app.R

# app.R
library(shiny)
#library(tidyverse)
library(ggplot2)
library(dplyr)
library(patchwork)
library(showtext)
library(magick)
library(grid)
library(gridExtra)
library(gtable)
library(httr)

is_barrel <- function(df) {
  df$hit_speedr <- round(df$hit_speed)
  df <- df |>
    mutate(barrel = ifelse((hit_speedr >= 124) &
                             (hit_angle >= 0 & hit_angle <= 50),1,0)) |>
    mutate(barrel = ifelse((hit_speedr == 123) &
                             (hit_angle >= 1 & hit_angle <= 50),1,barrel)) |>
    mutate(barrel = ifelse((hit_speedr == 122) &
                             (hit_angle >= 2 & hit_angle <= 50),1,barrel)) |>
    mutate(barrel = ifelse((hit_speedr == 121) &
                             (hit_angle >= 3 & hit_angle <= 50),1,barrel)) |>
    mutate(barrel = ifelse((hit_speedr == 120) &
                             (hit_angle >= 4 & hit_angle <= 50),1,barrel)) |>
    mutate(barrel = ifelse((hit_speedr == 119) &
                             (hit_angle >= 5 & hit_angle <= 50),1,barrel)) |>
    mutate(barrel = ifelse((hit_speedr == 118) &
                             (hit_angle >= 6 & hit_angle <= 50),1,barrel)) |>
    mutate(barrel = ifelse((hit_speedr == 117) &
                             (hit_angle >= 7 & hit_angle <= 50),1,barrel)) |>
    mutate(barrel = ifelse((hit_speedr == 116) &
                             (hit_angle >= 8 & hit_angle <= 50),1,barrel)) |>
    mutate(barrel = ifelse((hit_speedr == 115) &
                             (hit_angle >= 9 & hit_angle <= 50),1,barrel)) |>
    mutate(barrel = ifelse((hit_speedr == 114) &
                             (hit_angle >= 10 & hit_angle <= 50),1,barrel)) |>
    mutate(barrel = ifelse((hit_speedr == 113) &
                             (hit_angle >= 11 & hit_angle <= 50),1,barrel)) |>
    mutate(barrel = ifelse((hit_speedr == 112) &
                             (hit_angle >= 12 & hit_angle <= 50),1,barrel)) |>
    mutate(barrel = ifelse((hit_speedr == 111) &
                             (hit_angle >= 13 & hit_angle <= 50),1,barrel)) |>
    mutate(barrel = ifelse((hit_speedr == 110) &
                             (hit_angle >= 14 & hit_angle <= 48),1,barrel)) |>
    mutate(barrel = ifelse((hit_speedr == 109) &
                             (hit_angle >= 15 & hit_angle <= 46),1,barrel)) |>
    mutate(barrel = ifelse((hit_speedr == 108) &
                             (hit_angle >= 16 & hit_angle <= 45),1,barrel)) |>
    mutate(barrel = ifelse((hit_speedr == 107) &
                             (hit_angle >= 17 & hit_angle <= 43),1,barrel)) |>
    mutate(barrel = ifelse((hit_speedr == 106) &
                             (hit_angle >= 18 & hit_angle <= 42),1,barrel)) |>
    mutate(barrel = ifelse((hit_speedr == 105) &
                             (hit_angle >= 19 & hit_angle <= 40),1,barrel)) |>
    mutate(barrel = ifelse((hit_speedr == 104) &
                             (hit_angle >= 20 & hit_angle <= 39),1,barrel)) |>
    mutate(barrel = ifelse((hit_speedr == 103) &
                             (hit_angle >= 21 & hit_angle <= 37),1,barrel)) |>
    mutate(barrel = ifelse((hit_speedr == 102) &
                             (hit_angle >= 22 & hit_angle <= 36),1,barrel)) |>
    mutate(barrel = ifelse((hit_speedr == 101) &
                             (hit_angle >= 23 & hit_angle <= 34),1,barrel)) |>
    mutate(barrel = ifelse((hit_speedr == 100) &
                             (hit_angle >= 24 & hit_angle <= 33),1,barrel)) |>
    mutate(barrel = ifelse((hit_speedr == 99) &
                             (hit_angle >= 25 & hit_angle <= 31),1,barrel)) |>
    mutate(barrel = ifelse((hit_speedr == 98) &
                             (hit_angle >= 26 & hit_angle <= 30),1,barrel)) |>
    select(-hit_speedr)
  return(df)
}

