Introduction

Task

In this project, you’re given a text file with chess tournament results where the information has some structure. Your 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.

If you have questions about the meaning of the data or the results, please post them on the discussion forum. Data science, like chess, is a game of back and forth…

The chess rating system (invented by a Minnesota statistician named Arpad Elo) has been used in many other contexts, including assessing relative strength of employment candidates by human resource departments.

You may substitute another text file (or set of text files, or data scraped from web pages) of similar or greater complexity, and create your own assignment and solution. You may work in a small team. All of your code should be in an R markdown file (and published to rpubs.com); with your data accessible for the person running the script.

Overview

This project employs the use of data transformation techniques to make a messy text file a readable .csv file. The goal is to be able to pull important information, while leveraging patterns in a text file.

Procedure

Import Data

Read the data file from my github repository using the readLines() function.

# Read data file
chess.data <- readLines("https://raw.githubusercontent.com/SaneSky109/DATA607/main/Project1/tournamentinfo.txt")
## Warning in readLines("https://raw.githubusercontent.com/SaneSky109/DATA607/
## main/Project1/tournamentinfo.txt"): incomplete final line found on 'https://
## raw.githubusercontent.com/SaneSky109/DATA607/main/Project1/tournamentinfo.txt'
head(chess.data)
## [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    |"

Collecting the Desired Data

Acquire Player Name:

library(stringr)
# acquire player name
player.name <- unlist(str_extract_all(chess.data, "\\w+[A-Z] ?\\w+ \\w+")) 
player.name <- player.name[-1]
head(player.name)
## [1] "GARY HUA"            "DAKSHESH DARURI"     "ADITYA BAJAJ"       
## [4] "PATRICK H SCHILLING" "HANSHI ZUO"          "HANSEN SONG"

Acquire Player State:

I noticed that there were only three states in the dataset and they all had a space before the start of string. The following regular expression extracts that information.

# acquire player state

# there were only three states in the dataset and they all had a space before the start of string
player.state <- unlist(str_extract_all(chess.data, "\\sON | \\sMI | \\sOH"))
head(player.state)
## [1] " ON "  "  MI " "  MI " "  MI " "  MI " "  OH"

Acquire Total Number of Points:

To extract the total number of points a player recieved, I used a regular expression to only collect numbers that were separated by a period(.). The total points was the only number value that was separated by a period(.).

# acquire total number of points

# only number to include a . (ex. 6.0, 5.5, etc.)
player.total.points <- unlist(str_extract_all(chess.data, "\\d\\.\\d"))
head(player.total.points)
## [1] "6.0" "6.0" "6.0" "5.5" "5.5" "5.0"

Acquire Player Pre-Rating:

The Pre-Rating of a player was always after the string “R:”. To extract this metric, I collected the “R:” and the digits following, then proceeded by removing the “R:”.

# acquire player pre rating

# R: is always prior to pre rating
player.pre.rating <- unlist(str_extract_all(chess.data, "(R:\\s*)(\\d+)"))
# Remove the "R: " in the extracted string
player.pre.rating <- str_remove_all(player.pre.rating, "R: ")
head(player.pre.rating)
## [1] "1794" "1553" "1384" "1716" "1655" "1686"

Average Pre Chess Rating of Opponents:

There were multiple steps to calculate the average Pre Chess Rating of Opponents. First, I noticed that every three rows after row 5 stored the data related to opponents in a given round. I then extracted the opponent number in each round and used it in a for loop. The for loop calculated the average pre rating of opponents for each player.

# Average Pre Chess Rating of Opponents

# the data required to get the opponents per round is every three rows apart
new.data <- chess.data[seq(5, length(chess.data), 3)]

# player number
player.number <- as.integer(str_extract(new.data, "\\d+"))

# need to make player.pre.rating a numeric value to calculate mean
player.pre.rating <- as.numeric(player.pre.rating)

# extracts the opponent number in each round
opponent.in.each.round <- str_extract_all(str_extract_all(new.data, "\\d+\\|"), "\\d+")
## Warning in stri_extract_all_regex(string, pattern, simplify = simplify, :
## argument is not an atomic vector; coercing
# declare variables to be used in loop
n <- length(chess.data)
avg.pre.rating.of.opponents <- numeric(n / 3)

# calculates the avg pre rating of opponents for each player
for (i in 1:(n / 3)) { 
  avg.pre.rating.of.opponents[i] <- mean(player.pre.rating[as.numeric(unlist(opponent.in.each.round[player.number[i]]))]) 
}

# dropped last row because it was NA due to there only being 64 rows, not 65
avg.pre.rating.of.opponents <- avg.pre.rating.of.opponents[-65]
head(avg.pre.rating.of.opponents)
## [1] 1605.286 1469.286 1563.571 1573.571 1500.857 1518.714

Export to .csv

I made a new data frame with the variables collected previously and renamed the columns. I then proceeded to export the data as a .csv file.

# creating data frame with desired variables
cleaned.chess.data <- data.frame(player.name, player.state, player.total.points, player.pre.rating, avg.pre.rating.of.opponents)

# Creating column names
colnames(cleaned.chess.data) <- c("Players Name","Players State", "Total Points", "Players Pre-Rating", "Avg Pre Chess Rating of Opponents")

head(cleaned.chess.data)
##          Players Name Players State Total Points Players Pre-Rating
## 1            GARY HUA           ON           6.0               1794
## 2     DAKSHESH DARURI           MI           6.0               1553
## 3        ADITYA BAJAJ           MI           6.0               1384
## 4 PATRICK H SCHILLING           MI           5.5               1716
## 5          HANSHI ZUO           MI           5.5               1655
## 6         HANSEN SONG            OH          5.0               1686
##   Avg Pre Chess Rating of Opponents
## 1                          1605.286
## 2                          1469.286
## 3                          1563.571
## 4                          1573.571
## 5                          1500.857
## 6                          1518.714
write.csv(cleaned.chess.data, "EL_ChessTournament.csv", row.names=FALSE)