 5|L 51|D 60|L 56|W 63|D 55|W 58|"
## [46] "|3.0 |W 35|L 7|L 27|L 50|W 64|W 43|L 23|"
## [47] "|2.5 |L 18|W 24|L 21|W 61|L 8|D 51|L 25|"
## [48] "|2.5 |L 17|W 63|H |D 52|H |L 29|L 35|"
## [49] "|2.5 |L 26|L 20|D 63|D 64|W 58|H |U |"
## [50] "|2.5 |L 29|W 42|L 33|W 46|H |L 31|L 30|"
## [51] "|2.5 |L 27|W 45|L 36|W 57|L 32|D 47|L 33|"
## [52] "|2.5 |W 30|D 22|L 19|D 48|L 29|D 35|L 34|"
## [53] "|2.0 |H |L 25|H |L 44|U |W 57|U |"
## [54] "|2.0 |L 14|L 39|L 61|B |L 15|L 59|W 64|"
## [55] "|2.0 |L 62|D 31|L 10|L 30|B |D 45|L 43|"
## [56] "|2.0 |H |L 11|L 35|W 45|H |L 40|L 42|"
## [57] "|2.0 |L 7|L 36|W 42|L 51|L 35|L 53|B |"
## [58] "|2.0 |W 31|L 2|L 41|L 23|L 49|B |L 45|"
## [59] "|2.0 |L 41|B |L 9|L 40|L 43|W 54|L 44|"
## [60] "|1.5 |L 33|L 34|D 45|D 42|L 24|H |U |"
## [61] "|1.5 |L 32|L 3|W 54|L 47|D 42|L 30|L 37|"
## [62] "|1.0 |W 55|U |U |U |U |U |U |"
## [63] "|1.0 |L 2|L 48|D 49|L 43|L 45|H |U |"
## [64] "|1.0 |L 22|D 30|L 31|D 49|L 46|L 42|L 54|"Looking at this result, we see that some players do not have opponents in all 7 rounds. To make extracting the opponent numbers easier, We replace |B |, |U |, |X |, and |H | with |R 0| to maintain the same format as others.
oppo <- unlist(str_replace_all(opponent, pattern = "\\|[BUXH] ", replacement = "\\|R 0"))
oppo## [1] "|6.0 |W 39|W 21|W 18|W 14|W 7|D 12|D 4|"
## [2] "|6.0 |W 63|W 58|L 4|W 17|W 16|W 20|W 7|"
## [3] "|6.0 |L 8|W 61|W 25|W 21|W 11|W 13|W 12|"
## [4] "|5.5 |W 23|D 28|W 2|W 26|D 5|W 19|D 1|"
## [5] "|5.5 |W 45|W 37|D 12|D 13|D 4|W 14|W 17|"
## [6] "|5.0 |W 34|D 29|L 11|W 35|D 10|W 27|W 21|"
## [7] "|5.0 |W 57|W 46|W 13|W 11|L 1|W 9|L 2|"
## [8] "|5.0 |W 3|W 32|L 14|L 9|W 47|W 28|W 19|"
## [9] "|5.0 |W 25|L 18|W 59|W 8|W 26|L 7|W 20|"
## [10] "|5.0 |D 16|L 19|W 55|W 31|D 6|W 25|W 18|"
## [11] "|4.5 |D 38|W 56|W 6|L 7|L 3|W 34|W 26|"
## [12] "|4.5 |W 42|W 33|D 5|W 38|R 0|D 1|L 3|"
## [13] "|4.5 |W 36|W 27|L 7|D 5|W 33|L 3|W 32|"
## [14] "|4.5 |W 54|W 44|W 8|L 1|D 27|L 5|W 31|"
## [15] "|4.5 |D 19|L 16|W 30|L 22|W 54|W 33|W 38|"
## [16] "|4.0 |D 10|W 15|R 0|W 39|L 2|W 36|R 0|"
## [17] "|4.0 |W 48|W 41|L 26|L 2|W 23|W 22|L 5|"
## [18] "|4.0 |W 47|W 9|L 1|W 32|L 19|W 38|L 10|"
## [19] "|4.0 |D 15|W 10|W 52|D 28|W 18|L 4|L 8|"
## [20] "|4.0 |L 40|W 49|W 23|W 41|W 28|L 2|L 9|"
## [21] "|4.0 |W 43|L 1|W 47|L 3|W 40|W 39|L 6|"
## [22] "|4.0 |W 64|D 52|L 28|W 15|R 0|L 17|W 40|"
## [23] "|4.0 |L 4|W 43|L 20|W 58|L 17|W 37|W 46|"
## [24] "|4.0 |L 28|L 