apply_percentile_calcs <- function(data) {
  # List of columns to apply percent_rank
  percent_rank_cols <- c("Z-Con%", "Z-Swing%", "O-Con%", "Avg EV", "Max EV", "EV90", "Barrel%", "Swing%", "wOBA",
                         "wOBACON","xwOBA","xDamage")
  
  # List of columns to apply inverse percent_rank
  inverse_percent_rank_cols <- c("Chase%", "Whiff%", "stdev(LA)", "SwStr%")
  
  # Create an empty list to store results
  percentile_list <- list()
  
  # Calculate regular percentiles
  for(col in percent_rank_cols) {
    percentile_list[[col]] <- data.frame(
      `Batter Name` = data[["Batter Name"]],  # Using [[ ]] to preserve exact column name
      `Batter ID` = data[["Batter ID"]],      # Using [[ ]] to preserve exact column name
      metric = col,
      percentile = round(percent_rank(data[[col]]) * 100),
      value = data[[col]],
      stringsAsFactors = FALSE
    )
  }
  
  # Calculate inverse percentiles
  for(col in inverse_percent_rank_cols) {
    percentile_list[[col]] <- data.frame(
      `Batter Name` = data[["Batter Name"]],  # Using [[ ]] to preserve exact column name
      `Batter ID` = data[["Batter ID"]],      # Using [[ ]] to preserve exact column name
      metric = col,
      percentile = round((1 - percent_rank(data[[col]])) * 100),
      value = data[[col]],
      stringsAsFactors = FALSE
    )
  }
  
  # Combine all results into one data frame
  result <- do.call(rbind, percentile_list)
  
  # Reset row names
  rownames(result) <- NULL
  
  return(result)
}

get_player_image <- function(player_id) {
  # Try MLB silo image first
  silo_url <- sprintf("https://img.mlbstatic.com/mlb-photos/image/upload/w_200,q_auto:best/v1/people/%s/headshot/silo/current", player_id)
  
  # Check if silo works
  silo_result <- tryCatch({
    response <- httr::HEAD(silo_url)
    httr::status_code(response) == 200
  }, error = function(e) FALSE)
  
  # If silo fails, use MiLB with correct formatting
  if (!silo_result) {
    return(sprintf("https://img.mlbstatic.com/mlb-photos/image/upload/c_fill,g_auto,b_white,ar_1:1/w_180/v1/people/%s/headshot/milb/current", player_id))
  }
  
  # Return silo if it worked
  return(silo_url)
}

get_player_info <- function(player_id, season, level = "MLB") {
  # Initialize return values
  team <- "MLB"
  position <- NA
  
  # If MLB level, use original endpoint
  if(level == "MLB") {
    url <- paste0("https://statsapi.mlb.com/api/v1/people/", player_id, 
                  "/stats?stats=season&season=", season, "&group=hitting")
    
    response <- httr::GET(url)
    data <- httr::content(response, "parsed")
    
    if(length(data$stats) > 0 && length(data$stats[[1]]$splits) > 0) {
      team <- data$stats[[1]]$splits[[length(data$stats[[1]]$splits)]]$team$name
    }
  } else {
    # For minor leagues, get player info from sports endpoint
    sport_code <- if(level == "AAA") "11" else "14"  # 11 for AAA, 14 for FSL
    url <- paste0("https://statsapi.mlb.com/api/v1/sports/", sport_code, "/players?season=", season)
    
    response <- httr::GET(url)
    # Convert response to data frame
    players_df <- jsonlite::fromJSON(rawToChar(response$content), flatten = TRUE)$people
    
