Visualizing Chess Data With ggplot

灰太狼 2022-08-09 18:34 8阅读 0赞

October 29, 2015

By  [Joshua Kunst][]

(This article was first published on  **[Jkunst - R category ][Jkunst - R category]**, and kindly contributed to  [R-bloggers)][R-bloggers]

There are nice visualizations from chess data:  
[piece movement][],  
[piece survaviliy][],  
[square usage by player][].  
Sadly not always the authors shows the code/data for replicate the final result.  
So I wrote some code to show how to do some this great visualizations entirely in  
R. Just for fun.

1.  [The Data][]
2.  [Piece Movements][]
3.  [Survival rates][]
4.  [Square usage by player][]
5.  [Distributions for the first movement][]
6.  [Who captures whom][]

### The Data ###

The original data come from [here][]  
which was parsed and stored in the [rchess][] package.

library("rchess")
    data(chesswc)
    str(chesswc)

## Classes 'tbl_df', 'tbl' and 'data.frame':    1266 obs. of  11 variables:
    ##  $ event   : chr  "FIDE World Cup 2011" "FIDE World Cup 2011" "FIDE World Cup 2011" "FIDE World Cup 2011" ...
    ##  $ site    : chr  "Khanty-Mansiysk RUS" "Khanty-Mansiysk RUS" "Khanty-Mansiysk RUS" "Khanty-Mansiysk RUS" ...
    ##  $ date    : Date, format: "2011-08-28" "2011-08-28" ...
    ##  $ round   : num  1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 ...
    ##  $ white   : chr  "Kaabi, Mejdi" "Ivanchuk, Vassily" "Ibrahim, Hatim" "Ponomariov, Ruslan" ...
    ##  $ black   : chr  "Karjakin, Sergey" "Steel, Henry Robert" "Mamedyarov, Shakhriyar" "Gwaze, Robert" ...
    ##  $ result  : chr  "0-1" "1-0" "0-1" "1-0" ...
    ##  $ whiteelo: int  2344 2768 2402 2764 2449 2746 2477 2741 2493 2736 ...
    ##  $ blackelo: int  2788 2362 2765 2434 2760 2452 2744 2480 2739 2493 ...
    ##  $ eco     : chr  "D15" "E68" "E67" "B40" ...
    ##  $ pgn     : chr  "1. d4 d5 2. Nf3 Nf6 3. c4 c6 4. Nc3 dxc4 5. e3 b5 6. a4 b4 7. Nb1 Ba6 8. Ne5 e6 9. Nxc4 c5 10. b3 cxd4 11. exd4 Nc6 12. Be3 Be7"| __truncated__ "1. c4 Nf6 2. Nc3 g6 3. g3 Bg7 4. Bg2 O-O 5. d4 d6 6. Nf3 Nbd7 7. O-O e5 8. e4 c6 9. Rb1 exd4 10. Nxd4 Re8 11. h3 Nc5 12. Re1 a5"| __truncated__ "1. Nf3 Nf6 2. c4 g6 3. Nc3 Bg7 4. g3 O-O 5. Bg2 d6 6. O-O Nbd7 7. d4 e5 8. b3 exd4 9. Nxd4 Re8 10. Bb2 Nc5 11. Qc2 h5 12. Rad1 "| __truncated__ "1. e4 c5 2. Nf3 e6 3. d3 Nc6 4. g3 e5 5. Bg2 d6 6. O-O Be7 7. c3 Nf6 8. Nbd2 O-O 9. a3 b5 10. Re1 Kh8 11. d4 Bd7 12. b4 cxd4 13"| __truncated__ ...

chesswc %>% count(event)

chesswc <- chesswc %>% filter(event == "FIDE World Cup 2015")

The most important variable here is the [pgn][] game.  
This pgn is a long string which represent the game. However this format is not so visualization  
friendly. That's why I implemented the `history_detail()` method for a `Chess` object. Let's check.

set.seed(123)
    pgn <- sample(chesswc$pgn, size = 1)
    str_sub(pgn, 0, 50)

## [1] "1. d4 Nf6 2. Nf3 d5 3. c4 e6 4. e3 Be7 5. Nbd2 O-O"

Compare the previous string with the first 10 rows of the`history_detail()`

chss <- Chess$new()
    chss$load_pgn(pgn)

