Awesome

A `ggplot2` and `gganimate` Version of Pac-Man <img src="man/figures/ggpacman.gif" align="right" width="120" />

The goal of ggpacman is to …
Make a GIF of the game Pac-Man (not to develop an R version of Pac-Man …).

Installation

# Install ggpacman from CRAN:
install.packages("ggpacman")

# Or the the development version from GitHub:
# install.packages("remotes")
remotes::install_github("mcanouil/ggpacman")

Pac-Man in action

library(ggpacman)
animate_pacman(
  pacman = pacman,
  ghosts = list(blinky, pinky, inky, clyde),
  font_family = "xkcd"
)

The Story of `ggpacman`

It started on a Saturday evening …

It was the 21st of March (for the sake of precision), around 10 pm CET (also for the sake of precision and mostly because it is not relevant). I was playing around with my data on ‘all’ the movies I have seen so far (mcanouil/IMDbRating) and looking on possibly new ideas of visualisation on twitter using #ggplot2 and #gganimate (by the way the first time I played with gganimate was at useR-2018 (Brisbane, Australia), just before and when @thomasp85 released the actual framework). The only thing on the feed was “contaminated/deaths and covid-19” curves made with ggplot2 and a few with gganimate … Let’s say, it was not as funny and interesting as I was hoping for … Then, I’ve got an idea, what if I can do something funny and not expected with ggplot2 and gganimate? My first thought, was let’s draw and animate Pac-Man, that should not be that hard!

Well, it was not that easy after-all … But, I am going to go through my code here (you might be interested to actually look at the commits history.

<blockquote class="twitter-tweet"> Maybe I went too far with <a href="https://twitter.com/hashtag/ggplot2?src=hash&ref_src=twsrc%5Etfw">\#ggplot2</a> and <a href="https://twitter.com/hashtag/gganimate?src=hash&ref_src=twsrc%5Etfw">\#gganimate</a> …😅 What do you think <a href="https://twitter.com/hadleywickham?ref_src=twsrc%5Etfw">@hadleywickham</a> & <a href="https://twitter.com/thomasp85?ref_src=twsrc%5Etfw">@thomasp85</a> , did I go too far or not enough ? (I am planning to add the ghosts 😎) <a href="https://t.co/nkfbti1Etd">pic.twitter.com/nkfbti1Etd</a> — Mickaël CANOUIL (@mickaelcanouil) <a href="https://twitter.com/mickaelcanouil/status/1241760925499170824?ref_src=twsrc%5Etfw">March 22, 2020</a> </blockquote>

The packages
The maze layer
Pac-Man character
The Ghosts characters
1. Body
2. Eyes
3. Ghost shape
How Pac-Man interacts with the maze?
1. Bonus points
2. Ghost "weak" and "eaten" states
Plot time

The packages

library("stats")
library("utils")
library("rlang")
library("magrittr")
library("dplyr")
library("tidyr")
library("purrr")
library("ggplot2")
library("ggforce")
library("gganimate")
library("ggtext")

The maze layer

The base layer

First thing first, I needed to set-up the base layer, meaning, the maze from Pac-Man. I did start by setting the coordinates of the maze.

base_layer <- ggplot() +
  theme_void() +
  theme(
    legend.position = "none",
    plot.background = element_rect(fill = "black", colour = "black"),
    panel.background = element_rect(fill = "black", colour = "black"),
  ) +
  coord_fixed(xlim = c(0, 20), ylim = c(0, 26))

For later use, I defined some scales (actually those scales, where defined way after chronologically speaking). I am using those to define sizes and colours for all the geometries I am going to use to achieve the Pac-Man GIF.

map_colours <- c(
  "READY!" = "goldenrod1",
  "wall" = "dodgerblue3", "door" = "dodgerblue3",
  "normal" = "goldenrod1", "big" = "goldenrod1", "eaten" = "black",
  "Pac-Man" = "yellow",
  "eye" = "white", "iris" = "black",
  "Blinky" = "red", "Blinky_weak" = "blue", "Blinky_eaten" = "transparent",
  "Pinky" = "pink", "Pinky_weak" = "blue", "Pinky_eaten" = "transparent",
  "Inky" = "cyan", "Inky_weak" = "blue", "Inky_eaten" = "transparent",
  "Clyde" = "orange", "Clyde_weak" = "blue", "Clyde_eaten" = "transparent"
)

base_layer <- base_layer +
  scale_size_manual(values = c("wall" = 2.5, "door" = 1, "big" = 2.5, "normal" = 0.5, "eaten" = 3)) +
  scale_fill_manual(breaks = names(map_colours), values = map_colours) +
  scale_colour_manual(breaks = names(map_colours), values = map_colours)

My base_layer here is not really helpful, so I temporarily added some elements to help me draw everything on it. Note: I won’t use it in the following.

base_layer +
  scale_x_continuous(breaks = 0:21, sec.axis = dup_axis()) +
  scale_y_continuous(breaks = 0:26, sec.axis = dup_axis()) +
  theme(
    panel.grid.major = element_line(colour = "white"),
    axis.text = element_text(colour = "white")
  ) +
  annotate("rect", xmin = 0, xmax = 21, ymin = 0, ymax = 26, fill = NA)

Quite better, isn’t it?!

The grid layer

Here, I am calling “grid”, the walls of the maze. For this grid, I started drawing the vertical lines on the left side of the maze (as you may have noticed, the first level is symmetrical).

left_vertical_segments <- tribble(
  ~x, ~y, ~xend, ~yend,
  0, 0, 0, 9,
  0, 17, 0, 26,
  2, 4, 2, 5,
  2, 19, 2, 20,
  2, 22, 2, 24,
  4, 4, 4, 7,
  4, 9, 4, 12,
  4, 14, 4, 17,
  4, 19, 4, 20,
  4, 22, 4, 24,
  6, 2, 6, 5,
  6, 9, 6, 12,
  6, 14, 6, 20,
  6, 22, 6, 24,
  8, 4, 8, 5,
  8, 9, 8, 10,
  8, 12, 8, 15,
  8, 19, 8, 20,
  8, 22, 8, 24
)

base_layer +
  geom_segment(
    data = left_vertical_segments,
    mapping = aes(x = x, y = y, xend = xend, yend = yend),
    lineend = "round",
    inherit.aes = FALSE,
    colour = "white"
  )

