library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.2 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.3 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(stringr)text_data <- read_lines("https://raw.githubusercontent.com/jewelercart/R/main/tournamentinfo.txt")
#text_dataplayer_names <- character(0)
total_point <- numeric(0)
# Define a regular expression pattern to match player names
pattern <- "^\\s*\\d+\\s+\\|\\s+(.+?)\\s+\\|.*$"
# Iterate through lines in the file
for (line in text_data) {
# Use regular expression to extract player names
if (grepl(pattern, line)) {
match_data <- str_match(line, pattern)
player_name <- match_data[2]
player_names <- c(player_names, player_name)
point<- str_extract(line, "[[:digit:]]+\\.[[:digit:]]")
total_point<- c(total_point, as.numeric(point))
}
}
# Print the extracted player names
print("Players are : ")## [1] "Players are : "print(player_names)## [1] "GARY HUA" "DAKSHESH DARURI"
## [3] "ADITYA BAJAJ" "PATRICK H SCHILLING"
## [5] "HANSHI ZUO" "HANSEN SONG"
## [7] "GARY DEE SWATHELL" "EZEKIEL HOUGHTON"
## [9] "STEFANO LEE" "ANVIT RAO"
## [11] "CAMERON WILLIAM MC LEMAN" "KENNETH J TACK"
## [13] "TORRANCE HENRY JR" "BRADLEY SHAW"
## [15] "ZACHARY JAMES HOUGHTON" "MIKE NIKITIN"
## [17] "RONALD GRZEGORCZYK" "DAVID SUNDEEN"
## [19] "DIPANKAR ROY" "JASON ZHENG"
## [21] "DINH DANG BUI" "EUGENE L MCCLURE"
## [23] "ALAN BUI" "MICHAEL R ALDRICH"
## [25] "LOREN SCHWIEBERT" "MAX ZHU"
## [27] "GAURAV GIDWANI" "SOFIA ADINA STANESCU-BELLU"
## [29] "CHIEDOZIE OKORIE" "GEORGE AVERY JONES"
## [31] "RISHI SHETTY" "JOSHUA PHILIP MATHEWS"
## [33] "JADE GE" "MICHAEL JEFFERY THOMAS"
## [35] "JOSHUA DAVID LEE" "SIDDHARTH JHA"
## [37] "AMIYATOSH PWNANANDAM" "BRIAN LIU"
## [39] "JOEL R HENDON" "FOREST ZHANG"
## [41] "KYLE WILLIAM MURPHY" "JARED GE"
## [43] "ROBERT GLEN VASEY" "JUSTIN D SCHILLING"
## [45] "DEREK YAN" "JACOB ALEXANDER LAVALLEY"
## [47] "ERIC WRIGHT" "DANIEL KHAIN"
## [49] "MICHAEL J MARTIN" "SHIVAM JHA"
## [51] "TEJAS AYYAGARI" "ETHAN GUO"
## [53] "JOSE C YBARRA" "LARRY HODGE"
## [55] "ALEX KONG" "MARISA RICCI"
## [57] "MICHAEL LU" "VIRAJ MOHILE"
## [59] "SEAN M MC CORMICK" "JULIA SHEN"
## [61] "JEZZEL FARKAS" "ASHWIN BALAJI"
## [63] "THOMAS JOSEPH HOSMER" "BEN LI"print("Total points are: ")## [1] "Total points are: "print(total_point)## [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 1.0player_states=character(0)
## Firs I will select all the rows containg a player's state ON, MI or OH
states_data <- grep("\\b(ON|MI|OH)\\b", text_data, value = TRUE)
##Now I can match player's state and add to a variable
Pre_rating = numeric(0)
for (line in states_data){
st <- str_extract(line,'ON|MI|OH')
player_states <- c(player_states, st)
