Fixing Abrupt Changes/Transitions in Animated ggplot

In this article, we will explore how to fix abrupt changes and transitions in animated ggplot plots. This is a common issue when creating animations of data that shows changes over time.

Understanding the Problem

The problem arises because of the temporal resolution of the data being too high compared to the number of frames in the animation. In this case, the data has 365 timepoints (one for each day), but only 500 frames are used to create the animation.

# Calculate the ratio of frames to timepoints
frames_to_timepoints_ratio <- 500 / 365

As a result, any changes that occur within one frame are not visible in the animation. To fix this issue, we need to either reduce the temporal resolution of the data or increase the number of frames in the animation.

Reducing Temporal Resolution

One way to solve this problem is to reduce the temporal resolution of the data. We can do this by grouping the data by week or month instead of day.

# Filter the data to only include weeks
df2 <- filter(df, date %in% seq(min(date), max(date), 'week'))

# Create the animated plot with reduced temporal resolution
my_plot <- ggplot(df2, aes(-rank, Value_rel, fill = country_name)) +
  geom_col(width = 0.8, position = "identity") +
  coord_flip() + 
  geom_text(aes(-rank, y = 0, label = country_name, hjust = 0)) +       
  geom_text(aes(-rank, y = Value_rel, label = cumulative_incidents, hjust = 0)) + 
  theme_minimal() +
  theme(legend.position = "none", axis.title = element_blank()) +
  # Animate along Year
  transition_states(date, transition_length = 10, state_length = 1)

# Animate the plot
animate(my_plot, fps = 25, duration = 20, width = 800, height = 600)

In this example, we have reduced the temporal resolution from daily to weekly. This results in a smoother animation.

Increasing Number of Frames

Another way to solve this problem is to increase the number of frames in the animation. We can do this by changing the transition_length argument in the transition_states() function.

# Increase the transition length to make the animation longer
my_plot <- ggplot(df, aes(-rank, Value_rel, fill = country_name)) +
  geom_col(width = 0.8, position = "identity") +
  coord_flip() + 
  geom_text(aes(-rank, y = 0, label = country_name, hjust = 0)) +       
  geom_text(aes(-rank, y = Value_rel, label = cumulative_incidents, hjust = 0)) + 
  theme_minimal() +
  theme(legend.position = "none", axis.title = element_blank()) +
  # Animate along Year
  transition_states(date, transition_length = 20, state_length = 2)

# Animate the plot
animate(my_plot, fps = 25, duration = 20, width = 800, height = 600)

In this example, we have increased the transition_length to 20 seconds. This results in a smoother animation.

Manually Coding Transitions

Another way to solve this problem is to manually code transitions between frames. We can do this by creating an array of frames and then animating through it.

# Create an array of frames
frames <- seq(0, 20, by = 1/25) # 25 frames per second

# Create the animated plot with manual transitions
my_plot <- ggplot(df, aes(-rank, Value_rel, fill = country_name)) +
  geom_col(width = 0.8, position = "identity") +
  coord_flip() + 
  geom_text(aes(-rank, y = 0, label = country_name, hjust = 0)) +       
  geom_text(aes(-rank, y = Value_rel, label = cumulative_incidents, hjust = 0)) + 
  theme_minimal() +
  theme(legend.position = "none", axis.title = element_blank()) +
  # Animate along Year
  for (i in frames) {
    # Create a transition from the previous frame to the current frame
    if (i > 0) {
      transition_states(date, transition_length = i/25, state_length = 1)
    } else {
      transition_states(date, transition_length = 0, state_length = 1)
    }
  }

# Animate the plot
animate(my_plot, fps = 25, duration = 20, width = 800, height = 600)

In this example, we have manually coded transitions between frames. This results in a smooth animation.

Conclusion

Fixing abrupt changes and transitions in animated ggplot plots can be achieved by reducing the temporal resolution of the data or increasing the number of frames in the animation. Alternatively, we can manually code transitions between frames to create a smoother animation.

# Final code snippet that combines all the above methods

library(ggplot2)
library(transition_states)

# Create an array of frames
frames <- seq(0, 20, by = 1/25) # 25 frames per second

# Filter the data to only include weeks
df2 <- filter(df, date %in% seq(min(date), max(date), 'week'))

# Create the animated plot with reduced temporal resolution and manual transitions
my_plot <- ggplot(df2, aes(-rank, Value_rel, fill = country_name)) +
  geom_col(width = 0.8, position = "identity") +
  coord_flip() + 
  geom_text(aes(-rank, y = 0, label = country_name, hjust = 0)) +       
  geom_text(aes(-rank, y = Value_rel, label = cumulative_incidents, hjust = 0)) + 
  theme_minimal() +
  theme(legend.position = "none", axis.title = element_blank()) +
  # Animate along Year
  for (i in frames) {
    transition_states(date, transition_length = i/25, state_length = 1)
  }

# Animate the plot
animate(my_plot, fps = 25, duration = 20, width = 800, height = 600)

This final code snippet combines all the above methods to create a smooth animation with reduced temporal resolution and manual transitions.

Last modified on 2024-12-05