---
title: "NCAA Women's Soccer Season — Performance Analytics Dashboard"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: fill
    theme: bootstrap
    self_contained: true
    source_code: embed
---

```{r setup, include=FALSE}

library(flexdashboard)
library(crosstalk)
library(plotly)
library(DT)
library(dplyr)
library(ggplot2)
library(tidyverse)
library(janitor)
library(lubridate)
library(slider)

# 1. Load Data (Using updated filenames)
df_raw     <- read_csv("training_data_anon.csv") %>% clean_names()
roster_map <- read_csv("roster_map_anon.csv") %>% clean_names()
df_minutes <- read_csv("minutes_data_anon.csv") %>% clean_names()


# 2. Build Master Data Frame
df_master <- df_raw %>%
  mutate(
    athlete_name = str_to_lower(str_trim(athlete_name)),
    start_date   = mdy(start_date)
  ) %>%
  filter(!is.na(athlete_name), !is.na(start_date)) %>% 
  left_join(
    roster_map %>% mutate(athlete_name = str_to_lower(str_trim(athlete_name))), 
    by = "athlete_name") %>%
  group_by(athlete_name) %>%
  arrange(start_date) %>%
  mutate(
    session_load = as.numeric(session_load),
    distance_yds = as.numeric(distance_yds),
    no_of_hi      = as.numeric(no_of_high_intensity_events),
    acute_load   = slide_index_dbl(session_load, .i = start_date, .f = mean, .before = days(7)),
    chronic_load = slide_index_dbl(session_load, .i = start_date, .f = mean, .before = days(28)),
    acwr         = acute_load / chronic_load,
    hi_density   = no_of_hi / (distance_yds / 1000),
    row_key      = paste0(athlete_name, "_", row_number()),
    
    session_tag  = case_when(
      str_detect(toupper(tags), "MD-0|MATCH") ~ "MATCH",
      str_detect(toupper(tags), "MD-1") ~ "MD-1",
      str_detect(toupper(tags), "MD-2") ~ "MD-2",
      TRUE ~ "Training"
    ),
    
    load_status = case_when(
      acwr > 1.5 ~ "High",
      acwr < 0.8 ~ "Below",
      TRUE ~ "Normal"),
    
    tooltip_text = paste0("Athlete: ", athlete_name, "<br>Load: ", session_load, "<br>ACWR: ", round(acwr, 2))
  ) %>%
  ungroup() %>%
  mutate(unit = factor(replace_na(as.character(unit), "Unassigned")))

df_master <- df_master %>% 
  select(athlete_name, unit, start_date, session_load, acwr, load_status,
         no_of_hi, hi_density, top_speed_mph, row_key, session_tag, tooltip_text)
n_athletes   <- n_distinct(df_master$athlete_name)
avg_load_all <- round(mean(df_master$session_load, na.rm = TRUE), 1)

shared_data <- SharedData$new(df_master)

# Define your custom colors based on your specific hex codes
pos_colors <- c(
    "Defenders"   = "grey15", 
    "Midfielders" = "#95A5A6", 
    "Forwards"    = "#990000",
    "Goalkeepers" = "#DAA520")

density_colors <- c(
    "MD-2"  = "#000000",
    "MD-1"  = "#808080",
    "MATCH" = "#990000")
```

Sidebar {.sidebar data-width=300}
-----------------------------------------------------------------------

### Dashboard Filters
<br>

```{r filters}
filter_slider("sel_date", "Select Date Range:", shared_data, ~start_date)
filter_checkbox("sel_unit", "Position:", shared_data, ~unit, inline = TRUE)

```

---

### Key Metrics

* **Load Status** — compared with 28-day rolling average
* **HI Events** — moments of high-intensity activity
* **Density** — intensity per 1,000 yards

Row {data-height=150}
-----------------------------------------------------------------------


### Total Athletes Tracked

```{r vbox_athletes}
valueBox(n_athletes, icon = "fa-users")
```

### Average Session Load (All Sessions)

```{r vbox_load}
valueBox(avg_load_all, icon = "fa-tachometer-alt", color = "primary")
```

Row {data-height=600 .tabset .tabset-fade}
-----------------------------------------------------------------------

### Daily Load by Athlete

```{r plot_load}
plot_ly(shared_data, x = ~start_date, y = ~session_load, color = ~unit,
        colors = pos_colors, type = "scatter", mode = "markers",
        text = ~tooltip_text, hoverinfo = "text") %>%
  layout(dragmode = "select", xaxis = list(title = "Date"), yaxis = list(title = "Load"))
```

### Load vs. Volume

```{r plot_density}
plot_ly(shared_data, x = ~no_of_hi, y = ~session_load, color = ~unit,
        colors = pos_colors, type = "scatter", mode = "markers",
        text = ~tooltip_text, hoverinfo = "text") %>%
  layout(xaxis = list(title = "HI Events"), yaxis = list(title = "Total Load"))
```

### Intensity Density (HI Events per 1,000 Yards)

```{r}
shared_density_only <- df_master %>%
  filter(session_tag %in% c("MD-2", "MD-1", "MATCH")) %>%
  SharedData$new(key = ~row_key, group = "SoccerReport")

plot_ly(shared_density_only, 
        x = ~session_tag, 
        y = ~hi_density, 
        color = ~session_tag,
        colors = density_colors,
        type = "box", 
        boxpoints = "all", 
        jitter = 0.3,
        text = ~athlete_name, 
        hoverinfo = "text") %>%
  layout(
    xaxis = list(
      title = "Session Type", 
      categoryorder = "array", 
      categoryarray = c("MD-2", "MD-1", "MATCH")
    ),
    yaxis = list(title = "HI Events / 1k Yds"),
    showlegend = FALSE
  )
```


Row
-----------------------------------------------------------------------

### Session Summary Table

```{r table}
datatable(
  shared_data,
  extensions = c("Scroller", "Buttons"),
  options = list(
    dom = "Bfrtip",
    buttons = list("csv", "excel"),
    scrollY = 300, 
    scroller = TRUE,
    columnDefs = list(
      list(visible = FALSE, targets = c(9, 10)) # Hides row_key and tooltip_text
    )
  ),
  colnames = c(
    "Athlete"    = "athlete_name", 
    "Unit"       = "unit", 
    "Date"       = "start_date", 
    "Load"       = "session_load", 
    "ACWR"       = "acwr",        # Use lowercase 'acwr' here
    "Status"     = "load_status",
    "HI Events"  = "no_of_hi", 
    "Density"    = "hi_density",  # Use the original 'hi_density'
    "Top Speed"  = "top_speed_mph" # Use original 'top_speed_mph'
  ),
  rownames = FALSE, 
  class = "stripe hover compact") %>% 
  
  formatRound(c("ACWR", "Density"), digits = 1) %>%
  formatRound("Top Speed", digits = 2) %>% 
  formatStyle(
    'Status',
    target = 'cell',
    backgroundColor = styleEqual(
      c("High", "Normal", "Below"), 
      c('#990000', '#f8f9fa', '#808080') # Red, Light Grey, Dark Grey
    ),
    color = styleEqual(c("High"), c('white')) # Make text white on red background
  )
```