## [1] TRUE

chss$history_detail() %>%
      arrange(number_move) %>% 
      head(10)

<table style="border-collapse:collapse; border-spacing:0px; margin:1em 0px; color:rgb(68,68,68); font-family:Simsun; font-size:14px; line-height:18px"> 
 <thead> 
  <tr> 
   <th style="font-family:'Trebuchet MS',Verdana,Arial,Helvetica,sans-serif; font-weight:normal; border:1px solid rgb(204,204,204); text-transform:uppercase; padding:6px; background:rgb(238,238,238)"> PIECE</th> 
   <th style="font-family:'Trebuchet MS',Verdana,Arial,Helvetica,sans-serif; font-weight:normal; border:1px solid rgb(204,204,204); text-transform:uppercase; padding:6px; background:rgb(238,238,238)"> FROM</th> 
   <th style="font-family:'Trebuchet MS',Verdana,Arial,Helvetica,sans-serif; font-weight:normal; border:1px solid rgb(204,204,204); text-transform:uppercase; padding:6px; background:rgb(238,238,238)"> TO</th> 
   <th style="font-family:'Trebuchet MS',Verdana,Arial,Helvetica,sans-serif; font-weight:normal; border:1px solid rgb(204,204,204); text-transform:uppercase; padding:6px; text-align:right; background:rgb(238,238,238)"> NUMBER_MOVE</th> 
   <th style="font-family:'Trebuchet MS',Verdana,Arial,Helvetica,sans-serif; font-weight:normal; border:1px solid rgb(204,204,204); text-transform:uppercase; padding:6px; text-align:right; background:rgb(238,238,238)"> PIECE<em>NUMBER</em>MOVE</th> 
   <th style="font-family:'Trebuchet MS',Verdana,Arial,Helvetica,sans-serif; font-weight:normal; border:1px solid rgb(204,204,204); text-transform:uppercase; padding:6px; background:rgb(238,238,238)"> STATUS</th> 
   <th style="font-family:'Trebuchet MS',Verdana,Arial,Helvetica,sans-serif; font-weight:normal; border:1px solid rgb(204,204,204); text-transform:uppercase; padding:6px; text-align:right; background:rgb(238,238,238)"> NUMBER<em>MOVE</em>CAPTURE</th> 
   <th style="font-family:'Trebuchet MS',Verdana,Arial,Helvetica,sans-serif; font-weight:normal; border:1px solid rgb(204,204,204); text-transform:uppercase; padding:6px; background:rgb(238,238,238)"> CAPTURED_BY</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">d2 Pawn</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">d2</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">d4</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px; text-align:right">1</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px; text-align:right">1</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">NA</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px; text-align:right">NA</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">NA</td> 
  </tr> 
  <tr> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">g8 Knight</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">g8</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">f6</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px; text-align:right">2</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px; text-align:right">1</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">game over</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px; text-align:right">NA</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">NA</td> 
  </tr> 
  <tr> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">g1 Knight</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">g1</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">f3</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px; text-align:right">3</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px; text-align:right">1</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">NA</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px; text-align:right">NA</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">NA</td> 
  </tr> 
  <tr> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">d7 Pawn</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">d7</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">d5</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px; text-align:right">4</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px; text-align:right">1</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">NA</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px; text-align:right">NA</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">NA</td> 
  </tr> 
  <tr> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">c2 Pawn</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">c2</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">c4</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px; text-align:right">5</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px; text-align:right">1</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">captured</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px; text-align:right">14</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">d7 Pawn</td> 
  </tr> 
  <tr> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">e7 Pawn</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">e7</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">e6</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px; text-align:right">6</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px; text-align:right">1</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">game over</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px; text-align:right">NA</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">NA</td> 
  </tr> 
  <tr> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">e2 Pawn</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">e2</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">e3</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px; text-align:right">7</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px; text-align:right">1</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">game over</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px; text-align:right">NA</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">NA</td> 
  </tr> 
  <tr> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">f8 Bishop</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">f8</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">e7</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px; text-align:right">8</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px; text-align:right">1</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">NA</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px; text-align:right">NA</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">NA</td> 
  </tr> 
  <tr> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">b1 Knight</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">b1</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">d2</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px; text-align:right">9</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px; text-align:right">1</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">NA</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px; text-align:right">NA</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">NA</td> 
  </tr> 
  <tr> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">Black King</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">e8</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">g8</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px; text-align:right">10</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px; text-align:right">1</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">game over</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px; text-align:right">NA</td> 
   <td style="border:1px solid rgb(204,204,204); padding:6px">NA</td> 
  </tr> 
 </tbody> 
</table>

