Leaderboard Dashboards with sdvplotR, gt, and Quarto

Introduction

This vignette demonstrates how to create interactive leaderboard dashboards using sdvplotR, gt tables, and Quarto. These patterns are perfect for tracking standings, player stats, and team performance in real-time.

Setup

library(sdvplotR)
library(ggplot2)
library(dplyr)
library(gt)

# Get valid team abbreviations
nfl_teams <- valid_team_names("nfl")
nba_teams <- valid_team_names("nba")
cfb_teams <- valid_team_names("cfb")

Basic Leaderboard with Logos

Create a simple leaderboard table with team logos:

# Sample standings data
standings <- data.frame(
  team = c("KC", "BUF", "SF", "PHI", "DAL", "MIA", "CIN", "BAL"),
  wins = c(9, 8, 8, 7, 7, 6, 6, 6),
  losses = c(2, 3, 3, 4, 4, 5, 5, 5),
  ties = c(0, 0, 0, 0, 0, 0, 0, 0),
  pct = c(0.818, 0.727, 0.727, 0.636, 0.636, 0.545, 0.545, 0.545)
) |>
  mutate(
    logo = team,
    rank = row_number()
  ) |>
  select(rank, logo, team, wins, losses, ties, pct)

standings |>
  gt() |>
  gt_sdv_logos(columns = "logo", sport = "nfl", height = 35) |>
  fmt_number(columns = "pct", decimals = 3) |>
  cols_label(
    rank = "#",
    logo = "Team",
    team = "Abbrev",
    wins = "W",
    losses = "L",
    ties = "T",
    pct = "Win %"
  ) |>
  tab_header(
    title = "NFL Standings",
    subtitle = "Example data"
  ) |>
  tab_footnote(
    footnote = "Data: nflfastR | Viz: sdvplotR",
    locations = cells_title(groups = "title")
  )

Advanced Leaderboard with Team Colors

Create a leaderboard with team-colored rows:

# Add team colors to standings
standings_colored <- standings |>
  mutate(
    primary_color = sdv_team_colors("nfl", team, type = "primary"),
    secondary_color = sdv_team_colors("nfl", team, type = "secondary")
  )

standings_colored |>
  gt() |>
  gt_sdv_logos(columns = "logo", sport = "nfl", height = 35) |>
  fmt_number(columns = "pct", decimals = 3) |>
  cols_label(
    rank = "#",
    logo = "Team",
    team = "Abbrev",
    wins = "W",
    losses = "L",
    ties = "T",
    pct = "Win %"
  ) |>
  tab_header(
    title = "NFL Standings",
    subtitle = "Example data"
  ) |>
  data_color(
    columns = "pct",
    palette = c("lightblue", "darkblue")
  )

Multi-Sport Leaderboard

Create leaderboards for multiple sports in one table:

# Sample multi-sport data
multi_sport_standings <- data.frame(
  sport = c(rep("NFL", 4), rep("NBA", 4), rep("MLB", 4)),
  team = c("KC", "BUF", "SF", "PHI",
           "BOS", "DEN", "MIL", "PHX",
           "LAD", "ATL", "HOU", "BAL"),
  wins = c(9, 8, 8, 7, 25, 23, 22, 20, 95, 90, 88, 85),
  losses = c(2, 3, 3, 4, 8, 10, 11, 13, 67, 72, 74, 77)
) |>
  mutate(
    pct = wins / (wins + losses),
    rank = row_number()
  ) |>
  select(sport, rank, team, wins, losses, pct)

multi_sport_standings |>
  gt() |>
  gt_sdv_logos(columns = "team", sport = "nfl", height = 30) |>
  fmt_number(columns = "pct", decimals = 3) |>
  cols_label(
    sport = "Sport",
    rank = "#",
    team = "Team",
    wins = "W",
    losses = "L",
    pct = "Win %"
  ) |>
  tab_header(
    title = "Multi-Sport Leaderboard",
    subtitle = "Top Teams Across Sports"
  )

Player Leaderboard with Headshots

Create a player stats table with headshots:

# Sample player data
player_stats <- data.frame(
  player_id = c("00-0033873", "00-0026498", "00-0035228", "00-0033869"),
  player_name = c("P. Mahomes", "M. Stafford", "K. Murray", "J. Allen"),
  team = c("KC", "LAR", "ARI", "BUF"),
  pass_yds = c(4200, 3800, 3500, 4100),
  pass_td = c(32, 28, 25, 30),
  pass_int = c(8, 12, 10, 9)
) |>
  mutate(
    rank = row_number()
  ) |>
  select(rank, player_id, player_name, team, pass_yds, pass_td, pass_int)

player_stats |>
  gt() |>
  gt_sdv_headshots(columns = "player_id", sport = "nfl", height = 40) |>
  fmt_number(columns = c("pass_yds", "pass_td", "pass_int")) |>
  cols_label(
    rank = "#",
    player_id = "Player",
    player_name = "Name",
    team = "Team",
    pass_yds = "Yards",
    pass_td = "TD",
    pass_int = "INT"
  ) |>
  tab_header(
    title = "NFL Quarterback Leaderboard",
    subtitle = "Top Passers by Yards"
  )