47|W 43|L 25|W 60|W 44|W 39|"
## [25] "|3.5 |L 9|W 53|L 3|W 24|D 34|L 10|W 47|"
## [26] "|3.5 |W 49|W 40|W 17|L 4|L 9|D 32|L 11|"
## [27] "|3.5 |W 51|L 13|W 46|W 37|D 14|L 6|R 0|"
## [28] "|3.5 |W 24|D 4|W 22|D 19|L 20|L 8|D 36|"
## [29] "|3.5 |W 50|D 6|L 38|L 34|W 52|W 48|R 0|"
## [30] "|3.5 |L 52|D 64|L 15|W 55|L 31|W 61|W 50|"
## [31] "|3.5 |L 58|D 55|W 64|L 10|W 30|W 50|L 14|"
## [32] "|3.5 |W 61|L 8|W 44|L 18|W 51|D 26|L 13|"
## [33] "|3.5 |W 60|L 12|W 50|D 36|L 13|L 15|W 51|"
## [34] "|3.5 |L 6|W 60|L 37|W 29|D 25|L 11|W 52|"
## [35] "|3.5 |L 46|L 38|W 56|L 6|W 57|D 52|W 48|"
## [36] "|3.5 |L 13|W 57|W 51|D 33|R 0|L 16|D 28|"
## [37] "|3.5 |R 0|L 5|W 34|L 27|R 0|L 23|W 61|"
## [38] "|3.0 |D 11|W 35|W 29|L 12|R 0|L 18|L 15|"
## [39] "|3.0 |L 1|W 54|W 40|L 16|W 44|L 21|L 24|"
## [40] "|3.0 |W 20|L 26|L 39|W 59|L 21|W 56|L 22|"
## [41] "|3.0 |W 59|L 17|W 58|L 20|R 0|R 0|R 0|"
## [42] "|3.0 |L 12|L 50|L 57|D 60|D 61|W 64|W 56|"
## [43] "|3.0 |L 21|L 23|L 24|W 63|W 59|L 46|W 55|"
## [44] "|3.0 |R 0|L 14|L 32|W 53|L 39|L 24|W 59|"
## [45] "|3.0 |L 5|L 51|D 60|L 56|W 63|D 55|W 58|"
## [46] "|3.0 |W 35|L 7|L 27|L 50|W 64|W 43|L 23|"
## [47] "|2.5 |L 18|W 24|L 21|W 61|L 8|D 51|L 25|"
## [48] "|2.5 |L 17|W 63|R 0|D 52|R 0|L 29|L 35|"
## [49] "|2.5 |L 26|L 20|D 63|D 64|W 58|R 0|R 0|"
## [50] "|2.5 |L 29|W 42|L 33|W 46|R 0|L 31|L 30|"
## [51] "|2.5 |L 27|W 45|L 36|W 57|L 32|D 47|L 33|"
## [52] "|2.5 |W 30|D 22|L 19|D 48|L 29|D 35|L 34|"
## [53] "|2.0 |R 0|L 25|R 0|L 44|R 0|W 57|R 0|"
## [54] "|2.0 |L 14|L 39|L 61|R 0|L 15|L 59|W 64|"
## [55] "|2.0 |L 62|D 31|L 10|L 30|R 0|D 45|L 43|"
## [56] "|2.0 |R 0|L 11|L 35|W 45|R 0|L 40|L 42|"
## [57] "|2.0 |L 7|L 36|W 42|L 51|L 35|L 53|R 0|"
## [58] "|2.0 |W 31|L 2|L 41|L 23|L 49|R 0|L 45|"
## [59] "|2.0 |L 41|R 0|L 9|L 40|L 43|W 54|L 44|"
## [60] "|1.5 |L 33|L 34|D 45|D 42|L 24|R 0|R 0|"
## [61] "|1.5 |L 32|L 3|W 54|L 47|D 42|L 30|L 37|"
## [62] "|1.0 |W 55|R 0|R 0|R 0|R 0|R 0|R 0|"
## [63] "|1.0 |L 2|L 48|D 49|L 43|L 45|R 0|R 0|"
## [64] "|1.0 |L 22|D 30|L 31|D 49|L 46|L 42|L 54|"Remove the first part containing the total points as well as the letters and the pipe symbol
#Remove the first part containing the total points
oppon <- unlist(str_remove_all(oppo, pattern = "\\|\\d\\.\\d\\s"))
#Remove the Letters
oppone <- unlist(str_remove_all(oppon, pattern = "[:alpha:]"))
#Remove the pipe "|"
oppone <- unlist(str_remove_all(oppone, pattern = "\\|"))
