This is a quick tutorial on how to use cfbplotR to quickly and easily include college football team logos in your ggplot.
First we need to load the necessary libraries.
will get us the data we want to use for analysis.
will help us easily plot the results.
tidyverse will help
us do the necessary data manipulation and of course includes
ggplot2 that we will use for plotting. You can use the
commented out code to install these packages if you don’t already have
## Warning: package 'ggplot2' was built under R version 4.1.2
## Warning: package 'tibble' was built under R version 4.1.2
## Warning: package 'tidyr' was built under R version 4.1.2
## Warning: package 'readr' was built under R version 4.1.2
## Warning: package 'dplyr' was built under R version 4.1.2
This first chunk of code will pull the play-by-play data from the
first week of the 2021 season using the
cfbfastR data repo
and create the advanced metrics like EPA that we will be plotting (this
will take a second). We’ll also pull in the general team info so we can
filter down to just teams in a power 5 conference.
pbp <- cfbfastR::load_cfb_pbp(2021) %>% filter(week == 1) team_info <- cfbfastR::cfbd_team_info() team_info <- team_info %>% select(team = school,conference,mascot) %>% filter(conference %in% c("Pac-12","ACC","SEC","Big Ten","Big 12"))
Now we quickly roll up the EPA data and find the EPA per rush and EPA per pass for every team in week 1 and take a look at our plotting data.
team_plot_data <- pbp %>% group_by(team = offense_play) %>% summarize(rush_epa = mean(if_else(rush == 1,EPA,NA_real_),na.rm = TRUE), n_rush = sum(rush), pass_epa = mean(if_else(pass == 1,EPA,NA_real_),na.rm = TRUE), n_pass = sum(pass)) %>% filter(team %in% team_info$team) %>% left_join(team_info,by = "team") head(team_plot_data)
## # A tibble: 6 × 7 ## team rush_epa n_rush pass_epa n_pass conference mascot ## <chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr> ## 1 Alabama 0.0502 105 0.0589 131 SEC Crimson Tide ## 2 Arizona 0.178 31 -0.140 53 Pac-12 Wildcats ## 3 Arizona State 0.256 65 -0.123 46 Pac-12 Sun Devils ## 4 Arkansas 0.311 93 -0.543 48 SEC Razorbacks ## 5 Auburn 0.299 63 0.156 65 SEC Tigers ## 6 Baylor 0.185 86 -0.372 41 Big 12 Bears
Now that the data is prepped, we can being to use
cfbplotR. First we’ll plot all the teams with Passing EPA
on the x-axis and Rushing EPA on the y-axis with lines showing the
median value for each. It’s important to set width or height in
geom_cfb_logos to small values. The default of 1 will
create extremely large logos.
ggplot(team_plot_data, aes(x = pass_epa, y = rush_epa)) + geom_median_lines(aes(v_var = pass_epa, h_var = rush_epa)) + geom_cfb_logos(aes(team = team), width = 0.075) + labs(x = "EPA per Pass",y = "EPA per Rush") + theme_bw()
This is still pretty messy because of the large number of teams.
Let’s try to focus in on the Pac-12 teams with a couple of handy tools.
We’re going to add two columns to our data: one for the color and one
for the alpha. Then we just add those two columns as aesthetics to
geom_cfb_logos to turn the logos of non-Pac-12 teams black
and white and lower the alpha.
team_plot_data %>% mutate(color = if_else(conference == "Pac-12",NA_character_,"b/w"), alpha = if_else(conference == "Pac-12",1,.6)) %>% ggplot(aes(x = pass_epa, y = rush_epa)) + geom_median_lines(aes(v_var = pass_epa, h_var = rush_epa)) + geom_cfb_logos(aes(team = team, alpha = alpha, color = color), width = 0.075) + scale_alpha_identity() + scale_color_identity() + labs(x = "EPA per Pass",y = "EPA per Rush") + theme_bw()
Finally let’s make a bar chart showing the Pac-12 EPA per pass for
each team. Because
cfbplotR creates a custom geom for
ggplot, we can use
annotate() to place a log anywhere we’d
let us automatically use a teams primary color on a plot. The
alt_colors argument lets us pass through a vector of team
names that we want to use an alternate color for.
the axis labels that are team names into logos. Due to the way ggplot
works, you have to add the corresponding theme function
can be used for the axis.text argument in the theme function for
improved performance in using logos and headshots as axis labels.
team_plot_data %>% filter(conference == "Pac-12") %>% mutate(team = fct_reorder(team,pass_epa)) %>% ggplot(aes(x = team, y = pass_epa)) + # geom_col(aes(fill = team, color = team),size = 1.5) + annotate(cfbplotR::GeomCFBlogo,x = "California",y = 0.2,team = "Pac-12",height = .35,alpha = .3) + scale_fill_cfb(alpha = .8) + scale_color_cfb(alt_colors = team_plot_data$team) + #scale_x_cfb(size = 18) + labs(x = "", y = "EPA per Pass") + theme_bw() + #theme_x_cfb() theme(axis.text.x = element_cfb_logo())
cfbplotR also allows you to plot player headshots. Let’s
look at the top 10 rushing EPA players with more than 10 rushes for week
player_plot_data <- pbp %>% filter(!is.na(rush_player_id)) %>% group_by(rush_player_id) %>% summarize(epa = mean(EPA, na.rm = TRUE), player_name = first(rusher_player_name), team = first(pos_team), n = n()) %>% filter(n >= 10) %>% arrange(desc(epa)) %>% slice(1:10) player_plot_data %>% mutate(team_ordered = fct_reorder(team,epa)) %>% ggplot(aes(y = team_ordered, x = epa)) + geom_col(aes(color = team, fill = team)) + geom_label(aes(label = player_name, x = epa / 2), alpha = .6) + geom_cfb_headshots(aes(player_id = rush_player_id, x = epa + .1), height = .1) + scale_color_cfb(alt_colors = valid_team_names()) + scale_fill_cfb() + labs(y = "", x = "EPA per Rush") + #scale_y_cfb(size = 18) + theme_minimal() + theme(legend.position = "none", panel.grid.major.y = element_blank()) + #theme_y_cfb() theme(axis.text.y = element_cfb_logo())
gt package offers an easy way to create nice tables
of data and the
gtExtras package from Tom Mock provides a
number of convenient functions for styling those tables. The
functions are slightly modified versions of
gtExtras::gt_image_rows() to easily add team and conference
logos or wordmarks based on names from
gt_merge_stack_team_color() function is a slightly
modified version of
gtExtras::gt_merge_stack() that merges
two columns together and colors the text of the bottom row with the
color of the team referenced in a third column. We can quickly make a
table showing the top teams from week 1 by EPA per pass.
## Warning: package 'gt' was built under R version 4.1.2
team_plot_data %>% transmute(conference, team,logo = team, mascot, wordmark = team, pass_epa = round(pass_epa,2),n_pass, rush_epa = round(rush_epa,2),n_rush) %>% arrange(desc(pass_epa)) %>% head(8) %>% gt() %>% gt_fmt_cfb_logo(columns = c(conference,logo)) %>% gt_fmt_cfb_wordmark(columns = wordmark) %>% gt_merge_stack_team_color(team,mascot,team)
We can also use the
gt_fmt_cfb_headshot() function to
add headshots to a gt using the player_id or headshot_url available
player_plot_data %>% separate(player_name, into = c("first","last"), extra = "merge") %>% select(team,rush_player_id,first,last,n,epa) %>% gt() %>% gt_fmt_cfb_logo(team) %>% gt_fmt_cfb_headshot(rush_player_id) %>% gt_merge_stack_team_color(first,last,team)