library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(stringr)The code below is the original code with the results from the book:
strings <- c("12 Jun 2002", " 8 September 2004 ", "22-July-2009 ", "01 01 2001", "date",
"02.06.2000",
"xxx-yyy-zzzz", "$2,600")
dates <- "([0-9]{1,2})[- .]([a-zA-Z]+)[- .]([0-9]{4})"
str_detect(strings, dates)## [1] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSEThe above results were not capturing the other two dates in the string (example from “Handling and processing strings in R”, p. 80) The following changes were made to capture other styles of dates:
dates <- "([0-9]{1,2})[- .]\\s?([[:alnum:]]+)\\s?[- .]([0-9]{4})"
str_detect(strings, dates)## [1] TRUE TRUE TRUE TRUE FALSE TRUE FALSE FALSEAn example to extract all the digits from a string:
x <- "this sentence has 5 words, and 0 animal names"Note that the code only returns the first digit with this str_extract:
y <- str_extract(x, "\\d")
y## [1] "5"Note that the code now returns all the digits with the function str_extract_all:
y <- str_extract_all(x, "\\d")
y## [[1]]
## [1] "5" "0"Notes: This project presented various challenges. The first one was to decide how to read the text files. I had to consult various resources and some previous solutions to make the conclusion that the required data is in a line format so the best format would be to read it as lines (below).
A quick look at the lines below will reveal that the first few lines are irrelevant and the meaningful data starts from line 5.
chess_results <- readLines("/Users/rmirza/Documents/DATA607/tournamentinfo.txt")## Warning in readLines("/Users/rmirza/Documents/DATA607/tournamentinfo.txt"):
## incomplete final line found on '/Users/rmirza/Documents/DATA607/
## tournamentinfo.txt'head(chess_results, 10)## [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 |"
## [7] "-----------------------------------------------------------------------------------------"
## [8] " 2 | DAKSHESH DARURI |6.0 |W 63|W 58|L 4|W 17|W 16|W 20|W 7|"
## [9] " MI | 14598900 / R: 1553 ->1663 |N:2 |B |W |B |W |B |W |B |"
## [10] "-----------------------------------------------------------------------------------------"The following line was to count the number of rows in the data which turned out be 196.
n_rows <- length(chess_results)
n_rows## [1] 196Upon further consultation from the online sources it seemed reasonable to slice the data based on the information in the rows and start from the rows that have the meaningful information in them. The rows with that had the players’ numbers, name, and other information started from row 5 and repeated as every third row after.
row_names <- chess_results[seq(5, n_rows, 3)]
head(row_names, 10)## [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|"
## [7] " 7 | GARY DEE SWATHELL |5.0 |W 57|W 46|W 13|W 11|L 1|W 9|L 2|"
## [8] " 8 | EZEKIEL HOUGHTON |5.0 |W 3|W 32|L 14|L 9|W 47|W 28|W 19|"
## [9] " 9 | STEFANO LEE |5.0 |W 25|L 18|W 59|W 8|W 26|L 7|W 20|"
## [10] " 10 | ANVIT RAO |5.0 |D 16|L 19|W 55|W 31|D 6|W 25|W 18|"An interesting find was to keep checking for the length of the rows to ascertain the data matching.
length(row_names)## [1] 64The rows with that had the players’ states, and other information started from row 6 and repeated as every third row after.
row_states <- chess_results[seq(6, n_rows, 3)]
head(row_states, 10)## [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 |"
## [7] " MI | 11146376 / R: 1649 ->1673 |N:3 |W |B |W |B |B |W |W |"
## [8] " MI | 15142253 / R: 1641P17->1657P24 |N:3 |B |W |B |W |B |W |W |"
## [9] " ON | 14954524 / R: 1411 ->1564 |N:2 |W |B |W |B |W |B |B |"
## [10] " MI | 14150362 / R: 1365 ->1544 |N:3 |W |W |B |B |W |B |W |"length(row_states)## [1] 64To extract the players’ numbers for matching in calculating average pre rating scores:
player_no <- as.integer(str_extract(row_names, "\\d+"))
head(player_no, 10)## [1] 1 2 3 4 5 6 7 8 9 10length(player_no)## [1] 64To extract the players’ names:
player_name <- as.character(str_trim (str_extract(row_names,
"(\\w+\\s){2,3}")))
head(player_name, 10)## [1] "GARY HUA" "DAKSHESH DARURI" "ADITYA BAJAJ"
## [4] "PATRICK H SCHILLING" "HANSHI ZUO" "HANSEN SONG"
## [7] "GARY DEE SWATHELL" "EZEKIEL HOUGHTON" "STEFANO LEE"
## [10] "ANVIT RAO"length(player_name)## [1] 64To extract the players’ points:
player_points <- as.numeric(str_extract(row_names,
"\\d+\\.\\d+"))
head(player_points, 10)## [1] 6.0 6.0 6.0 5.5 5.5 5.0 5.0 5.0 5.0 5.0length(player_points)## [1] 64To extract the player numbers of the opponents:
player_opponents <- str_extract_all(str_extract_all(row_names,
"\\d+\\|"), "\\d+")## Warning in stri_extract_all_regex(string, pattern, simplify = simplify, :
## argument is not an atomic vector; coercinghead(player_opponents, 5)## [[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"length(player_opponents)## [1] 64To extract the players’ states:
player_state <- as.character(str_extract(row_states, "\\w+"))
head(player_state, 5)## [1] "ON" "MI" "MI" "MI" "MI"length(player_state)## [1] 64To extract the players’ ratings before the tournament:
player_prerating <- as.integer(str_extract(str_extract
(row_states,
"[^\\d]\\d{3,4}[^\\d]"),
"\\d+"))
head(player_prerating, 5)## [1] 1794 1553 1384 1716 1655length(player_prerating)## [1] 64The following code was written to calculate the average pre chess ratings of the players as asked in the question. Since there were 64 players, it was important to ensure that there were 64 iterations for the for loop and it is saved as a rounded number.
avg_opp_scores <- length(player_no)
for (i in 1:(length(player_no))) {
avg_opp_scores[i] <-
round(mean(player_prerating[as.numeric(unlist
(player_opponents
[player_no[i]]))]))
}A data frame was created to capture the results:
df <- data.frame(player_name,player_state,
player_points,player_prerating,avg_opp_scores)
head(df, 5)length(df)## [1] 5Updated column names to match the requirements of the project:
colnames(df) <- c("Player's Name","Player's State",
"Total Number of Points", "Player's Pre-Rating",
"Average Pre Chess Rating of Opponents")head(df, 5)Finally, a csv file was created as per the requirements:
write.csv(df, "Chess_results_Project1.csv", row.names=FALSE)