oppone## [1] " 39 21 18 14 7 12 4" " 63 58 4 17 16 20 7" " 8 61 25 21 11 13 12"
## [4] " 23 28 2 26 5 19 1" " 45 37 12 13 4 14 17" " 34 29 11 35 10 27 21"
## [7] " 57 46 13 11 1 9 2" " 3 32 14 9 47 28 19" " 25 18 59 8 26 7 20"
## [10] " 16 19 55 31 6 25 18" " 38 56 6 7 3 34 26" " 42 33 5 38 0 1 3"
## [13] " 36 27 7 5 33 3 32" " 54 44 8 1 27 5 31" " 19 16 30 22 54 33 38"
## [16] " 10 15 0 39 2 36 0" " 48 41 26 2 23 22 5" " 47 9 1 32 19 38 10"
## [19] " 15 10 52 28 18 4 8" " 40 49 23 41 28 2 9" " 43 1 47 3 40 39 6"
## [22] " 64 52 28 15 0 17 40" " 4 43 20 58 17 37 46" " 28 47 43 25 60 44 39"
## [25] " 9 53 3 24 34 10 47" " 49 40 17 4 9 32 11" " 51 13 46 37 14 6 0"
## [28] " 24 4 22 19 20 8 36" " 50 6 38 34 52 48 0" " 52 64 15 55 31 61 50"
## [31] " 58 55 64 10 30 50 14" " 61 8 44 18 51 26 13" " 60 12 50 36 13 15 51"
## [34] " 6 60 37 29 25 11 52" " 46 38 56 6 57 52 48" " 13 57 51 33 0 16 28"
## [37] " 0 5 34 27 0 23 61" " 11 35 29 12 0 18 15" " 1 54 40 16 44 21 24"
## [40] " 20 26 39 59 21 56 22" " 59 17 58 20 0 0 0" " 12 50 57 60 61 64 56"
## [43] " 21 23 24 63 59 46 55" " 0 14 32 53 39 24 59" " 5 51 60 56 63 55 58"
## [46] " 35 7 27 50 64 43 23" " 18 24 21 61 8 51 25" " 17 63 0 52 0 29 35"
## [49] " 26 20 63 64 58 0 0" " 29 42 33 46 0 31 30" " 27 45 36 57 32 47 33"
## [52] " 30 22 19 48 29 35 34" " 0 25 0 44 0 57 0" " 14 39 61 0 15 59 64"
## [55] " 62 31 10 30 0 45 43" " 0 11 35 45 0 40 42" " 7 36 42 51 35 53 0"
## [58] " 31 2 41 23 49 0 45" " 41 0 9 40 43 54 44" " 33 34 45 42 24 0 0"
## [61] " 32 3 54 47 42 30 37" " 55 0 0 0 0 0 0" " 2 48 49 43 45 0 0"
## [64] " 22 30 31 49 46 42 54"opponen <- unlist(str_extract_all(oppone, pattern = " [:digit:]{1,2}"))
opponen## [1] " 39" " 21" " 18" " 14" " 7" " 12" " 4" " 63" " 58" " 4" " 17" " 16"
## [13] " 20" " 7" " 8" " 61" " 25" " 21" " 11" " 13" " 12" " 23" " 28" " 2"
## [25] " 26" " 5" " 19" " 1" " 45" " 37" " 12" " 13" " 4" " 14" " 17" " 34"
## [37] " 29" " 11" " 35" " 10" " 27" " 21" " 57" " 46" " 13" " 11" " 1" " 9"
## [49] " 2" " 3" " 32" " 14" " 9" " 47" " 28" " 19" " 25" " 18" " 59" " 8"
## [61] " 26" " 7" " 20" " 16" " 19" " 55" " 31" " 6" " 25" " 18" " 38" " 56"
## [73] " 6" " 7" " 3" " 34" " 26" " 42" " 33" " 5" " 38" " 0" " 1" " 3"
## [85] " 36" " 27" " 7" " 5" " 33" " 3" " 32" " 54" " 44" " 8" " 1" " 27"
## [97] " 5" " 31" " 19" " 16" " 30" " 22" " 54" " 33" " 38" " 10" " 15" " 0"
## [109] " 39" " 2" " 36" " 0" " 48" " 41" " 26" " 2" " 23" " 22" " 5" " 47"
## [121] " 9" " 