gganimate with increasing sample size

# Note, this uses the new version of gganimate by Thomas Lin Pedersen (@thomasp85) available from:
# https://github.com/thomasp85/gganimate
# Apologies for clunkiness of code below...

library(tidyverse)
library(gganimate)

data <- NULL
sample <- NULL
d <- NULL
e <- 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)
  d <- mean(c(c_1, data$sample_mean))
  e <- mean_cl_boot(c(a, data$DV))[1,1] - mean_cl_boot(c(a, data$DV))[1,2]  
  
  sample <- as.data.frame(cbind(b, a, c, d, e))
  colnames(sample) <- c("sample", "DV", "sample_mean", "running_mean", "cl")
  
  data <- rbind(sample, data)
}

ggplot(data, aes(x = sample, y = running_mean)) + geom_point(size = 2, colour="red") + 
  geom_errorbar(aes(ymin = running_mean - cl, ymax = running_mean + cl)) +
  geom_hline(yintercept = 1000, colour = "blue") +
  ylim(980,1020) +
  labs(x = "Sample Number", y = "Moving Average", 
       title = "Moving average gets closer to the population \nmean (blue line) and CI bands narrow as \nsampling increases.") +
  theme_minimal() +
  theme(text = element_text(size = 15)) +
  transition_time(sample) + shadow_mark(size = 5, colour = "grey")