DATA607 Week 4 Project 1

Kleber Perez

Chess Tournament Summary

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 want to first click the below links.
Learn RMarkdown CUNY SPS DATA607 Project 1

1
Data Import

First, we load the library to import the text file.
Next, we will be using 'stringr' package 

library(stringr)
library(knitr)
library(kableExtra)

# tournamentinfo <- readLines("https://raw.githubusercontent.com/kleberperez1/CUNY-SPS-Data607-Project1/master/tournamentinfo.txt", header=F)
tournamentinfo <- read.csv(paste0("C:/Users/Kleber/Documents/MSDS2019/DATA607/Week4/Project1/tournamentinfo.txt"), header=F)
head(tournamentinfo)

##                                                                                           V1
## 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    |

tail(tournamentinfo)

##                                                                                            V1
## 191    63 | THOMAS JOSEPH HOSMER            |1.0  |L   2|L  48|D  49|L  43|L  45|H    |U    |
## 192    MI | 15057092 / R: 1175   ->1125     |     |W    |B    |W    |B    |B    |     |     |
## 193 -----------------------------------------------------------------------------------------
## 194    64 | BEN LI                          |1.0  |L  22|D  30|L  31|D  49|L  46|L  42|L  54|
## 195    MI | 15006561 / R: 1163   ->1112     |     |B    |W    |W    |B    |W    |B    |B    |
## 196 -----------------------------------------------------------------------------------------

 

2
Get the information
The first line contains data begins with a number of one or two digits, and is the only line that has this pattern.
The second line begins with a pair of upper case letters, and it is the line that follows this pattern. 

tournamentinfo <- tournamentinfo[-c(1:4),]
head(tournamentinfo)

## [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] -----------------------------------------------------------------------------------------
## 131 Levels: ----------------------------------------------------------------------------------------- ...

With the dataframe in place, let's build another data frame containing the data, along with the patterns for eacth participant. 

playerInfo <- tournamentinfo[seq(1, length(tournamentinfo), 3)]
ratingInfo <- tournamentinfo[seq(2, length(tournamentinfo), 3)]

 

3
Extracting & Transform

Players Name: Consist of separate uppercase letters and hyphens. 

pairNo <- as.integer(str_extract(playerInfo, "\\d+"))
Name <- str_trim(str_extract(playerInfo, "(\\w+\\s){2,3}"))
Region <- str_extract(ratingInfo, "\\w+")
Points <- as.numeric(str_extract(playerInfo, "\\d+\\.\\d+"))
Rating <- as.integer(str_extract(str_extract(ratingInfo, "[^\\d]\\d{3,4}[^\\d]"), "\\d+"))
Opponents <- str_extract_all(str_extract_all(playerInfo, "\\d+\\|"), "\\d+")

## Warning in stri_extract_all_regex(string, pattern, simplify = simplify, :
## argument is not an atomic vector; coercing

Won <- str_count(playerInfo, "\\Q|W  \\E")
Loose <- str_count(playerInfo, "\\Q|L  \\E")
Draw <- str_count(playerInfo, "\\Q|D  \\E")

 

4
Rating
Unique pattern: any two digits followed by a "|" on line 1. 

mRating <- length(playerInfo)
for (i in 1:length(playerInfo)) {
  mRating[i] <- round(mean(Rating[as.numeric(unlist(Opponents[pairNo[i]]))]), digits = 0)
}
opData <- data.frame(Name, Region, Points, Rating, mRating, Won, Loose, Draw);

 

