library(tidyverse)
library(magick)

# devtools::install_github("thomasp85/patchwork")
library(patchwork)

colours = c("#85d4e3",
            "#e39f85")

set.seed(40)

error_sd <- function(icc) {

  # From the reliability formula: rel = sigmaTrue^2 / sigmaTot^2 = sigmaTrue^2 / (sigmaTrue^2 + sigmaError^2)

  sqrt(1/icc - 1)
}


special_dnorm <- function(negative=FALSE, x, mean, sd) {

  # This makes a density plot that has the same height, regardless of the variance
  # Also, the negative argument was for another plot to have them facing left and right

  maxval <- dnorm(0, mean, sd)
  out <- dnorm(x, mean, sd) / maxval

  out <- out+0.08  # 0.08 is a little offset for aesthetics

  if(negative) {
    out <- out*-1
  }

  return(out)
}


# Make some points

reliability <- tibble(True = rnorm(40)) %>%
  arrange(True) %>%
  mutate(Subject = 1:n())


dir.create("gif_figs")

make_measplot <- function(icc, n=1) {

  # Add measurement error

  errorsd <- error_sd(icc)
  reliability$Measured = reliability$True + rnorm(length(reliability$True), sd = errorsd)


  # And error bars

  reliability$Error_upper <- reliability$True + 1.96*errorsd
  reliability$Error_lower <- reliability$True - 1.96*errorsd

  # Make the distribution plot

  distroplot <- ggplot(data.frame(x = c(-5, 5)), aes(x)) +   # The data is meaningless - not used
    stat_function(fun = special_dnorm, colour = colours[1],
                  size = 1.5, args = list(negative=F, mean=0, sd=1),
                  geom = "line") +
    annotate("segment", x = 1.96, xend = -1.96, y = 0.03, yend = 0.03,
             colour = colours[1], size=1) +
    stat_function(fun = special_dnorm, colour = colours[2],
                  size = 1.5, args = list(negative=F, mean=0, sd=errorsd),
                  geom = "line") +
    annotate("segment", x = 1.96*errorsd, xend = -1.96*errorsd, y = 0.05, yend = 0.05,
             colour = colours[2], size=1) +
    coord_flip() +
    theme_void() +
    lims(x=c(-5,5)) +
    annotate("text", x = 2.5, y = 0.7, label = "True", size=5, colour=colours[1]) +
    annotate("text", x = -2.5, y = 0.7, label = "Error", size=5, colour=colours[2])


  # Make the measurement plot

  relplot <- ggplot(reliability, aes(x=Subject, y=True)) +
    geom_point(size=5, alpha=0.3, colour=colours[1]) +
    theme_void() +
    geom_errorbar(aes(ymin=Error_lower, ymax=Error_upper), width=0, colour=colours[2]) +
    coord_cartesian(ylim = c(-5,5)) +
    annotate("text", x = 10, y = 4, label = paste("ICC =", round(icc, 2), sep=" "), size=5) +
    geom_point(aes(y=Measured))


  # Put them together

  out <- relplot + distroplot + plot_layout(ncol = 2, widths = c(3, 1))

  # Making a naming scheme so that they're in the right order

  icc_error <- 100-(100*icc)

  # Save

  ggsave(out, filename = sprintf(paste("gif_figs/", stringr::str_pad(icc_error, side="left", pad="0", width = 2),
                                       "_iccplot_%d.png", sep=""), n), width = 9, height = 5, dpi=200)

  return(out)

}


# Make a bunch of them

iccvals <- c(0.99, 0.9,  0.8, 0.7, 0.6, 0.5, 0.4, 0.2)
each_times <- 6

# I was too lazy to do this tidily. Forgive me Hadley for I have sinned.

for(i in 1:length(iccvals)) {
  for(j in 1:each_times) {
    make_measplot(iccvals[i], j)
  }
}


# Make a gif!

list.files(path = "./gif_figs/", pattern = "*.png", full.names = T) %>%
  map(image_read) %>% # reads each path file
  image_join() %>% # joins image
  image_animate(fps=2) %>% # animates, can opt for number of loops
  image_write("reliability_change.gif") # write to current dir