In this project, we are given a text file with chess tournament results where the information has some structure. Our 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.
# Libraries to be used
library(tidyverse)
library(data.table)tourney_file_name <- "C:\\tmp\\tournamentinfo.txt"
# Read the data from the tournament file and store it in a data.table (skip the first 4 rows containing unnecessary header data)
mdt <- data.table(read_csv(tourney_file_name, col_types = list(col_character()), col_names = c("data"), skip = 4))Let’s peak into the data read from the file.
# Take a peak at the data from the table
head(mdt,10)## data
## 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: -----------------------------------------------------------------------------------------
## 4: 2 | DAKSHESH DARURI |6.0 |W 63|W 58|L 4|W 17|W 16|W 20|W 7|
## 5: MI | 14598900 / R: 1553 ->1663 |N:2 |B |W |B |W |B |W |B |
## 6: -----------------------------------------------------------------------------------------
## 7: 3 | ADITYA BAJAJ |6.0 |L 8|W 61|W 25|W 21|W 11|W 13|W 12|
## 8: MI | 14959604 / R: 1384 ->1640 |N:2 |W |B |W |B |W |B |W |
## 9: -----------------------------------------------------------------------------------------
## 10: 4 | PATRICK H SCHILLING |5.5 |W 23|D 28|W 2|W 26|D 5|W 19|D 1|Extract the lines of text corresponding to the players and their ratings.
# From the table, extract the text rows that correspond to the players (skip the dashed lines)
players <- mdt[seq(1, nrow(mdt), 3)]
# From the table, extract the text rows that correspond to the players' ratings (skip the dashed lines)
player_ratings <- mdt[seq(2, nrow(mdt), 3)]Show the results of the extracted lines of text.
# take a peak at the players data lines
head(players, 10)## data
## 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|# take a peak at the players' ratings data lines
head(player_ratings, 10)## data
## 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 |
## 7: MI | 11146376 / R: 1649 ->1673 |N:3 |W |B |W |B |B |W |W |
## 8: MI | 15142253 / R: 1641P17->1657P24 |N:3 |B |W |B |W |B |W |W |
## 9: ON | 14954524 / R: 1411 ->1564 |N:2 |W |B |W |B |W |B |B |
## 10: MI | 14150362 / R: 1365 ->1544 |N:3 |W |W |B |B |W |B |W |Using regular expressions, parse out the values from each line of text.
# Extract the values from the players table
Pair_Num <- as.integer(str_extract(players$data, "\\d+")) # 1st sequence of numeric characters
Player_Name <- str_trim(str_extract(players$data, "(\\w+\\s){2,3}")) # 1st instance of 2 or 3 words followed by white space
Total_Points <- as.numeric(str_extract(players$data, "\\d+\\.\\d+")) # 2nd sequence of numeric characters
Opponents <- str_extract_all(str_extract_all(players$data, "\\d+\\|"), "\\d+") # All instances of numeric characters followed by a pipe |
# Extract the values from the player ratings table
Player_State <- str_extract(player_ratings$data, "\\w+") # 1st word on the line
Pre_Rating <- as.integer(str_extract(str_extract(player_ratings$data, "[^\\d]\\d{3,4}[^\\d]"), "\\d+")) # 1st instance of a number of at least 3 but no more than 4 digits
Post_Rating <- as.integer(str_extract(str_extract(player_ratings$data, "->\\s*\\d{3,4}"), "\\d+")) # 1st instance of a number of at least 3 but no more than 4 digits, which come after "-> ".Calculate the Arpad Elo’s mean rating for each player’s opponents.
For this, we traverse the vector of Pre-Ratings of each player and get the pre-ratings of their opponents. Because the opponents’ IDs (Pair number) are stored in a vector inside a vector, we need to flatten it before we can use the values from the inner vector. Using those IDs, we look up the pre-rating corresponding to each of those IDs (Pair number) and then calculate the mean of all of them. We round the mean to the nearest point (no decimal digits).
# Calculate the Arpad Elo's mean rating for each player's opponents
total_Players = nrow(players)
Avg_Opponent_Pre_Rating <- total_Players
for (i in 1:total_Players) {
# for each player
Avg_Opponent_Pre_Rating[i] <- round(
mean(
Pre_Rating[as.numeric(
flatten(
Opponents[Pair_Num[i]]
)
)]
), digits = 0
)
}# build a table to be used as the source for the CSV file
DT = data.table(
Player_Name,
Player_State,
Total_Points,
Pre_Rating,
Avg_Opponent_Pre_Rating
)| Player_Name | Player_State | Total_Points | Pre_Rating | Avg_Opponent_Pre_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 |
# Write the data to CSV file
write_csv(
x = DT,
file = "c:\\tmp\\chess_tournament_player_output.csv",
quote_escape = "double"
)# Which player scored the most points relative to his or her expected result?
