Project 1

Chess Tournament

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.

Load the data

The chess tournament data is loaded from the .txt file. A sample of the data loaded is shown below.

The data can be accessed here

load_txt <- readLines('tournamentinfo.txt')

## Warning in readLines("tournamentinfo.txt"): incomplete final line found on
## 'tournamentinfo.txt'

head(load_txt)

## [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    |"

Cleaning the data

The data loaded has an unfriendly format so I did some cleaning on it to make it easy to work with.

t0 <- 0
t1 <- unname(str_locate_all(pattern = '\\|', load_txt[5])[[1]][1,1])
t2 <- unname(str_locate_all(pattern = '\\|', load_txt[5])[[1]][2,1])
t3 <- unname(str_locate_all(pattern = '\\|', load_txt[5])[[1]][3,1])
t4 <- max(nchar(load_txt))

s1 <- seq(5, 196, 3)   #generate a sequence for the first group
s2 <- seq(6, 196, 3)   #generate a sequence for the second group

g1 <- load_txt[s1]     #based on first sequence first group is created
g2 <- load_txt[s2]     #based on second sequence second group is created

Extracting required fields

As each field is extracted it is loaded into the dataframe.

name <- substr(g1, t1+1, t2-2)
name <- str_trim(name)
Player_Name <- str_to_title(name)                                  #Player's Name

s_raw <- substr(g2, t0, t1-1)
State <- str_trim(s_raw)                                           #Player's State

chess_data <- data.frame(Player_Name, State)

point <- substr(g1, t2+1, t3-1)                                    #Player's point total
chess_data$TotalPoints <- sprintf("%.1f", as.numeric(point))

pre <- substr(g2, t1+1, t2-1)
pre <- str_extract(pre, ': *\\d{2,}')                              #Player's pre-rating
chess_data$PreRating <- as.integer(str_extract(pre, '\\d{2,}'))

FInd Average Pre Rating of Oppenent

I have used a function to calculate the average opponent pre rating. It uses a nested for loop.

oppenent <- substr(g1, t3+1, t4)
oppenent <- str_extract_all(oppenent, '\\b\\d{1,}')
oppenent <- as.matrix(oppenent)

calculate <- function(z, p) {
    temp <- z[p]
    
    for (place in temp){
        rate <- 0
        c <- 0
        for(i in place) {
            c <- c + 1
            rate <- rate + chess_data$PreRating[as.numeric(i)]
        }
        rate <- round(rate / c)
        
    }
    return(rate)
}

chess_data$AvgOppPreRating <- apply(oppenent, 1, calculate)

View the final data

During my research I found this cool way to present the data where I can search for record I want. So trying it out here.

datatable(chess_data, extensions = 'Scroller', options = list(scrollY = 300, scroller = TRUE ))

Writing the final output to .csv file

To see the .csv file in github click here

write.csv(chess_data,"ChessTournament.csv")

Plotting

ggplot(chess_data, aes(PreRating, AvgOppPreRating)) + geom_point(aes(color=TotalPoints)) + geom_smooth(method="lm")

## `geom_smooth()` using formula 'y ~ x'