Introduction to Sequential Animation with gganimate in R

In this article, we will delve into the world of sequential animation using the gganimate package in R. We will explore how to create a changing density plot that animates over time, showing how the density changes as new data is added to the dataset and the mean and standard deviation are updated.

Setting Up the Environment

To begin with, we need to make sure our environment is set up correctly. We will be using the tidyverse package for data manipulation and gganimate for creating animations.

Firstly, install and load the necessary packages:

library(tidyverse)
library(gganimate)

Next, let’s create a sample dataset with 14 rows and 4 columns. The Group column has two unique values (A), week is a factor with four levels, mean and st_dev are numeric values.

sampled_data <- data.frame(
  Group = rep("A", 14),
  week = c(0,2:5,7:14,20),
  mean = c(30, 24.76111, 28.02916, 29.1741, 26.75879, 29.80132, 30.33864, 32.02787, 30.39993, 31.53138, 33.36167, 33.64898, 33.25587, 32.76954),
  st_dev = c(10, 8.518451, 7.524797, 7.048826, 6.242289, 5.490741, 5.247176, 4.757057, 4.34478, 4.034823, 3.680196, 3.569206, 3.455505, 3.317643)
)

n = 20000

plot_df <- sampled_data %>%
  uncount(n) %>% # Add noise to the data
    mutate(value = rnorm(n(), mean, st_dev)) %>% # Create a normal distribution with specified parameters
    select(Group, week, value)

Creating the Initial Plot

Now that we have our dataset, let’s create an initial plot using ggplot. We will plot the density of the data using geom_density.

# Initialize a new plot
plot_df %>%
  ggplot(aes(x=value, fill=as.factor(week))) +
    geom_density(alpha = 1)

Animating the Plot

To create an animation, we need to use the transition_reveal function from gganimate. However, as per the question, this approach does not yield the desired result.

Instead, let’s try a different method that works:

# Initialize a new plot with transitions
plot_df %>%
  ggplot(aes(x=value, fill=as.factor(week))) +
    geom_density(alpha = 1) +
    transition_time(week) +
    shadow_mark()

Understanding the Animation

Now let’s break down what each part of this animation does:

• transition_time(week) tells gganimate to animate the plot over time. In this case, it will change the density plot for each week.
• shadow_mark() adds a shadow effect to the plot for better visibility.

Understanding the Issue with Reordering Weeks

The original issue mentioned in the question arises from how R assigns levels to factors when they are ordered. When we use seq_along(as.factor(week)), it reorders the weeks, which results in an inconsistent color fill. To avoid this, we can manually specify the order of the weeks.

# Initialize a new plot with transitions
plot_df %>%
  ggplot(aes(x=value, fill=as.factor(week))) +
    geom_density(alpha = 1) +
    transition_time(week) +
    shadow_mark() +
    # Specify the order of the weeks manually
    guides(fill = guide_color_bars(order = 2))

In this example, we have added guides(fill = guide_color_bars(order = 2)). This tells gganimate to maintain the color fill while reordering the weeks.

Conclusion

Sequential animation is a powerful tool in data visualization. By understanding how to use the transition_reveal function and manual ordering of weeks, we can create an informative and engaging plot that showcases changing density over time.

Further Reading

For those interested in learning more about gganimate and its capabilities, check out the official documentation for more information on transitions, animations, and customizations.

Additionally, exploring other packages such as animation or lme4 can also help you expand your skills in data visualization.

Last modified on 2023-11-13