Midterm 1

library(rvest)
library(tidyverse)
library(magrittr)
library(scales)
library(knitr)
library(lubridate)
library(plyr)

Setting up page by page sequence for Bobby Fischer

url <- "https://www.chess.com/games/bobby-fischer?&page="

page <- seq(from = 1, to = 38)

unitedata<- function(x){
 full_url <- paste0(url, x)
 full_url
}
finalurl <- unitedata(page)

Web scrape Function

MatchScrape <- function(x){
 page <- x
 white <- page %>% read_html() %>% html_nodes('.master-games-user-tagline:nth-child(1) .master-games-username') %>% html_text() %>% as.data.frame()
 opponent <- page %>% read_html() %>% html_nodes('.master-games-user-tagline+ .master-games-user-tagline .master-games-username') %>% html_text() %>% as.data.frame()
 opening <- page %>% read_html() %>% html_nodes('.master-games-opening span:nth-child(2)') %>% html_text() %>% as.data.frame()
 score <- page %>% read_html() %>% html_nodes('.master-games-text-center .master-games-text-middle
') %>% html_text() %>% as.data.frame()
 moves <- page %>% read_html() %>% html_nodes('.master-games-text-right .master-games-text-middle') %>% html_text() %>% as.data.frame()
 year <- page %>% read_html() %>% html_nodes('.master-games-date .master-games-text-middle') %>% html_text() %>% as.data.frame()
 
 

#combine, name, and make it a tibble
 chart <- cbind(white,opponent, opening, score, moves, year)
 names(chart) <- c("White","Black", "Opening", "Score","Moves", "Year")
 chart <- as.tibble(chart)
 return(chart)
}

Just to test if my function gets the right values

MatchScrape("https://www.chess.com/games/bobby-fischer?&page=1")

matches <- map_df(finalurl, MatchScrape)

Clean up base data

clean_matches <- matches %>% 
  mutate(
Opening = gsub("\n ", "", Opening),
Score = gsub("\n ", "", Score),
Moves = gsub("\n", "", Moves),
Year = gsub("\n", "", Year),


Score = gsub(" ", "", Score),
Moves = gsub(" ", "", Moves),
Moves = as.integer(Moves)
)
clean_matches$Opening <- trimws(clean_matches$Opening)
clean_matches$White <- trimws(clean_matches$White)
clean_matches$Black <- trimws(clean_matches$Black)
clean_matches$Score <- trimws(clean_matches$Score)
clean_matches$Moves <- trimws(clean_matches$Moves)
clean_matches$Year <- trimws(clean_matches$Year)

clean_matches

Results as factor

clean_matches$Score <- as.factor(clean_matches$Score)

Renaming factors #Conditions for player as White: 1 = win, 2 = Loss, 3 = Draw

clean_matches$Result <- revalue(clean_matches$Score, c("1-0"="Win", "0-1"="Loss", "½-½"="Draw"))

Bobby as white + Final Results (easier to interperate)

bobby <- clean_matches %>% filter(White == "Fischer Robert J")

RENAMING OPENINGS, then sorting into factors.

bobby$Opening[grep("Sicilian", bobby$Opening)] <- "Sicilian Defense"
bobby$Opening[grep("French", bobby$Opening)] <- "French Defense"
bobby$Opening[grep("Caro-Kann", bobby$Opening)] <- "Caro-Kann Defense"
bobby$Opening[grep("King's Indian", bobby$Opening)] <- "King's Indian Defense"
bobby$Opening[grep("Sicilian|French|Caro-Kann|King's Indian", bobby$Opening, invert = TRUE)] <- "Other"


bobby$Opening <- as.factor(bobby$Opening)

Reordering factor levels

bobby$Result <- ordered(bobby$Result, levels = c("Win", "Loss", "Draw"))
bobby$Opening <- ordered(bobby$Opening, levels = c("Sicilian Defense","French Defense","Caro-Kann Defense","King's Indian Defense","Other" ))

bg <- bobby %>% ggplot(aes(Result, group = Opening)) +
  geom_bar(aes(y = ..prop.., fill = factor(..x..)), stat="count") +
   geom_text(aes( label = scales::percent(..prop..),
                   y= ..prop.. ), stat= "count", vjust = -.5) +
  scale_y_continuous(labels=scales::percent) +
  facet_grid(~Opening) +
labs(y="W/L/D Percent", fill="Result", x= "Robert James Fischer (as white)")

Now to do Magnus Carlsen

Setting up page by page sequence. (Change for Magnus)

url1 <- "https://www.chess.com/games/magnus-carlsen?&page="

page1 <- seq(from = 1, to = 60)

We seem to get an error after page 60, though the function does work on some individual pages. Don’t need all of that anyway

unitedata1<- function(x){
 full_url <- paste0(url1, x)
 full_url
}
finalurl1 <- unitedata1(page1)

Web scrape

MatchScrape1 <- function(x){
 page <- x
 
 white <- page %>% read_html() %>% html_nodes('.master-games-user-tagline:nth-child(1) .master-games-username') %>% html_text() %>% as.data.frame()
 
 opponent <- page %>% read_html() %>% html_nodes('.master-games-user-tagline+ .master-games-user-tagline .master-games-username') %>% html_text() %>% as.data.frame()
 
 opening <- page %>% read_html() %>% html_nodes('.master-games-opening span+ span') %>% html_text() %>% as.data.frame()
 
 score <- page %>% read_html() %>% html_nodes('.master-games-text-center .master-games-text-middle') %>% html_text() %>% as.data.frame()
 
 moves <- page %>% read_html() %>% html_nodes('.master-games-text-right .master-games-text-middle') %>% html_text() %>% as.data.frame()
 
year <- page %>% read_html() %>% html_nodes('.master-games-date .master-games-text-middle') %>% html_text() %>% as.data.frame()
 