}
print(player_states)## [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"We cann also extract subpart of string without using the loop as follows:
rating<-str_extract_all(states_data, ".\\: \\s?[[:digit:]]{3,4}")
rating <- gsub(rating, pattern="R: ", replacement="", fixed = TRUE)
pre_rating <- as.numeric(rating)
print(pre_rating)## [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 1163text_data2 <- text_data[-c(0:4)]
#text_data2
text_data3<- text_data2[sapply(text_data2, nchar)>0]
#text_data3
text_data_od <- text_data3[c(seq(1, length(text_data3), 3))]
text_data_od## [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|"opponent_player <- str_extract_all(text_data_od, "[[:digit:]]{1,2}")
opponent_player## [[1]]
## [1] "1" "6" "0" "39" "21" "18" "14" "7" "12" "4"
##
## [[2]]
## [1] "2" "6" "0" "63" "58" "4" "17" "16" "20" "7"
##
## [[3]]
## [1] "3" "6" "0" "8" "61" "25" "21" "11" "13" "12"
##
## [[4]]
## [1] "4" "5" "5" "23" "28" "2" "26" "5" "19" "1"
##
## [[5]]
## [1] "5" "5" "5" "45" "37" "12" "13" "4" "14" "17"
##
## [[6]]
## [1] "6" "5" "0" "34" "29" "11" "35" "10" "27" "21"
##
## [[7]]
## [1] "7" "5" "0" "57" "46" "13" "11" "1" "9" "2"
##
## [[8]]
## [1] "8" "5" "0" "3" "32" "14" "9" "47" "28" "19"
##
## [[9]]
## [1] "9" "5" "0" "25" "18" "59" "8" "26" "7" "20"
##
## [[10]]
## [1] "10" "5" "0" "16" "19" "55" "31" "6" "25" "18"
##
## [[11]]
## [1] "11" "4" "5" "38" "56" "6" "7" "3" "34" "26"
##
## [[12]]
## [1] "12" "4" "5" "42" "33" "5" "38" "1" "3"
##
## [[13]]
## [1] "13" "4" "5" "36" "27" "7" "5" "33" "3" "32"
##
## [[14]]
## [1] "14" "4" "5" "54" "44" "8" "1" "27" "5" "31"
##
## [[15]]
## [1] "15" "4" "5" "19" "16" "30" "22" "54" "33" "38"
##
## [[16]]
## [1] "16" "4" "0" "10" "15" "39" "2" "36"
##
## [[17]]
## [1] "17" "4" "0" "48" "41" "26" "2" "23" "22" "5"
##
## [[18]]
## [1] "18" "4" "0" "47" "9" "1" "32" "19" "38" "10"
##
## [[19]]
## [1] "19" "4" "0" "15" "10" "52" "28" "18" "4" "8"
##
## [[20]]
## [1] "20" "4" "0" "40" "49" "23" "41" "28" "2" "9"
##
## [[21]]
## [1] "21" "4" "0" "43" "1" "47" "3" "40" "39" "6"
##
## [[22]]
## [1] "22" "4" "0" "64" "52" "28" "15" "17" "40"
##
## [[23]]
## [1] "23" "4" "0" "4" "43" "20" "58" "17" "37" "46"
##
## [[24]]
## [1] "24" "4" "0" "28" "47" "43" "25" "60" "44" "39"
##
## [[25]]
## [1] "25" "3" "5" "9" "53" "3" "24" "34" "10" "47"
##
## [[26]]
## [1] "26" "3" "5" "49" "40" "17" "4" "9" "32" "11"
##
## [[27]]
## [1] "27" "3" "5" "51" "13" "46" "37" "14" "6"
##
## [[28]]
## [1] "28" "3" "5" "24" "4" "22" "19" "20" "8" "36"
##
## [[29]]
## [1] "29" "3" "5" "50" "6" "38" "34" "52" "48"
##
## [[30]]
## [1] "30" "3" "5" "52" "64" "15" "55" "31" "61" "50"
##
## [[31]]
