Introduction
This vignette demonstrates best practices for creating shareable sports graphics using sdvplotR. These patterns are optimized for social media platforms like Twitter/X, Instagram, and Facebook.
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")Weekly Win-Probability Charts
Create win-probability charts for weekly recaps:
# Sample game data (replace with real data)
game_data <- data.frame(
quarter = rep(1:4, each = 10),
time_left = rep(seq(900, 0, length.out = 10), 4),
win_prob_home = c(seq(0.5, 0.3, length.out = 10),
seq(0.3, 0.6, length.out = 10),
seq(0.6, 0.4, length.out = 10),
seq(0.4, 0.8, length.out = 10)),
win_prob_away = c(seq(0.5, 0.7, length.out = 10),
seq(0.7, 0.4, length.out = 10),
seq(0.4, 0.6, length.out = 10),
seq(0.6, 0.2, length.out = 10))
)
ggplot(game_data, aes(x = time_left)) +
geom_area(aes(y = win_prob_home), fill = "#E31837", alpha = 0.6) +
geom_area(aes(y = win_prob_away), fill = "#003594", alpha = 0.6) +
geom_sdv_logos(
data = data.frame(x = 450, y = 0.5, team = "KC"),
aes(x = x, y = y, team = team),
sport = "nfl",
width = 0.05,
inherit.aes = FALSE
) +
geom_sdv_logos(
data = data.frame(x = 450, y = 0.5, team = "BUF"),
aes(x = x, y = y, team = team),
sport = "nfl",
width = 0.05,
inherit.aes = FALSE
) +
scale_x_reverse() +
scale_y_continuous(labels = scales::percent) +
labs(
title = "Win Probability: KC vs BUF",
subtitle = "Week 10 Recap",
x = "Time Remaining",
y = "Win Probability",
caption = "Data: nflfastR | Viz: sdvplotR"
) +
theme_minimal() +
theme(
plot.title = element_text(face = "bold", size = 16),
plot.subtitle = element_text(color = "grey40", size = 12),
plot.caption = element_text(color = "grey60", size = 9)
)Logo-Rich Scatter Plots
Create eye-catching scatter plots with team logos:
# Sample EPA data
epa_data <- data.frame(
team = sample(nfl_teams, 16),
offensive_epa = runif(16, -0.2, 0.3),
defensive_epa = runif(16, -0.3, 0.2)
)
ggplot(epa_data, aes(x = offensive_epa, y = defensive_epa)) +
geom_sdv_logos(
aes(team = team),
sport = "nfl",
width = 0.075
) +
geom_hline(yintercept = 0, linetype = "dashed", color = "grey50") +
geom_vline(xintercept = 0, linetype = "dashed", color = "grey50") +
labs(
title = "NFL Team EPA Analysis",
subtitle = "Offensive vs Defensive Performance",
x = "Offensive EPA per Play",
y = "Defensive EPA per Play",
caption = "Data: nflfastR | Viz: sdvplotR"
) +
theme_minimal() +
theme(
plot.title = element_text(face = "bold", size = 16),
plot.subtitle = element_text(color = "grey40", size = 12),
plot.caption = element_text(color = "grey60", size = 9)
)Instagram-Style Square Exports
Create square graphics optimized for Instagram:
# Top 9 teams by win percentage
top_9 <- data.frame(
team = sample(nfl_teams, 9),
win_pct = sort(runif(9, 0.5, 0.9), decreasing = TRUE),
x = rep(1:3, each = 3),
y = rep(3:1, 3)
)
ggplot(top_9, aes(x = x, y = y)) +
geom_sdv_logos(
aes(team = team),
sport = "nfl",
width = 0.15
) +
geom_label(
aes(label = paste0(round(win_pct * 100, 1), "%")),
nudge_y = -0.3,
size = 4,
alpha = 0.8
) +
coord_equal() +
labs(
title = "Top 9 NFL Teams by Win %",
caption = "Data: nflfastR | Viz: sdvplotR"
) +
theme_void() +
theme(
plot.title = element_text(face = "bold", size = 18, hjust = 0.5),
plot.caption = element_text(color = "grey60", size = 10, hjust = 0.5)
)Twitter/X Optimized Graphics
Create graphics optimized for Twitter/X (16:9 aspect ratio):
# Weekly power rankings
power_rankings <- data.frame(
rank = 1:10,
team = sample(nfl_teams, 10),
points = sort(runif(10, 50, 100), decreasing = TRUE)
)
ggplot(power_rankings, aes(x = rank, y = points)) +
geom_col(aes(fill = team), width = 0.7) +
geom_sdv_logos(
aes(team = team),
sport = "nfl",
width = 0.05
) +
scale_fill_sdv(sport = "nfl", alpha = 0.8) +
scale_y_continuous(limits = c(0, 120)) +
labs(
title = "Week 10 Power Rankings",
subtitle = "Top 10 NFL Teams",
x = "Rank",
y = "Power Rating",