1" " 32" " 19" " 38" " 10" " 15" " 10" " 52" " 28" " 18" " 4"
## [133] " 8" " 40" " 49" " 23" " 41" " 28" " 2" " 9" " 43" " 1" " 47" " 3"
## [145] " 40" " 39" " 6" " 64" " 52" " 28" " 15" " 0" " 17" " 40" " 4" " 43"
## [157] " 20" " 58" " 17" " 37" " 46" " 28" " 47" " 43" " 25" " 60" " 44" " 39"
## [169] " 9" " 53" " 3" " 24" " 34" " 10" " 47" " 49" " 40" " 17" " 4" " 9"
## [181] " 32" " 11" " 51" " 13" " 46" " 37" " 14" " 6" " 0" " 24" " 4" " 22"
## [193] " 19" " 20" " 8" " 36" " 50" " 6" " 38" " 34" " 52" " 48" " 0" " 52"
## [205] " 64" " 15" " 55" " 31" " 61" " 50" " 58" " 55" " 64" " 10" " 30" " 50"
## [217] " 14" " 61" " 8" " 44" " 18" " 51" " 26" " 13" " 60" " 12" " 50" " 36"
## [229] " 13" " 15" " 51" " 6" " 60" " 37" " 29" " 25" " 11" " 52" " 46" " 38"
## [241] " 56" " 6" " 57" " 52" " 48" " 13" " 57" " 51" " 33" " 0" " 16" " 28"
## [253] " 0" " 5" " 34" " 27" " 0" " 23" " 61" " 11" " 35" " 29" " 12" " 0"
## [265] " 18" " 15" " 1" " 54" " 40" " 16" " 44" " 21" " 24" " 20" " 26" " 39"
## [277] " 59" " 21" " 56" " 22" " 59" " 17" " 58" " 20" " 0" " 0" " 0" " 12"
## [289] " 50" " 57" " 60" " 61" " 64" " 56" " 21" " 23" " 24" " 63" " 59" " 46"
## [301] " 55" " 0" " 14" " 32" " 53" " 39" " 24" " 59" " 5" " 51" " 60" " 56"
## [313] " 63" " 55" " 58" " 35" " 7" " 27" " 50" " 64" " 43" " 23" " 18" " 24"
## [325] " 21" " 61" " 8" " 51" " 25" " 17" " 63" " 0" " 52" " 0" " 29" " 35"
## [337] " 26" " 20" " 63" " 64" " 58" " 0" " 0" " 29" " 42" " 33" " 46" " 0"
## [349] " 31" " 30" " 27" " 45" " 36" " 57" " 32" " 47" " 33" " 30" " 22" " 19"
## [361] " 48" " 29" " 35" " 34" " 0" " 25" " 0" " 44" " 0" " 57" " 0" " 14"
## [373] " 39" " 61" " 0" " 15" " 59" " 64" " 62" " 31" " 10" " 30" " 0" " 45"
## [385] " 43" " 0" " 11" " 35" " 45" " 0" " 40" " 42" " 7" " 36" " 42" " 51"
## [397] " 35" " 53" " 0" " 31" " 2" " 41" " 23" " 49" " 0" " 45" " 41" " 0"
## [409] " 9" " 40" " 43" " 54" " 44" " 33" " 34" " 45" " 42" " 24" " 0" " 0"
## [421] " 32" " 3" " 54" " 47" " 42" " 30" " 37" " 55" " 0" " 0" " 0" " 0"
## [433] " 0" " 0" " 2" " 48" " 49" " 43" " 45" " 0" " 0" " 22" " 30" " 31"
## [445] " 49" " 46" " 42" " 54"Remove the spaces before the numbers
opponen_char <- unlist(str_remove_all(opponen, pattern = " "))
opponen_char## [1] "39" "21" "18" "14" "7" "12" "4" "63" "58" "4" "17" "16" "20" "7" "8"
