Using gganimate to visualise the chainging computational complexity as n increases.

big_o_complexity.R

library(gganimate)
library(ggplot2)
library(dplyr)
library(reshape2)

# Get base n values
n <- 50
base <- data.frame(idx = seq(2, n, by = 0.01))

# Compute relative runtimes
runtimes <- base %>% 
  mutate(n = idx,
         logn = log(idx),
         nlogn = idx*log(idx),
         n2 = idx**2,
         n3 = idx**3,
         constant = 1,
         root_n = sqrt(idx),
         factorial = factorial(idx)) %>% 
  melt(id.vars = 'idx', variable.name = 'complexity') # %>% 
  
p <- runtimes %>% 
  ggplot(aes(x = idx, y = value, colour = complexity)) +
  geom_line(size = 1) +
  ylim(0, 100) + 
  labs(title = 'Big O Complexity', x = 'Number of Inputs', y = 'Runtime') +
  theme_bw() +
  transition_reveal(along = idx) +
  ease_aes('linear')

p_anim <- animate(p, fps = 20, nframes = 80)
anim_save('big_o.gif')