Then, I added the horizontal lines (still only on the left side of the maze)!

left_horizontal_segments <- tribble(
  ~x, ~y, ~xend, ~yend,
  0, 0, 10, 0,
  2, 2, 8, 2,
  0, 4, 2, 4,
  8, 4, 10, 4,
  0, 5, 2, 5,
  8, 5, 10, 5,
  2, 7, 4, 7,
  6, 7, 8, 7,
  0, 9, 4, 9,
  8, 9, 10, 9,
  8, 10, 10, 10,
  0, 12, 4, 12,
  8, 12, 10, 12,
  0, 14, 4, 14,
  8, 15, 9, 15,
  0, 17, 4, 17,
  6, 17, 8, 17,
  2, 19, 4, 19,
  8, 19, 10, 19,
  2, 20, 4, 20,
  8, 20, 10, 20,
  2, 22, 4, 22,
  6, 22, 8, 22,
  2, 24, 4, 24,
  6, 24, 8, 24,
  0, 26, 10, 26
)

left_segments <- bind_rows(left_vertical_segments, left_horizontal_segments)

base_layer +
  geom_segment(
    data = left_segments,
    mapping = aes(x = x, y = y, xend = xend, yend = yend),
    lineend = "round",
    inherit.aes = FALSE,
    colour = "white"
  )

The maze is slowly appearing, but surely. As I wrote earlier, the first level is symmetrical, so I used my left lines left_segments to compute all the lines on the right right_segments.

right_segments <-  mutate(
  .data = left_segments,
  x = abs(x - 20),
  xend = abs(xend - 20)
)

base_layer +
  geom_segment(
    data = bind_rows(left_segments, right_segments),
    mapping = aes(x = x, y = y, xend = xend, yend = yend),
    lineend = "round",
    inherit.aes = FALSE,
    colour = "white"
  )

The middle vertical lines were missing, i.e., I did not want to plot them twice, which would have happen, if I added these in left_segments. Also, the “door” of the ghost spawn area is missing. I added the door and the missing vertical walls in the end.

centre_vertical_segments <- tribble(
  ~x, ~y, ~xend, ~yend,
  10, 2, 10, 4,
  10, 7, 10, 9,
  10, 17, 10, 19,
  10, 22, 10, 26
)
door_segment <- tibble(x = 9, y = 15, xend = 11, yend = 15, type = "door")

Finally, I combined all the segments and drew them all.

maze_walls <- bind_rows(
  left_segments,
  centre_vertical_segments,
  right_segments
) %>%
  mutate(type = "wall") %>%
  bind_rows(door_segment)

base_layer +
  geom_segment(
    data = maze_walls,
    mapping = aes(x = x, y = y, xend = xend, yend = yend),
    lineend = "round",
    inherit.aes = FALSE,
    colour = "white"
  )

The maze is now complete, but no-one can actually see the door, since it appears the same way as the walls. You may have noticed, I added a column named type. type can currently hold two values: "wall" and "door". I am going to use type as values for two aesthetics, you may already have guessed which ones. The answer is the colour and size aesthetics.

base_layer +
  geom_segment(
    data = maze_walls,
    mapping = aes(x = x, y = y, xend = xend, yend = yend, colour = type, size = type),
    lineend = "round",
    inherit.aes = FALSE
  )

Note: maze_walls is a dataset of ggpacman (data("maze_walls", package = "ggpacman")).

The bonus points layer

The strategy was quite the same as for the grid layer:

Setting up the point coordinates for the left side and the middle.
Compute the coordinates for the right side.
Use a column type for the two types of bonus points, i.e., "normal" and "big" (the one who weaken the ghosts).

bonus_points_coord <- function() {
  left_bonus_points <- tribble(
    ~x, ~y, ~type,
    1, c(1:3, 7:8, 18:22, 24:25), "normal",
    1, c(6, 23), "big",
    2, c(1, 3, 6, 8, 18, 21, 25), "normal",
    3, c(1, 3:6, 8, 18, 21, 25), "normal",
    4, c(1, 3, 8, 18, 21, 25), "normal",
    5, c(1, 3:25), "normal",
    6, c(1, 6, 8, 21, 25), "normal",
    7, c(1, 3:6, 8, 18:21, 25), "normal",
    8, c(1, 3, 6, 8, 18, 21, 25), "normal",
    9, c(1:3, 6:8, 18, 21:25), "normal"
  )

  bind_rows(
    left_bonus_points,
    tribble(
      ~x, ~y, ~type,
      10, c(1, 21), "normal"
    ),
    mutate(left_bonus_points, x = abs(x - 20))
  ) %>%
    unnest("y")
}
maze_points <- bonus_points_coord()

maze_layer <- base_layer +
  geom_segment(
    data = maze_walls,
    mapping = aes(x = x, y = y, xend = xend, yend = yend, colour = type, size = type),
    lineend = "round",
    inherit.aes = FALSE
  ) +
  geom_point(
    data = maze_points,
    mapping = aes(x = x, y = y, size = type, colour = type),
    inherit.aes = FALSE
  )

Note: maze_points is a dataset of ggpacman (data("maze_points", package = "ggpacman")).

Pac-Man character

It is now time to draw the main character. To draw Pac-Man, I needed few things:

The Pac-Man moves, i.e., all the coordinates where Pac-Man is supposed to be at every step.

data("pacman", package = "ggpacman")
unnest(pacman, c("x", "y"))
#> # A tibble: 150 x 3
#>        x     y colour 
#>    <dbl> <dbl> <chr>  
#>  1    10     6 Pac-Man
#>  2    10     6 Pac-Man
#>  3    10     6 Pac-Man
#>  4    10     6 Pac-Man
#>  5    10     6 Pac-Man
#>  6    10     6 Pac-Man
#>  7    10     6 Pac-Man
#>  8    10     6 Pac-Man
#>  9    10     6 Pac-Man
#> 10    10     6 Pac-Man
#> # ... with 140 more rows

maze_layer +
  geom_point(
    data = unnest(pacman, c("x", "y")),
    mapping = aes(x = x, y = y, colour = colour),
    size = 4
  )