    # Find player directly
    found_player <- players_df[players_df$id == player_id, ]
    
    if(nrow(found_player) > 0) {
      team_id <- found_player$currentTeam.id
      
      # Get parent org using team id
      team_url <- paste0("https://statsapi.mlb.com/api/v1/teams/", team_id, "?season=", season)
      team_response <- httr::GET(team_url)
      team_data <- jsonlite::fromJSON(rawToChar(team_response$content))
      
      team <- team_data$teams$parentOrgName
    }
  }
  
  # Get position info (same for all levels)
  url2 <- paste0("https://statsapi.mlb.com/api/v1/people/", player_id)
  response2 <- httr::GET(url2)
  data2 <- httr::content(response2, "parsed")
  
  if(length(data2$people) > 0) {
    full_position <- data2$people[[1]]$primaryPosition$name
    position <- case_when(
      full_position == "First Base" ~ "1B",
      full_position == "Second Base" ~ "2B",
      full_position == "Third Base" ~ "3B",
      full_position == "Shortstop" ~ "SS",
      full_position == "Catcher" ~ "C",
      full_position == "Left Field" ~ "LF",
      full_position == "Center Field" ~ "CF",
      full_position == "Right Field" ~ "RF",
      full_position == "Designated Hitter" ~ "DH",
      full_position == "Pitcher" ~ "P",
      full_position == "Two-Way Player" ~ "TWP",
      TRUE ~ as.character(full_position)
    )
  }
  
  return(list(
    team = team,
    position = position
  ))
}
download_private_csv <- function(repo_id, filename) {
  url <- paste0("https://huggingface.co/datasets/", repo_id, "/resolve/main/", filename)
  response <- GET(url, add_headers(Authorization = paste("Bearer", Sys.getenv("GETCSV"))))
  
  if (status_code(response) == 200) {
    content <- content(response, "text")
    con <- textConnection(content)
    
    # Try different read options
    data <- read.csv(con, 
                     header = TRUE,
                     check.names = FALSE,  # This prevents R from modifying column names
                     fileEncoding = "UTF-8",
                     stringsAsFactors = FALSE)
    close(con)
    return(data)
  } else {
    stop("Failed to download dataset")
  }
}
MLB <- download_private_csv("TimStats/StatcastDataAll", "MLB.csv")
AAA <- download_private_csv("TimStats/StatcastDataAll", "AAA.csv")
FSLAll <- download_private_csv("TimStats/StatcastDataAll", "FSL.csv")

