Category Archives: Drawings

Sunflowers for COLOURlovers

Andar, lo que es andar, anduve encima siempre de las nubes (Del tiempo perdido, Robe)

If you give importance to colours, maybe you know already COLOURlovers. As can be read in their website, COLOURlovers is a creative community where people from around the world create and share colors, palettes and patterns, discuss the latest trends and explore colorful articles… All in the spirit of love.

There is a R package called colourlovers which provides access to the COLOURlovers API. It makes very easy to choose nice colours for your graphics. I used clpalettes function to search for the top palettes of the website. Their names are pretty suggestive as well: Giant Goldfish, Thought Provoking, Adrift in Dreams, let them eat cake … Inspired by this post I have done a Shiny app to create colored flowers using that palettes. Seeds are arranged according to the golden angle. One example:

Some others:

You can play with the app here.

If you want to do your own sunflowers, here you have the code. This is the ui.R file:

library(colourlovers)
library(rlist)
top=clpalettes('top')
sapply(1:length(top), function(x) list.extract(top, x)$title)->titles

fluidPage(
  titlePanel("Sunflowers for COLOURlovers"),
  fluidRow(
    column(3,
           wellPanel(
             selectInput("pal", label = "Palette:", choices = titles),
             sliderInput("nob", label = "Number of points:", min = 200, max = 500, value = 400, step = 50)
           )
    ),
    mainPanel(
      plotOutput("Flower")
    )
  )
  )

And this is the server.R one:

library(shiny)
library(ggplot2)
library(colourlovers)
library(rlist)
library(dplyr)

top=clpalettes('top')
sapply(1:length(top), function(x) list.extract(top, x)$title)->titles

CreatePlot = function (ang=pi*(3-sqrt(5)), nob=150, siz=15, sha=21, pal="LoversInJapan") {
  
  list.extract(top, which(titles==pal))$colors %>% 
    unlist %>% 
    as.vector() %>% 
    paste0("#", .) -> all_colors
  
  colors=data.frame(hex=all_colors, darkness=colSums(col2rgb(all_colors)))
  colors %>% arrange(-darkness)->colors
  
  background=colors[1,"hex"] %>% as.character

  colors %>% filter(hex!=background) %>% .[,1] %>% as.vector()->colors

  ggplot(data.frame(r=sqrt(1:nob), t=(1:nob)*ang*pi/180), aes(x=r*cos(t), y=r*sin(t)))+
    geom_point(colour=sample(colors, nob, replace=TRUE, prob=exp(1:length(colors))), aes(size=(nob-r)), shape=16)+
    scale_x_continuous(expand=c(0,0), limits=c(-sqrt(nob)*1.4, sqrt(nob)*1.4))+
    scale_y_continuous(expand=c(0,0), limits=c(-sqrt(nob)*1.4, sqrt(nob)*1.4))+
    theme(legend.position="none",
          panel.background = element_rect(fill=background),
          panel.grid=element_blank(),
          axis.ticks=element_blank(),
          axis.title=element_blank(),
          axis.text=element_blank())}

function(input, output) {
 output$Flower=renderPlot({
    CreatePlot(ang=180*(3-sqrt(5)), nob=input$nob, siz=input$siz, sha=as.numeric(input$sha), pal=input$pal)
  }, height = 550, width = 550 )}

Frankenstein

Remember me, remember me, but ah! forget my fate (Dido’s Lament, Henry Purcell)

A Voronoi diagram divides a plane based on a set of original points. Each polygon, or Voronoi cell, contains an original point and all that are closer to that point than any other.

This is a nice example of a Voronoi tesselation. You can find good explanations of Voronoi diagrams and Delaunay triangulations here (in English) or here (in Spanish).

A grayscale image is simply a matrix where darkness of pixel located in coordinates (i, j) is represented by the value of its corresponding element of the matrix: a grayscale image is a dataset. This is a Voronoi diagraman of Frankenstein:

To do it I followed the next steps:

  1. Read this image
  2. Convert it to gray scale
  3. Turn it into a pure black and white image
  4. Obtain a random sample of black pixels (previous image corresponds to a sample of 6.000 points)
  5. Computes the Voronoi tesselation

Steps 1 to 3 were done with imager, a very appealing package to proccess and analice images. Step 5 was done with deldir, also a convenient package which computes Delaunay triangulation and the Dirichlet or Voronoi tessellations.