## [16] "61" "25" "21" "11" "13" "12" "23" "28" "2" "26" "5" "19" "1" "45" "37"
## [31] "12" "13" "4" "14" "17" "34" "29" "11" "35" "10" "27" "21" "57" "46" "13"
## [46] "11" "1" "9" "2" "3" "32" "14" "9" "47" "28" "19" "25" "18" "59" "8"
## [61] "26" "7" "20" "16" "19" "55" "31" "6" "25" "18" "38" "56" "6" "7" "3"
## [76] "34" "26" "42" "33" "5" "38" "0" "1" "3" "36" "27" "7" "5" "33" "3"
## [91] "32" "54" "44" "8" "1" "27" "5" "31" "19" "16" "30" "22" "54" "33" "38"
## [106] "10" "15" "0" "39" "2" "36" "0" "48" "41" "26" "2" "23" "22" "5" "47"
## [121] "9" "1" "32" "19" "38" "10" "15" "10" "52" "28" "18" "4" "8" "40" "49"
## [136] "23" "41" "28" "2" "9" "43" "1" "47" "3" "40" "39" "6" "64" "52" "28"
## [151] "15" "0" "17" "40" "4" "43" "20" "58" "17" "37" "46" "28" "47" "43" "25"
## [166] "60" "44" "39" "9" "53" "3" "24" "34" "10" "47" "49" "40" "17" "4" "9"
## [181] "32" "11" "51" "13" "46" "37" "14" "6" "0" "24" "4" "22" "19" "20" "8"
## [196] "36" "50" "6" "38" "34" "52" "48" "0" "52" "64" "15" "55" "31" "61" "50"
## [211] "58" "55" "64" "10" "30" "50" "14" "61" "8" "44" "18" "51" "26" "13" "60"
## [226] "12" "50" "36" "13" "15" "51" "6" "60" "37" "29" "25" "11" "52" "46" "38"
## [241] "56" "6" "57" "52" "48" "13" "57" "51" "33" "0" "16" "28" "0" "5" "34"
## [256] "27" "0" "23" "61" "11" "35" "29" "12" "0" "18" "15" "1" "54" "40" "16"
## [271] "44" "21" "24" "20" "26" "39" "59" "21" "56" "22" "59" "17" "58" "20" "0"
## [286] "0" "0" "12" "50" "57" "60" "61" "64" "56" "21" "23" "24" "63" "59" "46"
## [301] "55" "0" "14" "32" "53" "39" "24" "59" "5" "51" "60" "56" "63" "55" "58"
## [316] "35" "7" "27" "50" "64" "43" "23" "18" "24" "21" "61" "8" "51" "25" "17"
## [331] "63" "0" "52" "0" "29" "35" "26" "20" "63" "64" "58" "0" "0" "29" "42"
## [346] "33" "46" "0" "31" "30" "27" "45" "36" "57" "32" "47" "33" "30" "22" "19"
## [361] "48" "29" "35" "34" "0" "25" "0" "44" "0" "57" "0" "14" "39" "61" "0"
## [376] "15" "59" "64" "62" "31" "10" "30" "0" "45" "43" "0" "11" "35" "45" "0"
## [391] "40" "42" "7" "36" "42" "51" "35" "53" "0" "31" "2" "41" "23" "49" "0"
## [406] "45" "41" "0" "9" "40" "43" "54" "44" "33" "34" "45" "42" "24" "0" "0"
## [421] "32" "3" "54" "47" "42" "30" "37" "55" "0" "0" "0" "0" "0" "0" "2"
## [436] "48" "49" "43" "45" "0" "0" "22" "30" "31" "49" "46" "42" "54"Convert these numbers to numeric
opponen_dig <- opponen_char %>% map_dbl(as.numeric)
opponen_dig## [1] 39 21 18 14 7 12 4 63 58 4 17 16 20 7 8 61 25 21 11 13 12 23 28 2 26
