Project 1

In this project, you’re given a text file with chess tournament results where the information has some structure. Your job is to create an R Markdown file that generates a .CSV file (that could for example be imported into a SQL database) with the following information for all of the players: Player’s Name, Player’s State, Total Number of Points, Player’s Pre-Rating, and Average Pre Chess Rating of Opponents. For the first player, the information would be: Gary Hua, ON, 6.0, 1794, 1605. 1605 was calculated by using the pre-tournament opponents’ ratings of 1436, 1563, 1600, 1610, 1649, 1663, 1716, and dividing by the total number of games played.

Loading Data

# Load Raw Text file Data from github

file_path <- 'https://raw.githubusercontent.com/Badigun/Data-607-Assignments/refs/heads/main/chess%20tournament%20file.txt'

tournament_data <- read_lines(file_path)

# View the first few lines of the Data
head(tournament_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    |"
tail(tournament_data)
## [1] "   63 | THOMAS JOSEPH HOSMER            |1.0  |L   2|L  48|D  49|L  43|L  45|H    |U    |"
## [2] "   MI | 15057092 / R: 1175   ->1125     |     |W    |B    |W    |B    |B    |     |     |"
## [3] "-----------------------------------------------------------------------------------------"
## [4] "   64 | BEN LI                          |1.0  |L  22|D  30|L  31|D  49|L  46|L  42|L  54|"
## [5] "   MI | 15006561 / R: 1163   ->1112     |     |B    |W    |W    |B    |W    |B    |B    |"
## [6] "-----------------------------------------------------------------------------------------"

Edit Player information in the Data

I observe that the data follows a specific pattern. The first four lines contain non-data information, so they are excluded from the dataset. After that, the player details and game statistics appear in a repeating sequence every three lines. The data is then organize into two matrices, one for player information and the other for their game statistics, to structure it like a more conventional dataset.

Each player has two lines in the file, which includes;

Line 1: Name, total points, and opponent numbers.

Line 2: State and pre-rating.

# Name, total points, and opponent numbers
edit_tournament_data <- matrix(unlist(tournament_data), byrow=TRUE)

m1 <- edit_tournament_data[seq(5,length(edit_tournament_data),3)]
head(m1)
## [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|"

Another way to extract player information for Line 1: Name, total points, and opponent numbers

player_lines <- tournament_data[grep("^\\s*\\d+", tournament_data)]

# Identify lines that contain player information 
player_lines <- tournament_data[grep("^\\s*\\d+", tournament_data)]

# Extract Player's Name, Points, and Opponents
players_info <- lapply(player_lines, function(line) {
  elements <- unlist(strsplit(line, "\\|"))
  elements <- trimws(elements)  # Remove leading and trailing spaces
  
  # Extract fields
  name <- elements[2]  # Player Name
  total_points <- as.numeric(elements[3])  # Total Points
  opponents <- unlist(str_extract_all(elements[6:length(elements)], "\\d+"))  # Opponent Numbers
  
  return(list(name, total_points, opponents))
})

# Convert to DataFrame
players_df <- data.frame(
  Name = sapply(players_info, `[[`, 1),
  Points = sapply(players_info, `[[`, 2),
  Opponents = sapply(players_info, function(x) paste(x[[3]], collapse = ",")), # Convert list to string
  stringsAsFactors = FALSE
)
head(players_df)
##                  Name Points      Opponents
## 1            GARY HUA    6.0   18,14,7,12,4
## 2     DAKSHESH DARURI    6.0   4,17,16,20,7
## 3        ADITYA BAJAJ    6.0 25,21,11,13,12
## 4 PATRICK H SCHILLING    5.5    2,26,5,19,1
## 5          HANSHI ZUO    5.5  12,13,4,14,17
## 6         HANSEN SONG    5.0 11,35,10,27,21

This code is to extract Line 2: State and pre-rating

m2 <- edit_tournament_data[seq(6,length(edit_tournament_data),3)]
head(m2)
## [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    |"

Another way to extract Line 2: State and pre-rating

# Find the second line for each player's entry (contains state & rating)
rating_lines <- tournament_data[grep("R:\\s*\\d+", tournament_data)]

# Extract State and Pre-Rating
ratings_info <- lapply(rating_lines, function(line) {
  elements <- unlist(strsplit(line, "\\|"))
  elements <- trimws(elements)

  state <- substr(elements[1], 1, 2)  # Extract first two letters as state
  pre_rating <- as.numeric(str_extract(elements[2], "(?<=R:\\s)\\d+"))  # Extract pre-rating

  return(list(state, pre_rating))
})

# Add to DataFrame
players_df$State <- sapply(ratings_info, `[[`, 1)
players_df$Pre_Rating <- sapply(ratings_info, `[[`, 2)

Capturing The Data

Since the Data is more organized, capturing of the data featured can be done

# Convert m1 and m2 to character vectors 
m1 <- as.character(m1)
m2 <- as.character(m2)

# matching first numbers
ID <- as.numeric(str_extract(m1, '\\d+'))

# matching the first combination of a letter, any amount of characters and "|"
Name <- str_extract(m1, '[A-z].{1,32}') 

# extracting the name
Name <- str_trim(str_extract(Name, '.+\\s{2,}'))

# matching the first two letters (state) in the second matrix 
State <- str_extract(m2, '[A-Z]{2}') 

# matching at least 1 number, a period, and 1 number
Total_Points <- as.numeric(str_extract(m1, '\\d+\\.\\d'))

# matching the combination of "R", any characters and "-"
PreRating <- str_extract(m2, 'R:.{8,}-')

# matching first 4 numbers
PreRating <- as.numeric(str_extract(PreRating, '\\d{1,4}'))

# matching all combinations of 1 letter 2 spaces and any numbers
Rounds <- str_extract_all(m1, '[A-Z]\\s{2,}\\d+')

# matching numbers
Rounds <- str_extract_all(Rounds, '\\d+')
## Warning in stri_extract_all_regex(string, pattern, simplify = simplify, :
## argument is not an atomic vector; coercing

Compute Average Opponent Pre-Rating

Compute the average pre-rating of opponents using the vectors from the previous step

AvgOpp_Rating <- c()

for(i in c(1:length(Rounds))){
  AvgOpp_Rating[i] <- round(mean(PreRating[as.numeric(Rounds[[i]])]),0)
}
AvgOpp_Rating
##  [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 1263
Project1_Data607 <- data.frame(ID,Name,State,Total_Points,PreRating,AvgOpp_Rating)

head(Project1_Data607)
##   ID                Name State Total_Points PreRating AvgOpp_Rating
## 1  1            GARY HUA    ON          6.0      1794          1605
## 2  2     DAKSHESH DARURI    MI          6.0      1553          1469
## 3  3        ADITYA BAJAJ    MI          6.0      1384          1564
## 4  4 PATRICK H SCHILLING    MI          5.5      1716          1574
## 5  5          HANSHI ZUO    MI          5.5      1655          1501
## 6  6         HANSEN SONG    OH          5.0      1686          1519

Writing/Saving Data to CSV

write_csv(Project1_Data607, "ChessTournament_results.csv")