The Data
For this example, we’ll use a play from Week 15 of the 2018 NFL season between the Chicago Bears and Green Bay Packers. Data made available for the Big Data Bowl 2021 Kaggle competition.
# Load the play data
example_nfl_play <- data.table::fread(
glue::glue(
"https://raw.githubusercontent.com/sportsdataverse/sportyR/",
"main/data-raw/example-pbp-data.csv"
)
)
# Convert to data frame
example_nfl_play <- as.data.frame(example_nfl_play)To keep things easy, let’s specify the colors for each team’s dots on the resulting GIF. We’ll make the Bears orange and the Packers yellow. The football will also need a dot to be seen; let’s make it brown.
# Prep data for plotting
example_nfl_play[example_nfl_play["team"] == "home", "color"] <- "#c83803"
example_nfl_play[example_nfl_play["team"] == "away", "color"] <- "#ffb612"
example_nfl_play[example_nfl_play["team"] == "football", "color"] <- "#624a2e"First, let’s draw an NFL field via geom_football("nfl").
We’ll adjust the origin to be in the lower left corner of the field, as
per the notes on the coordinate system on the Kaggle page describing the
data.
# Create the field
nfl_field <- geom_football("nfl", x_trans = 60, y_trans = 26.6667)
# Display the field
nfl_field
Looks good! Now, let’s animate using gganimate.
# Add the points on the field
play_anim <- nfl_field +
geom_point(
data = example_nfl_play,
aes(x, y),
color = example_nfl_play$color
) +
transition_time(example_nfl_play$frameId)
# Show the animation
play_anim
Easy peasy. As noted on the plotting-tracking-data vignette, this too works so long as the geospatial data is provided and contains a way to identify and order the frames of the resulting GIF.