The result is a dataframe where each row is a piece's movement showing explicitly the cells  
where the travel in a particular number move. Now we apply this function over the 433  
games in the FIDE World Cup 2015.

library("foreach")
    library("doParallel")
    workers <- makeCluster(parallel::detectCores())
    registerDoParallel(workers)
    
    chesswc <- chesswc %>% mutate(game_id = seq(nrow(.)))
    
    dfmoves <- adply(chesswc %>% select(pgn, game_id), .margins = 1, function(x){ 
      chss <- Chess$new()
      chss$load_pgn(x$pgn)
      chss$history_detail()
      }, .parallel = TRUE, .paropts = list(.packages = c("rchess")))
    
    dfmoves <- tbl_df(dfmoves) %>% select(-pgn)
    dfmoves %>% filter(game_id == 1, piece == "g1 Knight")

The `dfmoves` data frame will be the heart from all these plots due have a lot of information and  
it is easy to consume.

### Piece Movements ###

To try replicate the result it's necessary the data to represent (and then plot) the  
board. In the rchess package there are some helper functions like`chessboardata()`.

dfboard <- rchess:::.chessboarddata() %>%
      select(cell, col, row, x, y, cc)
    
    head(dfboard)

Now we add this information to the `dfmoves` data frame and calculates some field to  
to know how to draw the curves (see [here][here 1] for more details).

dfpaths <- dfmoves %>%
      left_join(dfboard %>% rename(from = cell, x.from = x, y.from = y),
                by = "from") %>%
      left_join(dfboard %>% rename(to = cell, x.to = x, y.to = y) %>% select(-cc, -col, -row),
                by = "to") %>%
      mutate(x_gt_y = abs(x.to - x.from) > abs(y.to - y.from),
             xy_sign = sign((x.to - x.from)*(y.to - y.from)) == 1,
             x_gt_y_equal_xy_sign = x_gt_y == xy_sign)

The data is ready! So we need now some `ggplot`, `geom_tile` for the board, the new `geom_curve`  
to represent the piece's path and some `jitter` to make this more artistic. Let's  
plot the f1 Bishop's movements.

ggplot() +
      geom_tile(data = dfboard, aes(x, y, fill = cc)) +
      geom_curve(data = dfpaths %>% filter(piece == "f1 Bishop", x_gt_y_equal_xy_sign),
                 aes(x = x.from, y = y.from, xend = x.to, yend = y.to),
                 position = position_jitter(width = 0.2, height = 0.2),
                 curvature = 0.50, angle = -45, alpha = 0.02, color = "white", size = 1.05) +
      geom_curve(data = dfpaths %>% filter(piece == "f1 Bishop", !x_gt_y_equal_xy_sign),
                 aes(x = x.from, y = y.from, xend = x.to, yend = y.to),
                 position = position_jitter(width = 0.2, height = 0.2),
                 curvature = -0.50, angle = 45, alpha = 0.02, color = "white", size = 1.05) +
      scale_fill_manual(values =  c("gray10", "gray20")) +
      ggtitle("f1 Bishop") +
      coord_equal()

![plot of chunk unnamed-chunk-8][]

In the same way we can plot every piece.

![plot of chunk unnamed-chunk-9][]

I think it's look very similar to the original work made by [Steve Tung][].