Quartile-Based Leaderboard

Create a leaderboard with quartile indicators:

# Add quartile information
standings_quartile <- standings |>
  mutate(
    quartile = case_when(
      pct >= 0.75 ~ "Q1 (Elite)",
      pct >= 0.50 ~ "Q2 (Good)",
      pct >= 0.25 ~ "Q3 (Average)",
      TRUE ~ "Q4 (Struggling)"
    )
  )

standings_quartile |>
  gt() |>
  gt_sdv_logos(columns = "logo", sport = "nfl", height = 35) |>
  fmt_number(columns = "pct", decimals = 3) |>
  cols_label(
    rank = "#",
    logo = "Team",
    team = "Abbrev",
    wins = "W",
    losses = "L",
    ties = "T",
    pct = "Win %",
    quartile = "Quartile"
  ) |>
  tab_header(
    title = "NFL Standings by Quartile",
    subtitle = "Example data"
  ) |>
  tab_row_group(
    label = "Elite Teams",
    rows = pct >= 0.75
  ) |>
  tab_row_group(
    label = "Playoff Teams",
    rows = pct >= 0.50 & pct < 0.75
  )

Quarto Dashboard Integration

Create a Quarto dashboard with sdvplotR visualizations:

# In a Quarto document (.qmd), use this structure:

# ---
# title: "NFL Dashboard"
# format: dashboard
# ---

# ## Standings

# ```{r}
# standings |>
#   gt() |>
#   gt_sdv_logos(columns = "logo", sport = "nfl")
# ```

# ## Team Performance

# ```{r}
# ggplot(standings, aes(x = reorder(team, pct), y = pct)) +
#   geom_col(aes(fill = team), width = 0.7) +
#   scale_fill_sdv(sport = "nfl") +
#   theme_minimal()
# ```

Real-Time Leaderboard Updates

Create a leaderboard that updates automatically:

# Function to fetch and display standings
update_standings <- function(sport = "nfl", week = NULL) {
  # In a real implementation, this would fetch live data
  # For demonstration, we'll use sample data

  message(paste("Updating", toupper(sport), "standings..."))

  # Sample standings (replace with real data fetch)
  standings <- data.frame(
    team = sample(valid_team_names(sport), 10),
    wins = sample(0:16, 10),
    losses = sample(0:16, 10)
  ) |>
    mutate(
      pct = wins / (wins + losses),
      logo = team,
      rank = row_number()
    ) |>
    arrange(desc(pct)) |>
    head(10)

  standings |>
    gt() |>
    gt_sdv_logos(columns = "logo", sport = sport, height = 30) |>
    fmt_number(columns = "pct", decimals = 3)
}

# Update standings
update_standings("nfl")

Conditional Formatting

Apply conditional formatting based on performance:

# Add performance indicators
standings_conditional <- standings |>
  mutate(
    performance = case_when(
      pct >= 0.75 ~ "🔥 Hot",
      pct >= 0.50 ~ "✅ Good",
      pct >= 0.25 ~ "⚠️ Average",
      TRUE ~ "❌ Struggling"
    )
  )

standings_conditional |>
  gt() |>
  gt_sdv_logos(columns = "logo", sport = "nfl", height = 35) |>
  fmt_number(columns = "pct", decimals = 3) |>
  cols_label(
    rank = "#",
    logo = "Team",
    team = "Abbrev",
    wins = "W",
    losses = "L",
    ties = "T",
    pct = "Win %",
    performance = "Status"
  ) |>
  tab_header(
    title = "NFL Standings with Status",
    subtitle = "Example data"
  )

Integrating with oddsapiR

Combine standings with betting odds:

# Sample odds data (replace with real oddsapiR data)
odds_data <- data.frame(
  team = c("KC", "BUF", "SF", "PHI"),
  spread = c(-7.5, -3.5, -6.5, -10.5),
  moneyline = c(-350, -180, -280, -550),
  total = c(48.5, 51.5, 47.5, 45.5)
)

# Merge with standings
standings_with_odds <- standings |>
  filter(team %in% odds_data$team) |>
  left_join(odds_data, by = "team") |>
  mutate(logo = team)

standings_with_odds |>
  gt() |>
  gt_sdv_logos(columns = "logo", sport = "nfl", height = 35) |>
  fmt_number(columns = c("pct", "spread", "total"), decimals = 1) |>
  fmt_number(columns = "moneyline", decimals = 0) |>
  cols_label(
    rank = "#",
    logo = "Team",
    team = "Abbrev",
    wins = "W",
    losses = "L",
    pct = "Win %",
    spread = "Spread",
    moneyline = "ML",
    total = "O/U"
  ) |>
  tab_header(
    title = "NFL Standings with Betting Odds",
    subtitle = "Combining Performance and Odds"
  )

Best Practices for Dashboards

Keep it Simple: Focus on key metrics that matter to your audience
Use Consistent Branding: Apply the same colors, fonts, and logos across all visualizations
Make it Interactive: Use Quarto dashboards for interactive exploration
Update Regularly: Set up automated updates to keep data fresh
Optimize for Mobile: Ensure dashboards work on mobile devices
Add Context: Include captions, footnotes, and data sources