1 Project

1.1 Load Following Libraries

  1. stringr
  2. dplyr
  3. reshape2
  4. htmlTable

1.2 Download The Text File From Github

# Download File
#--------------
# https://stackoverflow.com/questions/23028760/download-a-file-from-https-using-download-file

url<-"https://raw.githubusercontent.com/jameskuruvilla/DATA607/master/tournament.txt"

r_file<- "tournament.txt"

downloader::download(url, r_file)

# Read text File into vector
#---------------------------
#https://stackoverflow.com/questions/23001548/dealing-with-readlines-function-in-r


v_conn <-file(r_file,open="r")

tournament.raw <- readLines(v_conn, warn = FALSE)

close(v_conn)

1.3 Replace ‘->’ with ‘>>’ and Replace ‘-’ with “” and then cleanup empty lines and 2 heading lines

#use gsub() to replace all the text you don't want with an empty string.
#-----------------------------------------------------------------------
#https://stackoverflow.com/questions/13529360/replace-text-within-parenthesis-in-r

tournament <- gsub("-{3,}", "",gsub("->", ">>",tournament.raw))

# Following code also will work the same way
#tournament <- str_replace_all(str_replace_all(tournament.raw, "->", ">>"), "-{3,}", "")

tournament <- tournament[tournament != ""] # Remove the emtpy lines
tournament <- tournament[-(1:2)]           # Remove the First 2 lines as those are headings

head(tournament)
## [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] "    2 | DAKSHESH DARURI                 |6.0  |W  63|W  58|L   4|W  17|W  16|W  20|W   7|"
## [4] "   MI | 14598900 / R: 1553   >>1663     |N:2  |B    |W    |B    |W    |B    |W    |B    |"
## [5] "    3 | ADITYA BAJAJ                    |6.0  |L   8|W  61|W  25|W  21|W  11|W  13|W  12|"
## [6] "   MI | 14959604 / R: 1384   >>1640     |N:2  |W    |B    |W    |B    |W    |B    |W    |"

1.4 Split the 2 lines of a player information into 2 vectors.

# =====================================
# Str_sub OR substr OR substring can be used to cut the specific character set from the strng 
# https://www.rdocumentation.org/packages/stringr/versions/1.1.0/topics/str_sub
# http://rfunction.com/archives/1692
# =====================================

# extract only the lines starts with a digit
tournament.fmt1<- tournament[str_detect(substr(tournament, 1, 6), "[0-9]")]   

# extract only the lines starts with an alphabet
tournament.fmt2 <- tournament[str_detect(str_sub(tournament, 1, 6), "[A-Z]{2,2}")]

1.5 Parse both vectors (lines) into two dataframes and combine those by columns using cbind.

# Extract column values from these 2 vectors.
fmt1.df <- data.frame(   Player_num   = as.numeric(str_sub(tournament.fmt1, 1,  6)),
                         Player_name  = str_trim(str_sub(tournament.fmt1, 8,  40), side="both"),
                         Total_pts    = as.numeric(str_sub(tournament.fmt1, 42, 46)),
                         Round1       = str_sub(tournament.fmt1, 48, 52),
                         Round2       = str_sub(tournament.fmt1, 54, 58),
                         Round3       = str_sub(tournament.fmt1, 60, 64),
                         Round4       = str_sub(tournament.fmt1, 66, 70),
                         Round5       = str_sub(tournament.fmt1, 72, 76),
                         Round6       = str_sub(tournament.fmt1, 78, 82),
                         Round7       = str_sub(tournament.fmt1, 84, 88), stringsAsFactors=FALSE)

head(fmt1.df)
fmt2.df <- data.frame(   Player_state  = str_trim(str_sub(tournament.fmt2, 1,  6), side="both"),
                 Uscf_id       = str_extract(str_sub(tournament.fmt2, 8,  40), "\\d+"),
                 Pre_rating    = as.numeric(str_extract(str_sub(tournament.fmt2, 8,  40), "(?<=R: ).\\d+(?=)")),
                 Post_rating   = as.numeric(str_extract(str_sub(tournament.fmt2, 8,  40), "(?<=>>).\\d+(?=)")),
                 stringsAsFactors=FALSE)
head(fmt2.df)
# Combine both data frames by columns
# https://stat.ethz.ch/R-manual/R-devel/library/methods/html/cbind2.html 
tournament.df <-cbind(fmt1.df, fmt2.df)

head(tournament.df)

1.6 Steps to arrive at the final result

# Select all columns except round columns
player_df <- select(tournament.df, Player_num:Total_pts, Player_state:Post_rating)  #Select requires the dplyr package

