Introduction

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.

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.

Objective

The objective of this project is import the text file and present it in a sophisticated way as the file is given in text form and is very unstructured

# Loading the necessary libraries
library(stringr)
library(knitr)
# Reading the file
chess <- read.delim("chessinfo.txt", header= FALSE, stringsAsFactors=FALSE)
head(chess)
##                                                                                           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    |

Data cleaning Process

chess2  <- chess[-c(1:4),]
chess2 %>% head()
## [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] "-----------------------------------------------------------------------------------------"
#  Check the file if it is character
class(chess2)
## [1] "character"
# There are lines after every two rows which needs to be eliminated. Let's do that 

name <- chess2[seq(from=1, to = length(chess2), by=3)]
ratings <- chess2[seq(from=2, to = length(chess2), by=3)]
# Extracting relevant data
new_id <- as.integer(str_extract(name, "\\d+"))
new_name <- str_trim(str_extract(name, "(\\w+\\s){2,3}"))
new_point <- as.numeric(str_extract(name, "\\d.\\d"))
new_rate <- as.integer(str_extract(str_extract(ratings, "\\D\\d{3,4}\\D"), "\\d+"))
new_state <- str_extract(ratings, "\\w\\w")
new_oppid <- str_extract_all(str_extract_all(name, "\\d+\\|"), "\\d+")
## Warning in stri_extract_all_regex(string, pattern, simplify = simplify, :
## argument is not an atomic vector; coercing
# Now let's convert the lists into df
op1 <- sapply(new_oppid, length)
max <- seq_len(max(op1))
op2 <- t(sapply(new_oppid, "[", i=max))
df2 <- as.data.frame(op2)


# Now let's convert these with ratings

v <- data.frame(new_id, new_rate)
df3 <- df2
df3[] <- v$new_rate[match(unlist(df3), v$new_id)]
head(df3)
##     V1   V2   V3   V4   V5   V6   V7
## 1 1436 1563 1600 1610 1649 1663 1716
## 2 1175  917 1716 1629 1604 1595 1649
## 3 1641  955 1745 1563 1712 1666 1663
## 4 1363 1507 1553 1579 1655 1564 1794
## 5 1242  980 1663 1666 1716 1610 1629
## 6 1399 1602 1712 1438 1365 1552 1563
# Now let's calculate the average to print the final table afterwards

df3$opponentavg <- round(apply(df3,1,mean, na.rm=TRUE))
head(df3)
##     V1   V2   V3   V4   V5   V6   V7 opponentavg
## 1 1436 1563 1600 1610 1649 1663 1716        1605
## 2 1175  917 1716 1629 1604 1595 1649        1469
## 3 1641  955 1745 1563 1712 1666 1663        1564
## 4 1363 1507 1553 1579 1655 1564 1794        1574
## 5 1242  980 1663 1666 1716 1610 1629        1501
## 6 1399 1602 1712 1438 1365 1552 1563        1519
# We are done with data cleaning, let's print the final table 
new_table <- data.frame(new_id, new_name, new_state, new_point, new_rate, df3$opponentavg)