temp_players <- MLB %>% filter(season == 2024)
MLBC <- rbind(MLB,AAA,FSLAll)
data <- is_barrel(MLBC) %>%
  mutate(
    BBE = case_when(description %in% c('In play, run(s)','In play, out(s)','In play, no out') ~ TRUE, TRUE ~ FALSE),
    Swing = case_when(description %in% c('Foul','Foul Bunt','Foul Pitchout','Foul Tip',
                                         'In play, run(s)','In play, out(s)','In play, no out',
                                         'Swinging Strike','Swinging Strike (Blocked)',
                                         'Missed Bunt') ~ TRUE, TRUE ~ FALSE),
    Contact = case_when(description %in% c('In play, run(s)','In play, out(s)','In play, no out',
                                           'Foul','Foul Bunt','Foul Pitchout') ~ TRUE, TRUE ~ FALSE),
    Whiff = case_when(description %in% c('Swinging Strike','Swinging Strike (Blocked)',
                                         'Missed Bunt','Foul Tip') ~ TRUE, TRUE ~ FALSE),
    IZ = ifelse(zone <= 9, TRUE, FALSE),
    Single = case_when(result == "Single" & BBE == TRUE ~ TRUE, TRUE ~ FALSE),
    Double = case_when(result == "Double" & BBE == TRUE ~ TRUE, TRUE ~ FALSE),
    Triple = case_when(result == "Triple" & BBE == TRUE ~ TRUE, TRUE ~ FALSE),
    `Home Run` = case_when(result == "Home Run" & BBE == TRUE ~ TRUE, TRUE ~ FALSE),
    Walk = case_when(balls >= 4 & result == "Walk" ~ TRUE, TRUE ~ FALSE),
    HBP = case_when(description == "Hit By Pitch" & result == "Hit By Pitch" ~ TRUE, TRUE ~ FALSE),
    Strikeout = case_when(strikes >= 3 & result %in% c("Strikeout",'Stikeout Double Play') ~ TRUE, TRUE ~ FALSE),
    Sac = case_when(BBE == TRUE & result %in% c('Sac Fly','Sac Bunt',
                                                'Sac Fly Double Play','Sac Bunt Double Play') ~ TRUE, TRUE ~ FALSE),
    IBB = case_when(pitchNum == 1 & result == "Intent Walk" ~ TRUE, TRUE ~ FALSE),
    AB = Strikeout + BBE - Sac,
    PA = AB + Walk + HBP + IBB
  ) %>%
  group_by(`Batter Name`,`Batter ID`,season,level) %>%
  summarise(
    BIP = sum(BBE,na.rm = TRUE),
    wOBA = round((sum(Single,na.rm = TRUE) * .882 + sum(Double, na.rm = TRUE) * 1.254 + 
                    sum(Triple,na.rm = TRUE) * 1.59 + sum(`Home Run`,na.rm = TRUE) * 2.05 +
                    sum(Walk,na.rm = TRUE) * .689 + sum(HBP,na.rm = TRUE) * .720) / 
                   (sum(PA,na.rm = TRUE) - sum(IBB,na.rm = TRUE)), 3),
    wOBACON = round((sum(Single,na.rm = TRUE) * .882 + sum(Double, na.rm = TRUE) * 1.254 + 
                       sum(Triple,na.rm = TRUE) * 1.59 + sum(`Home Run`,na.rm = TRUE) * 2.05 )/ 
                      sum(BBE,na.rm = TRUE), 3),
    xwOBA = round(mean(expected_woba,na.rm = TRUE), 3),
    xDamage = round(mean(expected_woba[BBE == TRUE],na.rm = TRUE), 3),
    `Avg EV` = round(mean(hit_speed,na.rm = TRUE), 1),
    EV90 = round(quantile(hit_speed,0.9,na.rm = TRUE), 1),
    `Max EV` = round(max(hit_speed,na.rm = TRUE), 1),
    'stdev(LA)' = round(sd(hit_angle,na.rm = TRUE), 1),
    'Barrel%' = round(100 * mean(barrel[Swing == TRUE],na.rm = TRUE), 1),
    "Z-Con%" = round(100 * mean(Contact[IZ == TRUE & Swing == TRUE],na.rm = TRUE), 1),
    "Z-Swing%" = round(100 * mean(Swing[IZ == TRUE],na.rm = TRUE), 1),
    "O-Con%" = round(100 * mean(Contact[IZ == FALSE & Swing == TRUE],na.rm = TRUE), 1),
    "Chase%" = round(100 * mean(Swing[IZ == FALSE],na.rm = TRUE), 1),
    "Whiff%" = round(100 * mean(Whiff[Swing == TRUE],na.rm = TRUE), 1),
    "Swing%" = round(100 * mean(Swing,na.rm = TRUE), 1),
    "SwStr%" = round(100 * mean(Whiff,na.rm = TRUE), 1)
  )

