Update app.R

app.R

@@ -15,55 +15,48 @@ summarise(
   .groups = 'drop'
   )
 
-pitch_type_lookup <- data.frame(
-    pitch_name = c("Changeup", "Curveball", "Cutter", "Eephus", "Forkball", 
-                   "Four-Seam Fastball", "Knuckle Ball", "Knuckle Curve", 
-                   "Screwball", "Sinker", "Slider", "Slurve", "Splitter", "Sweeper"),
-    pitch_abbr = c("CH", "CU", "FC", "EP", "FO", "FF", "KN", "KC", 
-                   "SC", "SI", "SL", "SV", "FS", "ST"),
-    stringsAsFactors = FALSE
+pitch_colors <- c(
+  "FF" = "#FF4136", 
+  "SI" = "#FF851B", 
+  "FC" = "#FFDC00",
+  "CH" = "#2ECC40", 
+  "SL" = "#0074D9", 
+  "ST" = "#ED68ED",
+  "CU" = "#B10DC9", 
+  "FS" = "#01FF70", 
+  "KC" = "#85144b",
+  "SV" = "#3D9970", 
+  "KN" = "#39CCCC", 
+  "FO" = "#F012BE",
+  "EP" = "#AAAAAA", 
+  "FA" = "#7FDBFF", 
+  "SC" = "#FF69B4"
 )
 
-break_plot_Szn <- function(game,data1,sdate,edate) {
-  game <- game %>% left_join(pitch_type_lookup, by = c("pitch_name"))
+break_plot_Szn <- function(game, data1, sdate, edate) {
+  # Calculate pitch usage percentages
+  
+  game <- game %>% filter(between(as.Date(date), sdate, edate))
+  total_pitches <- nrow(game)
+  usage_stats <- game %>%
+    group_by(pitch_name) %>%
+    summarise(
+      count = n(),
+      usage = sprintf("%.1f%%", (count/total_pitches) * 100)
+    )
+  
+  game <- game %>% 
+    left_join(pitch_type_lookup, by = c("pitch_name")) %>%
+    left_join(usage_stats, by = "pitch_name")
+  
+  
   game <- game %>% filter(!is.na(pitch_abbr))
   title <- paste0(unique(game$`Pitcher Name`)," ",sdate," to ",edate)
-  # Get pitcher's handedness
   pitcher_hand <- unique(game$phand)
   
   angle_degrees <- round(mean(game$arm_angle,na.rm = TRUE),digits = 1)
-  # Calculate average movement by arm angle and pitch type from season data
-  # AAaveragePT <- data1 %>%
-  #   mutate(arm_angle = round(arm_angle, digits = 0)) %>%
-  #   group_by(arm_angle, phand, pitch_name) %>%
-  #   summarise(
-  #     AvgIVB = mean(IVB, na.rm = TRUE),
-  #     AvgHB = mean(HB, na.rm = TRUE),
-  #     .groups = 'drop'
-  #   )
-  
-  pitch_colors <- c(
-    "FF" = "#FF4136",
-    "SI" = "#FF851B",
-    "FC" = "#FFDC00",
-    "CH" = "#2ECC40",
-    "SL" = "#0074D9",
-    "ST" = "#ED68ED",
-    "CU" = "#B10DC9",
-    "FS" = "#01FF70",
-    "KC" = "#85144b",
-    "SV" = "#3D9970",
-    "KN" = "#39CCCC",
-    "FO" = "#F012BE",
-    "EP" = "#AAAAAA",
-    "FA" = "#7FDBFF",
-    "SC" = "#FF69B4"
-  )
-  
-  # Convert angle to radians
   angle_radians <- angle_degrees * (pi / 180)
   
-  # Legend location based on handedness
   if(pitcher_hand == "L") {
     leg <- c(0.08, .22)
     factor <- -1
@@ -72,70 +65,64 @@ break_plot_Szn <- function(game,data1,sdate,edate) {
     factor <- 1
   }
   