### Survival Rates ###

In this plot we need filter `dfmoves` by `!is.na(status)` so we can know what happend with  
every piece in at the end of the game: if a piece was caputered of or not. Then get summary  
across all the games.

dfsurvrates <- dfmoves %>%
      filter(!is.na(status)) %>%
      group_by(piece) %>%
      summarize(games = n(),
                was_captured = sum(status == "captured")) %>%
      mutate(surv_rate = 1 - was_captured/games)
    
    dfsurvrates %>% arrange(desc(surv_rate)) %>% head()

This helps as validation because the kings are never captured. Now we use a helper function in the  
rchess package `rchess:::.chesspiecedata()` to get the start position for every piece and then plot  
the survival rates in the cell where the piece start in the game.

dfsurvrates <- dfsurvrates %>%
      left_join(rchess:::.chesspiecedata() %>% select(start_position, piece = name, color, unicode),
                by = "piece") %>%
      full_join(dfboard %>% rename(start_position = cell),
                by = "start_position")
    
    # Auxiliar data to plot the board
    dfboard2 <- data_frame(x = 0:8 + 0.5, y = 0 + 0.5, xend = 0:8 + 0.5, yend = 8 + 0.5)
    
    ggplot(dfsurvrates) +
      geom_tile(data = dfsurvrates %>% filter(!is.na(surv_rate)),
                aes(x, y, fill = surv_rate)) +
      scale_fill_gradient(low = "darkred",  high = "white") +
      geom_text(data = dfsurvrates %>% filter(!is.na(surv_rate)),
                aes(x, y, label = scales::percent(surv_rate)),
                color = "gray70", size = 5) +
      scale_x_continuous(breaks = 1:8, labels = letters[1:8]) +
      scale_y_continuous(breaks = 1:8, labels = 1:8)  +
      geom_segment(data = dfboard2, aes(x, y, xend = xend, yend = yend), color = "gray70") +
      geom_segment(data = dfboard2, aes(y, x, xend = yend, yend = xend), color = "gray70") +
      ggtitle("Survival Rates for each piece") + 
      coord_equal() + 
      theme_minimal() +
      theme(legend.position = "none")

![plot of chunk unnamed-chunk-11][]

Obviously the plot show same data in text and color, and there a lot of space without  
information but the idea is use the chess board to represent the initial position in a chess game.

We can replace the texts with the piece's icons:

ggplot(dfsurvrates) +
      geom_tile(data = dfsurvrates %>% filter(!is.na(surv_rate)),
                aes(x, y, fill = 100*surv_rate)) +
      scale_fill_gradient(NULL, low = "darkred",  high = "white") +
      geom_text(data = dfsurvrates %>% filter(!is.na(surv_rate)),
                aes(x, y, label = unicode), size = 11, color = "gray20", alpha = 0.7) +
      scale_x_continuous(breaks = 1:8, labels = letters[1:8]) +
      scale_y_continuous(breaks = 1:8, labels = 1:8)  +
      geom_segment(data = dfboard2, aes(x, y, xend = xend, yend = yend), color = "gray70") +
      geom_segment(data = dfboard2, aes(y, x, xend = yend, yend = xend), color = "gray70") +
      ggtitle("Survival Rates for each piece") + 
      coord_equal() +
      theme_minimal() +
      theme(legend.position = "bottom")

![plot of chunk unnamed-chunk-12][]

### Square Usage By Player ###

For this visualization we will use the `to` variable. First of all we select the player  
who have more games in the table `chesswc`. Then for each of them get the `to` counts.

players <- chesswc %>% count(white) %>% arrange(desc(n)) %>% .$white %>% head(4)
    players

## [1] "Karjakin, Sergey" "Svidler, Peter"   "Wei, Yi"         
    ## [4] "Adams, Michael"

dfmov_players <- ldply(players, function(p){  # p <- sample(players, size = 1)
      games <- chesswc %>% filter(white == p) %>% .$game_id
      dfres <- dfmoves %>%
        filter(game_id %in% games, !is.na(to)) %>%
        count(to) %>%
        mutate(player = p,
               p = n/length(games))
      dfres
    })
    
    dfmov_players <- dfmov_players %>%
      rename(cell = to) %>%
      left_join(dfboard, by = "cell")
    
    ggplot(dfmov_players) +
      geom_tile(aes(x, row, fill = p)) +
      scale_fill_gradient("Movements to every celln(normalized by number of games)",
                          low = "white",  high = "darkblue") +
      geom_text(aes(x, row, label = round(p, 1)), size = 3, color = "white", alpha = 0.5) +
      facet_wrap(~player) +
      scale_x_continuous(breaks = 1:8, labels = letters[1:8]) +
      scale_y_continuous(breaks = 1:8, labels = 1:8)  +
      geom_segment(data = dfboard2, aes(x, y, xend = xend, yend = yend), color = "gray70") +
      geom_segment(data = dfboard2, aes(y, x, xend = yend, yend = xend), color = "gray70") +
      coord_equal() +
      theme_minimal() +
      theme(legend.position = "bottom")