## [26] 5 19 1 45 37 12 13 4 14 17 34 29 11 35 10 27 21 57 46 13 11 1 9 2 3
## [51] 32 14 9 47 28 19 25 18 59 8 26 7 20 16 19 55 31 6 25 18 38 56 6 7 3
## [76] 34 26 42 33 5 38 0 1 3 36 27 7 5 33 3 32 54 44 8 1 27 5 31 19 16
## [101] 30 22 54 33 38 10 15 0 39 2 36 0 48 41 26 2 23 22 5 47 9 1 32 19 38
## [126] 10 15 10 52 28 18 4 8 40 49 23 41 28 2 9 43 1 47 3 40 39 6 64 52 28
## [151] 15 0 17 40 4 43 20 58 17 37 46 28 47 43 25 60 44 39 9 53 3 24 34 10 47
## [176] 49 40 17 4 9 32 11 51 13 46 37 14 6 0 24 4 22 19 20 8 36 50 6 38 34
## [201] 52 48 0 52 64 15 55 31 61 50 58 55 64 10 30 50 14 61 8 44 18 51 26 13 60
## [226] 12 50 36 13 15 51 6 60 37 29 25 11 52 46 38 56 6 57 52 48 13 57 51 33 0
## [251] 16 28 0 5 34 27 0 23 61 11 35 29 12 0 18 15 1 54 40 16 44 21 24 20 26
## [276] 39 59 21 56 22 59 17 58 20 0 0 0 12 50 57 60 61 64 56 21 23 24 63 59 46
## [301] 55 0 14 32 53 39 24 59 5 51 60 56 63 55 58 35 7 27 50 64 43 23 18 24 21
## [326] 61 8 51 25 17 63 0 52 0 29 35 26 20 63 64 58 0 0 29 42 33 46 0 31 30
## [351] 27 45 36 57 32 47 33 30 22 19 48 29 35 34 0 25 0 44 0 57 0 14 39 61 0
## [376] 15 59 64 62 31 10 30 0 45 43 0 11 35 45 0 40 42 7 36 42 51 35 53 0 31
## [401] 2 41 23 49 0 45 41 0 9 40 43 54 44 33 34 45 42 24 0 0 32 3 54 47 42
## [426] 30 37 55 0 0 0 0 0 0 2 48 49 43 45 0 0 22 30 31 49 46 42 54The next task is to replace this opponent Vectors with their corresponding preratings.
#check the structure of the vectors in question to make sure they are numeric
str(opponen_dig)## num [1:448] 39 21 18 14 7 12 4 63 58 4 ...str(player_prerating)## num [1:64] 1794 1553 1384 1716 1655 ...Transform the opponent_dig to a matrix and then to dataframe
opponent_matrix <- matrix(opponen_dig, byrow = TRUE, ncol = 7)
opponent_matrix## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## [1,] 39 21 18 14 7 12 4
## [2,] 63 58 4 17 16 20 7
## [3,] 8 61 25 21 11 13 12
## [4,] 23 28 2 26 5 19 1
## [5,] 45 37 12 13 4 14 17
## [6,] 34 29 11 35 10 27 21
## [7,] 57 46 13 11 1 9 2
## [8,] 3 32 14 9 47 28 19
## [9,] 25 18 59 8 26 7 20
## [10,] 16 19 55 31 6 25 18
## [11,] 38 56 6 7 3 34 26
## [12,] 42 33 5 38 0 1 3
## [13,] 36 27 7 5 33 3 32
## [14,] 54 44 8 1 27 5 31
## [15,] 19 16 30 22 54 33 38
## [16,] 10 15 0 39 2 36 0
## [17,] 48 41 26 2 23 22 5
## [18,] 47 9 1 32 19 38 10
## [19,] 15 10 52 28 18 4 8
## [20,] 40 49 23 41 28 2 9
## [21,] 43 1 47 3 40 39 6
## [22,] 64 52 28 15 0 17 40
## [23,] 4 43 20 58 17 37 46
## [24,] 28 47 43 25 60 44 39
## [25,] 9 53 3 24 34 10 47
## [26,] 49 40 17 4 9 32 