-  # Calculate the endpoint coordinates
   x_end <- 50 * cos(angle_radians) * factor
   y_end <- 50 * sin(angle_radians)
   
+  # Create the base color mapping
+  game_pitches <- unique(game$pitch_abbr)
+  used_colors <- pitch_colors[game_pitches]
+  
+  # Add usage to the game data
   avg_locations <- game %>%
     group_by(pitch_abbr) %>%
     summarize(
       avg_HB = mean(HB, na.rm = TRUE),
-      avg_IVB = mean(IVB, na.rm = TRUE)
-    )  
+      avg_IVB = mean(IVB, na.rm = TRUE),
+      usage = first(usage)  # Keep the usage info
+    )
   
-  # Get unique pitch types from the game data
-  game_pitches <- unique(game$pitch_abbr)
+  # Create labels with percentages
+  legend_labels <- paste0(names(used_colors), " (", 
+                          avg_locations$usage[match(names(used_colors), avg_locations$pitch_abbr)], ")")
+  
+  # Create the color scale with usage labels
+  fill_values <- used_colors
+  names(fill_values) <- legend_labels
   
-  # Filter season data for the current arm angle, handedness, and only pitches in the game
-arm_angle_data <- data1 %>%
-  # First remove NAs from relevant columns
-  filter(!is.na(arm_angle), !is.na(angle_degrees), !is.na(phand), !is.na(pitcher_hand), !is.na(pitch_name)) %>%
-  # Then proceed with your original filtering
-  filter(abs(round(arm_angle) - angle_degrees) <= 2, phand == pitcher_hand) %>%
-  left_join(pitch_type_lookup, by = c("pitch_name")) %>%
-  # Remove NAs that might have been introduced by the join
-  filter(!is.na(pitch_abbr)) %>%
-  filter(pitch_abbr %in% game_pitches)
+  arm_angle_data <- data1 %>%
+    filter(!is.na(arm_angle), !is.na(phand), !is.na(pitch_name)) %>%
+    filter(abs(round(arm_angle) - angle_degrees) <= 2, phand == pitcher_hand) %>%
+    left_join(pitch_type_lookup, by = c("pitch_name")) %>%
+    filter(!is.na(pitch_abbr)) %>%
+    filter(pitch_abbr %in% game_pitches)
   
