Project 1

Chess Tournament Details

Approach

I would start by cleaning the input file by removing everything that does not constitute the information about any particular player in the tournament: the header of the file, as well as the separator lines. At this point, the file consists of two lines for each player where the first line contains the name, total points, and details of how every single round went for him/her and the second line – his/her state and rating.

I extract the data from every single line using “|” as a delimiter because of its predictability. The rating of some players is followed by additional comments that I strip using regular expressions to get only the number. I also extract the name of each player’s opponent in every round using the same technique and skipping rounds when he/she got a bye or did not participate.

Now I match the player number to his/her rating and calculate the average rating of opponents for each individual player by averaging the ratings of all the players whom he/she played against. Finally, I combine all the required information into one table.

Code Base

library(stringr)
library(dplyr)

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

data_url <- "https://raw.githubusercontent.com/daanishrasheed/DATA607/refs/heads/main/Project%201/tournamentinfo.txt"

raw_lines <- readLines(data_url)

Warning in readLines(data_url): incomplete final line found on
'https://raw.githubusercontent.com/daanishrasheed/DATA607/refs/heads/main/Project%201/tournamentinfo.txt'

# Drop blank lines, separator lines (all dashes), and the two header lines
clean_lines <- raw_lines[
  str_trim(raw_lines) != "" &
    !str_detect(str_trim(raw_lines), "^-+$") &
    !str_detect(str_trim(raw_lines), "^(Pair|Num)")
]

length(clean_lines)

[1] 128

parse1 <- function(x) {
  line1 <- clean_lines[2 * x - 1]
  line2 <- clean_lines[2 * x]
  f1 <- str_trim(str_split(line1, "\\|")[[1]])
  f2 <- str_trim(str_split(line2, "\\|")[[1]])
  
  numpair <- as.integer(f1[1])
  name     <- f1[2]
  points   <- as.numeric(f1[3])
  state <- f2[1]
  pre_rating <- as.integer(str_extract(str_extract(f2[2], "R:\\s*\\d+"), "\\d+"))
  rounded <- f1[4:10]
  
  o <- rounded |>
    str_extract("\\d+") |>
    as.integer()
  o <- o[!is.na(o)]
  
  list(pair = numpair, name = name, state = state,
       points = points, pre_rating = pre_rating, opponents = list(o))
}

n <- length(clean_lines) / 2

players <- lapply(seq_len(n), parse1)

players_df <- bind_rows(lapply(players, function(p) {
  tibble(pair = p$pair, name = p$name, state = p$state,
         points = p$points, pre_rating = p$pre_rating)
}))

head(players_df, 10)

# A tibble: 10 × 5
    pair name                state points pre_rating
   <int> <chr>               <chr>  <dbl>      <int>
 1     1 GARY HUA            ON       6         1794
 2     2 DAKSHESH DARURI     MI       6         1553
 3     3 ADITYA BAJAJ        MI       6         1384
 4     4 PATRICK H SCHILLING MI       5.5       1716
 5     5 HANSHI ZUO          MI       5.5       1655
 6     6 HANSEN SONG         OH       5         1686
 7     7 GARY DEE SWATHELL   MI       5         1649
 8     8 EZEKIEL HOUGHTON    MI       5         1641
 9     9 STEFANO LEE         ON       5         1411
10    10 ANVIT RAO           MI       5         1365

The above code chunk returns the pair #, name, state, points, and pre-rating for each person in the .txt file.

ratings <- setNames(players_df$pre_rating, players_df$pair)

avg_opp <- sapply(players, function(p) {
  opp_ratings <- ratings[as.character(p$opponents[[1]])]
  opp_ratings <- opp_ratings[!is.na(opp_ratings)]
  if (length(opp_ratings) == 0) return(NA_real_)
  round(mean(opp_ratings))
})

players_df$avg_opponent_pre_rating <- avg_opp

head(players_df, 10)

# A tibble: 10 × 6
    pair name                state points pre_rating avg_opponent_pre_rating
   <int> <chr>               <chr>  <dbl>      <int>                   <dbl>
 1     1 GARY HUA            ON       6         1794                    1605
 2     2 DAKSHESH DARURI     MI       6         1553                    1469
 3     3 ADITYA BAJAJ        MI       6         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         1686                    1519
 7     7 GARY DEE SWATHELL   MI       5         1649                    1372
 8     8 EZEKIEL HOUGHTON    MI       5         1641                    1468
 9     9 STEFANO LEE         ON       5         1411                    1523
10    10 ANVIT RAO           MI       5         1365                    1554

To get the average opponents rating for each player, the code builds a lookup table so any rating can be found by a player’s number. Then for each player, it grabs their opponents’ ratings, drops any missing ones and averages the data that is available.

write.csv(players_df, "tournament_ratings.csv", row.names = FALSE)

Conclusion

This parser will take this fixed-width data structure and convert it into a CSV with all of the relevant data formatted nicely in tabular format with the columns, name, state, total score, rating before the tournament and the average rating of their opponents before the tournament, all for each player. The hardest part of doing this was calculating the average of the opponents’ rating as I had to find a way to access the pair number of each opponent. I wasn’t able to do this with the data as a number, so I had to reformat it as a string and individually extracted each of them.