The next grid shows tesselations for sample size from 500 to 12.000 points and step equal to 500:

I gathered all previous images in this gif created with magick, another amazing package of R I discovered recently:

This is the code:

library(imager)
library(dplyr)
library(deldir)
library(ggplot2)
library(scales)

# Download the image
file="http://ereaderbackgrounds.com/movies/bw/Frankenstein.jpg"
download.file(file, destfile = "frankenstein.jpg", mode = 'wb')

# Read and convert to grayscale
load.image("frankenstein.jpg") %>% grayscale() -> x

# This is just to define frame limits
x %>% 
  as.data.frame() %>% 
  group_by() %>% 
  summarize(xmin=min(x), xmax=max(x), ymin=min(y), ymax=max(y)) %>% 
  as.vector()->rw

# Filter image to convert it to bw
x %>%
  threshold("45%") %>% 
  as.cimg() %>% 
  as.data.frame() -> df

# Function to compute and plot Voronoi tesselation depending on sample size
doPlot = function(n)
{
  #Voronoi tesselation
  df %>% 
  sample_n(n, weight=(1-value)) %>% 
  select(x,y) %>% 
  deldir(rw=rw, sort=TRUE) %>% 
  .$dirsgs -> data

  # This is just to add some alpha to lines depending on its longitude
  data %>% 
    mutate(long=sqrt((x1-x2)^2+(y1-y2)^2),
         alpha=findInterval(long, quantile(long, probs = seq(0, 1, length.out = 20)))/21)-> data

  # A little bit of ggplot to plot results
  data %>% 
    ggplot(aes(alpha=(1-alpha))) +
    geom_segment(aes(x = x1, y = y1, xend = x2, yend = y2), color="black", lwd=1) +
    scale_x_continuous(expand=c(0,0))+
    scale_y_continuous(expand=c(0,0), trans=reverse_trans())+
    theme(legend.position  = "none",
            panel.background = element_rect(fill="white"),
            axis.ticks       = element_blank(),
            panel.grid       = element_blank(),
            axis.title       = element_blank(),
            axis.text        = element_blank())->plot

return(plot)
}

# I call the previous function and store resulting plot in jpeg format
i=5000
name=paste0("frankie",i,".jpeg")
jpeg(name, width = 600, height = 800, units = "px", quality = 100)
doPlot(i)
dev.off()

# Once all images are stored I can create gif
library(magick)
frames=c()
images=list.files(pattern="jpeg")

for (i in length(images):1)
{
  x=image_read(images[i])
  x=image_scale(x, "300")
  c(x, frames) -> frames
}
animation=image_animate(frames, fps = 2)
image_write(animation, "Frankenstein.gif")

The Ex Libris Generator

Go ahead stomp your feet on the floorboards
Clap your hands if that’s really what you came here for
(Heaven, The Milk Carton Kids)

Inspired by curves created by the harmonograph, I have done a Shiny app to generate random images that you can personalize and use as an Exlibris.  You can try the App here. For me, an exlibris (also known as bookplates) can be a nice, original and useful present for book-lovers. This is an example:
exlibris8
More examples:

I always put the code at the end of my posts. Since I always have doubts about how many people are interested in what I do, today will be different. I will share the code with those who ask it to me in any of the following ways:

  • Sending me a direct message on Twitter
  • Droping me an email

Cheers!

The Somnambulist and Pi

How wary we are of something warm and unborn. Something calmly by zero will divide (Unbegotten, The Somnambulist)

Some time ago, I assumed the mission to draw a plot for the cover of the new album of The Somnambulist, a music band from Berlin. They wanted a circlization of Pi, which is a graphic where numbers are represented in a circular layout. The idea is connecting each digit of Pi to its successive digit with links to the position of the numerically corresponding external sectors. I used a color palette composed by 10 nuances of the visible spectrum as a tribute for Planck, as Marco (the vocalist) requested me. After a number of attempts:

somnambulist

The album is named Unbegotten, a german word which means archaic. As Marco told me, in theology it also means kind of eternal because of being never born and so never dying. I like how π is integrated into the title to substitute the string “tt” in the middle. Pi is also eternal so the association is genuine.

The music of The Somnambulist is intense, dark and powerful and is waiting for you here to listen it. My favorite song is the one that gives name to the album.

If you want to know more about circlizong numbers, you can visit this post, where you also can see the code I used as starting point to do this plot.