   ggplot(game, aes(x = HB, y = IVB)) +
-    # Base layers
     geom_vline(xintercept = 0, color = "lightblue", linewidth = 1, linetype = 4) +
     geom_hline(yintercept = 0, color = "lightblue", linewidth = 1, linetype = 4) +
-    
-    # Add ellipses from season data for this arm angle
+    # Ellipses
     stat_ellipse(
       data = arm_angle_data,
-      aes(x = HB, y = IVB, fill = pitch_abbr),
+      aes(fill = paste0(pitch_abbr, " (", 
+                        avg_locations$usage[match(pitch_abbr, avg_locations$pitch_abbr)], ")")),
       geom = "polygon",
       alpha = 0.2,
       level = 0.68,
       show.legend = FALSE
     ) +
-    
-    # Individual pitch points from the game
-    # geom_point(aes(fill = pitch_abbr), color = "#c6c6c6", size = 3, stroke = .5, shape = 21) +
-    
-    # # Average location points from season data
-    # geom_point(
-    #   data = AAaveragePT %>% 
-    #     filter(arm_angle == angle_degrees,
-    #            phand == pitcher_hand) %>%
-    #     left_join(pitch_type_lookup, by = c("pitch_name")) %>%
-    #     filter(pitch_abbr %in% game_pitches),
-    #   aes(x = AvgHB, y = AvgIVB, fill = pitch_abbr),
-    #   color = "black",
-    #   size = 6,
-    #   stroke = .5,
-    #   shape = 21
-    # ) +
-    geom_point(data = avg_locations, aes(x = avg_HB, y = avg_IVB, fill = pitch_abbr),
-               color = "black", size = 6, stroke = .5, shape = 21) +
+    # Average points
+    geom_point(
+      data = avg_locations,
+      aes(x = avg_HB, y = avg_IVB, 
+          fill = paste0(pitch_abbr, " (", usage, ")")),
+      color = "black", 
+      size = 6, 
+      stroke = .5, 
+      shape = 21
+    ) +
     # Arm angle line
-    geom_segment(x = 0, y = 0, xend = x_end, yend = y_end, color = "red", linewidth = 1, linetype = 5) +
-    
-    # Aesthetics
-    scale_fill_manual(values = pitch_colors) +
-    scale_color_manual(values = pitch_colors) +
+    geom_segment(x = 0, y = 0, xend = x_end, yend = y_end, 
+                 color = "red", linewidth = 1, linetype = 5) +
+    scale_fill_manual(values = fill_values) +
     labs(
       x = "Horizontal Break (in)",
       y = "Induced Vertical Break (in)", 
@@ -170,46 +157,29 @@ arm_angle_data <- data1 %>%
       panel.border = element_blank()
     )
 }
-break_plot_tot <- function(game,data1,sdate,edate) {
-  game <- game %>% left_join(pitch_type_lookup, by = c("pitch_name"))
+
+break_plot_tot <- function(game, data1, sdate, edate) {
+  # Calculate pitch usage percentages
+  game <- game %>% filter(between(as.Date(date), sdate, edate))
+  total_pitches <- nrow(game)
+  usage_stats <- game %>%
+    group_by(pitch_name) %>%
+    summarise(
+      count = n(),
+      usage = sprintf("%.1f%%", (count/total_pitches) * 100)
+    )
+
+  game <- game %>% 
+    left_join(pitch_type_lookup, by = c("pitch_name")) %>%
+    left_join(usage_stats, by = "pitch_name")
+
   game <- game %>% filter(!is.na(pitch_abbr))
   title <- paste0(unique(game$`Pitcher Name`)," ",sdate," to ",edate)
-  # Get pitcher's handedness
   pitcher_hand <- unique(game$phand)
   
   angle_degrees <- round(mean(game$arm_angle,na.rm = TRUE),digits = 1)
-  # Calculate average movement by arm angle and pitch type from season data
-  # AAaveragePT <- data1 %>%
-  #   mutate(arm_angle = round(arm_angle, digits = 0)) %>%
-  #   group_by(arm_angle, phand, pitch_name) %>%
-  #   summarise(
-  #     AvgIVB = mean(IVB, na.rm = TRUE),
-  #     AvgHB = mean(HB, na.rm = TRUE),
-  #     .groups = 'drop'
-  #   )
-  
-  pitch_colors <- c(
-    "FF" = "#FF4136",
-    "SI" = "#FF851B",
-    "FC" = "#FFDC00",
-    "CH" = "#2ECC40",
-    "SL" = "#0074D9",
-    "ST" = "#ED68ED",
-    "CU" = "#B10DC9",
-    "FS" = "#01FF70",
-    "KC" = "#85144b",
-    "SV" = "#3D9970",
-    "KN" = "#39CCCC",
-    "FO" = "#F012BE",
-    "EP" = "#AAAAAA",
-    "FA" = "#7FDBFF",
-    "SC" = "#FF69B4"
-  )
-  
-  # Convert angle to radians
   angle_radians <- angle_degrees * (pi / 180)
   
-  # Legend location based on handedness
   if(pitcher_hand == "L") {
     leg <- c(0.08, .22)
     factor <- -1
@@ -218,71 +188,72 @@ break_plot_tot <- function(game,data1,sdate,edate) {
     factor <- 1
   }
   
-  # Calculate the endpoint coordinates
   x_end <- 50 * cos(angle_radians) * factor
   y_end <- 50 * sin(angle_radians)
   
+  # Create the base color mapping
+  game_pitches <- unique(game$pitch_abbr)
+  used_colors <- pitch_colors[game_pitches]
+  
+  # Add usage to the game data
   avg_locations <- game %>%
     group_by(pitch_abbr) %>%
     summarize(
       avg_HB = mean(HB, na.rm = TRUE),
-      avg_IVB = mean(IVB, na.rm = TRUE)
-    )  
+      avg_IVB = mean(IVB, na.rm = TRUE),
+      usage = first(usage)  # Keep the usage info
+    )
   
