library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.3 ✔ 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")
head(text_data)## [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 |"player_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_data <- text_data[-c(0:4)]
text_data<- text_data[sapply(text_data, nchar)>0]
text_data_od <- text_data[c(seq(1, length(text_data), 3))]opponent_player <- str_extract_all(text_data_od, "[[:digit:]]{1,2}")
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"opponent_avg_rating<-list()
for (i in 1:length(opp_numeric)){
opponent_avg_rating[i]<- round(mean(as.numeric(unlist(opp_numeric[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 20.33
## [13] 20.43 24.29 30.29 20.40 23.86 22.29 19.29 27.43 25.57 36.00 32.14 40.86
## [25] 25.71 23.14 27.83 19.00 38.00 46.86 40.14 31.57 33.86 31.43 43.29 33.00
## [37] 30.00 20.00 28.57 34.71 38.50 51.43 41.57 36.83 49.71 35.57 29.71 39.20
## [49] 46.20 35.17 39.57 31.00 42.00 42.00 36.83 34.60 37.33 31.83 38.50 35.60
## [61] 35.00 55.00 37.40 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")
head(df)write.csv(df, "chess_rating.csv")