![plot of chunk unnamed-chunk-13][]

### Distributions For The First Movement ###

Now, with the same data and using the `piece_number_move` and`number_move` we can obtain  
the distribution for the first movement for each piece.

piece_lvls <- rchess:::.chesspiecedata() %>%
      mutate(col = str_extract(start_position, "\w{1}"),
             row = str_extract(start_position, "\d{1}")) %>%
      arrange(desc(row), col) %>%
      .$name
    
    dfmoves_first_mvm <- dfmoves %>%
      mutate(piece = factor(piece, levels = piece_lvls),
             number_move_2 = ifelse(number_move %% 2 == 0, number_move/2, (number_move + 1)/2 )) %>%
      filter(piece_number_move == 1)

ggplot(dfmoves_first_mvm) +
      geom_density(aes(number_move_2), fill = "#B71C1C", alpha = 0.8, color = NA) +
      scale_y_continuous(breaks = NULL) +
      facet_wrap(~piece, nrow = 4, ncol = 8, scales = "free_y")  +
      xlab("Density") + ylab("Number Move") + 
      xlim(0, 40) +
      theme_gray() +
      theme(panel.background = element_rect(fill = "gray90"))

![plot of chunk unnamed-chunk-15][]

Notice the similarities between the White King and h1 Rook due the castling, the same  
effect is present between the Black King and the h8 Rook.

### Who Captures Whom ###

For this plot we'll use the igraph package and [ForceAtlas2][]  
package an R implementation by [Adolfo Alvarez][] of the Force Atlas 2 graph layout  
designed for Gephi.

We get the rows with `status == "captured"` and summarize by `piece`and `captured_by` variables. The result data  
frame will be the edges in our igraph object using the`graph.data.frame` function.

library("igraph")
    library("ForceAtlas2")
    
    dfcaputures <- dfmoves %>%
      filter(status == "captured") %>%
      count(captured_by, piece) %>%
      ungroup() %>% 
      arrange(desc(n))
    
    dfvertices <- rchess:::.chesspiecedata() %>%
      select(-fen, -start_position) %>%
      mutate(name2 = str_replace(name, " \w+$", unicode),
             name2 = str_replace(name2, "White|Black", ""))
    
    g <- graph.data.frame(dfcaputures %>% select(captured_by, piece, weight = n),
                          directed = TRUE,
                          vertices = dfvertices)
    
    set.seed(123)
    # lout <- layout.kamada.kawai(g)
    lout <- layout.forceatlas2(g, iterations = 10000, plotstep = 0)
    
    dfvertices <- dfvertices %>%
      mutate(x = lout[, 1],
             y = lout[, 2])
    
    dfedges <- as_data_frame(g, "edges") %>%
      tbl_df() %>%
      left_join(dfvertices %>% select(from = name, x, y), by = "from") %>%
      left_join(dfvertices %>% select(to = name, xend = x, yend = y), by = "to")

To plot the the network I prefer use ggplot2 instead igraph just you get more control in the style  
and colors.

ggplot() +
      geom_curve(data = dfedges %>%
                   filter((str_extract(from, "\d+") %in% c(1, 2) |
                             str_detect(from, "White"))),
                 aes(x, y, xend = xend, yend = yend, alpha = weight, size = weight),
                 curvature = 0.1, color = "red") +
      geom_curve(data = dfedges %>%
                   filter(!(str_extract(from, "\d+") %in% c(1, 2) |
                              str_detect(from, "White"))),
                 aes(x, y, xend = xend, yend = yend, alpha = weight, size = weight),
                 curvature = 0.1, color = "blue") +
      scale_alpha(range = c(0.01, 0.5)) +
      scale_size(range = c(0.01, 2)) +
      geom_point(data = dfvertices, aes(x, y, color = color), size = 15, alpha = 0.9) +
      scale_color_manual(values = c("gray10", "gray90")) +
      geom_text(data = dfvertices %>% filter(str_length(name2) != 1),
                aes(x, y, label = name2), size = 5, color = "gray50") +
      geom_text(data = dfvertices %>% filter(str_length(name2) == 1),
                aes(x, y, label = name2), size = 9, color = "gray50") +
      ggtitle("Red: white captures black | Blue: black captures white")