# UI definition
ui <- fluidPage(
  titlePanel(NULL, windowTitle = "Baseball Stats Visualization"),
  
  sidebarLayout(
    sidebarPanel(
      selectInput("szn", "Season:", c(2024, 2023, 2022, 2021, 2021)),
      selectInput("level", "Level:", c("MLB", "AAA", "FSL")),
      selectInput("type", "Player Type:", c("Batter", "Pitcher")),
      selectInput("player", "Player:", choices = unique(temp_players$`Batter Name`)),
      # Add toggle for custom team
      checkboxInput("use_custom_team", "Use Custom Team", FALSE),
      # Conditional panel for team selection
      conditionalPanel(
        condition = "input.use_custom_team == true",
        selectInput(
          inputId = "team",
          label = "Select Team",
          choices = c(
            # Regular teams (sorted alphabetically)
            "Angels" = "LAA",
            "Astros" = "HOU", 
            "Athletics" = "OAK",
            "Blue Jays" = "TOR",
            "Braves" = "ATL",
            "Brewers" = "MIL",
            "Cardinals" = "STL",
            "Cubs" = "CHC",
            "D-backs" = "ARI",
            "Dodgers" = "LAD",
            "Giants" = "SF",
            "Guardians" = "CLE",
            "Mariners" = "SEA",
            "Marlins" = "MIA",
            "Mets" = "NYM",
            "Nationals" = "WSH",
            "Orioles" = "BAL",
            "Padres" = "SD",
            "Phillies" = "PHI",
            "Pirates" = "PIT",
            "Rangers" = "TEX",
            "Rays" = "TB",
            "Red Sox" = "BOS",
            "Reds" = "CIN",
            "Rockies" = "COL",
            "Royals" = "KC",
            "Tigers" = "DET",
            "Twins" = "MIN",
            "White Sox" = "CHW",
            "Yankees" = "NYY",
            # MLB option at the top
            "MLB" = "MLB"
          ),
          selected = "MLB"
        )
      ),
    ),
    
    mainPanel(
      plotOutput("statsPlot", height = "750px", width = "750px")
    )
  )
)

# Server logic
server <- function(input, output,session) {
  
  observeEvent(c(input$szn,input$level), {
    # Filter data based on selected season
    filtered_data <- MLBC[MLBC$season == input$szn & MLBC$level == input$level,]
    
    updateSelectInput(session,
                      inputId = "player",
                      choices = unique(filtered_data$`Batter Name`))
  })
  # Create reactive value to store team
  team_value <- reactiveVal("MLB")
  position_value <- reactiveVal("")
  # Watch for player or season changes to update team
  observeEvent(c(input$player, input$szn), {
    if (!input$use_custom_team && !is.null(input$player)) {
      
      player_id <- MLBC %>%
        filter(`Batter Name` == input$player) %>%
        pull(`Batter ID`) %>%
        unique() %>%
        first()
      
      if (!is.null(player_id)) {
        player_info <- get_player_info(player_id, input$szn, input$level)
        
        team_abb <- switch(player_info$team,
                           "Los Angeles Angels" = "LAA",
                           "Houston Astros" = "HOU",
                           "Oakland Athletics" = "OAK",
                           "Toronto Blue Jays" = "TOR",
                           "Atlanta Braves" = "ATL",
                           "Milwaukee Brewers" = "MIL",
                           "St. Louis Cardinals" = "STL",
                           "Chicago Cubs" = "CHC",
                           "Arizona Diamondbacks" = "ARI",
                           "Los Angeles Dodgers" = "LAD",
                           "San Francisco Giants" = "SF",
                           "Cleveland Guardians" = "CLE",
                           "Seattle Mariners" = "SEA",
                           "Miami Marlins" = "MIA",
                           "New York Mets" = "NYM",
                           "Washington Nationals" = "WSH",
                           "Baltimore Orioles" = "BAL",
                           "San Diego Padres" = "SD",
                           "Philadelphia Phillies" = "PHI",
                           "Pittsburgh Pirates" = "PIT",
                           "Texas Rangers" = "TEX",
                           "Tampa Bay Rays" = "TB",
                           "Boston Red Sox" = "BOS",
                           "Cincinnati Reds" = "CIN",
                           "Colorado Rockies" = "COL",
                           "Kansas City Royals" = "KC",
                           "Detroit Tigers" = "DET",
                           "Minnesota Twins" = "MIN",
                           "Chicago White Sox" = "CHW",
                           "New York Yankees" = "NYY",
                           "MLB") 
        if(is.na(team_abb)){
          team_abb <- "MLB"
        }
        team_value(team_abb)
        position_value(player_info$position)
      }
    }
  })
  
