In this project, my goal is to generate a csv file with the chess player information:
I have placed the given text file in my github repo. Exploring it below:
data_src <- "https://raw.githubusercontent.com/cdube89128/DATA-607/refs/heads/main/project-01/tournamentinfo.txt"
# Read file lines
lines <- readLines(data_src)## Warning in readLines(data_src): incomplete final line found on
## 'https://raw.githubusercontent.com/cdube89128/DATA-607/refs/heads/main/project-01/tournamentinfo.txt'It looks like there was a warning from reading in the file, but my rudimentary Google suggests that this is due to the lack of a newline character at the end of my text file, so I’m continuing onward as normal.
head(lines, 10)## [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 |"
## [7] "-----------------------------------------------------------------------------------------"
## [8] " 2 | DAKSHESH DARURI |6.0 |W 63|W 58|L 4|W 17|W 16|W 20|W 7|"
## [9] " MI | 14598900 / R: 1553 ->1663 |N:2 |B |W |B |W |B |W |B |"
## [10] "-----------------------------------------------------------------------------------------"There are 4 lines of header/misc text. Each entry takes up two lines, and different pieces of information that are needed are distinguished in different ways within this text file. I.e. there is nothing perfectly standard like comma delimiters here.
I am going to clean up these lines to get the data for each entry consolidated together.
# Remove header lines in file
lines <- lines[-c(1:4)]
# Each entry is 2 lines, followed by a divider line (---)
# Remove those divider lines
lines <- lines[!grepl("^-", lines)]
# Checking in again
head(lines, 10)## [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] " 2 | DAKSHESH DARURI |6.0 |W 63|W 58|L 4|W 17|W 16|W 20|W 7|"
## [4] " MI | 14598900 / R: 1553 ->1663 |N:2 |B |W |B |W |B |W |B |"
## [5] " 3 | ADITYA BAJAJ |6.0 |L 8|W 61|W 25|W 21|W 11|W 13|W 12|"
## [6] " MI | 14959604 / R: 1384 ->1640 |N:2 |W |B |W |B |W |B |W |"
## [7] " 4 | PATRICK H SCHILLING |5.5 |W 23|D 28|W 2|W 26|D 5|W 19|D 1|"
## [8] " MI | 12616049 / R: 1716 ->1744 |N:2 |W |B |W |B |W |B |B |"
## [9] " 5 | HANSHI ZUO |5.5 |W 45|W 37|D 12|D 13|D 4|W 14|W 17|"
## [10] " MI | 14601533 / R: 1655 ->1690 |N:2 |B |W |B |W |B |W |B |"# Group into chunks of 2 lines per player
player_lines <- split(lines, ceiling(seq_along(lines)/2))
# Combine each pair into one string
combined <- sapply(player_lines, paste, collapse = "")
# The whitespace is messy, cleaning that up
combined <- gsub("\\s+", " ", combined)
combined <- trimws(combined)
# Checking in again
head(combined, 5)## 1
## "1 | GARY HUA |6.0 |W 39|W 21|W 18|W 14|W 7|D 12|D 4| ON | 15445895 / R: 1794 ->1817 |N:2 |W |B |W |B |W |B |W |"
## 2
## "2 | DAKSHESH DARURI |6.0 |W 63|W 58|L 4|W 17|W 16|W 20|W 7| MI | 14598900 / R: 1553 ->1663 |N:2 |B |W |B |W |B |W |B |"
## 3
## "3 | ADITYA BAJAJ |6.0 |L 8|W 61|W 25|W 21|W 11|W 13|W 12| MI | 14959604 / R: 1384 ->1640 |N:2 |W |B |W |B |W |B |W |"
## 4
## "4 | PATRICK H SCHILLING |5.5 |W 23|D 28|W 2|W 26|D 5|W 19|D 1| MI | 12616049 / R: 1716 ->1744 |N:2 |W |B |W |B |W |B |B |"
## 5
## "5 | HANSHI ZUO |5.5 |W 45|W 37|D 12|D 13|D 4|W 14|W 17| MI | 14601533 / R: 1655 ->1690 |N:2 |B |W |B |W |B |W |B |"The data looks much more easily parsable now. Next I will focus on pulling out the individual pieces of data per entry (e.g. Player’s Number, Player Name, Player’s Rating etc.)