The Pac-Man shape (open and closed mouth). Since, Pac-Man is not a complete circle shape, I used geom_arc_bar() (from ggforce), and defined the properties of each state of Pac-Man based on the aesthetics required by this function. Note: At first, I wanted a smooth animation/transition of Pac-Man opening and closing its mouth, this is why there are four "close_" states.

pacman_state <- tribble(
  ~state, ~start, ~end,
  "open_right", 14 / 6 * pi, 4 / 6 * pi,
  "close_right", 15 / 6 * pi, 3 / 6 * pi,
  "open_up", 11 / 6 * pi, 1 / 6 * pi,
  "close_up", 12 / 3 * pi, 0 / 6 * pi,
  "open_left", 8 / 6 * pi, - 2 / 6 * pi,
  "close_left", 9 / 6 * pi, - 3 / 6 * pi,
  "open_down", 5 / 6 * pi, - 5 / 6 * pi,
  "close_down", pi, - pi
)

ggplot() +
  geom_arc_bar(
    data = pacman_state,
    mapping = aes(x0 = 0, y0 = 0, r0 = 0, r = 0.5, start = start, end = end),
    fill = "yellow",
    inherit.aes = FALSE
  ) +
  facet_wrap(vars(state), ncol = 4)

<blockquote class="twitter-tweet"> Next mission, should you choose to accept, is to make Pac-Man face the direction of movement🎖 — Thomas Lin Pedersen (@thomasp85) <a href="https://twitter.com/thomasp85/status/1241767912370774020?ref_src=twsrc%5Etfw">March 22, 2020</a> </blockquote>

Once those things available, how to make Pac-Man look where he is headed? Short answer, I just computed the differences between two successive positions of Pac-Man and added both open/close state to a new column state.

pacman %>%
  unnest(c("x", "y")) %>%
  mutate(
    state_x = sign(x - lag(x)),
    state_y = sign(y - lag(y)),
    state = case_when(
      (is.na(state_x) | state_x %in% 0) & (is.na(state_y) | state_y %in% 0) ~ list(c("open_right", "close_right")),
      state_x == 1 & state_y == 0 ~ list(c("open_right", "close_right")),
      state_x == -1 & state_y == 0 ~ list(c("open_left", "close_left")),
      state_x == 0 & state_y == -1 ~ list(c("open_down", "close_down")),
      state_x == 0 & state_y == 1 ~ list(c("open_up", "close_up"))
    )
  )  %>%
  unnest("state")
#> # A tibble: 300 x 6
#>        x     y colour  state_x state_y state      
#>    <dbl> <dbl> <chr>     <dbl>   <dbl> <chr>      
#>  1    10     6 Pac-Man      NA      NA open_right 
#>  2    10     6 Pac-Man      NA      NA close_right
#>  3    10     6 Pac-Man       0       0 open_right 
#>  4    10     6 Pac-Man       0       0 close_right
#>  5    10     6 Pac-Man       0       0 open_right 
#>  6    10     6 Pac-Man       0       0 close_right
#>  7    10     6 Pac-Man       0       0 open_right 
#>  8    10     6 Pac-Man       0       0 close_right
#>  9    10     6 Pac-Man       0       0 open_right 
#> 10    10     6 Pac-Man       0       0 close_right
#> # ... with 290 more rows

Here, in preparation for gganimate, I also added a column step before merging the new upgraded pacman (i.e., with the Pac-Man state column) with the pacman_state defined earlier.

pacman_moves <- ggpacman::compute_pacman_coord(pacman)
pacman_moves
#> # A tibble: 300 x 9
#>        x     y colour  state_x state_y state        step start   end
#>    <dbl> <dbl> <chr>     <dbl>   <dbl> <chr>       <int> <dbl> <dbl>
#>  1    10     6 Pac-Man      NA      NA open_right      1  7.33  2.09
#>  2    10     6 Pac-Man      NA      NA close_right     2  7.85  1.57
#>  3    10     6 Pac-Man       0       0 open_right      3  7.33  2.09
#>  4    10     6 Pac-Man       0       0 close_right     4  7.85  1.57
#>  5    10     6 Pac-Man       0       0 open_right      5  7.33  2.09
#>  6    10     6 Pac-Man       0       0 close_right     6  7.85  1.57
#>  7    10     6 Pac-Man       0       0 open_right      7  7.33  2.09
#>  8    10     6 Pac-Man       0       0 close_right     8  7.85  1.57
#>  9    10     6 Pac-Man       0       0 open_right      9  7.33  2.09
#> 10    10     6 Pac-Man       0       0 close_right    10  7.85  1.57
#> # ... with 290 more rows

maze_layer +
  geom_arc_bar(
    data = pacman_moves,
    mapping = aes(x0 = x, y0 = y, r0 = 0, r = 0.5, start = start, end = end, colour = colour, fill = colour, group = step),
    inherit.aes = FALSE
  )

You can’t see much?! Ok, perhaps it’s time to use gganimate. I am going to animate Pac-Man based on the column step, which is, if you looked at the code above, just the line number of pacman_moves.

animated_pacman <- maze_layer +
  geom_arc_bar(
    data = pacman_moves,
    mapping = aes(x0 = x, y0 = y, r0 = 0, r = 0.5, start = start, end = end, colour = colour, fill = colour, group = step),
    inherit.aes = FALSE
  ) +
  transition_manual(step)

Note: pacman is a dataset of ggpacman (data("pacman", package = "ggpacman")).

The Ghosts characters

Time to draw the ghosts, namely: Blinky, Pinky, Inky and Clyde.