caption = "Data: nflfastR | Viz: sdvplotR"
) +
theme_minimal() +
theme(
plot.title = element_text(face = "bold", size = 18),
plot.subtitle = element_text(color = "grey40", size = 14),
plot.caption = element_text(color = "grey60", size = 10),
legend.position = "none"
)ggsave Best Practices
Export graphics with optimal settings for social media:
# Save for Twitter (16:9, high DPI)
# ggsave("twitter_graphic.png",
# width = 16, height = 9, dpi = 300,
# bg = "white")
# Save for Instagram (1:1 square)
# ggsave("instagram_square.png",
# width = 10, height = 10, dpi = 300,
# bg = "white")
# Save for Instagram Stories (9:16)
# ggsave("instagram_story.png",
# width = 9, height = 16, dpi = 300,
# bg = "white")
# Save for Facebook (varies, but 1200x630 is common)
# ggsave("facebook_post.png",
# width = 12, height = 6.3, dpi = 300,
# bg = "white")Branding and Watermarks
Add consistent branding to your graphics:
# Create a branded footer function
add_branding <- function(plot, username = "@YourHandle") {
plot +
labs(caption = paste0("Data: nflfastR | Viz: sdvplotR | ", username)) +
theme(
plot.caption = element_text(
color = "grey60",
size = 9,
hjust = 1
)
)
}
# Apply branding to a plot
sample_plot <- ggplot(data.frame(x = 1:5, y = 1:5), aes(x, y)) +
geom_point() +
theme_minimal()
add_branding(sample_plot, "@SportsDataverse")Multi-Sport Weekly Recap
Create a multi-sport recap graphic:
# Sample data for multiple sports
multi_sport_data <- data.frame(
sport = c("NFL", "NBA", "MLB", "NHL", "CFB"),
team = c("KC", "BOS", "LAD", "COL", "UGA"),
record = c("8-2", "12-3", "95-67", "45-12-5", "11-1"),
rank = 1:5
)
ggplot(multi_sport_data, aes(x = rank, y = 1)) +
geom_sdv_logos(
data = multi_sport_data[1, ],
aes(x = rank, y = 1, team = team),
sport = "nfl",
width = 0.075,
inherit.aes = FALSE
) +
geom_sdv_logos(
data = multi_sport_data[2, ],
aes(x = rank, y = 1, team = team),
sport = "nba",
width = 0.075,
inherit.aes = FALSE
) +
geom_sdv_logos(
data = multi_sport_data[3, ],
aes(x = rank, y = 1, team = team),
sport = "mlb",
width = 0.075,
inherit.aes = FALSE
) +
geom_sdv_logos(
data = multi_sport_data[4, ],
aes(x = rank, y = 1, team = team),
sport = "nhl",
width = 0.075,
inherit.aes = FALSE
) +
geom_sdv_logos(
data = multi_sport_data[5, ],
aes(x = rank, y = 1, team = team),
sport = "cfb",
width = 0.075,
inherit.aes = FALSE
) +
geom_label(
aes(label = sport),
nudge_y = -0.3,
size = 3,
alpha = 0.8
) +
geom_label(
aes(label = record),
nudge_y = -0.6,
size = 3,
alpha = 0.8
) +
scale_x_continuous(breaks = 1:5) +
labs(
title = "Weekly Power Rankings",
subtitle = "Top Teams Across All Sports",
caption = "Data: SportsDataverse | Viz: sdvplotR"
) +
theme_minimal() +
theme(
axis.text.y = element_blank(),
panel.grid = element_blank(),
plot.title = element_text(face = "bold", size = 16),
plot.subtitle = element_text(color = "grey40", size = 12)
)Color Palette Showcases
Create graphics showcasing team color palettes:
# Get team colors for top teams
color_data <- data.frame(
team = c("KC", "BUF", "SF", "PHI", "DAL"),
x = 1:5,
y = 1
)
ggplot(color_data, aes(x = x, y = y)) +
geom_col(aes(fill = team), width = 0.8) +
scale_fill_sdv(sport = "nfl", alpha = 1) +
labs(
title = "NFL Team Color Palettes",
subtitle = "Primary Colors",
caption = "Data: sdvplotR"
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
legend.position = "none",
plot.title = element_text(face = "bold", size = 16),
plot.subtitle = element_text(color = "grey40", size = 12)
)Tips for Social Media Success
Aspect Ratios: Use platform-specific aspect ratios (16:9 for Twitter, 1:1 for Instagram posts, 9:16 for Stories)
Text Size: Use large, readable fonts (minimum 14pt for titles)
High DPI: Export at 300 DPI for crisp images
Branding: Include consistent watermarks and handles
Accessibility: Use high-contrast colors and alt text
Consistency: Post at regular intervals with consistent styling