# This looks more easily parsable. Almost all of the distinct values are separated by pipes (|).
split_data <- str_split(combined, "\\|")
# Checking in again
head(split_data, 2)## [[1]]
## [1] "1 " " GARY HUA "
## [3] "6.0 " "W 39"
## [5] "W 21" "W 18"
## [7] "W 14" "W 7"
## [9] "D 12" "D 4"
## [11] " ON " " 15445895 / R: 1794 ->1817 "
## [13] "N:2 " "W "
## [15] "B " "W "
## [17] "B " "W "
## [19] "B " "W "
## [21] ""
##
## [[2]]
## [1] "2 " " DAKSHESH DARURI "
## [3] "6.0 " "W 63"
## [5] "W 58" "L 4"
## [7] "W 17" "W 16"
## [9] "W 20" "W 7"
## [11] " MI " " 14598900 / R: 1553 ->1663 "
## [13] "N:2 " "B "
## [15] "W " "B "
## [17] "W " "B "
## [19] "W " "B "
## [21] ""class(split_data)## [1] "list"class(split_data[[1]])## [1] "character"# It looks like split_data is a list of character vectors
# Create a function to parse each entry
parse_player <- function(x) {
x <- str_trim(x) # trim whitespace because it was still slightly irregular
tibble(
Pair = as.numeric(x[1]),
Name = x[2],
Total = as.numeric(x[3]),
Round_1 = as.numeric(str_extract(x[4], "\\d+")),
Round_2 = as.numeric(str_extract(x[5], "\\d+")),
Round_3 = as.numeric(str_extract(x[6], "\\d+")),
Round_4 = as.numeric(str_extract(x[7], "\\d+")),
Round_5 = as.numeric(str_extract(x[8], "\\d+")),
Round_6 = as.numeric(str_extract(x[9], "\\d+")),
Round_7 = as.numeric(str_extract(x[10], "\\d+")),
State = x[11],
#After this, more complicated parsing is needed
ID = str_extract(x[12], "\\d+"), # get 1st group of digits
Pre_Rating = as.numeric(str_extract(x[12], "(?<=R: )\\d+")), # get group of digits after R:
Post_Rating = as.numeric(str_extract(x[12], "(?<=->)\\d+")) # get group of digits after ->
)
}
# Apply my function to each element of split_data
# Bind the resulting rows together into a new dataframe
my_df <- bind_rows(lapply(split_data, parse_player))
# Checking in
head(my_df, 5)## # A tibble: 5 × 14
## Pair Name Total Round_1 Round_2 Round_3 Round_4 Round_5 Round_6 Round_7
## <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 GARY HUA 6 39 21 18 14 7 12 4
## 2 2 DAKSHESH … 6 63 58 4 17 16 20 7
## 3 3 ADITYA BA… 6 8 61 25 21 11 13 12
## 4 4 PATRICK H… 5.5 23 28 2 26 5 19 1
## 5 5 HANSHI ZUO 5.5 45 37 12 13 4 14 17
## # ℹ 4 more variables: State <chr>, ID <chr>, Pre_Rating <dbl>,
## # Post_Rating <dbl>I have almost everything I wanted from this data now. Next I will calculate the average pre chess rating of opponents for each entry/player. (I was struggling to find a really clean way to do this, so I ultimately have opted to use another self-defined function to help the process.)
# Get the columns names for the rounds
round_cols <- paste0("Round_", 1:7)
# Adding in the Average Pre Chess Rating of Opponents
my_df <- my_df %>%
rowwise() %>%
mutate(
Avg_Opp_Rating = round(
mean(
sapply(c_across(all_of(round_cols)), function(opponent_pair) {
if (!is.na(opponent_pair)) {
my_df$Pre_Rating[my_df$Pair == opponent_pair]
} else {
NA_real_ # handling NAs from rounds without opponents
}
}),
na.rm = TRUE # ignore NAs in the mean calculation
),
0 # round to 0 decimal places
)
) %>%
ungroup()Lastly, I am selecting only the columns of information that I wanted from this data.
# Reduce this to only get the columns on interest
my_df <- my_df %>%
select(Name, State, Total, Pre_Rating, Avg_Opp_Rating)