5
Show Data 
colnames(opData) <- c("Player's Name", "Player's State", "Total Number of Points", "Player's Pre-Rating", " Average Pre Chess Rating of Opponents", "Won", "Lost", "Draw")
kable(data.frame(opData))
Player.s.Name Player.s.State Total.Number.of.Points Player.s.Pre.Rating X.Average.Pre.Chess.Rating.of.Opponents Won Lost Draw
GARY HUA ON 6.0 1794 1605 5 0 2
DAKSHESH DARURI MI 6.0 1553 1469 6 1 0
ADITYA BAJAJ MI 6.0 1384 1564 6 1 0
PATRICK H SCHILLING MI 5.5 1716 1574 4 0 3
HANSHI ZUO MI 5.5 1655 1501 4 0 3
HANSEN SONG OH 5.0 1686 1519 4 1 2
GARY DEE SWATHELL MI 5.0 1649 1372 5 2 0
EZEKIEL HOUGHTON MI 5.0 1641 1468 5 2 0
STEFANO LEE ON 5.0 1411 1523 5 2 0
ANVIT RAO MI 5.0 1365 1554 4 1 2
CAMERON WILLIAM MC MI 4.5 1712 1468 4 2 1
KENNETH J TACK MI 4.5 1663 1506 3 1 2
TORRANCE HENRY JR MI 4.5 1666 1498 4 2 1
BRADLEY SHAW MI 4.5 1610 1515 4 2 1
ZACHARY JAMES HOUGHTON MI 4.5 1220 1484 4 2 1
MIKE NIKITIN MI 4.0 1604 1386 3 1 1
RONALD GRZEGORCZYK MI 4.0 1629 1499 4 3 0
DAVID SUNDEEN MI 4.0 1600 1480 4 3 0
DIPANKAR ROY MI 4.0 1564 1426 3 2 2
JASON ZHENG MI 4.0 1595 1411 4 3 0
DINH DANG BUI ON 4.0 1563 1470 4 3 0
EUGENE L MCCLURE MI 4.0 1555 1300 3 2 1
ALAN BUI ON 4.0 1363 1214 4 3 0
MICHAEL R ALDRICH MI 4.0 1229 1357 4 3 0
LOREN SCHWIEBERT MI 3.5 1745 1363 3 3 1
MAX ZHU ON 3.5 1579 1507 3 3 1
GAURAV GIDWANI MI 3.5 1552 1222 3 2 1
SOFIA ADINA MI 3.5 1507 1522 2 2 3
CHIEDOZIE OKORIE MI 3.5 1602 1314 3 2 1
GEORGE AVERY JONES ON 3.5 1522 1144 3 3 1
RISHI SHETTY MI 3.5 1494 1260 3 3 1
JOSHUA PHILIP MATHEWS ON 3.5 1441 1379 3 3 1
JADE GE MI 3.5 1449 1277 3 3 1
MICHAEL JEFFERY THOMAS MI 3.5 1399 1375 3 3 1
JOSHUA DAVID LEE MI 3.5 1438 1150 3 3 1
SIDDHARTH JHA MI 3.5 1355 1388 2 2 2
AMIYATOSH PWNANANDAM MI 3.5 980 1385 2 3 0
BRIAN LIU MI 3.0 1423 1539 2 3 1
JOEL R HENDON MI 3.0 1436 1430 3 4 0
FOREST ZHANG MI 3.0 1348 1391 3 4 0
KYLE WILLIAM MURPHY MI 3.0 1403 1248 2 2 0
JARED GE MI 3.0 1332 1150 2 3 2
ROBERT GLEN VASEY MI 3.0 1283 1107 3 4 0
JUSTIN D SCHILLING MI 3.0 1199 1327 2 4 0
DEREK YAN MI 3.0 1242 1152 2 3 2
JACOB ALEXANDER LAVALLEY MI 3.0 377 1358 3 4 0
ERIC WRIGHT MI 2.5 1362 1392 2 4 1
DANIEL KHAIN MI 2.5 1382 1356 1 3 1
MICHAEL J MARTIN MI 2.5 1291 1286 1 2 2
SHIVAM JHA MI 2.5 1056 1296 2 4 0
TEJAS AYYAGARI MI 2.5 1011 1356 2 4 1
ETHAN GUO MI 2.5 935 1495 1 3 3
JOSE C YBARRA MI 2.0 1393 1345 1 2 0
LARRY HODGE MI 2.0 1270 1206 1 5 0
ALEX KONG MI 2.0 1186 1406 0 4 2
MARISA RICCI MI 2.0 1153 1414 1 4 0
MICHAEL LU MI 2.0 1092 1363 1 5 0
VIRAJ MOHILE MI 2.0 917 1391 1 5 0
SEAN M MC MI 2.0 853 1319 1 5 0
JULIA SHEN MI 1.5 967 1330 0 3 2
JEZZEL FARKAS ON 1.5 955 1327 1 5 1
ASHWIN BALAJI MI 1.0 1530 1186 1 0 0
THOMAS JOSEPH HOSMER MI 1.0 1175 1350 0 4 1
BEN LI MI 1.0 1163 1263 0 5 2

 

6
Create CSV
Create CSV file chessInfo.csv in the working directory. 

write.csv(opData, file = "chessCSV.csv")