Awesome
<!-- README.md is generated from README.Rmd. Please edit that file -->GeomMLBStadiums
This package defines a couple of Geoms to draw MLB stadiums in ggplot2.
It also provides a Geom to draw a “spraychart” - x and y locations
of batted balls with a stadium overlay.
Example use
Install from github and load the necessary libraries
devtools::install_github("bdilday/GeomMLBStadiums")
library(GeomMLBStadiums)
library(ggplot2)
library(dplyr)
The stadium data
When you load the GeomMLBStadiums package it will attach the stadium
paths as a data frame, MLBStadiumsPathData
head(MLBStadiumsPathData)
#> # A tibble: 6 × 4
#> team x y segment
#> <chr> <dbl> <dbl> <chr>
#> 1 angels 125. 205. foul_lines
#> 2 angels 121. 201. foul_lines
#> 3 angels 117. 196. foul_lines
#> 4 angels 113. 192. foul_lines
#> 5 angels 109. 188. foul_lines
#> 6 angels 105. 184. foul_lines
The data comprise the 30 current MLB stadiums, in addition to a “generic” stadium. The stadia are identified by team name, with the following conventions
unique(MLBStadiumsPathData$team)
#> [1] "angels" "astros" "athletics" "blue_jays" "braves"
#> [6] "brewers" "cardinals" "cubs" "diamondbacks" "dodgers"
#> [11] "giants" "guardians" "mariners" "marlins" "mets"
#> [16] "nationals" "orioles" "padres" "phillies" "pirates"
#> [21] "rangers" "rays" "red_sox" "reds" "rockies"
#> [26] "royals" "tigers" "twins" "white_sox" "yankees"
#> [31] "generic"
The segments are split up into outfield_outer, outfield_inner,
infield_inner, infield_outer, foul_lines, and home_plate
unique(MLBStadiumsPathData$segment)
#> [1] "foul_lines" "home_plate" "infield_inner" "infield_outer"
#> [5] "outfield_inner" "outfield_outer"
Coordinates
The stadium paths are in the system of the hc_x and hc_y coordinates
of MLBAM. These are inverted (because they’re based on a display device
where y=0 is at top, IIRC) which means by default the field gets
displayed upside down. This package provides a helper function,
mlbam_xy_transformation, that transforms these values to a system
where y increases from bottom to top and home plate is located at
(0, 0).
set.seed(101)
batted_ball_data = data.frame(hc_x = rnorm(20, 125, 10),
hc_y = rnorm(20, 100, 20))
head(batted_ball_data)
#> hc_x hc_y
#> 1 121.7396 96.72489
#> 2 130.5246 114.17044
#> 3 118.2506 94.64039
#> 4 127.1436 70.72156
#> 5 128.1077 114.88872
#> 6 136.7397 71.79220
head(mlbam_xy_transformation(batted_ball_data))
#> hc_x hc_y hc_x_ hc_y_
#> 1 121.7396 96.72489 -8.136798 255.2450
#> 2 130.5246 114.17044 13.787630 211.7067
#> 3 118.2506 94.64039 -16.844378 260.4473
#> 4 127.1436 70.72156 5.349707 320.1408
#> 5 128.1077 114.88872 7.755777 209.9141
#> 6 136.7397 71.79220 29.298336 317.4688
summary(mlbam_xy_transformation(batted_ball_data))
#> hc_x hc_y hc_x_ hc_y_
#> Min. :104.5 Min. : 58.54 Min. :-51.169 Min. :187.7
#> 1st Qu.:118.0 1st Qu.: 92.36 1st Qu.:-17.592 1st Qu.:211.3
#> Median :123.5 Median :107.28 Median : -3.819 Median :228.9
#> Mean :124.0 Mean : 99.90 Mean : -2.429 Mean :247.3
#> 3rd Qu.:130.3 3rd Qu.:114.35 3rd Qu.: 13.301 3rd Qu.:266.1
#> Max. :139.3 Max. :123.80 Max. : 35.632 Max. :350.5
geom_mlb_stadium
This uses geom_mlb_stadium, which implicitly loads the
MLBStadiumsPathData data, to plot the 30 current stadiums.
ggplot() +
geom_mlb_stadium(stadium_ids = "all_mlb",
stadium_segments = "all") +
facet_wrap(~team) +
coord_fixed() +
theme_void()
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An alternative way is to explicitly pass the data to geom_path.
MLBStadiumsPathData %>%
filter(team != 'generic') %>%
mutate(g=paste(team, segment, sep="_")) %>%
ggplot(aes(x, y)) +
geom_path(aes(group=g)) +
facet_wrap(~team) +
coord_fixed() +
theme_void()
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This shows the generic stadium, which is the default,
ggplot() +
geom_mlb_stadium(stadium_segments = "all") +
facet_wrap(~team) +
coord_fixed() +
theme_void()
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geom_spraychart
This generates some simulated data.
# first generate the data
set.seed(101)
batted_ball_data = data.frame(hc_x = rnorm(20, 125, 10),
hc_y = rnorm(20, 100, 20))
batted_ball_data$team = rep(c("angels", "yankees"), each=10)
This plots the data as a spraychart. By default it uses the “generic” stadium.
batted_ball_data %>%
ggplot(aes(x=hc_x, y=hc_y)) +
geom_spraychart()
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Add some styling using theme_void and coord_fixed
batted_ball_data %>%
ggplot(aes(x=hc_x, y=hc_y)) +
geom_spraychart() +
theme_void() +
coord_fixed()
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This transforms the data and the stadium before plotting, passes the
team names in stadium_ids, draws all segments, and facets by field.
batted_ball_data %>% mlbam_xy_transformation() %>%
ggplot(aes(x=hc_x_, y=hc_y_, color=team)) +
geom_spraychart(stadium_ids = unique(batted_ball_data$team),
stadium_transform_coords = TRUE,
stadium_segments = "all") +
theme_void() +
coord_fixed() +
facet_wrap(~team) +
theme(legend.position = "bottom")
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You can make use of any of the other ggplot2 functions, for example,
contours from stat_density2d. The mapping argument for
geom_spraychart gets passed to the underlying geom_point, as do any
extra parameters passed into the ... argument of geom_spraychart,
e.g. size=5 in the below.
batted_ball_data %>% mlbam_xy_transformation() %>%
ggplot(aes(x=hc_x_, y=hc_y_, color=team)) +
geom_spraychart(mapping = aes(shape=team),
stadium_ids = unique(batted_ball_data$team),
stadium_transform_coords = TRUE,
stadium_segments = "all", size=5) +
theme_void() +
coord_fixed() +
facet_wrap(~team) +
theme(legend.position = "bottom") +
stat_density2d(color='gray')
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