Chaotic Galaxies

Tell me, which side of the earth does this nose come from? Ha! (ALF)

Reading about strange attractors I came across with this book, where I discovered a way to generate two dimensional chaotic maps. The generic equation is pretty simple:

x_{n+1}= a_{1}+a_{2}x_{n}+a_{3}x_{n}^{2}+a_{4}x_{n}y_{n}+a_{5}y_{n}+a_{6}y_{n}^{2}
y_{n+1}= a_{7}+a_{8}x_{n}+a_{9}x_{n}^{2}+a_{10}x_{n}y_{n}+a_{11}y_{n}+a_{12}y_{n}^{2}

I used it to generate these chaotic galaxies:

Changing the vector of parameters you can obtain other galaxies. Do you want to try?

library(ggplot2)
library(dplyr)
#Generic function
attractor = function(x, y, z)
{
  c(z[1]+z[2]*x+z[3]*x^2+ z[4]*x*y+ z[5]*y+ z[6]*y^2, 
    z[7]+z[8]*x+z[9]*x^2+z[10]*x*y+z[11]*y+z[12]*y^2)
}
#Function to iterate the generic function over the initial point c(0,0)
galaxy= function(iter, z)
{
  df=data.frame(x=0,y=0)
  for (i in 2:iter) df[i,]=attractor(df[i-1, 1], df[i-1, 2], z)
  df %>% rbind(data.frame(x=runif(iter/10, min(df$x), max(df$x)), 
                          y=runif(iter/10, min(df$y), max(df$y))))-> df
  return(df)
}
opt=theme(legend.position="none",
          panel.background = element_rect(fill="#00000c"),
          plot.background = element_rect(fill="#00000c"),
          panel.grid=element_blank(),
          axis.ticks=element_blank(),
          axis.title=element_blank(),
          axis.text=element_blank(),
          plot.margin=unit(c(-0.1,-0.1,-0.1,-0.1), "cm"))
#First galaxy
z1=c(1.0, -0.1, -0.2,  1.0,  0.3,  0.6,  0.0,  0.2, -0.6, -0.4, -0.6,  0.6)
galaxy1=galaxy(iter=2400, z=z1) %>% ggplot(aes(x,y))+
  geom_point(shape= 8, size=jitter(12, factor=4), color="#ffff99", alpha=jitter(.05, factor=2))+
  geom_point(shape=16, size= jitter(4, factor=2), color="#ffff99", alpha=jitter(.05, factor=2))+
  geom_point(shape=46, size= 0, color="#ffff00")+opt
#Second galaxy
z2=c(-1.1, -1.0,  0.4, -1.2, -0.7,  0.0, -0.7,  0.9,  0.3,  1.1, -0.2,  0.4)
galaxy2=galaxy(iter=2400, z=z2) %>% ggplot(aes(x,y))+
  geom_point(shape= 8, size=jitter(12, factor=4), color="#ffff99", alpha=jitter(.05, factor=2))+
  geom_point(shape=16, size= jitter(4, factor=2), color="#ffff99", alpha=jitter(.05, factor=2))+
  geom_point(shape=46, size= 0, color="#ffff00")+opt
#Third galaxy
z3=c(-0.3,  0.7,  0.7,  0.6,  0.0, -1.1,  0.2, -0.6, -0.1, -0.1,  0.4, -0.7)
galaxy3=galaxy(iter=2400, z=z3) %>% ggplot(aes(x,y))+
  geom_point(shape= 8, size=jitter(12, factor=4), color="#ffff99", alpha=jitter(.05, factor=2))+
  geom_point(shape=16, size= jitter(4, factor=2), color="#ffff99", alpha=jitter(.05, factor=2))+
  geom_point(shape=46, size= 0, color="#ffff00")+opt
#Fourth galaxy
z4=c(-1.2, -0.6, -0.5,  0.1, -0.7,  0.2, -0.9,  0.9,  0.1, -0.3, -0.9,  0.3)
galaxy4=galaxy(iter=2400, z=z4) %>% ggplot(aes(x,y))+
  geom_point(shape= 8, size=jitter(12, factor=4), color="#ffff99", alpha=jitter(.05, factor=2))+
  geom_point(shape=16, size= jitter(4, factor=2), color="#ffff99", alpha=jitter(.05, factor=2))+
  geom_point(shape=46, size= 0, color="#ffff00")+opt

Gummy Worms

Just keep swimming (Dory in Finding Nemo)

Inspired by this post, I decided to create gummy worms like this:

worm
Or these:

When I was young I used to eat them.

