Approach This assignment is asking for a R markdown file that can process the given data set in text file: player’s name, state, total points, pre-rating, average pre-rating of opponents and create a csv file to record the result.
Read and parse the data might be an challenge. Given text file is formated two lines for each player, separate by dash lines and vertical dash lines, so we need to remove those separator lines before process each player’s data. Will need to extract name, total points, state and rating, pre-rating, opponent numbers.
To calculate average opponent rating, a function will need be able to lockup opponent’s stats through opponent number. In the end write.csv function can be used to convert result in csv deliverable.
Load and Read Data A sample of data set is saved in github repository. Grep() function can be used to identify where separator dash lines are, which can be removed once being identified. Similary we can remove header, so all the info left are the ones we need.
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.6
## ✔ forcats 1.0.1 ✔ stringr 1.6.0
## ✔ ggplot2 4.0.1 ✔ tibble 3.3.0
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.2
## ✔ purrr 1.2.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(stringr)
file_path <- "tournamentinfo.txt"
data <- readLines(file_path, warn = FALSE)
#search pattern inside the txt file locate separator - dash lines only leave info data.
separator_indices <- grep("^-+", data)
data_lines <- data[-separator_indices]
# Remove potential empty lines
data_lines <- data_lines[data_lines != ""]
data_lines <- trimws(data_lines)
# use grep function again to locate header
header_indices <- grep("Pair|Player Name|USCF ID|Num", data_lines, ignore.case = TRUE)
data_lines <- data_lines[-header_indices]
head(data_lines,6)## [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 |"Create data frame to save all opponents. Since each player are referenced by their player numbers, which can be used to locate pregame rating info. Player info such as name, states, opponment numbers can be extract and saved into player data frame.
players <- data.frame()
# parsing in pairs, 2 lines each
for (i in seq(1, length(data_lines), by = 2)) {
if (i + 1 <= length(data_lines)) {
line1 <- data_lines[i] # Player info
line2 <- data_lines[i + 1] # State/Rating
# first element - player ID
parts1 <- strsplit(line1, "\\|")[[1]]
parts2 <- strsplit(line2, "\\|")[[1]]
player_name <- trimws(parts1[2])
total_points <- trimws(parts1[3])
# opponent numbers
opponents <- c()
for (j in 4:length(parts1)) {
result <- trimws(parts1[j])
if (result == "" || result == " ") {
next
}
opp_num <- str_extract(result, "\\d+")
if (!is.na(opp_num)) {
opponents <- c(opponents, as.numeric(opp_num))
}
}
# state
state_info <- trimws(parts2[1])
state <- substr(state_info, 1, 2)
# pre-rating
rating_text <- parts2[2]
pattern <- "R:\\s*(\\d+)"
match <- regexpr(pattern, rating_text)
if (match != -1) {
pre_rating <- as.numeric(regmatches(rating_text, regexec(pattern, rating_text))[[1]][2])
} else {
pre_rating <- NA
}
player_data <- data.frame(
Name = player_name,
State = state,
Points = as.numeric(total_points),
PreRating = pre_rating,
stringsAsFactors = FALSE
)
#save all opponents to calculate average pre-game rating. adding rows
player_data$Opponents <- list(opponents)
players <- rbind(players, player_data)
}
}Player data frame has player’s name and all parsed opponent numbers. We still need to get opponent pre-ratings and calculate average. Few functions will be created, to get opponent rating, get average, and sapply() can be used to apply all created function to each player. This will provide us with final result.
# Function to calculate average opponent rating, pair player number to prerating
rating_lookup <- setNames(players$PreRating, 1:nrow(players))
calc_avg_opponent_rating <- function(opponent_numbers, rating_lookup) {
if (length(opponent_numbers) == 0) {
return(NA)
}
# ratings for opponents
opponent_ratings <- rating_lookup[as.character(opponent_numbers)]
# Remove NA values
opponent_ratings <- opponent_ratings[!is.na(opponent_ratings)]
#get average
if (length(opponent_ratings) > 0) {
return(mean(opponent_ratings))
} else {
return(NA)
}
}
# sapply function to apply all
players$AvgOppRating <- sapply(players$Opponents,
calc_avg_opponent_rating,
rating_lookup = rating_lookup)
# Round to whole
players$AvgOppRating <- round(players$AvgOppRating, 0)
final_data <- players[, c("Name", "State", "Points", "PreRating", "AvgOppRating")]
# sprintf to format show one decimal place
final_data$Points <- sprintf("%.1f", final_data$Points)Let’s do some verification, first I used Gary Hua, which has info exactly matches with the number from assignment. I then did the verification for Mike Nikitin, with stats as follow. I choose Mike because he had 2 games he didn’t play, and few of his opponent had pre-rating that include “P” which we should ignore and only use rating before P, which make Mike’s case more challenging. Result below correctly listed his opponents and average opponent score. Everything looks good, next step would be save all results into csv file.
