readiness_report.Rmd

---
title: "Player Readiness Report"
output: html_document
runtime: shiny
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
# Load libraries
library(ggplot2)
library(shiny)
library(lavaan)
library(semPlot)
library(lavaanPlot)
library(kableExtra)
library(leaflet)
library(DT)
# Look at the dataset
Data<-read.csv('stress_only.csv') #created in stress_only_data.py
Data$Date <- as.Date(Data$Date)

# Define your model specification

model.specs <-'
# measurment model
physical_readiness =~ Physical_Performance_Capability  + Overall_Recovery + Overall_Stress_Score+   Muscular_Stress_Score +  Number_of_Sore_Areas
emotional_readiness=~ Mental_Performance_Capability  + Hours_of_Sleep_Previous_Night+ Lack_of_Activation_Score + Negative_Emotional_State_Score+ Emotional_Balance


# residual correlations

Negative_Emotional_State_Score ~~  Emotional_Balance
Mental_Performance_Capability ~~               Emotional_Balance
Mental_Performance_Capability ~~  Negative_Emotional_State_Score
Lack_of_Activation_Score ~~  Negative_Emotional_State_Score
#Countermovement_Depth_cm ~~           Muscular_Stress_Score
Muscular_Stress_Score ~~            Number_of_Sore_Areas
Physical_Performance_Capability ~~   Mental_Performance_Capability
Overall_Stress_Score ~~           Muscular_Stress_Score
Overall_Recovery ~~   Mental_Performance_Capability
'
fit <-sem(model= model.specs, data = Data)

## ------------------------------------------
## merge factor scores to original data.frame 
## and calculate percentiles
## ------------------------------------------
fscores <- lavPredict(fit)
idx <- lavInspect(fit, "case.idx")
## loop over factors to append factor scores to Data
for (fs in colnames(fscores)) {
  Data[idx, fs] <- fscores[ , fs]
}
# Calculate the percentiles
physical_readiness_ecdf <- ecdf(Data$physical_readiness)
Data$physical_readiness_percentile <- physical_readiness_ecdf(Data$physical_readiness) * 100

emotional_readiness_ecdf <- ecdf(Data$emotional_readiness)
Data$emotional_readiness_percentile <- emotional_readiness_ecdf(Data$emotional_readiness) * 100

## ------------------------------------------

```

```{r}
ui <- fluidPage(
  # UI for selecting a player
  selectInput("player_id", "Select Player:", choices = unique(Data$Player_ID)),
  
  # Output plot for the selected player
  plotOutput("readiness_plot"),
  
  # UI for selecting a date
  dateInput("selected_date", "Select Date:", as.Date("2021-10-20"),
            min = min(Data$Date), max = max(Data$Date),
            format = "yyyy-mm-dd", dates = unique(Data$Date)),
  
  # Output table for the selected date
  DTOutput("readiness_table") # Change this to DTOutput for the datatable
)




server <- function(input, output, session) {
  output$readiness_plot <- renderPlot({
    # Filter data for the specific player
    player_data <- Data[Data$Player_ID == input$player_id, ]
    
    # Check if there's data for the player
    if (nrow(player_data) == 0) {
      return(NULL)
    }
    
    # Create the plot
    ggplot(player_data, aes(x = Date)) +
      geom_line(aes(y = physical_readiness_percentile, color = "Physical Readiness")) +
      geom_line(aes(y = emotional_readiness_percentile, color = "Emotional Readiness")) +
      scale_color_manual(values = c("Physical Readiness" = "blue", "Emotional Readiness" = "red")) +
      labs(title = paste("Readiness Scores for Player", input$player_id),
           x = "Date",
           y = "Readiness Percentile",
           color = "Readiness Type") +
      theme_minimal()
  })

  output$readiness_table <- renderDT({
    # Filter data for the selected date
    date_data <- Data[Data$Date == input$selected_date, ]
    
    # Check if there's data for the selected date
    if (nrow(date_data) == 0) {
      return(NULL)
    }
    
    # Select relevant columns
    date_data <- date_data[, c("Player_ID", "Date", "physical_readiness_percentile", "emotional_readiness_percentile")]

    # Round the last two columns to one decimal place
    date_data$physical_readiness_percentile <- round(date_data$physical_readiness_percentile, 1)
    date_data$emotional_readiness_percentile <- round(date_data$emotional_readiness_percentile, 1)

    # Render DataTable
    datatable(date_data, 
              options = list(pageLength = 25, autoWidth = TRUE, order = list(list(1, 'asc'))),
              rownames = FALSE,
              escape = FALSE, class = 'cell-border stripe') %>%
      formatDate(columns = "Date", method = "toLocaleDateString")
  })

}

# Add this line to render the Shiny app
shinyApp(ui, server, options = list(height = 1080))
```