Point_Diff <- Post_Rating - Pre_Rating
elo_rating = data.table (
Player_Name,
Pre_Rating,
Post_Rating,
Point_Diff,
Avg_Opponent_Pre_Rating
)
elo_rating <- elo_rating[order(-Point_Diff)]| Player_Name | Pre_Rating | Post_Rating | Point_Diff | Avg_Opponent_Pre_Rating |
|---|---|---|---|---|
| JACOB ALEXANDER LAVALLEY | 377 | 1076 | 699 | 1358 |
| ADITYA BAJAJ | 1384 | 1640 | 256 | 1564 |
| ZACHARY JAMES HOUGHTON | 1220 | 1416 | 196 | 1484 |
| ANVIT RAO | 1365 | 1544 | 179 | 1554 |
| ETHAN GUO | 935 | 1092 | 157 | 1495 |
| STEFANO LEE | 1411 | 1564 | 153 | 1523 |
| DAKSHESH DARURI | 1553 | 1663 | 110 | 1469 |
| AMIYATOSH PWNANANDAM | 980 | 1077 | 97 | 1385 |
| TEJAS AYYAGARI | 1011 | 1097 | 86 | 1356 |
| MICHAEL R ALDRICH | 1229 | 1300 | 71 | 1357 |
| SHIVAM JHA | 1056 | 1111 | 55 | 1296 |
| HANSHI ZUO | 1655 | 1690 | 35 | 1501 |
| PATRICK H SCHILLING | 1716 | 1744 | 28 | 1574 |
| SEAN M MC | 853 | 878 | 25 | 1319 |
| GARY DEE SWATHELL | 1649 | 1673 | 24 | 1372 |
| VIRAJ MOHILE | 917 | 941 | 24 | 1391 |
| JEZZEL FARKAS | 955 | 979 | 24 | 1327 |
| GARY HUA | 1794 | 1817 | 23 | 1605 |
| JULIA SHEN | 967 | 984 | 17 | 1330 |
| EZEKIEL HOUGHTON | 1641 | 1657 | 16 | 1468 |
| BRIAN LIU | 1423 | 1439 | 16 | 1539 |
| SIDDHARTH JHA | 1355 | 1367 | 12 | 1388 |
| MIKE NIKITIN | 1604 | 1613 | 9 | 1386 |
| BRADLEY SHAW | 1610 | 1618 | 8 | 1515 |
| ALAN BUI | 1363 | 1371 | 8 | 1214 |
| KENNETH J TACK | 1663 | 1670 | 7 | 1506 |
| DIPANKAR ROY | 1564 | 1570 | 6 | 1426 |
| SOFIA ADINA | 1507 | 1513 | 6 | 1522 |
| ASHWIN BALAJI | 1530 | 1535 | 5 | 1186 |
| HANSEN SONG | 1686 | 1687 | 1 | 1519 |
| MICHAEL JEFFERY THOMAS | 1399 | 1400 | 1 | 1375 |
| DAVID SUNDEEN | 1600 | 1600 | 0 | 1480 |
| JUSTIN D SCHILLING | 1199 | 1199 | 0 | 1327 |
| DINH DANG BUI | 1563 | 1562 | -1 | 1470 |
| FOREST ZHANG | 1348 | 1346 | -2 | 1391 |
| TORRANCE HENRY JR | 1666 | 1662 | -4 | 1498 |
| JOSHUA PHILIP MATHEWS | 1441 | 1433 | -8 | 1379 |
| GAURAV GIDWANI | 1552 | 1539 | -13 | 1222 |
| MARISA RICCI | 1153 | 1140 | -13 | 1414 |
| MICHAEL LU | 1092 | 1079 | -13 | 1363 |
| MAX ZHU | 1579 | 1564 | -15 | 1507 |
| CAMERON WILLIAM MC | 1712 | 1696 | -16 | 1468 |
| RONALD GRZEGORCZYK | 1629 | 1610 | -19 | 1499 |
| ERIC WRIGHT | 1362 | 1341 | -21 | 1392 |
| JOEL R HENDON | 1436 | 1413 | -23 | 1430 |
| ALEX KONG | 1186 | 1163 | -23 | 1406 |
| JASON ZHENG | 1595 | 1569 | -26 | 1411 |
| EUGENE L MCCLURE | 1555 | 1529 | -26 | 1300 |
| JADE GE | 1449 | 1421 | -28 | 1277 |
| MICHAEL J MARTIN | 1291 | 1259 | -32 | 1286 |
| JOSE C YBARRA | 1393 | 1359 | -34 | 1345 |
| ROBERT GLEN VASEY | 1283 | 1244 | -39 | 1107 |
| JOSHUA DAVID LEE | 1438 | 1392 | -46 | 1150 |
| DANIEL KHAIN | 1382 | 1335 | -47 | 1356 |
| RISHI SHETTY | 1494 | 1444 | -50 | 1260 |
| THOMAS JOSEPH HOSMER | 1175 | 1125 | -50 | 1350 |
| DEREK YAN | 1242 | 1191 | -51 | 1152 |
| BEN LI | 1163 | 1112 | -51 | 1263 |
| KYLE WILLIAM MURPHY | 1403 | 1341 | -62 | 1248 |
| LOREN SCHWIEBERT | 1745 | 1681 | -64 | 1363 |
| LARRY HODGE | 1270 | 1200 | -70 | 1206 |
| JARED GE | 1332 | 1256 | -76 | 1150 |
| GEORGE AVERY JONES | 1522 | 1444 | -78 | 1144 |
| CHIEDOZIE OKORIE | 1602 | 1508 | -94 | 1314 |
PLayer with the highest difference between his/her Pre-rating and Post-rating
| Player_Name | Pre_Rating | Post_Rating | Point_Diff | Avg_Opponent_Pre_Rating |
|---|---|---|---|---|
| JACOB ALEXANDER LAVALLEY | 377 | 1076 | 699 | 1358 |