  current_team <- reactive({
    if (input$use_custom_team) {
      return(input$team)
    } else {
      return(team_value())
    }
  })
  
  output$statsPlot <- renderPlot({
    req(position_value())
    data <- data %>% filter(season == input$szn,level == input$level)
    
    BBE <- MLBC %>%
      filter(season == input$szn) %>%
      filter(`Batter Name` == input$player) %>%
      mutate(
        BBE = case_when(description %in% c('In play, run(s)','In play, out(s)','In play, no out') ~ TRUE, TRUE ~ FALSE)
      )
    
  
    indv <- data %>% filter(`Batter Name` == input$player,level == input$level)
    #if(indv[1,5] >= 149){
    qual <- data %>% filter(BIP > 249)
    data <- rbind(indv,qual)
    data <- unique(data)
    #}
    current_data <- apply_percentile_calcs(data %>% select(-BIP)) %>% 
      filter(`Batter.Name` == input$player) %>%
      mutate(metric = factor(metric, levels = c(
        "wOBA", "wOBACON", "xwOBA", "xDamage",
        "Avg EV", "EV90", "Max EV", 
        "stdev(LA)", "Barrel%",
        "Z-Con%", "Z-Swing%", "O-Con%", 
        "Chase%", "Whiff%", "Swing%", "SwStr%"
      ))) %>%
      arrange(metric)
    #current_data <- data
    pos <- position_value()

    BBE <- sum(BBE$BBE,na.rm = TRUE)
    
    # Add Roboto Condensed font
    font_add_google("Roboto Condensed", "roboto")
    showtext_auto()
    
    # Color function
    current_data$color <- scales::gradient_n_pal(c("#325aa1","#90A4AE", "#D82129"))(current_data$percentile/100)
    
    # Labels plot
    labels_plot <- ggplot() +
      annotate("text", x = c(10, 50, 90), y = 1,
               label = c("Poor", "Average", "Great"),
               color = c("#3661ad", "#90A4AE", "#DC3545"),
               family = "roboto", size = 6) +
      annotate("text", x = c(10, 50, 90), y = 0.5,
               label = "▲",
               color = c("#3661ad", "#90A4AE", "#DC3545"), size = 12) +
      scale_x_continuous(limits = c(-16, 113), expand = c(0, 0)) +
      scale_y_continuous(limits = c(0.5, 1.5)) +
      theme_void()
    
