Load libraries

library(tidyverse)
library(knitr)

Read in txt file to clean

chess_data <- as.data.frame(read.delim("https://raw.githubusercontent.com/cassandra-coste/CUNY607/main/tournamentinfo.txt", header = FALSE, stringsAsFactors = FALSE, sep = "|"))

Remove junk rows (code developed by classmate Gabriel Campos and shared via Slack)

toDelete <- seq(1, length(chess_data$V1), 3)
toDelete
##  [1]   1   4   7  10  13  16  19  22  25  28  31  34  37  40  43  46  49  52  55
## [20]  58  61  64  67  70  73  76  79  82  85  88  91  94  97 100 103 106 109 112
## [39] 115 118 121 124 127 130 133 136 139 142 145 148 151 154 157 160 163 166 169
## [58] 172 175 178 181 184 187 190 193 196
chess_data  <- chess_data[-toDelete ,]

Tidying the dataframe

Pull data for player pre-tournament rating by extracting the digit code following ‘R:’

chess_data_clean  <- extract(
  chess_data,
  col=V2,
  into="Player_Rating",
  regex = "(R: +\\d+)", 
  remove = FALSE
)

Remove extraneous characters and convert to numeric for player rating column as there is no other pertinent information there

chess_data_clean  <- extract(
  chess_data_clean,
  col=Player_Rating,
  into="Player_Rating",
  regex = "(\\d+)", 
  remove = FALSE,
  convert = TRUE
)

Extract round data for all 7 rounds removing the original data column

chess_data_clean  <- extract(
  chess_data_clean,
  col = V4,
  into = c("Round_1"),
  regex = "(\\d+)",
  remove = TRUE,
  convert = TRUE
)

chess_data_clean  <- extract(
  chess_data_clean,
  col = V5,
  into = c("Round_2"),
  regex = "(\\d+)",
  remove = TRUE,
  convert = TRUE
)

chess_data_clean  <- extract(
  chess_data_clean,
  col = V6,
  into = c("Round_3"),
  regex = "(\\d+)",
  remove = TRUE,
  convert = TRUE
)

chess_data_clean  <- extract(
  chess_data_clean,
  col = V7,
  into = c("Round_4"),
  regex = "(\\d+)",
  remove = TRUE,
  convert = TRUE
)

chess_data_clean  <- extract(
  chess_data_clean,
  col = V8,
  into = c("Round_5"),
  regex = "(\\d+)",
  remove = TRUE,
  convert = TRUE
)

chess_data_clean  <- extract(
  chess_data_clean,
  col = V9,
  into = c("Round_6"),
  regex = "(\\d+)",
  remove = TRUE,
  convert = TRUE
)

chess_data_clean  <- extract(
  chess_data_clean,
  col = V10,
  into = c("Round_7"),
  regex = "(\\d+)",
  remove = TRUE,
  convert = TRUE
)

Create two data frames containing necessary rows for each column and clean for join

The following steps were employed:

  1. Create indexes of odd and even rows to separate the data into two dataframes containing the pertinent data

  2. Remove first rows with messy data

  3. Create ‘Pair’ column in the even dataframe for joining them back together later

  4. Renames columns as necessary

  5. Convert the odd dataframe ‘Pair’ column to numeric for join

  6. Select which columns from each dataframe have relevant data

even_indexes<-seq(2,130,2)
odd_indexes<-seq(1,129,2)

chess_data_even  <- chess_data_clean[-odd_indexes ,]

chess_data_even = chess_data_even[-1,]

chess_data_even$Pair <- seq.int(nrow(chess_data_even))

chess_data_odd  <- chess_data_clean[-even_indexes ,]

chess_data_odd = chess_data_odd[-1,]

chess_data_odd  <- rename(chess_data_odd, Pair = V1)

chess_data_odd$Pair <- as.integer(chess_data_odd$Pair)

chess_data_odd <- select(chess_data_odd, -c(Player_Rating, V11)) %>% rename(Player_Name = V2) %>% rename(Total_Score = V3)

chess_data_even <- select(chess_data_even, c(V1, Player_Rating, Pair)) %>% rename(Player_State = V1)

Make final dataframe with full join

Using a full join, the even and odd dataframes are joined via the ‘Pair’ column

chess_data_final <- full_join(chess_data_even, chess_data_odd, by = "Pair")

Replace pair ID with player ratings for opponent for each round

Create a loop that replaces players Pair ID with their player rating pre-match

for (i in 1:nrow(chess_data_final)) {
  for (j in 6:12) {
    if (!is.na(chess_data_final[i,j])) {
      chess_data_final[i,j] <- as.numeric(chess_data_final[chess_data_final[i,j],2])
    }
  }
}

Calculate average opponents rating

chess_data_final$Average_Opponent <- rowMeans(chess_data_final[6:12], na.rm = TRUE)

View final table

chess_data_output <- chess_data_final %>%select(Player_Name, Player_State, Total_Score, Player_Rating, Average_Opponent) %>% mutate_if(is.numeric, format, digits=4)

kable(chess_data_output, col.names = c('Player Name', 'Player State', 'Total Number of Points', 'Player Pre-Rating', 'Average Rating of Opponents'), align = "ccrrr", caption = "Chess Tournament Scorecard")
Chess Tournament Scorecard
Player Name Player State Total Number of Points Player Pre-Rating Average Rating of Opponents
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

Write CSV

Code to export final dataframe to CSV

write.csv(chess_data_output,"chess_data_output.csv", row.names = FALSE)