Body

I started with the body, especially the top and the bottom part of the ghost which are half circle (or at least I chose this) and use again geom_arc_bar().

ghost_arc <- tribble(
  ~x0, ~y0, ~r, ~start, ~end, ~part,
  0, 0, 0.5, - 1 * pi / 2, 1 * pi / 2, "top",
  -0.5, -0.5 + 1/6, 1 / 6,  pi / 2, 2 * pi / 2, "bottom",
  -1/6, -0.5 + 1/6, 1 / 6,  pi / 2, 3 * pi / 2, "bottom",
  1/6, -0.5 + 1/6, 1 / 6,  pi / 2, 3 * pi / 2, "bottom",
  0.5, -0.5 + 1/6, 1 / 6,  3 * pi / 2,  2 * pi / 2, "bottom"
)

top <- ggplot() +
  geom_arc_bar(
    data = ghost_arc[1, ],
    mapping = aes(x0 = x0, y0 = y0, r0 = 0, r = r, start = start, end = end)
  ) +
  coord_fixed(xlim = c(-1, 1), ylim = c(-1, 1))

I retrieved the coordinates of the created polygon, using ggplot_build().

top_polygon <- ggplot_build(top)$data[[1]][, c("x", "y")]

And I proceeded the same way for the bottom part of the ghost.

bottom <- ggplot() +
  geom_arc_bar(
    data = ghost_arc[-1, ],
        mapping = aes(x0 = x0, y0 = y0, r0 = 0, r = r, start = start, end = end)
  ) +
  coord_fixed(xlim = c(-1, 1), ylim = c(-1, 1))

bottom_polygon <- ggplot_build(bottom)$data[[1]][, c("x", "y")]

Then, I just added one point to “properly” link the top and the bottom part.

ghost_body <- dplyr::bind_rows(
  top_polygon,
  dplyr::tribble(
    ~x, ~y,
    0.5, 0,
    0.5, -0.5 + 1/6
  ),
  bottom_polygon,
  dplyr::tribble(
    ~x, ~y,
    -0.5, -0.5 + 1/6,
    -0.5, 0
  )
)

I finally got the whole ghost shape I was looking for.

ggplot() +
  coord_fixed(xlim = c(-1, 1), ylim = c(-1, 1)) +
  geom_polygon(
    data = ghost_body,
    mapping = aes(x = x, y = y),
    inherit.aes = FALSE
  )

Note: ghost_body is a dataset of ggpacman (data("ghost_body", package = "ggpacman")).
Note: ghost_body definitely needs some code refactoring.

Eyes

The eyes are quite easy to draw, they are just circles, but … As for Pac-Man before, I wanted the ghosts to look where they are headed. This implies moving the iris one way or the other, and so I defined five states for the iris: right, down, left, up and middle.

ghost_eyes <- tribble(
  ~x0, ~y0, ~r, ~part, ~direction,
  1/5, 1/8, 1/8, "eye", c("up", "down", "right", "left", "middle"),
  -1/5, 1/8, 1/8, "eye", c("up", "down", "right", "left", "middle"),
  5/20, 1/8, 1/20, "iris", "right",
  -3/20, 1/8, 1/20, "iris", "right",
  1/5, 1/16, 1/20, "iris", "down",
  -1/5, 1/16, 1/20, "iris", "down",
  3/20, 1/8, 1/20, "iris", "left",
  -5/20, 1/8, 1/20, "iris", "left",
  1/5, 3/16, 1/20, "iris", "up",
  -1/5, 3/16, 1/20, "iris", "up",
  1/5, 1/8, 1/20, "iris", "middle",
  -1/5, 1/8, 1/20, "iris", "middle"
) %>%
  unnest("direction")

map_eyes <- c("eye" = "white", "iris" = "black")
ggplot() +
  coord_fixed(xlim = c(-0.5, 0.5), ylim = c(-0.5, 0.5)) +
  scale_fill_manual(breaks = names(map_eyes), values = map_eyes) +
  scale_colour_manual(breaks = names(map_eyes), values = map_eyes) +
  geom_circle(
    data = ghost_eyes,
    mapping = aes(x0 = x0, y0 = y0, r = r, colour = part, fill = part),
    inherit.aes = FALSE,
    show.legend = FALSE
  ) +
  facet_wrap(vars(direction), ncol = 3)

Note: ghost_eyes is a dataset of ggpacman (data("ghost_eyes", package = "ggpacman")).

Ghost shape

I had the whole ghost shape and the eyes.

ggplot() +
  coord_fixed(xlim = c(-1, 1), ylim = c(-1, 1)) +
  scale_fill_manual(breaks = names(map_colours), values = map_colours) +
  scale_colour_manual(breaks = names(map_colours), values = map_colours) +
  geom_polygon(
    data = get(data("ghost_body", package = "ggpacman")),
    mapping = aes(x = x, y = y),
    inherit.aes = FALSE
  ) +
  geom_circle(
    data = get(data("ghost_eyes", package = "ggpacman")),
    mapping = aes(x0 = x0, y0 = y0, r = r, colour = part, fill = part),
    inherit.aes = FALSE,
    show.legend = FALSE
  ) +
  facet_wrap(vars(direction), ncol = 3)

Again, same as for Pac-Man, in order to know where the ghosts are supposed to look, I computed the differences of each successive positions of the ghosts and I added the corresponding directions.

blinky_ghost <- tibble(x = c(0, 1, 1, 0, 0), y = c(0, 0, 1, 1, 0), colour = "Blinky") %>%
  unnest(c("x", "y")) %>%
  mutate(
    X0 = x,
    Y0 = y,
    state_x = sign(round(x) - lag(round(x))),
    state_y = sign(round(y) - lag(round(y))),
    direction = case_when(
      (is.na(state_x) | state_x %in% 0) & (is.na(state_y) | state_y %in% 0) ~ "middle",
      state_x == 1 & state_y == 0 ~ "right",
      state_x == -1 & state_y == 0 ~ "left",
      state_x == 0 & state_y == -1 ~ "down",
      state_x == 0 & state_y == 1 ~ "up"
    )
  ) %>%
  unnest("direction")

#> # A tibble: 5 x 8
#>       x     y colour    X0    Y0 state_x state_y direction
#>   <dbl> <dbl> <chr>  <dbl> <dbl>   <dbl>   <dbl> <chr>    
#> 1     0     0 Blinky     0     0      NA      NA middle   
#> 2     1     0 Blinky     1     0       1       0 right    
#> 3     1     1 Blinky     1     1       0       1 up       
#> 4     0     1 Blinky     0     1      -1       0 left     
#> 5     0     0 Blinky     0     0       0      -1 down

I also added some noise around the position, i.e., four noised position at each actual position of a ghost.

blinky_ghost <- blinky_ghost %>%
  mutate(state = list(1:4)) %>%
  unnest("state") %>%
  mutate(
    step = 1:n(),
    noise_x = rnorm(n(), mean = 0, sd = 0.05),
    noise_y = rnorm(n(), mean = 0, sd = 0.05)
  )