![plot of chunk unnamed-chunk-17][]

It's know we usually exchange pieces with the same values: queen by queen, knight by bishop, etc. The interesting  
fact we see here is the d2 pawn/c7 pawn/g1 knight relationship beacuse d2 pawn/c7 pawn is not so symmetrical and  
it's explained by the popular use the  
[Sicilian Opening][]  
in a master level (`1.e4 c5 2.Nf3 d6 3.d4 cxd4 4.Nxd4`).

I hope you enjoyed this post in the same way I enjoyed doing it :D. If you notice a mistake please let me know.

[Joshua Kunst]: http://www.r-bloggers.com/author/joshua-kunst/
[Jkunst - R category]: http://jkunst.com/r/visualizing-chess-data-with-ggplot/
[R-bloggers]: http://www.r-bloggers.com/
[piece movement]: http://www.visualnews.com/2015/06/15/visualizing-the-movements-of-chess-pieces-using-millions-of-games/
[piece survaviliy]: https://github.com/ojb500/SurvivingPieces
[square usage by player]: http://en.chessbase.com/post/seth-kadish-visualizing-chess
[The Data]: http://jkunst.com/r/visualizing-chess-data-with-ggplot/#the-data
[Piece Movements]: http://jkunst.com/r/visualizing-chess-data-with-ggplot/#piece-movements
[Survival rates]: http://jkunst.com/r/visualizing-chess-data-with-ggplot/#survival-rates
[Square usage by player]: http://jkunst.com/r/visualizing-chess-data-with-ggplot/#square-usage-by-player
[Distributions for the first movement]: http://jkunst.com/r/visualizing-chess-data-with-ggplot/#distributions-for-the-first-movement
[Who captures whom]: http://jkunst.com/r/visualizing-chess-data-with-ggplot/#who-captures-whom
[here]: http://www.theweekinchess.com/chessnews/events/fide-world-cup-2015
[rchess]: http://github.com/jbkunst/rchess
[pgn]: https://en.wikipedia.org/wiki/Portable_Game_Notation
[here 1]: http://rpubs.com/jbkunst/geom_curve
[plot of chunk unnamed-chunk-8]: http://i0.wp.com/jkunst.com/images/visualizing-chess-data-with-ggplot/unnamed-chunk-8-1.png?w=456
[plot of chunk unnamed-chunk-9]: http://i0.wp.com/jkunst.com/images/visualizing-chess-data-with-ggplot/unnamed-chunk-9-1.png?w=456
[Steve Tung]: https://twitter.com/_tungs_
[plot of chunk unnamed-chunk-11]: http://i0.wp.com/jkunst.com/images/visualizing-chess-data-with-ggplot/unnamed-chunk-11-1.png?w=456
[plot of chunk unnamed-chunk-12]: http://i1.wp.com/jkunst.com/images/visualizing-chess-data-with-ggplot/unnamed-chunk-12-1.png?w=456
[plot of chunk unnamed-chunk-13]: http://i2.wp.com/jkunst.com/images/visualizing-chess-data-with-ggplot/unnamed-chunk-13-1.png?w=456
[plot of chunk unnamed-chunk-15]: http://i0.wp.com/jkunst.com/images/visualizing-chess-data-with-ggplot/unnamed-chunk-15-1.png?w=456
[ForceAtlas2]: https://github.com/analyxcompany/ForceAtlas2
[Adolfo Alvarez]: http://adolfoalvarez.cl/
[plot of chunk unnamed-chunk-17]: http://i2.wp.com/jkunst.com/images/visualizing-chess-data-with-ggplot/unnamed-chunk-17-1.png?w=456
[Sicilian Opening]: https://en.wikibooks.org/wiki/Chess_Opening_Theory/1._e4/1...c5/2._Nf3/2...d6/3._d4/3...cxd4/4._Nxd4