# Select only player number and all the rounds the player played
rounds <- tournament.df %>% select(Player_num, Round1:Round7)

head(rounds)
# Following link says how melt works 
# https://tgmstat.wordpress.com/2013/10/31/reshape-and-aggregate-data-with-the-r-package-reshape2/

melt_rounds <- rounds%>% melt(id.var=c("Player_num"), value.name="Result_opp")

head(melt_rounds)
mut_melt_rounds <- 
    melt_rounds%>% #Add the columns round, Result and opp_num (opponent Number)
    mutate(Round = as.numeric(gsub("Round", "",variable)), # Replace "round" with "" in the column 'variable'
    Result  = str_extract(Result_opp, "^\\w+"),            # Extract the begginning word
    Opp_num = as.numeric(str_extract(Result_opp, "\\d+$")) # Extract the ending Digits.
                          ) 
head(mut_melt_rounds)
#sel_mut_melt_rounds <- mut_melt_rounds %>% select(c(Player_num,Round,Result,Opp_num)) # 

sel_mut_melt_rounds <- mut_melt_rounds %>% select(-c(variable, Result_opp)) # Remove the columns 'variable' and 'Result_opp'

head(sel_mut_melt_rounds);head(player_df )
# Join sel_mut_melt_rounds and player_df on op_num and Payer_num 
# Following Link gives more information on Joins
# http://dplyr.tidyverse.org/reference/join.html 

join_sel_mut_melt_rds <- sel_mut_melt_rounds %>% 
                          inner_join(select(player_df, Player_num, Pre_rating,Post_rating), by = c("Opp_num" = "Player_num"))

head(join_sel_mut_melt_rds)
sel_p <- join_sel_mut_melt_rds %>% select(Player_num, Round, Result, Opp_num, Pre_rating)

head(sel_p)
sort_sel_p <- sel_p %>% arrange(Player_num, Round) ;head(sort_sel_p)
names(sort_sel_p)[names(sort_sel_p) == "Pre_rating"] <- "Opp_pre_rating" ;head(sort_sel_p)

1.7 Aggreate to Find the Average pre-rating of Opponents

player.opp_avg_rating <- sort_sel_p%>%group_by(Player_num) %>% summarise(Opp_avg_pre_rating = round(mean(Opp_pre_rating)))

player_df <- player.opp_avg_rating %>% inner_join(player_df, by="Player_num")

player_df_final <- player_df %>%  select(Player_name, Player_state, Total_pts,Pre_rating, Opp_avg_pre_rating)

2 Result : Chess Player Details

htmlTable(player_df_final)
Player_name Player_state Total_pts Pre_rating Opp_avg_pre_rating
1 GARY HUA ON 6 1794 1605
2 DAKSHESH DARURI MI 6 1553 1469
3 ADITYA BAJAJ MI 6 1384 1564
4 PATRICK H SCHILLING MI 5.5 1716 1574
5 HANSHI ZUO MI 5.5 1655 1501
6 HANSEN SONG OH 5 1686 1519
7 GARY DEE SWATHELL MI 5 1649 1372
8 EZEKIEL HOUGHTON MI 5 1641 1468
9 STEFANO LEE ON 5 1411 1523
10 ANVIT RAO MI 5 1365 1554
11 CAMERON WILLIAM MC LEMAN 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 1604 1386
17 RONALD GRZEGORCZYK MI 4 1629 1499
18 DAVID SUNDEEN MI 4 1600 1480
19 DIPANKAR ROY MI 4 1564 1426
20 JASON ZHENG MI 4 1595 1411
21 DINH DANG BUI ON 4 1563 1470
22 EUGENE L MCCLURE MI 4 1555 1300
23 ALAN BUI ON 4 1363 1214
24 MICHAEL R ALDRICH MI 4 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 STANESCU-BELLU 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 1423 1539
39 JOEL R HENDON MI 3 1436 1430
40 FOREST ZHANG MI 3 1348 1391
41 KYLE WILLIAM MURPHY MI 3 1403 1248
42 JARED GE MI 3 1332 1150
43 ROBERT GLEN VASEY MI 3 1283 1107
44 JUSTIN D SCHILLING MI 3 1199 1327
45 DEREK YAN MI 3 1242 1152
46 JACOB ALEXANDER LAVALLEY MI 3 377 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 1393 1345
54 LARRY HODGE MI 2 1270 1206
55 ALEX KONG MI 2 1186 1406
56 MARISA RICCI MI 2 1153 1414
57 MICHAEL LU MI 2 1092 1363
58 VIRAJ MOHILE MI 2 917 1391
59 SEAN M MC CORMICK MI 2 853 1319
60 JULIA SHEN MI 1.5 967 1330
61 JEZZEL FARKAS ON 1.5 955 1327
62 ASHWIN BALAJI MI 1 1530 1186
63 THOMAS JOSEPH HOSMER MI 1 1175 1350
64 BEN LI MI 1 1163 1263

The table is exported into a file ’CHESS PLAYERS.csv using following command into the local drive and then loaded into GitHub. The following link gives the output from GitHub.

write.csv(player_df_final, "CHESS PLAYERS.csv", row.names=FALSE)