## [1] "31" "3" "5" "58" "55" "64" "10" "30" "50" "14"
##
## [[32]]
## [1] "32" "3" "5" "61" "8" "44" "18" "51" "26" "13"
##
## [[33]]
## [1] "33" "3" "5" "60" "12" "50" "36" "13" "15" "51"
##
## [[34]]
## [1] "34" "3" "5" "6" "60" "37" "29" "25" "11" "52"
##
## [[35]]
## [1] "35" "3" "5" "46" "38" "56" "6" "57" "52" "48"
##
## [[36]]
## [1] "36" "3" "5" "13" "57" "51" "33" "16" "28"
##
## [[37]]
## [1] "37" "3" "5" "5" "34" "27" "23" "61"
##
## [[38]]
## [1] "38" "3" "0" "11" "35" "29" "12" "18" "15"
##
## [[39]]
## [1] "39" "3" "0" "1" "54" "40" "16" "44" "21" "24"
##
## [[40]]
## [1] "40" "3" "0" "20" "26" "39" "59" "21" "56" "22"
##
## [[41]]
## [1] "41" "3" "0" "59" "17" "58" "20"
##
## [[42]]
## [1] "42" "3" "0" "12" "50" "57" "60" "61" "64" "56"
##
## [[43]]
## [1] "43" "3" "0" "21" "23" "24" "63" "59" "46" "55"
##
## [[44]]
## [1] "44" "3" "0" "14" "32" "53" "39" "24" "59"
##
## [[45]]
## [1] "45" "3" "0" "5" "51" "60" "56" "63" "55" "58"
##
## [[46]]
## [1] "46" "3" "0" "35" "7" "27" "50" "64" "43" "23"
##
## [[47]]
## [1] "47" "2" "5" "18" "24" "21" "61" "8" "51" "25"
##
## [[48]]
## [1] "48" "2" "5" "17" "63" "52" "29" "35"
##
## [[49]]
## [1] "49" "2" "5" "26" "20" "63" "64" "58"
##
## [[50]]
## [1] "50" "2" "5" "29" "42" "33" "46" "31" "30"
##
## [[51]]
## [1] "51" "2" "5" "27" "45" "36" "57" "32" "47" "33"
##
## [[52]]
## [1] "52" "2" "5" "30" "22" "19" "48" "29" "35" "34"
##
## [[53]]
## [1] "53" "2" "0" "25" "44" "57"
##
## [[54]]
## [1] "54" "2" "0" "14" "39" "61" "15" "59" "64"
##
## [[55]]
## [1] "55" "2" "0" "62" "31" "10" "30" "45" "43"
##
## [[56]]
## [1] "56" "2" "0" "11" "35" "45" "40" "42"
##
## [[57]]
## [1] "57" "2" "0" "7" "36" "42" "51" "35" "53"
##
## [[58]]
## [1] "58" "2" "0" "31" "2" "41" "23" "49" "45"
##
## [[59]]
## [1] "59" "2" "0" "41" "9" "40" "43" "54" "44"
##
## [[60]]
## [1] "60" "1" "5" "33" "34" "45" "42" "24"
##
## [[61]]
## [1] "61" "1" "5" "32" "3" "54" "47" "42" "30" "37"
##
## [[62]]
## [1] "62" "1" "0" "55"
##
## [[63]]
## [1] "63" "1" "0" "2" "48" "49" "43" "45"
##
## [[64]]
## [1] "64" "1" "0" "22" "30" "31" "49" "46" "42" "54"opp_numeric = numeric(0)
for (line in opponent_player){
players<- line[4: length(line)]
opp_numeric <- c(opp_numeric, list((players)))
}
print(head(opp_numeric))## [[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"max_length <- max(sapply(opp_numeric, length))
vec_to_list= function(vec) {
c(vec, rep("0", max_length - length(vec)))
}
# Fill the vectors with zeros to make them of equal length
padded_vectors <- lapply(opp_numeric,vec_to_list)
padded_vectors## [[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" "0"
