Project 1

Nick Oliver

Project 1

Load Libraries

library(RCurl)
library(stringr)
library(readr)
library(dplyr)
library(tidyr)
library(knitr)
library(kableExtra)

Load Raw Data

Using the RCurl library download the raw text data from my GitHub repository. Set the result as raw_data

file_url <- 'https://raw.githubusercontent.com/nolivercuny/data607/master/project1/tournamentinfo.txt'
raw_data <- getURL(file_url)

Processing

Converting raw text string to dataframe

Get rid of dashes

raw_data <- str_replace_all(raw_data,"-", "")

Split the data on newlines then on the pipe character. Newlines being the logical separator between the rows and the pipe character being the separator of the columns.

split_data<- raw_data %>% 
  str_split('\n', simplify = TRUE) %>% 
  str_split('\\|', simplify = TRUE)

Convert to a dataframe and use the filter function to remove empty rows

df <- as.data.frame(split_data) %>%
  filter(V1 != "", V1 != "\r")

Manipulate the dataframe

At this point we have a dataframe which contains all our data in individual rows but there are two rows representing a single player record. This is because the raw data was structured in such a way that a logical row was actually represented as two newline separated lines in the text file.

Break up the dataframe into two dataframes so they can be recombined as a single row. This is done by using the modulus operator to split into even and odd rows.

df_one <- df %>% filter(row_number() %% 2 == 1) 
df_two <- df %>% filter(row_number() %% 2 == 0)

Set the column names to the first row.

names(df_one) <- df_one[1,]
names(df_one) <- df_one[1,]

Merge the two dataframes back into a single data frame. I am also using the subset combined with the select function to drop two columns that contain no data.

data <- bind_cols(df_one,df_two) %>% 
  subset(select=-c(11,22))
## New names:
## * Round -> Round...4
## * Round -> Round...5
## * Round -> Round...6
## * Round -> Round...7
## * Round -> Round...8
## * ...

Drop the first row because it contained the column names and we no longer need that.

data <- data[-c(1),]

Split column 12 which contains the USCF Id and the Pre and Post ratings for the the player. Then split the pre and post ratings into separate columns as well.

data <- separate(data, 12, sep="/", into = c('USCF_ID','PRE_POST'))
data <- separate(data, 13, sep=">", into = c('Pre_Rating','Post_Rating'))

Parse the Pre rating column as a number so it can be used in subsequent calculation

data$Pre_Rating <- parse_number(data$Pre_Rating)

Computing Opponent’s Pre-Rating Mean

  1. Loop over every row.
  2. Select the “Rounds” columns (4 through 10).
  3. Because those contain the row number of the opponent. Parse out that value as a number using gsub to replace all non-numeric characters with no value.
  4. Then us as.numeric to cast the values as numbers to be used as indexes in the dataframe.
  5. Use the opponent index vector to select the opponents pre-ratings.
  6. Use the mean function to obtain the average opponent pre-rating with the na.rm argument set to TRUE in order to remove NA values where opponents were not part of the round for the player.
  7. Round the mean to the nearest whole value using the round function and add that value as a new column in the dataframe.
for (i in 1:nrow(data)){
  opponent_indexes <- as.numeric(gsub("[^0-9]","",data[i,4:10]))
  opponents_ratings<-data[opponent_indexes,13]
  rating_average <- mean(opponents_ratings, na.rm = TRUE)
  data$Mean_Opponent_Pre_Rating[i] <- round(rating_average)
}

Clean up for final output

  1. Select only relevant columns
  2. Remove the R: from the player’s pre-rating.
  3. Rename the columns
  4. Trim whitespace
  5. Reset the row numbers
final_output <- data[,c(2,3,11,13,23)]
final_output$Pre_Rating <- gsub("R:", "", final_output$Pre_Rating)
names(final_output) <- c('Player’s Name', 'Total Number of Points','Player’s State', 'Player’s Pre-Rating', 'Average Pre Chess Rating of Opponents')
final_output <- final_output %>% 
  mutate(across(where(is.character), str_trim))
rownames(final_output) <- NULL