# Checking in
head(my_df, 5)## # A tibble: 5 × 5
## Name State Total Pre_Rating Avg_Opp_Rating
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 GARY HUA ON 6 1794 1605
## 2 DAKSHESH DARURI MI 6 1553 1469
## 3 ADITYA BAJAJ MI 6 1384 1564
## 4 PATRICK H SCHILLING MI 5.5 1716 1574
## 5 HANSHI ZUO MI 5.5 1655 1501The cleaned data (that will go into the csv file) is prepared.
kable(my_df)| Name | State | Total | Pre_Rating | Avg_Opp_Rating |
|---|---|---|---|---|
| GARY HUA | ON | 6.0 | 1794 | 1605 |
| DAKSHESH DARURI | MI | 6.0 | 1553 | 1469 |
| ADITYA BAJAJ | MI | 6.0 | 1384 | 1564 |
| PATRICK H SCHILLING | MI | 5.5 | 1716 | 1574 |
| HANSHI ZUO | MI | 5.5 | 1655 | 1501 |
| HANSEN SONG | OH | 5.0 | 1686 | 1519 |
| GARY DEE SWATHELL | MI | 5.0 | 1649 | 1372 |
| EZEKIEL HOUGHTON | MI | 5.0 | 1641 | 1468 |
| STEFANO LEE | ON | 5.0 | 1411 | 1523 |
| ANVIT RAO | MI | 5.0 | 1365 | 1554 |
| CAMERON WILLIAM MC LEMAN | MI | 4.5 | 1712 | 1468 |
| KENNETH J TACK | MI | 4.5 | 1663 | 1506 |
| TORRANCE HENRY JR | MI | 4.5 | 1666 | 1498 |
| BRADLEY SHAW | MI | 4.5 | 1610 | 1515 |
| ZACHARY JAMES HOUGHTON | MI | 4.5 | 1220 | 1484 |
| MIKE NIKITIN | MI | 4.0 | 1604 | 1386 |
| RONALD GRZEGORCZYK | MI | 4.0 | 1629 | 1499 |
| DAVID SUNDEEN | MI | 4.0 | 1600 | 1480 |
| DIPANKAR ROY | MI | 4.0 | 1564 | 1426 |
| JASON ZHENG | MI | 4.0 | 1595 | 1411 |
| DINH DANG BUI | ON | 4.0 | 1563 | 1470 |
| EUGENE L MCCLURE | MI | 4.0 | 1555 | 1300 |
| ALAN BUI | ON | 4.0 | 1363 | 1214 |
| MICHAEL R ALDRICH | MI | 4.0 | 1229 | 1357 |
| LOREN SCHWIEBERT | MI | 3.5 | 1745 | 1363 |
| MAX ZHU | ON | 3.5 | 1579 | 1507 |
| GAURAV GIDWANI | MI | 3.5 | 1552 | 1222 |
| SOFIA ADINA STANESCU-BELLU | MI | 3.5 | 1507 | 1522 |
| CHIEDOZIE OKORIE | MI | 3.5 | 1602 | 1314 |
| GEORGE AVERY JONES | ON | 3.5 | 1522 | 1144 |
| RISHI SHETTY | MI | 3.5 | 1494 | 1260 |
| JOSHUA PHILIP MATHEWS | ON | 3.5 | 1441 | 1379 |
| JADE GE | MI | 3.5 | 1449 | 1277 |
| MICHAEL JEFFERY THOMAS | MI | 3.5 | 1399 | 1375 |
| JOSHUA DAVID LEE | MI | 3.5 | 1438 | 1150 |
| SIDDHARTH JHA | MI | 3.5 | 1355 | 1388 |
| AMIYATOSH PWNANANDAM | MI | 3.5 | 980 | 1385 |
| BRIAN LIU | MI | 3.0 | 1423 | 1539 |
| JOEL R HENDON | MI | 3.0 | 1436 | 1430 |
| FOREST ZHANG | MI | 3.0 | 1348 | 1391 |
| KYLE WILLIAM MURPHY | MI | 3.0 | 1403 | 1248 |
| JARED GE | MI | 3.0 | 1332 | 1150 |
| ROBERT GLEN VASEY | MI | 3.0 | 1283 | 1107 |
| JUSTIN D SCHILLING | MI | 3.0 | 1199 | 1327 |
| DEREK YAN | MI | 3.0 | 1242 | 1152 |
| JACOB ALEXANDER LAVALLEY | MI | 3.0 | 377 | 1358 |
| ERIC WRIGHT | MI | 2.5 | 1362 | 1392 |
| DANIEL KHAIN | MI | 2.5 | 1382 | 1356 |
| MICHAEL J MARTIN | MI | 2.5 | 1291 | 1286 |
| SHIVAM JHA | MI | 2.5 | 1056 | 1296 |
| TEJAS AYYAGARI | MI | 2.5 | 1011 | 1356 |
| ETHAN GUO | MI | 2.5 | 935 | 1495 |
| JOSE C YBARRA | MI | 2.0 | 1393 | 1345 |
| LARRY HODGE | MI | 2.0 | 1270 | 1206 |
| ALEX KONG | MI | 2.0 | 1186 | 1406 |
| MARISA RICCI | MI | 2.0 | 1153 | 1414 |
| MICHAEL LU | MI | 2.0 | 1092 | 1363 |
| VIRAJ MOHILE | MI | 2.0 | 917 | 1391 |
| SEAN M MC CORMICK | MI | 2.0 | 853 | 1319 |
| JULIA SHEN | MI | 1.5 | 967 | 1330 |
| JEZZEL FARKAS | ON | 1.5 | 955 | 1327 |
| ASHWIN BALAJI | MI | 1.0 | 1530 | 1186 |
| THOMAS JOSEPH HOSMER | MI | 1.0 | 1175 | 1350 |
| BEN LI | MI | 1.0 | 1163 | 1263 |
It is time to create a csv of this information.
write.csv(my_df, "chess_tournament_player_info.csv", row.names = TRUE)From the given text file, we now have a csv with each Player’s Name, Player’s State, Total Number of Points, Player’s Pre-Rating, and Average Pre Chess Rating of Opponents from this tournament