11
## [27,] 51 13 46 37 14 6 0
## [28,] 24 4 22 19 20 8 36
## [29,] 50 6 38 34 52 48 0
## [30,] 52 64 15 55 31 61 50
## [31,] 58 55 64 10 30 50 14
## [32,] 61 8 44 18 51 26 13
## [33,] 60 12 50 36 13 15 51
## [34,] 6 60 37 29 25 11 52
## [35,] 46 38 56 6 57 52 48
## [36,] 13 57 51 33 0 16 28
## [37,] 0 5 34 27 0 23 61
## [38,] 11 35 29 12 0 18 15
## [39,] 1 54 40 16 44 21 24
## [40,] 20 26 39 59 21 56 22
## [41,] 59 17 58 20 0 0 0
## [42,] 12 50 57 60 61 64 56
## [43,] 21 23 24 63 59 46 55
## [44,] 0 14 32 53 39 24 59
## [45,] 5 51 60 56 63 55 58
## [46,] 35 7 27 50 64 43 23
## [47,] 18 24 21 61 8 51 25
## [48,] 17 63 0 52 0 29 35
## [49,] 26 20 63 64 58 0 0
## [50,] 29 42 33 46 0 31 30
## [51,] 27 45 36 57 32 47 33
## [52,] 30 22 19 48 29 35 34
## [53,] 0 25 0 44 0 57 0
## [54,] 14 39 61 0 15 59 64
## [55,] 62 31 10 30 0 45 43
## [56,] 0 11 35 45 0 40 42
## [57,] 7 36 42 51 35 53 0
## [58,] 31 2 41 23 49 0 45
## [59,] 41 0 9 40 43 54 44
## [60,] 33 34 45 42 24 0 0
## [61,] 32 3 54 47 42 30 37
## [62,] 55 0 0 0 0 0 0
## [63,] 2 48 49 43 45 0 0
## [64,] 22 30 31 49 46 42 54opponent_headers <- c("O1","O2", "O3", "O4", "O5", "O6", "O7")
opponent_df <- as.data.frame(opponent_matrix)
colnames(opponent_df) <- opponent_headers
head(opponent_df, n = 10)## O1 O2 O3 O4 O5 O6 O7
## 1 39 21 18 14 7 12 4
## 2 63 58 4 17 16 20 7
## 3 8 61 25 21 11 13 12
## 4 23 28 2 26 5 19 1
## 5 45 37 12 13 4 14 17
## 6 34 29 11 35 10 27 21
## 7 57 46 13 11 1 9 2
## 8 3 32 14 9 47 28 19
## 9 25 18 59 8 26 7 20
## 10 16 19 55 31 6 25 18Transform the player pre rating to a matrix and then to a dataframe
#Transform to matrix
player_pre_rating_matrix <- matrix(player_prerating, byrow = TRUE)
#Transform to dataframe
pre_rating_header <- "rating"
pre_rating <- as.data.frame(player_pre_rating_matrix, row.names = 1:length(player_pre_rating_matrix))
colnames(pre_rating) <- pre_rating_header
head(pre_rating, n = 10)## rating
## 1 1794
## 2 1553
## 3 1384
## 4 1716
## 5 1655
## 6 1686
## 7 1649
## 8 1641
## 9 1411
## 10 1365Add another column called rating_index for the player number corresponding to each prerating
rating_index <- 1:64
rating_index <- as.data.frame(rating_index)
pre_rating <- cbind(rating_index, pre_rating)
head(pre_rating, n = 10)## rating_index rating
## 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 1365Write a for loop to replace the opponent numbers in opponent_df dataframe with their corresponding player rating.