    # Main plot
    main_plot <- ggplot(current_data, aes(y = factor(metric, levels = rev(metric)))) +
      geom_tile(aes(x = 50, width = 100),
                fill = "#c7dcdc", alpha = 0.3, height = 0.25) +
      geom_tile(aes(x = percentile/2, width = percentile, fill = color),
                height = 0.7) +
      annotate("segment", x = c(10, 50, 90), xend = c(10, 50, 90),
               y = 0, yend = 16.35,
               color = c("white"),
               linewidth = 1.5, alpha = 0.5) +
      geom_segment(aes(x = -2, xend = -16,
                       y = as.numeric(factor(metric, levels = rev(metric))) - 0.3,
                       yend = as.numeric(factor(metric, levels = rev(metric))) - 0.3),
                   linetype = "longdash", color = "#399098", size = 1) +
      geom_segment(aes(x = 102, xend = 113,
                       y = as.numeric(factor(metric, levels = rev(metric))) - 0.3,
                       yend = as.numeric(factor(metric, levels = rev(metric))) - 0.3),
                   linetype = "longdash", color = "#399098", size = 1) +
      geom_text(aes(x = -3, label = metric),
                hjust = 1, size = 6, family = "roboto") +
      geom_text(aes(x = 103, label = value),
                hjust = 0, size = 6, family = "roboto") +
      geom_point(aes(x = percentile, color = "white", fill = color),
                 size = 6, shape = 21, stroke = 3) +
      geom_text(aes(x = percentile, label = percentile),
                size = 6, color = "white", fontface = "bold", family = "roboto") +
      scale_x_continuous(limits = c(-16, 113), expand = c(0, 0)) +
      scale_fill_identity() +
      scale_color_identity() +
      theme_minimal() +
      theme(
        axis.text = element_blank(),
        axis.title = element_blank(),
        panel.grid = element_blank(),
        plot.margin = margin(t = 0, r = 0, b = -20, l = 0),  # Reduced bottom margin
        text = element_text(family = "roboto")
      )
    
    # Load and process team logo
    if(current_team() == "MLB"){
      logo_url <- "https://a.espncdn.com/combiner/i?img=/i/teamlogos/leagues/500/mlb.png?w=400&h=400&transparent=true"
    } else {
      logo_url <- sprintf("https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/%s.png&h=200&w=200",
                          current_team())
    }
    logo_img <- image_read(logo_url)
    logo_raster <- as.raster(logo_img)
    
    
    #player_url <- sprintf(paste0("https://img.mlbstatic.com/mlb-photos/image/upload/d_headshot_silo_generic.png,ar_1:1,b_auto:border,c_pad,q_auto:best/w_60/v1/people/427012/headshot/milb/current"))
    player_url <- get_player_image(current_data[1,2])
    player_img <- image_read(player_url)
    player_raster <- as.raster(player_img)
    # Create title with logo
    title_grob <- textGrob(paste0(input$player, " - ", pos,
                                  "\n BBE - ", BBE, "\n",
                                  "Percentile Rankings - ",input$szn),
                           gp = gpar(fontsize = 25, fontface = "bold",
                                     fontfamily = "roboto"))
    logo_grob <- rasterGrob(logo_raster, x = 0, width = unit(.5, "npc"),hjust = 0)
    player_grob <- rasterGrob(player_raster, x = 0.5, width = unit(.5, "npc"),hjust = 0)
    title_with_logo <- arrangeGrob(logo_grob, title_grob,player_grob, ncol = 3,
                                   widths = c(.25,.5,.25))
    caption_grob <- textGrob("Viz by: @TimStats | tim-stats.com | Data: MLB",gp = gpar(fontsize = 15, fontface = "bold",
                                                                                       fontfamily = "roboto"))
    
    # Final arrangement with logo in title
   # Set fixed dimensions for the plot
    plot_width <- 750
    plot_height <- 750
    
    # Create a viewport with fixed dimensions
    grid.newpage()
    pushViewport(viewport(width = unit(1, "npc"), height = unit(1, "npc")))
    
    # Adjust the layout heights to be proportional
    layout_heights <- c(0.15, 0.05, 0.75, 0.05)
    
    # Create the final plot with fixed dimensions
    final_plot <- arrangeGrob(
      title_with_logo,
      labels_plot,
      main_plot,
      caption_grob,
      heights = layout_heights
    )
    
    # Add padding with fixed units
    padded_plot <- gtable_add_padding(
      final_plot,
      padding = unit(c(20, 20, 20, 20), "points")
    )
    
    # Draw the plot in the viewport
    grid.draw(padded_plot)
  }, height = 750, width = 750, res = 96)
}

# Run the app
shinyApp(ui = ui, server = server)