-  # Get unique pitch types from the game data
-  game_pitches <- unique(game$pitch_abbr)
+  # Create labels with percentages
+  legend_labels <- paste0(names(used_colors), " (", 
+                          avg_locations$usage[match(names(used_colors), avg_locations$pitch_abbr)], ")")
+  
+  # Create the color scale with usage labels
+  fill_values <- used_colors
+  names(fill_values) <- legend_labels
   
-  # Filter season data for the current arm angle, handedness, and only pitches in the game
   arm_angle_data <- data1 %>%
-    # First remove NAs from relevant columns
-    filter(!is.na(arm_angle), !is.na(angle_degrees), !is.na(phand), !is.na(pitcher_hand), !is.na(pitch_name)) %>%
-    # Then proceed with your original filtering
+    filter(!is.na(arm_angle), !is.na(phand), !is.na(pitch_name)) %>%
     filter(abs(round(arm_angle) - angle_degrees) <= 2, phand == pitcher_hand) %>%
     left_join(pitch_type_lookup, by = c("pitch_name")) %>%
-    # Remove NAs that might have been introduced by the join
     filter(!is.na(pitch_abbr)) %>%
     filter(pitch_abbr %in% game_pitches)
   
-  game <- game %>% filter(!is.na(pitch_abbr))
   ggplot(game, aes(x = HB, y = IVB)) +
-    # Base layers
     geom_vline(xintercept = 0, color = "lightblue", linewidth = 1, linetype = 4) +
     geom_hline(yintercept = 0, color = "lightblue", linewidth = 1, linetype = 4) +
-    
-    # Add ellipses from season data for this arm angle
+    # Ellipses
     stat_ellipse(
       data = arm_angle_data,
-      aes(x = HB, y = IVB, fill = pitch_abbr),
+      aes(fill = paste0(pitch_abbr, " (", 
+                        avg_locations$usage[match(pitch_abbr, avg_locations$pitch_abbr)], ")")),
       geom = "polygon",
       alpha = 0.2,
       level = 0.68,
       show.legend = FALSE
     ) +
-    
-    # Individual pitch points from the game
-    geom_point(aes(fill = pitch_abbr), color = "#c6c6c6", size = 3, stroke = .5, shape = 21) +
-    
-    # # Average location points from season data
-    # geom_point(
-    #   data = AAaveragePT %>% 
-    #     filter(arm_angle == angle_degrees,
-    #            phand == pitcher_hand) %>%
-    #     left_join(pitch_type_lookup, by = c("pitch_name")) %>%
-    #     filter(pitch_abbr %in% game_pitches),
-    #   aes(x = AvgHB, y = AvgIVB, fill = pitch_abbr),
-    #   color = "black",
-    #   size = 6,
-    #   stroke = .5,
-    #   shape = 21
-    # ) +
-    geom_point(data = avg_locations, aes(x = avg_HB, y = avg_IVB, fill = pitch_abbr),
-               color = "black", size = 6, stroke = .5, shape = 21) +
+    # Individual points
+    geom_point(
+      aes(fill = paste0(pitch_abbr, " (", usage, ")")), 
+      color = "#c6c6c6", 
+      size = 3, 
+      stroke = .5, 
+      shape = 21
+    ) +
+    # Average points
+    geom_point(
+      data = avg_locations,
+      aes(x = avg_HB, y = avg_IVB, 
+          fill = paste0(pitch_abbr, " (", usage, ")")),
+      color = "black", 
+      size = 6, 
+      stroke = .5, 
+      shape = 21
+    ) +
     # Arm angle line
-    geom_segment(x = 0, y = 0, xend = x_end, yend = y_end, color = "red", linewidth = 1, linetype = 5) +
-    
-    # Aesthetics
-    scale_fill_manual(values = pitch_colors) +
-    scale_color_manual(values = pitch_colors) +
+    geom_segment(x = 0, y = 0, xend = x_end, yend = y_end, 
+                 color = "red", linewidth = 1, linetype = 5) +
+    scale_fill_manual(values = fill_values) +
     labs(
       x = "Horizontal Break (in)",
       y = "Induced Vertical Break (in)", 
@@ -317,11 +288,10 @@ break_plot_tot <- function(game,data1,sdate,edate) {
       panel.border = element_blank()
     )
 }
-
 ui <- fluidPage(
   # Application title
   titlePanel("2020-2024 MLB Pitch Plots"),
-
+  
   sidebarLayout(
     sidebarPanel(
       width = 3,  
@@ -340,7 +310,7 @@ ui <- fluidPage(
                      class = "btn btn-success btn-block") 
     ),
     
