Visualizing Chess Data With ggplot

A collection of chess visualization using only… yeah, you know! only ggplot2.

data-visualization
ggplot2
chess
Author

Joshua Kunst Fuentes

Published

October 30, 2015

There are nice visualizations from chess data: piece movement, piece survaviliy, square usage by player, etc. 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.

The Data

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

Code
library(tidyverse)
library(ggplot2)
library(rchess)

theme_set(
  theme_void(base_family = "Segoe UI") + 
    theme(legend.position = "none")
  )

data(chesswc)

chesswc
# A tibble: 1,266 × 11
   event   site  date       round white black result white…¹ black…² eco   pgn  
   <chr>   <chr> <date>     <dbl> <chr> <chr> <chr>    <int>   <int> <chr> <chr>
 1 FIDE W… Khan… 2011-08-28   1.1 Kaab… Karj… 0-1       2344    2788 D15   1. d…
 2 FIDE W… Khan… 2011-08-28   1.1 Ivan… Stee… 1-0       2768    2362 E68   1. c…
 3 FIDE W… Khan… 2011-08-28   1.1 Ibra… Mame… 0-1       2402    2765 E67   1. N…
 4 FIDE W… Khan… 2011-08-28   1.1 Pono… Gwaz… 1-0       2764    2434 B40   1. e…
 5 FIDE W… Khan… 2011-08-28   1.1 Hans… Gash… 0-1       2449    2760 A61   1. d…
 6 FIDE W… Khan… 2011-08-28   1.1 Gris… Genb… 1-0       2746    2452 D37   1. d…
 7 FIDE W… Khan… 2011-08-28   1.1 De L… Radj… 0-1       2477    2744 B30   1. e…
 8 FIDE W… Khan… 2011-08-28   1.1 Kams… Di B… 1-0       2741    2480 B90   1. e…
 9 FIDE W… Khan… 2011-08-28   1.1 Lima… Svid… 1/2-1…    2493    2739 D85   1. d…
10 FIDE W… Khan… 2011-08-28   1.1 Jako… Sale… 1-0       2736    2493 B69   1. e…
# … with 1,256 more rows, and abbreviated variable names ¹​whiteelo, ²​blackelo
Code
chesswc %>% 
  count(event)
# A tibble: 3 × 2
  event                   n
  <chr>               <int>
1 FIDE World Cup 2011   398
2 FIDE World Cup 2013   435
3 FIDE World Cup 2015   433
Code
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.

Code
set.seed(123)
pgn <- sample(chesswc$pgn, size = 1)
str_sub(pgn, 0, 50)
[1] "1. d4 Nf6 2. c4 e6 3. Nc3 Bb4 4. Nf3 b6 5. g3 Bb7 "

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

Code
chss <- Chess$new()
chss$load_pgn(pgn)
[1] TRUE
Code
chss$history_detail() %>%
  arrange(number_move)
# A tibble: 178 × 8
   piece     from  to    number_move piece_number_move status    numbe…¹ captu…²
   <chr>     <chr> <chr>       <int>             <int> <chr>       <int> <chr>  
 1 d2 Pawn   d2    d4              1                 1 <NA>           NA <NA>   
 2 g8 Knight g8    f6              2                 1 <NA>           NA <NA>   
 3 c2 Pawn   c2    c4              3                 1 game over      NA <NA>   
 4 e7 Pawn   e7    e6              4                 1 <NA>           NA <NA>   
 5 b1 Knight b1    c3              5                 1 captured       12 f8 Bis…
 6 f8 Bishop f8    b4              6                 1 <NA>           NA <NA>   
 7 g1 Knight g1    f3              7                 1 <NA>           NA <NA>   
 8 b7 Pawn   b7    b6              8                 1 game over      NA <NA>   
 9 g2 Pawn   g2    g3              9                 1 <NA>           NA <NA>   
10 c8 Bishop c8    b7             10                 1 <NA>           NA <NA>   
# … with 168 more rows, and abbreviated variable names ¹​number_move_capture,
#   ²​captured_by

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.

Code
chesswc <- chesswc %>%
  mutate(game_id = row_number())

library(furrr)
plan(multisession, workers = 4)

dfmoves <- chesswc %>% 
  select(game_id, pgn) %>% 
  mutate(
    data = future_map(pgn, function(p) {
      chss <- Chess$new()
      chss$load_pgn(p)
      chss$history_detail()
    })
  ) %>% select(-pgn) %>% 
  unnest()

