Introduction

After submitting Project 1, I was informed that my submission relied too heavily on Excel due to the fact that I completed the calculations in Excel. Fortunately I was given an opportunity to re-do Project 1 after getting the chance to take a look at other students’ completed work.

For this project, we are given a text file with chess tournament results where the information has some structure. We are responsible for creating an R Markdown file that generates a .csv with the following information for all the players:

Player’s Name
Player’s State
Total Number of Points
Player’s Pre-Rating
Average Pre-Chess Rating of Opponents

We need to calculate the Average Pre Chess Rating of Opponents by adding the pre-rating of each opponent and dividing it by the number of matches played.

Load libraries that are required:

‘stringr’: we will be using the ‘stringr’ package for manipulating the data

library(stringr)

Read in the text file:

chessinfo <- readLines('chessinfo.txt', warn = FALSE)  
#I get a warning message when this line runs so I added the warn component to eliminate the warning. 
head(chessinfo)

## [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    |"

Once the file has been loaded, we will start manipulating our data by extracting all the pertinent information such as player’s names, state and opponent information using str_extract_all().

chessinfo <- chessinfo[-c(1:4)]
l <- length(chessinfo)
row.one <- as.factor(chessinfo[seq(1,l,3)])
#we have to create a subset that contains the names

head(row.one)

## [1]     1 | GARY HUA                        |6.0  |W  39|W  21|W  18|W  14|W   7|D  12|D   4|
## [2]     2 | DAKSHESH DARURI                 |6.0  |W  63|W  58|L   4|W  17|W  16|W  20|W   7|
## [3]     3 | ADITYA BAJAJ                    |6.0  |L   8|W  61|W  25|W  21|W  11|W  13|W  12|
## [4]     4 | PATRICK H SCHILLING             |5.5  |W  23|D  28|W   2|W  26|D   5|W  19|D   1|
## [5]     5 | HANSHI ZUO                      |5.5  |W  45|W  37|D  12|D  13|D   4|W  14|W  17|
## [6]     6 | HANSEN SONG                     |5.0  |W  34|D  29|L  11|W  35|D  10|W  27|W  21|
## 64 Levels:     1 | GARY HUA                        |6.0  |W  39|W  21|W  18|W  14|W   7|D  12|D   4| ...

row.two <- as.factor(chessinfo[seq(2,l,3)])   
#this will contain all the alternate row information, basically just dividing up by every other row

head(row.two)

## [1]    ON | 15445895 / R: 1794   ->1817     |N:2  |W    |B    |W    |B    |W    |B    |W    |
## [2]    MI | 14598900 / R: 1553   ->1663     |N:2  |B    |W    |B    |W    |B    |W    |B    |
## [3]    MI | 14959604 / R: 1384   ->1640     |N:2  |W    |B    |W    |B    |W    |B    |W    |
## [4]    MI | 12616049 / R: 1716   ->1744     |N:2  |W    |B    |W    |B    |W    |B    |B    |
## [5]    MI | 14601533 / R: 1655   ->1690     |N:2  |B    |W    |B    |W    |B    |W    |B    |
## [6]    OH | 15055204 / R: 1686   ->1687     |N:3  |W    |B    |W    |B    |B    |W    |B    |
## 64 Levels:    MI | 10131499 / R: 1610   ->1618     |N:3  |W    |B    |W    |W    |B    |B    |W    | ...

player.name <- str_trim(str_extract(row.one, "(\\w+\\s){2,3}"))  
#this will extract all words with 2 or more letters, with a space and up to 3 words and then it str_trim will trim down to a new string.

head(player.name)

## [1] "GARY HUA"            "DAKSHESH DARURI"     "ADITYA BAJAJ"       
## [4] "PATRICK H SCHILLING" "HANSHI ZUO"          "HANSEN SONG"

scores <- as.double(unlist(str_extract_all(row.one, "[:digit:][.][:digit:]")))  
#this will extract all numbers in the format x.x and sets them as doubles 

head(scores)

## [1] 6.0 6.0 6.0 5.5 5.5 5.0

opponent <- str_extract_all(row.one, "[:digit:]+?\\|") #this is extract all digits followed by |
opponent <- str_extract_all(opponent, "\\d+")
head(opponent)

## [[1]]
## [1] "39" "21" "18" "14" "7"  "12" "4" 
## 
## [[2]]
## [1] "63" "58" "4"  "17" "16" "20" "7" 
## 
## [[3]]
## [1] "8"  "61" "25" "21" "11" "13" "12"
## 
## [[4]]
## [1] "23" "28" "2"  "26" "5"  "19" "1" 
## 
## [[5]]
## [1] "45" "37" "12" "13" "4"  "14" "17"
## 
## [[6]]
## [1] "34" "29" "11" "35" "10" "27" "21"

states <- str_extract(row.two, "\\w+")
head(states)

## [1] "ON" "MI" "MI" "MI" "MI" "OH"

pre.rating <- str_extract(row.two, "[^[:digit:]][:digit:]{1,4}[^[:digit:]]")
pre.rating <- str_extract(pre.rating, "\\d+")
#I initially tried code that would extract all four digit numbers with spaces before and after but that didn't take into account elements such as 955P11. This approach worked much better

head(pre.rating)

## [1] "1794" "1553" "1384" "1716" "1655" "1686"

Now that we have the names, states, scores, and pre-rating extracted, we need to calculated the average opponent pre-rating. We will use a loop to do this.

average.preRating <- 0

for (i in 1:length(player.name)){
  average.preRating[i] <- round(mean(as.numeric(pre.rating[as.numeric(unlist(opponent[i]))]), na.rm = TRUE, digits = 0))
}
head(average.preRating)

## [1] 1605 1469 1564 1574 1501 1519

Finally, I will combine all the parts into a data frame, rename the columns and convert to a table.

chess.output <- data.frame(player.name, states, scores, pre.rating, average.preRating)
names(chess.output) <- c("Player's Name","State", "Score", "Pre Rating","Ave.Opp.Pre Rating")
chess.output

##               Player's Name State Score Pre Rating Ave.Opp.Pre Rating
## 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

knitr::kable(chess.output)

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

Now that my data our data is neat and contains our necessary information, we can write a .csv file.

write.csv(chess.output, file = "chess_output.csv")

Project #1 Re-Do

Oluwakemi Omotunde

October 7, 2016

Introduction

After submitting Project 1, I was informed that my submission relied too heavily on Excel due to the fact that I completed the calculations in Excel. Fortunately I was given an opportunity to re-do Project 1 after getting the chance to take a look at other students’ completed work.

For this project, we are given a text file with chess tournament results where the information has some structure. We are responsible for creating an R Markdown file that generates a .csv with the following information for all the players:

We need to calculate the Average Pre Chess Rating of Opponents by adding the pre-rating of each opponent and dividing it by the number of matches played.

Load libraries that are required:

Read in the text file:

Once the file has been loaded, we will start manipulating our data by extracting all the pertinent information such as player’s names, state and opponent information using str_extract_all().

Now that we have the names, states, scores, and pre-rating extracted, we need to calculated the average opponent pre-rating. We will use a loop to do this.

Finally, I will combine all the parts into a data frame, rename the columns and convert to a table.

Now that my data our data is neat and contains our necessary information, we can write a .csv file.