##
## [[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" "0" "0"
##
## [[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" "0"
##
## [[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" "0"
##
## [[28]]
## [1] "24" "4" "22" "19" "20" "8" "36"
##
## [[29]]
## [1] "50" "6" "38" "34" "52" "48" "0"
##
## [[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" "0"
##
## [[37]]
## [1] "5" "34" "27" "23" "61" "0" "0"
##
## [[38]]
## [1] "11" "35" "29" "12" "18" "15" "0"
##
## [[39]]
## [1] "1" "54" "40" "16" "44" "21" "24"
##
## [[40]]
## [1] "20" "26" "39" "59" "21" "56" "22"
##
## [[41]]
## [1] "59" "17" "58" "20" "0" "0" "0"
##
## [[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" "0"
##
## [[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" "0" "0"
##
## [[49]]
## [1] "26" "20" "63" "64" "58" "0" "0"
##
## [[50]]
## [1] "29" "42" "33" "46" "31" "30" "0"
##
## [[51]]
## [1] "27" "45" "36" "57" "32" "47" "33"
##
## [[52]]
## [1] "30" "22" "19" "48" "29" "35" "34"
##
## [[53]]
## [1] "25" "44" "57" "0" "0" "0" "0"
##
## [[54]]
## [1] "14" "39" "61" "15" "59" "64" "0"
##
## [[55]]
## [1] "62" "31" "10" "30" "45" "43" "0"
##
## [[56]]
## [1] "11" "35" "45" "40" "42" "0" "0"
##
## [[57]]
## [1] "7" "36" "42" "51" "35" "53" "0"
##
## [[58]]
## [1] "31" "2" "41" "23" "49" "45" "0"
##
## [[59]]
## [1] "41" "9" "40" "43" "54" "44" "0"
##
## [[60]]
## [1] "33" "34" "45" "42" "24" "0" "0"
##
## [[61]]
## [1] "32" "3" "54" "47" "42" "30" "37"
##
## [[62]]
## [1] "55" "0" "0" "0" "0" "0" "0"
##
## [[63]]
## [1] "2" "48" "49" "43" "45" "0" "0"
##
## [[64]]
## [1] "22" "30" "31" "49" "46" "42" "54"df <- as.data.frame(do.call(rbind, padded_vectors))
# Convert character columns to numeric, replacing non-numeric values with NA
df <- data.frame(sapply(df, function(x) as.numeric(as.character(x))))
# Replace NA with 0
df[is.na(df)] <- 0
# Print the resulting data frame
dfopponent_avg_rating<-list()
for (i in 1:nrow(df)){
opponent_avg_rating[i]<- round(mean(as.numeric(unlist(df[i,]))), 2)
}
opponent_avg_rating<- unlist(opponent_avg_rating)
opponent_avg_rating## [1] 16.43 26.43 21.57 14.86 20.29 23.86 19.86 21.71 23.29 24.29 24.29 17.43
## [13] 20.43 24.29 30.29 14.57 23.86 22.29 19.29 27.43 25.57 30.86 32.14 40.86
## [25] 25.71 23.14 23.86 19.00 32.57 46.86 40.14 31.57 33.86 31.43 43.29 28.29
## [37] 21.43 17.14 28.57 34.71 22.00 51.43 41.57 31.57 49.71 35.57 29.71 28.00
## [49] 33.00 30.14 39.57 31.00 18.00 36.00 31.57 24.71 32.00 27.29 33.00 25.43
## [61] 35.00 7.86 26.71 39.14df<- cbind.data.frame(player_names, player_states, total_point, pre_rating, opponent_avg_rating)
colnames(df)<- c("Player's name", "Player's state", "Total number of points", "Player's Pre-Rating", "Opponent's Average Pre-Rating")
dfwrite.csv(df, "chess_rating.csv")