# library(doParallel)
# workers <- makeCluster(parallel::detectCores())
# registerDoParallel(workers)
# moves <- plyr::llply(chesswc %>% pull(pgn), function(p) {
#   chss <- Chess$new()
#   chss$load_pgn(p)
#   chss$history_detail()
#   },  .parallel = TRUE, .paropts = list(.packages = c("rchess")))

dfmoves
# A tibble: 41,731 × 9
   game_id piece     from  to    number_move piece_numb…¹ status numbe…² captu…³
     <int> <chr>     <chr> <chr>       <int>        <int> <chr>    <int> <chr>  
 1       1 a1 Rook   a1    c1             57            1 <NA>        NA <NA>   
 2       1 a1 Rook   c1    h1             65            2 <NA>        NA <NA>   
 3       1 a1 Rook   h1    h5             73            3 <NA>        NA <NA>   
 4       1 a1 Rook   h5    h7             89            4 <NA>        NA <NA>   
 5       1 a1 Rook   h7    e7             95            5 captu…      96 Black …
 6       1 b1 Knight b1    d2             13            1 <NA>        NA <NA>   
 7       1 b1 Knight d2    f1             69            2 <NA>        NA <NA>   
 8       1 b1 Knight f1    e3             75            3 captu…      76 g8 Kni…
 9       1 c1 Bishop c1    b2             31            1 <NA>        NA <NA>   
10       1 c1 Bishop b2    c1             87            2 game …      NA <NA>   
# … with 41,721 more rows, and abbreviated variable names ¹​piece_number_move,
#   ²​number_move_capture, ³​captured_by

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().

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

dfboard
# A tibble: 64 × 6
   cell  col     row     x     y cc   
   <chr> <chr> <int> <int> <int> <chr>
 1 a1    a         1     1     1 b    
 2 b1    b         1     2     1 w    
 3 c1    c         1     3     1 b    
 4 d1    d         1     4     1 w    
 5 e1    e         1     5     1 b    
 6 f1    f         1     6     1 w    
 7 g1    g         1     7     1 b    
 8 h1    h         1     8     1 w    
 9 a2    a         2     1     2 w    
10 b2    b         2     2     2 b    
# … with 54 more rows

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

Code
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.

Code
pieces <- "f1 Bishop"

dfpaths_piece <- dfpaths %>% 
  filter(piece == pieces)

ggplot() +
  geom_tile(data = dfboard, aes(x, y, fill = cc)) +
  geom_curve(
    data = dfpaths_piece %>% filter(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_piece %>% filter(!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()

In the same way we can plot every piece.

Code
pieces <- c("White Queen",
            "h1 Rook",
            "b8 Knight",
            "g2 Pawn",
            "c1 Bishop",
            "f7 Pawn")

dfpaths_pieces <- dfpaths %>% 
  filter(piece %in% pieces)

ggplot() +
  geom_tile(data = dfboard, aes(x, y, fill = cc)) +
  geom_curve(
    data = dfpaths_pieces %>% filter(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_pieces %>% filter(!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")) +
  coord_equal() +
  facet_wrap(vars(piece), ncol = 3)

I think it’s look very nice and 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.

Code
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))
# A tibble: 32 × 4
   piece      games was_captured surv_rate
   <chr>      <int>        <int>     <dbl>
 1 Black King   433            0     1    
 2 White King   433            0     1    
 3 h2 Pawn      433          121     0.721
 4 h7 Pawn      433          148     0.658
 5 g2 Pawn      433          150     0.654
 6 g7 Pawn      433          160     0.630
 7 f2 Pawn      433          178     0.589
 8 a2 Pawn      433          183     0.577
 9 a7 Pawn      433          185     0.573
10 f7 Pawn      433          185     0.573
# … with 22 more rows

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.

Code
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 = 3) +
  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(legend.position = "none")

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:

Code
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(legend.position = "bottom")

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.

Code
players <- chesswc %>% 
  count(white) %>% 
  arrange(desc(n)) %>%
  pull(white) %>% 
  head(4)

players
[1] "Karjakin, Sergey" "Svidler, Peter"   "Wei, Yi"          "Adams, Michael"
Code
dfmov_players <- map_df(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 cell\n(normalized by number of games)",
                      low = "white",  high = "darkblue") +
  geom_text(aes(x, row, label = round(p, 1)), size = 2, 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(legend.position = "bottom")

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.

Code
piece_lvls <- rchess:::.chesspiecedata() %>%
  mutate(col = str_extract(start_position, "\\w{1}"),
         row = str_extract(start_position, "\\d{1}")) %>%
  arrange(desc(row), col) %>%
  pull(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) +
  scale_x_continuous(breaks = c(0, 20, 40), limits = c(0, 40)) + 
  facet_wrap(~piece, nrow = 4, ncol = 8, scales = "free_y")  +
  labs(x = "Number Move", y = "Density") +
  theme_minimal(base_size = 7) +
  theme(strip.text = element_text(hjust = 0))

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.

Code
library(igraph)

# devtools::install_github("analyxcompany/ForceAtlas2")
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.

Code
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 = 13, 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 = 5, color = "gray50") +
  ggtitle("Red: white captures black | Blue: black captures white") +
  theme(legend.position = "none")

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 because 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.

Reuse

https://creativecommons.org/licenses/by/4.0/

Citation

BibTeX citation:
@online{kunstfuentes2015,
  author = {Joshua Kunst Fuentes},
  title = {Visualizing {Chess} {Data} {With} Ggplot},
  date = {2015-10-30},
  url = {https://jkunst.com/blog/posts/2015-10-30-visualizing-chess-data-with-ggplot},
  langid = {en}
}
For attribution, please cite this work as:
Joshua Kunst Fuentes. 2015. “Visualizing Chess Data With Ggplot.” October 30, 2015. https://jkunst.com/blog/posts/2015-10-30-visualizing-chess-data-with-ggplot.