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# r animation package tutorial
# How To Make Animated Graphs using R
# Example of animated transitions in statistical data graphics
library(animation)

#Set delay between frames when replaying
ani.options(interval = .05)

# Set up a vector of colors for use below
col.range <- heat.colors(15)

# Begin animation loop
# Note the brackets within the parentheses
saveGIF({
  # For the most part, it’s safest to start with graphical settings in
  # the animation loop, as the loop adds a layer of complexity to
  # manipulating the graphs. For example, the layout specification needs to
  # be within animation loop to work properly.
  layout(matrix(c(1, rep(2, 5)), 6, 1))
  
  # Adjust the margins a little
  par(mar = c(4, 4, 2, 1) + 0.1)
  
  # Begin the loop that creates the 150 individual graphs
  for (i in 1:150) {
    # Pull 100 observations from a normal distribution
    # and add a constant based on the iteration to move the distribution
    chunk <- rnorm(100) + sqrt(abs((i) - 51))
    
    # Reset the color of the top chart every time (so that it doesn’t change as the
    # bottom chart changes)
    par(fg = 1)
    
    # Set up the top chart that keeps track of the current frame/iteration
    # Dress it up a little just for fun
    plot(
      -5,
      xlim = c(1, 150),
      ylim = c(0, .3),
      axes = F,
      xlab =  "",
      ylab =  "",
      main =  "Iteration"
    )
    abline(
      v = i,
      lwd = 5,
      col = rgb(0, 0, 255, 255, maxColorValue = 255)
    )
    abline(
      v = i - 1,
      lwd = 5,
      col = rgb(0, 0, 255, 50, maxColorValue = 255)
    )
    abline(
      v = i - 2,
      lwd = 5,
      col = rgb(0, 0, 255, 25, maxColorValue = 255)
    )
    
    # Bring back the X axis
    axis(1)
    
    # Set the color of the bottom chart based on the distance of the distribution’s mean from 0
    par(fg = col.range[mean(chunk) + 3])
    
    # Set up the bottom chart
    
    plot(
      density(chunk),
      main =  "",
      xlab =  "X Value",
      xlim = c(-5, 15),
      ylim = c(0, .6)
    )
    
    # Add a line that indicates the mean of the distribution. Add additional lines to track
    # previous means
    abline(v = mean(chunk),
           col = rgb(255, 0, 0, 255, maxColorValue = 255))
    if (exists("lastmean")) {
      abline(v = lastmean,
             col = rgb(255, 0, 0, 50, maxColorValue = 255))
      prevlastmean <- lastmean
    }
    if (exists("prevlastmean")) {
      abline(v = prevlastmean,
             col = rgb(255, 0, 0, 25, maxColorValue = 255))
    }
    #Fix last mean calculation
    lastmean <- mean(chunk)
  }
})

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