Upload 2 files

FB.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0634ac1f0f8e9b37ea215740d541a0f5ca48cec0c7f048d655ac204de20ea2be
+size 2442683

app.R

@@ -1,58 +1,88 @@
-library(shiny)
-library(bslib)
-library(dplyr)
-library(ggplot2)
-
-df <- readr::read_csv("penguins.csv")
-# Find subset of columns that are suitable for scatter plot
-df_num <- df |> select(where(is.numeric), -Year)
-
-ui <- page_sidebar(
-  theme = bs_theme(bootswatch = "minty"),
-  title = "Penguins explorer",
-  sidebar = sidebar(
-    varSelectInput("xvar", "X variable", df_num, selected = "Bill Length (mm)"),
-    varSelectInput("yvar", "Y variable", df_num, selected = "Bill Depth (mm)"),
-    checkboxGroupInput("species", "Filter by species",
-      choices = unique(df$Species), selected = unique(df$Species)
-    ),
-    hr(), # Add a horizontal rule
-    checkboxInput("by_species", "Show species", TRUE),
-    checkboxInput("show_margins", "Show marginal plots", TRUE),
-    checkboxInput("smooth", "Add smoother"),
-  ),
-  plotOutput("scatter")
-)
-
-server <- function(input, output, session) {
-  subsetted <- reactive({
-    req(input$species)
-    df |> filter(Species %in% input$species)
-  })
-
-  output$scatter <- renderPlot(
-    {
-      p <- ggplot(subsetted(), aes(!!input$xvar, !!input$yvar)) +
-        theme_light() +
-        list(
-          theme(legend.position = "bottom"),
-          if (input$by_species) aes(color = Species),
-          geom_point(),
-          if (input$smooth) geom_smooth()
-        )
-
-      if (input$show_margins) {
-        margin_type <- if (input$by_species) "density" else "histogram"
-        p <- p |> ggExtra::ggMarginal(
-          type = margin_type, margins = "both",
-          size = 8, groupColour = input$by_species, groupFill = input$by_species
-        )
-      }
-
-      p
-    },
-    res = 100
-  )
-}
-
-shinyApp(ui, server)
+#
+# This is a Shiny web application. You can run the application by clicking
+# the 'Run App' button above.
+#
+# Find out more about building applications with Shiny here:
+#
+#    https://shiny.posit.co/
+#
+
+library(shiny)
+library(dplyr)
+library(data.table)
+library(xgboost)
+library(caret)
+# Define UI for application that draws a histogram
+ui <- fluidPage(
+
+    # Application title
+    titlePanel("TimStuff Fastball Calculator"),
+
+    # Sidebar with a slider input for number of bins 
+    sidebarLayout(
+        sidebarPanel(
+            selectInput("phand",
+                        "Hand",
+                        c("R","L")),
+            numericInput("velo",
+                        "Velocity /mph",90),
+            numericInput("ivb",
+                         "Induced Vertical Break (IVB) /in. (IVB)",16),
+            numericInput("hb",
+                         "Horizontal Break (HB) /in. (Pitcher's Perspective)",6),
+            numericInput("spinrate",
+                         "Spin Rate /rpm",2300),
+            numericInput("ext",
+                         "Extension /ft",6),
+            numericInput("spinaxisdiff",
+                         "Spin Axis Difference (-180 - 180) /degrees",0),
+            numericInput("x0","Horizntal Release Point /ft. (Batter's Perspective)",0),
+            numericInput("z0","Vertical Release Point /ft. (Batter's Perspective)", 0),
+            
+            
+        ),
+
+        # Show a plot of the generated distribution
+        mainPanel(
+           textOutput("distPlot")
+        )
+    )
+)
+
+# Define server logic required to draw a histogram
+server <- function(input, output) {
+
+    output$distPlot <- renderText({
+       data <- as.data.frame(5)
+       setnames(data,"5","start_speed")
+       data$start_speed <- input$velo
+       data$IVB <- input$ivb
+       if(input$phand == "L"){
+         data$HB <- input$hb * -1
+       }
+       else{
+       data$HB <- input$hb
+       }
+       data$EAA <- input$ext / 6.3
+       if(input$phand == "L"){
+         data$x0 <- input$x0 * -1
+       }
+       else{
+         data$x0 <- input$x0
+       }
+       data$z0 <- input$z0
+       data$spin_rate <- input$spinrate
+       data$SADiff <- input$spinaxisdiff
+       
+       data <- as.matrix(data)
+       
+       prediction <-predict(FBMod,data)
+       TimStuff <- (50 - (prediction - -0.0004397118)/ 0.2180433 * 10)
+       # FBMod <- xgb.load("FB.model")
+       # vip(FBMod)
+       return(TimStuff)
+    })
+}
+
+# Run the application 
+shinyApp(ui = ui, server = server)