DATA607_Project1
Niteen Kumar
February 18, 2018
- 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.
Data Acquisition
library(stringr)
library(tidyverse)## -- Attaching packages ----------------------------------------------------------------- tidyverse 1.2.1 --## v ggplot2 2.2.1 v readr 1.1.1
## v tibble 1.4.1 v purrr 0.2.4
## v tidyr 0.7.2 v dplyr 0.7.4
## v ggplot2 2.2.1 v forcats 0.2.0## -- Conflicts -------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()Importing tounrament project data
tournament.data <- readLines('https://raw.githubusercontent.com/niteen11/MSDS/master/DATA607/Week4/dataset/tournamentinfo.txt')## Warning in readLines("https://raw.githubusercontent.com/niteen11/MSDS/
## master/DATA607/Week4/dataset/tournamentinfo.txt"): incomplete final line
## found on 'https://raw.githubusercontent.com/niteen11/MSDS/master/DATA607/
## Week4/dataset/tournamentinfo.txt'head(tournament.data,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] "-----------------------------------------------------------------------------------------"typeof(tournament.data)## [1] "character"tournament.data <- tournament.data[-c(0:4)]
head(tournament.data)## [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] "-----------------------------------------------------------------------------------------"Data Wrangling
Cleaning torunament data
tournament.data <- tournament.data[sapply(tournament.data, nchar) > 0]
head(tournament.data)## [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] "-----------------------------------------------------------------------------------------"It appears from the dataset that information about each player is organized in two consecutive rows. First row indicates player’s information and match result. The second row holds information about the players state, USCF information such as id, pre and post rating. So, each player’s information is embdedded in two consecutive rows in the dataset.
# odd data points represent player match info
player.data <- c(seq(1, length(tournament.data), 3))
player.info <- tournament.data[player.data]
head(player.info)## [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|"# even data points represent player rating info
player.rating.data <- c(seq(2, length(tournament.data), 3))
player.rating.info <- tournament.data[player.rating.data]
head(player.rating.info)## [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 |"Regex functions to extract information
Extract player name
player.name <- str_extract(player.info, "\\s+([[:alpha:]- ]+)\\b\\s*\\|")
player.name <- gsub(player.name, pattern = "|", replacement = "", fixed = T)
player.name <- trimws(player.name)
head(player.name)## [1] "GARY HUA" "DAKSHESH DARURI" "ADITYA BAJAJ"
## [4] "PATRICK H SCHILLING" "HANSHI ZUO" "HANSEN SONG"Extract player state
player.state <- str_extract(player.rating.info, "[[:alpha:]]{2}")
head(player.state)## [1] "ON" "MI" "MI" "MI" "MI" "OH"Extract Player Pre rating score value
player.prerating.score <- str_extract(player.rating.info, ".\\: \\s?[[:digit:]]{3,4}")
player.prerating.score <- gsub(player.prerating.score, pattern = "R: ", replacement = "", fixed = T)
player.prerating.score <- as.numeric(as.character(player.prerating.score))
head(player.prerating.score)## [1] 1794 1553 1384 1716 1655 1686Extract Players total points
player.total.points <- str_extract(player.info, "[[:digit:]]+\\.[[:digit:]]")
player.total.points <- as.numeric(as.character(player.total.points))
head(player.total.points)## [1] 6.0 6.0 6.0 5.5 5.5 5.0Now extract players opponent info
player.opponent.info <- str_extract_all(player.info, "[[:digit:]]{1,2}\\|")
player.opponent.info <- str_extract_all(player.opponent.info, "[[:digit:]]{1,2}")
player.opponent.info <- lapply(player.opponent.info, as.numeric)
head(player.opponent.info)## [[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 21let’s calculate Player’s opponent avg. rating and store in a list
opponent.avg.rating <- list()
for (i in 1:length(player.opponent.info)){
opponent.avg.rating[i] <- round(mean(player.prerating.score[unlist(player.opponent.info[i])]),2)
}
opponent.avg.rating <- lapply(opponent.avg.rating, as.numeric)
opponent.avg.rating <- data.frame(unlist(opponent.avg.rating))
head(opponent.avg.rating)## unlist.opponent.avg.rating.
## 1 1605.29
## 2 1469.29
## 3 1563.57
## 4 1573.57
## 5 1500.86
## 6 1518.71Prepare the final players data frame
player.df <- cbind.data.frame(player.name, player.state, player.total.points,
player.prerating.score,round(opponent.avg.rating,0))
head(player.df)## player.name player.state player.total.points
## 1 GARY HUA ON 6.0
## 2 DAKSHESH DARURI MI 6.0
## 3 ADITYA BAJAJ MI 6.0
## 4 PATRICK H SCHILLING MI 5.5
## 5 HANSHI ZUO MI 5.5
## 6 HANSEN SONG OH 5.0
## player.prerating.score unlist.opponent.avg.rating.
## 1 1794 1605
## 2 1553 1469
## 3 1384 1564
## 4 1716 1574
## 5 1655 1501
## 6 1686 1519summary(player.df)## player.name player.state player.total.points
## ADITYA BAJAJ : 1 MI:55 Min. :1.000
## ALAN BUI : 1 OH: 1 1st Qu.:2.500
## ALEX KONG : 1 ON: 8 Median :3.500
## AMIYATOSH PWNANANDAM: 1 Mean :3.438
## ANVIT RAO : 1 3rd Qu.:4.000
## ASHWIN BALAJI : 1 Max. :6.000
## (Other) :58
## player.prerating.score unlist.opponent.avg.rating.
## Min. : 377 Min. :1107
## 1st Qu.:1227 1st Qu.:1310
## Median :1407 Median :1382
## Mean :1378 Mean :1379
## 3rd Qu.:1583 3rd Qu.:1481
## Max. :1794 Max. :1605
## Data Visualization
ggplot(player.df, aes(player.prerating.score, opponent.avg.rating, color = player.state)) +
geom_point(aes(size = player.total.points, shape = player.state))+
ggtitle('Players info: Pre-rating Vs. Opponent Avg. Pre-rating')+
xlab('Player Opponent Avg. Pre-rating')+
ylab('Opponent Avg. Pre-rating')## Don't know how to automatically pick scale for object of type data.frame. Defaulting to continuous.
Export the final player data frame in csv format
write.csv(player.df,'playerChessData.csv')