#> # A tibble: 20 x 12
#>        x     y colour    X0    Y0 state_x state_y direction state  step  noise_x
#>    <dbl> <dbl> <chr>  <dbl> <dbl>   <dbl>   <dbl> <chr>     <int> <int>    <dbl>
#>  1     0     0 Blinky     0     0      NA      NA middle        1     1 -0.0292 
#>  2     0     0 Blinky     0     0      NA      NA middle        2     2  0.0207 
#>  3     0     0 Blinky     0     0      NA      NA middle        3     3  0.0291 
#>  4     0     0 Blinky     0     0      NA      NA middle        4     4  0.0948 
#>  5     1     0 Blinky     1     0       1       0 right         1     5 -0.0245 
#>  6     1     0 Blinky     1     0       1       0 right         2     6 -0.0230 
#>  7     1     0 Blinky     1     0       1       0 right         3     7 -0.00576
#>  8     1     0 Blinky     1     0       1       0 right         4     8  0.00934
#>  9     1     1 Blinky     1     1       0       1 up            1     9  0.0386 
#> 10     1     1 Blinky     1     1       0       1 up            2    10 -0.0661 
#> 11     1     1 Blinky     1     1       0       1 up            3    11 -0.0216 
#> 12     1     1 Blinky     1     1       0       1 up            4    12 -0.0353 
#> 13     0     1 Blinky     0     1      -1       0 left          1    13  0.0242 
#> 14     0     1 Blinky     0     1      -1       0 left          2    14 -0.0156 
#> 15     0     1 Blinky     0     1      -1       0 left          3    15 -0.119  
#> 16     0     1 Blinky     0     1      -1       0 left          4    16  0.0289 
#> 17     0     0 Blinky     0     0       0      -1 down          1    17  0.0488 
#> 18     0     0 Blinky     0     0       0      -1 down          2    18 -0.0200 
#> 19     0     0 Blinky     0     0       0      -1 down          3    19  0.0608 
#> 20     0     0 Blinky     0     0       0      -1 down          4    20  0.0106 
#> # ... with 1 more variable: noise_y <dbl>

Then, I added (in a weird way I might say) the polygons coordinates for the body and the eyes.

blinky_ghost <- blinky_ghost %>%
  mutate(
    body = pmap(
      .l = list(x, y, noise_x, noise_y),
      .f = function(.x, .y, .noise_x, .noise_y) {
        mutate(
          .data = get(data("ghost_body")),
          x = x + .x + .noise_x,
          y = y + .y + .noise_y
        )
      }
    ),
    eyes = pmap(
      .l = list(x, y, noise_x, noise_y, direction),
      .f = function(.x, .y, .noise_x, .noise_y, .direction) {
        mutate(
          .data = filter(get(data("ghost_eyes")), direction == .direction),
          x0 = x0 + .x + .noise_x,
          y0 = y0 + .y + .noise_y,
          direction = NULL
        )
      }
    ),
    x = NULL,
    y = NULL
  )

#> # A tibble: 20 x 12
#>    colour    X0    Y0 state_x state_y direction state  step  noise_x  noise_y
#>    <chr>  <dbl> <dbl>   <dbl>   <dbl> <chr>     <int> <int>    <dbl>    <dbl>
#>  1 Blinky     0     0      NA      NA middle        1     1 -0.0292  -0.0541 
#>  2 Blinky     0     0      NA      NA middle        2     2  0.0207   0.0203 
#>  3 Blinky     0     0      NA      NA middle        3     3  0.0291   0.0664 
#>  4 Blinky     0     0      NA      NA middle        4     4  0.0948   0.0407 
#>  5 Blinky     1     0       1       0 right         1     5 -0.0245  -0.00714
#>  6 Blinky     1     0       1       0 right         2     6 -0.0230  -0.0977 
#>  7 Blinky     1     0       1       0 right         3     7 -0.00576  0.0145 
#>  8 Blinky     1     0       1       0 right         4     8  0.00934 -0.0348 
#>  9 Blinky     1     1       0       1 up            1     9  0.0386   0.0268 
#> 10 Blinky     1     1       0       1 up            2    10 -0.0661  -0.0559 
#> 11 Blinky     1     1       0       1 up            3    11 -0.0216   0.0248 
#> 12 Blinky     1     1       0       1 up            4    12 -0.0353   0.0693 
#> 13 Blinky     0     1      -1       0 left          1    13  0.0242  -0.0551 
#> 14 Blinky     0     1      -1       0 left          2    14 -0.0156   0.0320 
#> 15 Blinky     0     1      -1       0 left          3    15 -0.119   -0.0295 
#> 16 Blinky     0     1      -1       0 left          4    16  0.0289   0.0512 
#> 17 Blinky     0     0       0      -1 down          1    17  0.0488  -0.00597
#> 18 Blinky     0     0       0      -1 down          2    18 -0.0200  -0.00374
#> 19 Blinky     0     0       0      -1 down          3    19  0.0608  -0.0797 
#> 20 Blinky     0     0       0      -1 down          4    20  0.0106   0.0380 
#> # ... with 2 more variables: body <list>, eyes <list>

For ease, it is now a call to one function directly on the potion matrix of a ghost.

blinky_ghost <- tibble(x = c(0, 1, 1, 0, 0), y = c(0, 0, 1, 1, 0), colour = "Blinky")
blinky_moves <- ggpacman::compute_ghost_coord(blinky_ghost)

blinky_plot <- base_layer +
  coord_fixed(xlim = c(-1, 2), ylim = c(-1, 2)) +
  geom_polygon(
    data = unnest(blinky_moves, "body"),
    mapping = aes(x = x, y = y, fill = colour, colour = colour, group = step),
    inherit.aes = FALSE
  ) +
  geom_circle(
    data = unnest(blinky_moves, "eyes"),
    mapping = aes(x0 = x0, y0 = y0, r = r, colour = part, fill = part, group = step),
    inherit.aes = FALSE
  )

Again, it is better with an animated GIF.

animated_blinky <- blinky_plot + transition_manual(step)

How Pac-Man interacts with the maze?