for (row in 1:nrow(opponent_df)){
for (col in 1:ncol(opponent_df)){
if (opponent_df[row, col] != 0){
opponent_df[row, col] = pre_rating$rating[opponent_df[row,col]]
}else {
opponent_df[row,col] = NA
}
}
}
head(opponent_df, n = 10)## O1 O2 O3 O4 O5 O6 O7
## 1 1436 1563 1600 1610 1649 1663 1716
## 2 1175 917 1716 1629 1604 1595 1649
## 3 1641 955 1745 1563 1712 1666 1663
## 4 1363 1507 1553 1579 1655 1564 1794
## 5 1242 980 1663 1666 1716 1610 1629
## 6 1399 1602 1712 1438 1365 1552 1563
## 7 1092 377 1666 1712 1794 1411 1553
## 8 1384 1441 1610 1411 1362 1507 1564
## 9 1745 1600 853 1641 1579 1649 1595
## 10 1604 1564 1186 1494 1686 1745 1600sum_opponent_df <- opponent_df %>% mutate(rowSums(opponent_df, na.rm = TRUE))
head(sum_opponent_df, n = 10)## O1 O2 O3 O4 O5 O6 O7 rowSums(opponent_df, na.rm = TRUE)
## 1 1436 1563 1600 1610 1649 1663 1716 11237
## 2 1175 917 1716 1629 1604 1595 1649 10285
## 3 1641 955 1745 1563 1712 1666 1663 10945
## 4 1363 1507 1553 1579 1655 1564 1794 11015
## 5 1242 980 1663 1666 1716 1610 1629 10506
## 6 1399 1602 1712 1438 1365 1552 1563 10631
## 7 1092 377 1666 1712 1794 1411 1553 9605
## 8 1384 1441 1610 1411 1362 1507 1564 10279
## 9 1745 1600 853 1641 1579 1649 1595 10662
## 10 1604 1564 1186 1494 1686 1745 1600 10879Compute the average oponnent prerating for each row.
avg_opponent <- opponent_df %>% mutate(round(rowMeans(opponent_df, na.rm = TRUE),0))
head(avg_opponent, n = 10)## O1 O2 O3 O4 O5 O6 O7
## 1 1436 1563 1600 1610 1649 1663 1716
## 2 1175 917 1716 1629 1604 1595 1649
## 3 1641 955 1745 1563 1712 1666 1663
## 4 1363 1507 1553 1579 1655 1564 1794
## 5 1242 980 1663 1666 1716 1610 1629
## 6 1399 1602 1712 1438 1365 1552 1563
## 7 1092 377 1666 1712 1794 1411 1553
## 8 1384 1441 1610 1411 1362 1507 1564
## 9 1745 1600 853 1641 1579 1649 1595
## 10 1604 1564 1186 1494 1686 1745 1600
## round(rowMeans(opponent_df, na.rm = TRUE), 0)
## 1 1605
## 2 1469
## 3 1564
## 4 1574
## 5 1501
## 6 1519
## 7 1372
## 8 1468
## 9 1523
## 10 1554Rename the column names
opponent_headers_withAvg <- c("O1","O2", "O3", "O4", "O5", "O6", "O7", "Average_opponent_rating")
colnames(avg_opponent) <- opponent_headers_withAvg
head(avg_opponent, n = 4)## O1 O2 O3 O4 O5 O6 O7 Average_opponent_rating
## 1 1436 1563 1600 1610 1649 1663 1716 1605
## 2 1175 917 1716 1629 1604 1595 1649 1469
## 3 1641 955 1745 1563 1712 1666 1663 1564
## 4 1363 1507 1553 1579 1655 1564 1794 1574Extract the Average_opponent_rating from this dataframe into a standalone vector.
opponent_avg_prerating <- avg_opponent$Average_opponent_rating
opponent_avg_prerating## [1] 1605 1469 1564 1574 1501 1519 1372 1468 1523 1554 1468 1506 1498 1515 1484
## [16] 1386 1499 1480 1426 1411 1470 1300 1214 1357 1363 1507 1222 1522 1314 1144
## [31] 1260 1379 1277 1375 1150 1388 1385 1539 1430 1391 1248 1150 1107 1327 1152
## [46] 1358 1392 1356 1286 1296 1356 1495 1345 1206 1406 1414 1363 1391 1319 1330
## [61] 1327 1186 1350 1263Merge the player_names, player_state, player_total_points, player_prerating, and opponent_avg_prerating into a single data frame.
name <- pl_name
state <- player_state
points <- player_total_points
prerating <- player_prerating
avg_opponent_prerating <- opponent_avg_prerating
tournament <- data.frame(name, state, points, prerating, avg_opponent_prerating)
tournament## name state points prerating avg_opponent_prerating
## 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 STANESCUBELLU 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 1263Write the tournament data frame to a csv file.
write.csv(tournament, file = "tournament.csv")