16 | MIKE NIKITIN |4.0 |D 10|W 15|H |W 39|L 2|W 36|U | MI | 10295068 / R: 1604 ->1613 |N:3 |B |W | |B |W |B | |
# Verify Gary Hua
gary_hua <- final_data[grepl("GARY HUA", final_data$Name, ignore.case = TRUE), ]
if (nrow(gary_hua) > 0) {
gary_hua <- gary_hua[1, ]
gary_index <- grep("^GARY HUA$", players$Name, ignore.case = TRUE)[1]
if (!is.na(gary_index)) {
# Extract opponents
gary_opponents <- players$Opponents[[gary_index]]
opp_ratings <- c()
for (opp in gary_opponents) {
opp_rating <- rating_lookup[as.character(opp)]
if (is.character(opp_rating)) {
opp_rating <- as.numeric(gsub("P.*$", "", opp_rating))
}
opp_ratings <- c(opp_ratings, opp_rating)
}
avg_calc <- mean(opp_ratings)
avg_rounded <- round(avg_calc, 0)
cat("PLAYER INFORMATION:\n",
" Name: ", gary_hua$Name, "\n",
" State: ", gary_hua$State, "\n",
" Points: ", gary_hua$Points, "\n",
" Pre-Rating: ", gary_hua$PreRating, "\n",
" Avg Opp Rating:", gary_hua$AvgOppRating, "\n\n", sep="")
}
}## PLAYER INFORMATION:
## Name: GARY HUA
## State: ON
## Points: 6.0
## Pre-Rating: 1794
## Avg Opp Rating:1605mike <- final_data[grepl("MIKE NIKITIN", final_data$Name, ignore.case = TRUE), ]
if (nrow(mike) > 0) {
mike <- mike[1, ]
mike_index <- grep("MIKE NIKITIN", players$Name, ignore.case = TRUE)[1]
if (!is.na(mike_index)) {
mike_opponents <- players$Opponents[[mike_index]]
opp_ratings <- c()
for (opp in mike_opponents) {
opp_rating <- rating_lookup[as.character(opp)]
if (is.character(opp_rating)) {
opp_rating <- as.numeric(gsub("P.*$", "", opp_rating))
}
opp_ratings <- c(opp_ratings, opp_rating)
}
avg_calc <- mean(opp_ratings)
avg_rounded <- round(avg_calc, 0)
cat("PLAYER INFORMATION:\n",
" Name: ", mike$Name, "\n",
" State: ", mike$State, "\n",
" Points: ", mike$Points, "\n",
" Pre-Rating: ", mike$PreRating, "\n",
" Avg Opp Rating: ", mike$AvgOppRating, "\n\n", sep="")
cat("OPPONENT INFORMATION:\n",
" Opponents played: ", paste(mike_opponents, collapse = ", "), "\n",
" Opponent ratings: ", paste(opp_ratings, collapse = ", "), "\n\n", sep="")
}
}## PLAYER INFORMATION:
## Name: MIKE NIKITIN
## State: MI
## Points: 4.0
## Pre-Rating: 1604
## Avg Opp Rating: 1386
##
## OPPONENT INFORMATION:
## Opponents played: 10, 15, 39, 2, 36
## Opponent ratings: 1365, 1220, 1436, 1553, 1355write.csv(final_data, "chess_results.csv", row.names = FALSE, quote = TRUE)
print(head(final_data, 10))## Name State Points PreRating AvgOppRating
## 1 GARY HUA ON 6.0 1794 1605
## 2 DAKSHESH DARURI MI 6.0 1553 1469
## 3 ADITYA BAJAJ MI 6.0 1384 1564
## 4 PATRICK H SCHILLING MI 5.5 1716 1574
## 5 HANSHI ZUO MI 5.5 1655 1501
## 6 HANSEN SONG OH 5.0 1686 1519
## 7 GARY DEE SWATHELL MI 5.0 1649 1372
## 8 EZEKIEL HOUGHTON MI 5.0 1641 1468
## 9 STEFANO LEE ON 5.0 1411 1523
## 10 ANVIT RAO MI 5.0 1365 1554Summary I think overall some challenging parts in this assignment were to correctly extract player and opponent information, then look through appointment’ data to calculate average rating. There were some variation in the format as well such as not all players completed 7 games, and some opponents’ rating include P rating which we need to ignore.