Bonus points

For ease, I am using some functions I defined to go quickly to the results of the first part of this readme. The idea here is to look at all the position in common between Pac-Man (pacman_moves) and the bonus points (maze_points). Each time Pac-Man was at the same place as a bonus point, I defined a status "eaten" for all values of step after. I ended up with a big table with position and the state of the bonus points.

pacman_moves <- ggpacman::compute_pacman_coord(get(data("pacman", package = "ggpacman")))
right_join(get(data("maze_points")), pacman_moves, by = c("x", "y")) %>%
  distinct(step, x, y, type) %>%
  mutate(
    step = map2(step, max(step), ~ seq(.x, .y, 1)),
    colour = "eaten"
  ) %>%
  unnest("step")
#> # A tibble: 45,150 x 5
#>        x     y type    step colour
#>    <dbl> <dbl> <chr>  <dbl> <chr> 
#>  1     1     1 normal    61 eaten 
#>  2     1     1 normal    62 eaten 
#>  3     1     1 normal    63 eaten 
#>  4     1     1 normal    64 eaten 
#>  5     1     1 normal    65 eaten 
#>  6     1     1 normal    66 eaten 
#>  7     1     1 normal    67 eaten 
#>  8     1     1 normal    68 eaten 
#>  9     1     1 normal    69 eaten 
#> 10     1     1 normal    70 eaten 
#> # ... with 45,140 more rows

Again, for ease, I am using a function I defined to compute everything.

pacman_moves <- ggpacman::compute_pacman_coord(get(data("pacman", package = "ggpacman")))
bonus_points_eaten <- ggpacman::compute_points_eaten(get(data("maze_points")), pacman_moves)

If you don’t recall, maze_layer already includes a geometry with the bonus points.

I could have change this geometry (i.e., geom_point()), but I did not, and draw a new geometry on top of the previous ones. Do you remember the values of the scale for the size aesthetic?

scale_size_manual(values = c("wall" = 2.5, "door" = 1, "big" = 2.5, "normal" = 0.5, "eaten" = 3))

maze_layer_points <- maze_layer +
  geom_point(
    data = bonus_points_eaten,
    mapping = aes(x = x, y = y, colour = colour, size = colour, group = step),
    inherit.aes = FALSE
  )

A new animation to see, how the new geometry is overlapping the previous one as step increases.

animated_points <- maze_layer_points + transition_manual(step)

Ghost `"weak"` and `"eaten"` states

The ghosts were more tricky (I know, they are ghosts …).

I first retrieved all the positions where a "big" bonus point was eaten by Pac-Man.

ghosts_vulnerability <- bonus_points_eaten %>%
  filter(type == "big") %>%
  group_by(x, y) %>%
  summarise(step_init = min(step)) %>%
  ungroup() %>%
  mutate(
    step = map(step_init, ~ seq(.x, .x + 30, 1)),
    vulnerability = TRUE,
    x = NULL,
    y = NULL
  ) %>%
  unnest("step")

#> # A tibble: 93 x 3
#>    step_init  step vulnerability
#>        <dbl> <dbl> <lgl>        
#>  1        79    79 TRUE         
#>  2        79    80 TRUE         
#>  3        79    81 TRUE         
#>  4        79    82 TRUE         
#>  5        79    83 TRUE         
#>  6        79    84 TRUE         
#>  7        79    85 TRUE         
#>  8        79    86 TRUE         
#>  9        79    87 TRUE         
#> 10        79    88 TRUE         
#> # ... with 83 more rows

This is part of a bigger function (I won’t dive too deep into it).

ggpacman::compute_ghost_status
#> function (ghost, pacman_moves, bonus_points_eaten) 
#> {
#>     ghosts_vulnerability <- bonus_points_eaten %>% dplyr::filter(.data[["type"]] == 
#>         "big") %>% dplyr::group_by(.data[["x"]], .data[["y"]]) %>% 
#>         dplyr::summarise(step_init = min(.data[["step"]])) %>% 
#>         dplyr::ungroup() %>% dplyr::mutate(step = purrr::map(.data[["step_init"]], 
#>         ~seq(.x, .x + 30, 1)), vulnerability = TRUE, x = NULL, 
#>         y = NULL) %>% tidyr::unnest("step")
#>     ghost_out <- dplyr::left_join(x = compute_ghost_coord(ghost), 
#>         y = pacman_moves %>% dplyr::mutate(ghost_eaten = TRUE) %>% 
#>             dplyr::select(c(X0 = "x", Y0 = "y", "step", "ghost_eaten")), 
#>         by = c("X0", "Y0", "step")) %>% dplyr::left_join(y = ghosts_vulnerability, 
#>         by = "step") %>% dplyr::mutate(vulnerability = tidyr::replace_na(.data[["vulnerability"]], 
#>         FALSE), ghost_name = .data[["colour"]], ghost_eaten = .data[["ghost_eaten"]] & 
#>         .data[["vulnerability"]], colour = ifelse(.data[["vulnerability"]], 
#>         paste0(.data[["ghost_name"]], "_weak"), .data[["colour"]]))
#>     pos_eaten_start <- which(ghost_out[["ghost_eaten"]])
#>     ghosts_home <- which(ghost_out[["X0"]] == 10 & ghost_out[["Y0"]] == 
#>         14)
#>     for (ipos in pos_eaten_start) {
#>         pos_eaten_end <- min(ghosts_home[ghosts_home >= ipos])
#>         ghost_out[["colour"]][ipos:pos_eaten_end] <- paste0(unique(ghost_out[["ghost_name"]]), 
#>             "_eaten")
#>     }
#>     dplyr::left_join(x = ghost_out, y = ghost_out %>% dplyr::filter(.data[["step"]] == 
#>         .data[["step_init"]] & grepl("eaten", .data[["colour"]])) %>% 
#>         dplyr::mutate(already_eaten = TRUE) %>% dplyr::select(c("step_init", 
#>         "already_eaten")), by = "step_init") %>% dplyr::mutate(colour = dplyr::case_when(.data[["already_eaten"]] & 
#>         .data[["X0"]] == 10 & .data[["Y0"]] == 14 ~ paste0(.data[["ghost_name"]], 
#>         "_eaten"), grepl("weak", .data[["colour"]]) & .data[["already_eaten"]] ~ 
#>         .data[["ghost_name"]], TRUE ~ .data[["colour"]]))
#> }
#> <bytecode: 0x000000001864c448>
#> <environment: namespace:ggpacman>

