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<img = >

source("https://raw.githubusercontent.com/ptallon/SportsAnalytics_Fall2026/refs/heads/main/SharedCode.R")



load_packages(c("data.table", "dplyr", "ggplot2", "stringr", "scales", "hms", "gganimate", "gifski", "ggforce", "sf"))


suppressMessages(library(data.table))

y1 <- fread("tracking2018.csv")
y2 <- fread("tracking2019.csv")
y3 <- fread("tracking2020.csv")
##3 These are data frames

tracking_df <- rbind(y1, y2, y3)
rm(y1, y2, y3)
plays <- fread("C:\\Users\\toria\\OneDrive\\Documents\\Rstudio\\nfl-big-data-bowl-2022\\plays.csv")

players <- fread("C:\\Users\\toria\\OneDrive\\Documents\\Rstudio\\nfl-big-data-bowl-2022\\players.csv")

games <- fread("C:\\Users\\toria\\OneDrive\\Documents\\Rstudio\\nfl-big-data-bowl-2022\\games.csv")

plays_df <- left_join(plays, players, by = c("kickerId" = "nflId"))
plays_df <- left_join(plays_df, games, by = c("gameId"))

df <- tracking_df |>
  filter(gameId == 2018123000, playId == 36) |>
  left_join(plays |> select(gameId, playId,
                            absoluteYardlineNumber, playDescription),
            by = c("gameId", "playId")) |>
  data.frame()

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source("https://raw.githubusercontent.com/mlfurman3/gg_field/main/gg_field.R")

ggplot(df) +
   # change the colors, shapes, and sizes of the points
  scale_size_manual(values = c(6, 4, 6), guide = "none") +
  scale_shape_manual(values = c(21, 16, 21), guide = "none") +
  scale_fill_manual(values = c("firebrick1", "black", "purple"), guide = "none") +
  scale_color_manual(values = c("black", "#663300", "black"), guide = "none") +
  
  # visualize the field of play
  gg_field(yardmin = max(-5, min(df$x)-5), yardmax = min(125, max(df$x)+5)) +
  
  # Add points for each players, and the ball
  geom_point(aes(x = x, y = y, shape = team, color = team,
                 size = team, fill = team))

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