# Table above is finalized but let's rename the column's name to make it more sophisticated

colnames(new_table) <- c("ID", "Player_Name", "State", "Point", "PreRating", "OpponentRating")
kable(new_table)
ID Player_Name State Point PreRating OpponentRating
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 1554
11 CAMERON WILLIAM MC MI 4.5 1712 1468
12 KENNETH J TACK MI 4.5 1663 1506
13 TORRANCE HENRY JR MI 4.5 1666 1498
14 BRADLEY SHAW MI 4.5 1610 1515
15 ZACHARY JAMES HOUGHTON MI 4.5 1220 1484
16 MIKE NIKITIN MI 4.0 1604 1386
17 RONALD GRZEGORCZYK MI 4.0 1629 1499
18 DAVID SUNDEEN MI 4.0 1600 1480
19 DIPANKAR ROY MI 4.0 1564 1426
20 JASON ZHENG MI 4.0 1595 1411
21 DINH DANG BUI ON 4.0 1563 1470
22 EUGENE L MCCLURE MI 4.0 1555 1300
23 ALAN BUI ON 4.0 1363 1214
24 MICHAEL R ALDRICH MI 4.0 1229 1357
25 LOREN SCHWIEBERT MI 3.5 1745 1363
26 MAX ZHU ON 3.5 1579 1507
27 GAURAV GIDWANI MI 3.5 1552 1222
28 SOFIA ADINA MI 3.5 1507 1522
29 CHIEDOZIE OKORIE MI 3.5 1602 1314
30 GEORGE AVERY JONES ON 3.5 1522 1144
31 RISHI SHETTY MI 3.5 1494 1260
32 JOSHUA PHILIP MATHEWS ON 3.5 1441 1379
33 JADE GE MI 3.5 1449 1277
34 MICHAEL JEFFERY THOMAS MI 3.5 1399 1375
35 JOSHUA DAVID LEE MI 3.5 1438 1150
36 SIDDHARTH JHA MI 3.5 1355 1388
37 AMIYATOSH PWNANANDAM MI 3.5 980 1385
38 BRIAN LIU MI 3.0 1423 1539
39 JOEL R HENDON MI 3.0 1436 1430
40 FOREST ZHANG MI 3.0 1348 1391
41 KYLE WILLIAM MURPHY MI 3.0 1403 1248
42 JARED GE MI 3.0 1332 1150
43 ROBERT GLEN VASEY MI 3.0 1283 1107
44 JUSTIN D SCHILLING MI 3.0 1199 1327
45 DEREK YAN MI 3.0 1242 1152
46 JACOB ALEXANDER LAVALLEY MI 3.0 377 1358
47 ERIC WRIGHT MI 2.5 1362 1392
48 DANIEL KHAIN MI 2.5 1382 1356
49 MICHAEL J MARTIN MI 2.5 1291 1286
50 SHIVAM JHA MI 2.5 1056 1296
51 TEJAS AYYAGARI MI 2.5 1011 1356
52 ETHAN GUO MI 2.5 935 1495
53 JOSE C YBARRA MI 2.0 1393 1345
54 LARRY HODGE MI 2.0 1270 1206
55 ALEX KONG MI 2.0 1186 1406
56 MARISA RICCI MI 2.0 1153 1414
57 MICHAEL LU MI 2.0 1092 1363
58 VIRAJ MOHILE MI 2.0 917 1391
59 SEAN M MC MI 2.0 853 1319
60 JULIA SHEN MI 1.5 967 1330
61 JEZZEL FARKAS ON 1.5 955 1327
62 ASHWIN BALAJI MI 1.0 1530 1186
63 THOMAS JOSEPH HOSMER MI 1.0 1175 1350
64 BEN LI MI 1.0 1163 1263 
# Convert the file into csv

write.table(new_table, file = "chessratings.csv", row.names=FALSE,sep = ",")

Analysis and Visualization

Player’s Pre Ratings compared to Opponent’s Average Ratings

There seems to be a slight positive association between each player’s ratings and the opponent’s average pre rating. However, the R square is too low for any significant association.

reg1<-lm(PreRating~ OpponentRating, data=new_table)
summary(reg1)
## 
## Call:
## lm(formula = PreRating ~ OpponentRating, data = new_table)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -988.58 -137.73   50.47  200.56  376.29 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)    514.7559   371.8226   1.384    0.171  
## OpponentRating   0.6265     0.2687   2.332    0.023 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 256.6 on 62 degrees of freedom
## Multiple R-squared:  0.08062,    Adjusted R-squared:  0.06579 
## F-statistic: 5.437 on 1 and 62 DF,  p-value: 0.02298
plot(new_table$`PreRating`, new_table$`OpponentRating`, xlim=c(800,1800), ylim=c(1000,1800), main="PreRating vs Oppononent avg Rating", xlab="Player PreRating", ylab="Opponent Average PreRating", abline(reg1))

Pre Ratings statistics by State

We can see that highest number of players in the dataset are from MI and the lowest number from OH. Players from OH have a much higher mean Pre-ratings score compared to the other two states.

boxplot(PreRating~State, data = new_table, xlab = "State",
   ylab = "Preratings by State", main = "Statewise Average Player Pre-ratings")