The goal of this function, is to compute the different states of a ghost, according to the bonus points eaten and, of course, the current Pac-Man position at a determined step.

pacman_moves <- ggpacman::compute_pacman_coord(get(data("pacman", package = "ggpacman")))
bonus_points_eaten <- ggpacman::compute_points_eaten(get(data("maze_points")), pacman_moves)
ghost_moves <- ggpacman::compute_ghost_status(
  ghost = get(data("blinky", package = "ggpacman")),
  pacman_moves = pacman_moves,
  bonus_points_eaten = bonus_points_eaten
)
ghost_moves %>%
  filter(state == 1) %>%
  distinct(step, direction, colour, vulnerability) %>%
  as.data.frame()
#>          colour direction step vulnerability
#> 1        Blinky    middle    1         FALSE
#> 2        Blinky    middle    5         FALSE
#> 3        Blinky    middle    9         FALSE
#> 4        Blinky    middle   13         FALSE
#> 5        Blinky    middle   17         FALSE
#> 6        Blinky    middle   21         FALSE
#> 7        Blinky    middle   25         FALSE
#> 8        Blinky    middle   29         FALSE
#> 9        Blinky    middle   33         FALSE
#> 10       Blinky      left   37         FALSE
#> 11       Blinky      left   41         FALSE
#> 12       Blinky      left   45         FALSE
#> 13       Blinky      down   49         FALSE
#> 14       Blinky      down   53         FALSE
#> 15       Blinky      down   57         FALSE
#> 16       Blinky      left   61         FALSE
#> 17       Blinky      left   65         FALSE
#> 18       Blinky      down   69         FALSE
#> 19       Blinky      down   73         FALSE
#> 20       Blinky      down   77         FALSE
#> 21  Blinky_weak      down   81          TRUE
#> 22  Blinky_weak      down   85          TRUE
#> 23 Blinky_eaten      left   89          TRUE
#> 24 Blinky_eaten     right   93          TRUE
#> 25 Blinky_eaten    middle   97          TRUE
#> 26 Blinky_eaten    middle  101          TRUE
#> 27 Blinky_eaten     right  105          TRUE
#> 28 Blinky_eaten        up  109          TRUE
#> 29 Blinky_eaten     right  113         FALSE
#> 30 Blinky_eaten        up  117         FALSE
#> 31 Blinky_eaten     right  121         FALSE
#> 32 Blinky_eaten        up  125         FALSE
#> 33 Blinky_eaten     right  129         FALSE
#> 34 Blinky_eaten        up  133         FALSE
#> 35 Blinky_eaten     right  137         FALSE
#> 36 Blinky_eaten        up  141          TRUE
#> 37 Blinky_eaten        up  145          TRUE
#> 38 Blinky_eaten    middle  149          TRUE
#> 39 Blinky_eaten    middle  153          TRUE
#> 40 Blinky_eaten    middle  157          TRUE
#> 41       Blinky        up  161          TRUE
#> 42       Blinky        up  165          TRUE
#> 43       Blinky     right  169          TRUE
#> 44       Blinky     right  173         FALSE
#> 45       Blinky     right  177         FALSE
#> 46       Blinky      down  181         FALSE
#> 47       Blinky      down  185         FALSE
#> 48       Blinky      down  189         FALSE
#> 49       Blinky      down  193         FALSE
#> 50       Blinky      down  197         FALSE
#> 51       Blinky      down  201         FALSE
#> 52       Blinky      down  205         FALSE
#> 53       Blinky      down  209         FALSE
#> 54       Blinky      left  213         FALSE
#> 55  Blinky_weak      left  217          TRUE
#> 56  Blinky_weak      down  221          TRUE
#> 57  Blinky_weak      down  225          TRUE
#> 58  Blinky_weak     right  229          TRUE
#> 59  Blinky_weak     right  233          TRUE
#> 60  Blinky_weak     right  237          TRUE
#> 61  Blinky_weak     right  241          TRUE
#> 62  Blinky_weak    middle  245          TRUE
#> 63       Blinky      down  249         FALSE
#> 64       Blinky      down  253         FALSE
#> 65       Blinky      down  257         FALSE
#> 66       Blinky     right  261         FALSE
#> 67       Blinky     right  265         FALSE
#> 68       Blinky        up  269         FALSE
#> 69       Blinky        up  273         FALSE
#> 70       Blinky        up  277         FALSE
#> 71       Blinky    middle  281         FALSE
#> 72       Blinky     right  285         FALSE
#> 73       Blinky     right  289         FALSE
#> 74       Blinky        up  293         FALSE
#> 75       Blinky        up  297         FALSE

To simplify a little, below a small example of a ghost moving in one direction with predetermined states.

blinky_ghost <- bind_rows(
  tibble(x = 1:4, y = 0, colour = "Blinky"),
  tibble(x = 5:8, y = 0, colour = "Blinky_weak"),
  tibble(x = 9:12, y = 0, colour = "Blinky_eaten")
)
blinky_moves <- ggpacman::compute_ghost_coord(blinky_ghost)

#> # A tibble: 48 x 12
#>    colour    X0    Y0 state_x state_y direction state  step noise_x  noise_y
#>    <chr>  <int> <dbl>   <dbl>   <dbl> <chr>     <int> <int>   <dbl>    <dbl>
#>  1 Blinky     1     0      NA      NA middle        1     1  0.127  -0.0141 
#>  2 Blinky     1     0      NA      NA middle        2     2  0.0415  0.0486 
#>  3 Blinky     1     0      NA      NA middle        3     3  0.0760  0.0939 
#>  4 Blinky     1     0      NA      NA middle        4     4 -0.0422 -0.0986 
#>  5 Blinky     2     0       1       0 right         1     5 -0.0761 -0.0399 
#>  6 Blinky     2     0       1       0 right         2     6 -0.0731 -0.00982
#>  7 Blinky     2     0       1       0 right         3     7 -0.0512  0.0151 
#>  8 Blinky     2     0       1       0 right         4     8 -0.0328 -0.0335 
#>  9 Blinky     3     0       1       0 right         1     9 -0.0151  0.0816 
#> 10 Blinky     3     0       1       0 right         2    10 -0.0248  0.00195
#> # ... with 38 more rows, and 2 more variables: body <list>, eyes <list>

blinky_plot <- base_layer +
  coord_fixed(xlim = c(0, 13), ylim = c(-1, 1)) +
  geom_polygon(
    data = unnest(blinky_moves, "body"),
    mapping = aes(x = x, y = y, fill = colour, colour = colour, group = step),
    inherit.aes = FALSE
  ) +
  geom_circle(
    data = unnest(blinky_moves, "eyes"),
    mapping = aes(x0 = x0, y0 = y0, r = r, colour = part, fill = part, group = step),
    inherit.aes = FALSE
  )

I am sure, you remember all the colours and their mapped values from the beginning, so you probably won’t need the following to understand of the ghost disappeared.