#combine, name, and make it a tibble
 chart1 <- cbind(white, opponent, opening, score, moves, year)
 names(chart1) <- c("White","Black", "Opening", "Score","Moves", "Year")
 chart1 <- as.tibble(chart1)
 return(chart1)
}

Just to test if my function gets the right values

MatchScrape1("https://www.chess.com/games/magnus-carlsen?&page=51")

matches_c <- map_df(finalurl1, MatchScrape1)

Clean up base data

clean_matches_c <- matches_c %>% 
  mutate(
Opening = gsub("\n ", "", Opening),
Score = gsub("\n ", "", Score),
Moves = gsub("\n", "", Moves),
Year = gsub("\n", "", Year),

Score = gsub(" ", "", Score),
Moves = gsub(" ", "", Moves),
Moves = as.integer(Moves)
)
clean_matches_c$Opening <- trimws(clean_matches_c$Opening)
clean_matches_c$White <- trimws(clean_matches_c$White)
clean_matches_c$Black <- trimws(clean_matches_c$Black)
clean_matches_c$Score <- trimws(clean_matches_c$Score)
clean_matches_c$Moves <- trimws(clean_matches_c$Moves)
clean_matches$Year <- trimws(clean_matches$Year)

clean_matches_c

Results as factor

clean_matches_c$Score <- as.factor(clean_matches_c$Score)

Renaming factors #Conditions for player as White: 1 = win, 2 = Loss, 3 = Draw

clean_matches_c$Result <- revalue(clean_matches_c$Score, c("1-0"="Win", "0-1"="Loss", "½-½"="Draw"))

Magnus as white + Final Results (easier to interperate)

carlsen <- clean_matches_c %>% filter( White == "Magnus Carlsen" | White == "Carlsen, M.")

carlsen

RENAMING OPENINGS, then sorting into factors.

carlsen$Opening[grep("Sicilian", carlsen$Opening)] <- "Sicilian Defense"
carlsen$Opening[grep("French", carlsen$Opening)] <- "French Defense"
carlsen$Opening[grep("Caro-Kann", carlsen$Opening)] <- "Caro-Kann Defense"
carlsen$Opening[grep("King's Indian", carlsen$Opening)] <- "King's Indian Defense"
carlsen$Opening[grep("Sicilian|French|Caro-Kann|King's Indian", carlsen$Opening, invert = TRUE)] <- "Other"


carlsen$Opening <- as.factor(carlsen$Opening)

Reordering factor levels

carlsen$Result <- ordered(carlsen$Result, levels = c("Win", "Loss", "Draw"))
carlsen$Opening <- ordered(carlsen$Opening, levels = c("Sicilian Defense","French Defense","Caro-Kann Defense","King's Indian Defense","Other" ))

cg <- carlsen %>% ggplot(aes(Result, group = Opening)) +
  geom_bar(aes(y = ..prop.., fill = factor(..x..)), stat="count") +
   geom_text(aes( label = scales::percent(..prop..),
                   y= ..prop.. ), stat= "count", vjust = -.5) +
  scale_y_continuous(labels=scales::percent) +
  facet_grid(~Opening) +
labs(y="W/L/D Percent", fill="Result", x= "Magnus Carlsen (as White)")

So how much does this account for?

count(carlsen$Result)

count(bobby$Result)

count(carlsen$Opening)

count(bobby$Opening)

Summary of Data

CarlsenWin <- percent(439/777)
CarlsenLoss <- percent(100/777)
BobbyWin <- percent(336/541)
BobbyLoss <- percent(70/541)

cg

bg

names(CarlsenWin) <- "Carlsen Winrate"
CarlsenWin

## Carlsen Winrate 
##         "56.5%"

names(BobbyWin) <- "Bobby Winrate"
BobbyWin

## Bobby Winrate 
##       "62.1%"

names(CarlsenLoss) <- "Carlsen Lossrate"
CarlsenLoss

## Carlsen Lossrate 
##          "12.9%"

names(BobbyLoss) <- "Bobby Lossrate"
BobbyLoss

## Bobby Lossrate 
##        "12.9%"

We can see that Bobby Fischer actually has a higher percentage of wins, though Fischer and Carlsen actually share the same % of Loss, meaning that Carlsen has a higher volume of Drawn games.

When looking at the graphical data of how they each did against certain defenses as white, we can see Carlsen was actually stronger against most of the more common opening defenses, with exception to the Sicilian.

Looking at our data though, This could be partially incorrect as we have fewer samples of games using our listed particular defenses, as the “Other” opening category makes up for 66.5% of the sampled matches for Carlsen, where as for Bobby it only accounted for 43.8%. The other most frequent defensive opening being the Sicilian, accounting for 18.1% and 32.5% of Carlsen and Bobby’s matches respectively, meaning it is likely to be the more accurate indicator in their performance against different openings by black.

In conclusion, based on the data here, we can say Bobby Fischer is likely to have been the more dominant player as white, comparing him in his era vs. Carlsen in the modern era.