Display the values as a table for double checking before outputing to a CSV

kable(final_output,caption="Players",digits = 2, format = "html", row.names = TRUE)
Players
Player’s Name Total Number of Points Player’s State Player’s Pre-Rating Average Pre Chess Rating of Opponents
1 GARY HUA 6.0 ON 1794 1605
2 DAKSHESH DARURI 6.0 MI 1553 1469
3 ADITYA BAJAJ 6.0 MI 1384 1564
4 PATRICK H SCHILLING 5.5 MI 1716 1574
5 HANSHI ZUO 5.5 MI 1655 1501
6 HANSEN SONG 5.0 OH 1686 1519
7 GARY DEE SWATHELL 5.0 MI 1649 1372
8 EZEKIEL HOUGHTON 5.0 MI 1641 1468
9 STEFANO LEE 5.0 ON 1411 1523
10 ANVIT RAO 5.0 MI 1365 1554
11 CAMERON WILLIAM MC LEMAN 4.5 MI 1712 1468
12 KENNETH J TACK 4.5 MI 1663 1506
13 TORRANCE HENRY JR 4.5 MI 1666 1498
14 BRADLEY SHAW 4.5 MI 1610 1515
15 ZACHARY JAMES HOUGHTON 4.5 MI 1220 1484
16 MIKE NIKITIN 4.0 MI 1604 1386
17 RONALD GRZEGORCZYK 4.0 MI 1629 1499
18 DAVID SUNDEEN 4.0 MI 1600 1480
19 DIPANKAR ROY 4.0 MI 1564 1426
20 JASON ZHENG 4.0 MI 1595 1411
21 DINH DANG BUI 4.0 ON 1563 1470
22 EUGENE L MCCLURE 4.0 MI 1555 1300
23 ALAN BUI 4.0 ON 1363 1214
24 MICHAEL R ALDRICH 4.0 MI 1229 1357
25 LOREN SCHWIEBERT 3.5 MI 1745 1363
26 MAX ZHU 3.5 ON 1579 1507
27 GAURAV GIDWANI 3.5 MI 1552 1222
28 SOFIA ADINA STANESCUBELLU 3.5 MI 1507 1522
29 CHIEDOZIE OKORIE 3.5 MI 1602 1314
30 GEORGE AVERY JONES 3.5 ON 1522 1144
31 RISHI SHETTY 3.5 MI 1494 1260
32 JOSHUA PHILIP MATHEWS 3.5 ON 1441 1379
33 JADE GE 3.5 MI 1449 1277
34 MICHAEL JEFFERY THOMAS 3.5 MI 1399 1375
35 JOSHUA DAVID LEE 3.5 MI 1438 1150
36 SIDDHARTH JHA 3.5 MI 1355 1388
37 AMIYATOSH PWNANANDAM 3.5 MI 980 1385
38 BRIAN LIU 3.0 MI 1423 1539
39 JOEL R HENDON 3.0 MI 1436 1430
40 FOREST ZHANG 3.0 MI 1348 1391
41 KYLE WILLIAM MURPHY 3.0 MI 1403 1248
42 JARED GE 3.0 MI 1332 1150
43 ROBERT GLEN VASEY 3.0 MI 1283 1107
44 JUSTIN D SCHILLING 3.0 MI 1199 1327
45 DEREK YAN 3.0 MI 1242 1152
46 JACOB ALEXANDER LAVALLEY 3.0 MI 377 1358
47 ERIC WRIGHT 2.5 MI 1362 1392
48 DANIEL KHAIN 2.5 MI 1382 1356
49 MICHAEL J MARTIN 2.5 MI 1291 1286
50 SHIVAM JHA 2.5 MI 1056 1296
51 TEJAS AYYAGARI 2.5 MI 1011 1356
52 ETHAN GUO 2.5 MI 935 1495
53 JOSE C YBARRA 2.0 MI 1393 1345
54 LARRY HODGE 2.0 MI 1270 1206
55 ALEX KONG 2.0 MI 1186 1406
56 MARISA RICCI 2.0 MI 1153 1414
57 MICHAEL LU 2.0 MI 1092 1363
58 VIRAJ MOHILE 2.0 MI 917 1391
59 SEAN M MC CORMICK 2.0 MI 853 1319
60 JULIA SHEN 1.5 MI 967 1330
61 JEZZEL FARKAS 1.5 ON 955 1327
62 ASHWIN BALAJI 1.0 MI 1530 1186
63 THOMAS JOSEPH HOSMER 1.0 MI 1175 1350
64 BEN LI 1.0 MI 1163 1263

Write out the results as CSV

Making an attempt at being agnostic about file systems by trying to get the working directory and using that as the location to write out the CSV.

working_directory <- getwd()
write_csv(final_output,paste(working_directory, "/chess_tournement_players.csv",sep=""))