-
+    
     mainPanel(
       width = 9, 
       plotOutput("Plot")
@@ -349,17 +319,29 @@ ui <- fluidPage(
 )
 
 # Define server
-server <- function(input, output) {
+server <- function(input, output, session) {
+  # Create a reactive value to store current plot settings
+  plotSettings <- reactiveVal(list(
+    player = NULL,
+    type = "Season Average",
+    dates = c(as.Date("2024-03-20"), as.Date("2024-10-01"))
+  ))
   
-  filtered_data <- eventReactive(input$submit, {
-    data1 %>%
-      filter(`Pitcher Name` == input$player,
-             between(as.Date(date), input$date1[1], input$date1[2]))
+  # Update settings only when submit is clicked
+  observeEvent(input$submit, {
+    plotSettings(list(
+      player = input$player,
+      type = input$type,
+      dates = c(input$date1[1], input$date1[2])
+    ))
   })
   
-  current_plot <- reactive({
-    game <- filtered_data()
-    season_data <- data1
+  output$Plot <- renderPlot({
+    settings <- plotSettings()
+    req(settings$player)  # Wait until we have a player selected
+    
+    game <- data1 %>%
+      filter(`Pitcher Name` == settings$player)
     
     if(nrow(game) == 0) {
       return(ggplot() + 
@@ -370,37 +352,42 @@ server <- function(input, output) {
                theme(plot.background = element_rect(fill = "#333333", color = NA)))
     }
     
-    if(input$type == "Season Average"){
-      break_plot_Szn(game, season_data, input$date1[1], input$date1[2])
+    if(settings$type == "Season Average"){
+      break_plot_Szn(game, data1, settings$dates[1], settings$dates[2])
     } else{
-      break_plot_tot(game, season_data, input$date1[1], input$date1[2])
+      break_plot_tot(game, data1, settings$dates[1], settings$dates[2])
     }
-  })
-  
-  output$Plot <- renderPlot({
-    current_plot()
   }, width = 1000, height = 1000)
   
   output$download <- downloadHandler(
     filename = function() {
-      
+      settings <- plotSettings()
       paste0(
-        gsub(" ", "_", input$player), "_",
-        format(input$date1[1], "%Y%m%d"), "_to_",
-        format(input$date1[2], "%Y%m%d"), ".png"
+        gsub(" ", "_", settings$player), "_",
+        format(settings$dates[1], "%Y%m%d"), "_to_",
+        format(settings$dates[2], "%Y%m%d"), ".png"
       )
     },
     content = function(file) {
-
+      settings <- plotSettings()
+      
+      game <- data1 %>%
+        filter(`Pitcher Name` == settings$player)
+      
+      plot <- if(settings$type == "Season Average"){
+        break_plot_Szn(game, data1, settings$dates[1], settings$dates[2])
+      } else{
+        break_plot_tot(game, data1, settings$dates[1], settings$dates[2])
+      }
+      
       ggsave(file,
-             plot = current_plot(),
-             width = 7,
-             height = 7,
+             plot = plot,
+             width = 10,
+             height = 10,
              dpi = 300,
              bg = "#333333") 
     }
   )
 }
 
-# Run the application 
 shinyApp(ui = ui, server = server)