moving averages animation

gistfile1.txt

library(tidyverse)
library(gganimate)

data <- NULL
sample <- NULL
d <- NULL

for (i in (1:100)) {
  set.seed(i+1234)
  a <- rnorm(25, 1000, 50)
  b <- rep(i, 25)
  c <- mean(a)
  
  c_1 <- rep(c, 25)
  
  if (is.null(d)) {d <- c_1}
    else {d <- mean(c(c_1, data$sample_mean))}

  sample <- as.data.frame(cbind(b, a, c, d))
  colnames(sample) <- c("sample", "DV", "sample_mean", "running_mean")
  
  data <- rbind(sample, data)
}

ggplot(data, aes(x = sample, y = running_mean)) + geom_point(size = 5) + 
  geom_hline(yintercept = 1000, colour = "blue") +
  scale_y_continuous(breaks = 996:1004) +
  labs(x = "Sample Number", y = "Moving Average", 
       title = "Moving average gets closer to the population \nmean (blue line) as sampling increases.") +
  theme_minimal() +
  theme(text = element_text(size = 15)) +
  transition_time(sample) + shadow_mark(size = 5, colour = "grey")

ggplot(data) +
  geom_vline(xintercept = 1000, colour="blue", size=2) + 
  geom_vline(aes(xintercept = running_mean, colour = "red", size = 1.5)) +
  guides(colour = FALSE, size = FALSE) + coord_flip() +
  scale_x_continuous(breaks = 996:1004) +
  labs(x = "Estimtaed population mean", 
       title = "Estimated mean gets closer to the true \nmean (blue line) as sampling increases up to \nand including sample number {frame_time}.") +
  theme_minimal() +
  theme(text = element_text(size = 14)) +
  transition_time(sample)