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# 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") |
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