In a hilarious experiment the anonymous mathematician behind the website Possibly Wrong estimated that s/he only needed to open “about 400-500” packs of Skittles to find an identifical pack.
From January 12th up to April 6th, s/he put it to the test and counted the contents of an astonishing 468 packs, containing over 27.000 individual Skittles! Read all about the experiment here.
A while back I discovered this free game called Screeps: an RTS colony-simulation game specifically directed AI programmers. I was immediately intrigued by the concept, but it took me a while to find the time and courage to play. When I finally got to playing though, I lost myself in the game for several days on end.
Screeps means “scripting creeps.”
It’s an open-source sandbox MMO RTS game for programmers, wherein the core mechanic is programming your units’ AI. You control your colony by writing JavaScript which operate 24/7 in the single persistent real-time world filled by other players on par with you.
Basically, screeps is very little game. You start with in a randomly generated canyon of some 400 by 400 pixels, with nothing more than some basic resources and your base. Nothing fun will happen. Even better, nothing at all will happen. Unless you program it yourself.
As a player, it is your job to “script” your own creeps’ AI. And your buildings AI for that matter. You will need to write a program that makes your base spawn workers. And next those workers will need to be programmed to actually work. You need to direct them to go to the resources, explain them how to mine the resources, when to stop mining, and how to return the mined resources to your base. You will probably also want some soldiers and some other defenses, so those need to be spawned with their own special instructions as well.
Everything needs to be scripted well, as the game (and thus your screeps) runs on special servers, 24/7, so also when you are not playing yourself. Truly your personal, virtual, mini-AI colony.
The programming mostly occurs in JavaScript. This can be difficult for those like myself who do not know JavaScript, but even I managed to have some basic workers running up and down my screen in a matter of hours. Step by step, you will learn (be forced) to create different worker types (harvesters, builders, repairmen, and even some stupid soldiers) and even some basic colony management scripts (spawning workers, spending resources, upgrading stuff). In the mean time, you will silently learn some JavaScript while playing. As I put in more and more hours, I could even see how to improve on my earlier scripts. This makes screeps a fun and rewarding gaming and learning experience.
Do expect to run into frustrations though! If you’re no JavaScript expert you will personally create a lot of bugs. Of which the game by default send you messages, as your colony will get stuck overnight. Moreover, you will likely need to Google every single thing you want to do at the start. I found great help in this YouTube tutorial to get me started. Finally, you are only under nooby-protection for the first so-many hours, after which you will quickly get slaughtered by all the advanced multi-CPU players on the servers.
Heck, it was fun while it lasted : )
PS. I read here that, using WebAssembly, one could also compile code written in different languages and run it in Screeps: C/C++ or Rust code, as well as other supported languages.
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Useful base functions
str() – explore structure of R object
trimws() – trim trailing and/or leading whitespaces
dput() – dump an R object in form of R code
cut()– categorize values into intervals
intersect() – returns similar elements in two vectors
union() – find intersecting items in two vectors
setdiff() – returns different elements in two vectors
interaction() – computes a factor which represents the interaction of the given factors
formatC()can be used to round numbers and force trailing zero’s
formatC() and sprintf() can be used to add leading/trailing characters
expand.grid() – create a data frame from all combinations of the supplied vectors or factors
seq_along(myvec) – generates a vector of 1:length(myvec)
Generate distributions in ggplot2 using the stat_function function. Normal distributions, student t-distributions, beta distributions, anything. See also here.
Due to the recent updates to the gganimate package, the code below no longer produces the desired animation. A working, updated version can be foundhere.
if(!"tidyverse" %in% installed.packages()) install.packages("tidyverse")
library("tidyverse")
n <- 100
tibble(x = runif(n),
y = runif(n),
s = runif(n, min = 4, max = 20)) %>%
ggplot(aes(x, y, size = s)) +
geom_point(color = "white", pch = 42) +
scale_size_identity() +
coord_cartesian(c(0,1), c(0,1)) +
theme_void() +
theme(panel.background = element_rect("black"))
This greatly fits the Christmas theme we have going here. Inspired by Ilya’s script, I decided to make an animated snowy GIF! Sure R is able to make something like the lively visualizations Daniel Shiffman (Coding Train) usually makes in Processing/JavaScript? It seems so:
### ANIMATED SNOW === BY PAULVANDERLAKEN.COM
### PUT THIS FILE IN AN RPROJECT FOLDER
# load in packages
pkg <- c("here", "tidyverse", "gganimate", "animation")
sapply(pkg, function(x){
if (!x %in% installed.packages()){install.packages(x)}
library(x, character.only = TRUE)
})
# parameters
n <- 100 # number of flakes
times <- 100 # number of loops
xstart <- runif(n, max = 1) # random flake start x position
ystart <- runif(n, max = 1.1) # random flake start y position
size <- runif(n, min = 4, max = 20) # random flake size
xspeed <- seq(-0.02, 0.02, length.out = 100) # flake shift speeds to randomly pick from
yspeed <- runif(n, min = 0.005, max = 0.025) # random flake fall speed
# create storage vectors
xpos <- rep(NA, n * times)
ypos <- rep(NA, n * times)
# loop through simulations
for(i in seq(times)){
if(i == 1){
# initiate values
xpos[1:n] <- xstart
ypos[1:n] <- ystart
} else {
# specify datapoints to update
first_obs <- (n*i - n + 1)
last_obs <- (n*i)
# update x position
# random shift
xpos[first_obs:last_obs] <- xpos[(first_obs-n):(last_obs-n)] - sample(xspeed, n, TRUE)
# update y position
# lower by yspeed
ypos[first_obs:last_obs] <- ypos[(first_obs-n):(last_obs-n)] - yspeed
# reset if passed bottom screen
xpos <- ifelse(ypos < -0.1, runif(n), xpos) # restart at random x
ypos <- ifelse(ypos < -0.1, 1.1, ypos) # restart just above top
}
}
# store in dataframe
data_fluid <- cbind.data.frame(x = xpos,
y = ypos,
s = size,
t = rep(1:times, each = n))
# create animation
snow <- data_fluid %>%
ggplot(aes(x, y, size = s, frame = t)) +
geom_point(color = "white", pch = 42) +
scale_size_identity() +
coord_cartesian(c(0, 1), c(0, 1)) +
theme_void() +
theme(panel.background = element_rect("black"))
# save animation
gganimate(snow, filename = here("snow.gif"), title_frame = FALSE, interval = .1)
Updates:
21/12/2017: Keith combined sound and image to create this very merry video.
paulvanderlaken.com 