"Blinky" = "red", "Blinky_weak" = "blue", "Blinky_eaten" = "transparent",

Note: yes, "transparent" is a colour and a very handy one.

A new animation to see our little Blinky in action?

animated_blinky <- blinky_plot + transition_manual(step)

Plot time (to summarise a little (or a lot))

In the current version, nearly everything is either a dataset or a function and could be used like this.

Load and compute the data.

data("pacman", package = "ggpacman")
data("maze_points", package = "ggpacman")
data("maze_walls", package = "ggpacman")
data("blinky", package = "ggpacman")
data("pinky", package = "ggpacman")
data("inky", package = "ggpacman")
data("clyde", package = "ggpacman")
ghosts <- list(blinky, pinky, inky, clyde)
pacman_moves <- ggpacman::compute_pacman_coord(pacman)
bonus_points_eaten <- ggpacman::compute_points_eaten(maze_points, pacman_moves)
map_colours <- c(
  "READY!" = "goldenrod1",
  "wall" = "dodgerblue3", "door" = "dodgerblue3",
  "normal" = "goldenrod1", "big" = "goldenrod1", "eaten" = "black",
  "Pac-Man" = "yellow",
  "eye" = "white", "iris" = "black",
  "Blinky" = "red", "Blinky_weak" = "blue", "Blinky_eaten" = "transparent",
  "Pinky" = "pink", "Pinky_weak" = "blue", "Pinky_eaten" = "transparent",
  "Inky" = "cyan", "Inky_weak" = "blue", "Inky_eaten" = "transparent",
  "Clyde" = "orange", "Clyde_weak" = "blue", "Clyde_eaten" = "transparent"
)

Build the base layer with the maze.

base_grid <- ggplot() +
  theme_void() +
  theme(
    legend.position = "none",
    plot.caption = element_textbox_simple(halign = 0.5, colour = "white"),
    plot.caption.position = "plot",
    plot.background = element_rect(fill = "black", colour = "black"),
    panel.background = element_rect(fill = "black", colour = "black")
  ) +
  labs(caption = "&copy; Micka&euml;l '<i style='color:#21908CFF;'>Coeos</i>' Canouil") +
  scale_size_manual(values = c("wall" = 2.5, "door" = 1, "big" = 2.5, "normal" = 0.5, "eaten" = 3)) +
  scale_fill_manual(breaks = names(map_colours), values = map_colours) +
  scale_colour_manual(breaks = names(map_colours), values = map_colours) +
  coord_fixed(xlim = c(0, 20), ylim = c(0, 26)) +
  geom_segment(
    data = maze_walls,
    mapping = aes(x = x, y = y, xend = xend, yend = yend, size = type, colour = type),
    lineend = "round",
    inherit.aes = FALSE
  ) +
  geom_point(
    data = maze_points,
    mapping = aes(x = x, y = y, size = type, colour = type),
    inherit.aes = FALSE
  ) +
  geom_text(
    data = tibble(x = 10, y = 11, label = "READY!", step = 1:20),
    mapping = aes(x = x, y = y, label = label, colour = label, group = step),
    size = 6
  )

base_grid

Draw the "eaten" bonus points geometry.

p_points <- list(
  geom_point(
    data = bonus_points_eaten,
    mapping = aes(x = x, y = y, colour = colour, size = colour, group = step),
    inherit.aes = FALSE
  )
)

base_grid + p_points

Draw the main character (I am talking about Pac-Man …)

p_pacman <- list(
  geom_arc_bar(
    data = pacman_moves,
    mapping = aes(
      x0 = x, y0 = y,
      r0 = 0, r = 0.5,
      start = start, end = end,
      colour = colour, fill = colour,
      group = step
    ),
    inherit.aes = FALSE
  )
)

base_grid + p_pacman

Draw the ghosts, using the trick that + works also on a list of geometries.

p_ghosts <- map(.x = ghosts, .f = function(data) {
  ghost_moves <- compute_ghost_status(
    ghost = data,
    pacman_moves = pacman_moves,
    bonus_points_eaten = bonus_points_eaten
  )
  list(
    geom_polygon(
      data = unnest(ghost_moves, "body"),
      mapping = aes(
        x = x, y = y,
        fill = colour, colour = colour,
        group = step
      ),
      inherit.aes = FALSE
    ),
    geom_circle(
      data = unnest(ghost_moves, "eyes"),
      mapping = aes(
        x0 = x0, y0 = y0,
        r = r,
        colour = part, fill = part,
        group = step
      ),
      inherit.aes = FALSE
    )
  )
})

base_grid + p_ghosts

Draw everything.

base_grid + p_points + p_pacman + p_ghosts

Animate everything.

PacMan <- base_grid + p_points + p_pacman + p_ghosts + transition_manual(step)

Getting help

If you encounter a clear bug, please file a minimal reproducible example on github. For questions and other discussion, please contact the package maintainer.

Please note that this project is released with a Contributor Code of Conduct. participating in this project you agree to abide by its terms.

Awesome

A ggplot2 and gganimate Version of Pac-Man <img src="man/figures/ggpacman.gif" align="right" width="120" />

Installation

Pac-Man in action

The Story of ggpacman

The packages

The maze layer

The base layer

The grid layer

The bonus points layer

Pac-Man character

The Ghosts characters

Body

Eyes

Ghost shape

How Pac-Man interacts with the maze?

Bonus points

Ghost "weak" and "eaten" states

Plot time (to summarise a little (or a lot))

Getting help

A `ggplot2` and `gganimate` Version of Pac-Man <img src="man/figures/ggpacman.gif" align="right" width="120" />

The Story of `ggpacman`

Ghost `"weak"` and `"eaten"` states