Do you want to try? This is the code:

library(rgl)
library(RColorBrewer)
t=seq(1, 6, by=.04)
f = function(a, b, c, d, e, f, t) exp(-a*t)*sin(t*b+c)+exp(-d*t)*sin(t*e+f)
v1=runif(6,0,1e-02)
v2=runif(6, 2, 3)
v3=runif(6,-pi/2,pi/2)
open3d()
spheres3d(x=f(v1[1], v2[1], v3[1], v1[4], v2[4], v3[4], t),
          y=f(v1[2], v2[2], v3[2], v1[5], v2[5], v3[5], t),
          z=f(v1[3], v2[3], v3[3], v1[6], v2[6], v3[6], t),
          radius=.3, color=sample(brewer.pal(8, "Dark2"),1))

Playing With Julia (Set)

Viento, me pongo en movimiento y hago crecer las olas del mar que tienes dentro (Tercer Movimiento: Lo de Dentro, Extremoduro)

I really enjoy drawing complex numbers: it is a huge source of entertainment for me. In this experiment I play with the Julia Set, another beautiful fractal like this one. This is what I have done:

  • Choosing the function f(z)=exp(z3)-0.621
  • Generating a grid of complex numbers with both real and imaginary parts in [-2, 2]
  • Iterating f(z) over the grid a number of times so zn+1 = f(zn)
  • Drawing the resulting grid as I did here
  • Gathering all plots into a GIF with ImageMagick as I did in my previous post: each frame corresponds to a different number of iterations

This is the result:

julia

I love how easy is doing difficult things in R. You can play with the code changing f(z) as well as color palettes. Be ready to get surprised:

library(ggplot2)
library(dplyr)
library(RColorBrewer)
setwd("YOUR WORKING DIRECTORY HERE")
dir.create("output")
setwd("output")
f = function(z,c) exp(z^3)+c
# Grid of complex
z0 <- outer(seq(-2, 2, length.out = 1200),1i*seq(-2, 2, length.out = 1200),'+') %>% c()
opt <-  theme(legend.position="none",
              panel.background = element_rect(fill="white"),
              plot.margin=grid::unit(c(1,1,0,0), "mm"),
              panel.grid=element_blank(),
              axis.ticks=element_blank(),
              axis.title=element_blank(),
              axis.text=element_blank())
for (i in 1:35)
{
  z=z0
  # i iterations of f(z)
  for (k in 1:i) z <- f(z, c=-0.621) df=data.frame(x=Re(z0), y=Im(z0), z=as.vector(exp(-Mod(z)))) %>% na.omit() 
  p=ggplot(df, aes(x=x, y=y, color=z)) + 
    geom_tile() + 
    scale_x_continuous(expand=c(0,0))+
    scale_y_continuous(expand=c(0,0))+
    scale_colour_gradientn(colours=brewer.pal(8, "Paired")) + opt
  ggsave(plot=p, file=paste0("plot", stringr::str_pad(i, 4, pad = "0"),".png"), width = 1.2, height = 1.2)
}
# Place the exact path where ImageMagick is installed
system('"C:\\Program Files\\ImageMagick-6.9.3-Q16\\convert.exe" -delay 20 -loop 0 *.png julia.gif')
# cleaning up
file.remove(list.files(pattern=".png"))

Zooming

You don’t have to be beautiful to turn me on (Kiss, Prince)

I discovered recently how easy is to create GIFs with R using ImageMagick and I feel like a kid with a new toy. To begin this new era of my life as R programmer I have done this:

zooming
First of all, read this article: it explains very well how to start doing GIFs from scratch. The one I have done is inspired in this previous post where I take a set of complex numbers to transform and color it using HSV technique. In this case I use this next transformation: f(z)= -Im(z)+(Re(z)+0.5*Im(z))*1i

Modifying the range of Real and Imaginary parts of complex numbers I obtain the zooming  effect. The code is very simple. Play with it changing the transformation or the animation options. Send me your creations, I would love to see them:

library(dplyr)
library(ggplot2)
dir.create("output")
setwd("output")
id=1 # label tO name plots
for (i in seq(from=320, to=20, length.out = 38)){
z=outer(seq(from = -i, to = i, length.out = 300),1i*seq(from = -i, to = i, length.out = 500),'+') %>% c()
z0=z
for (k in 1:100) z <- -Im(z)+(Re(z)+0.5*Im(z))*1i
df=data.frame(x=Re(z0),
y=Im(z0),
h=(Arg(z)<0)*1+Arg(z)/(2*pi), s=(1+sin(2*pi*log(1+Mod(z))))/2, v=(1+cos(2*pi*log(1+Mod(z))))/2) %>% mutate(col=hsv(h,s,v))
ggplot(df, aes(x, y)) +
geom_tile(fill=df$col)+
scale_x_continuous(expand=c(0,0))+
scale_y_continuous(expand=c(0,0))+
labs(x=NULL, y=NULL)+
theme(legend.position="none",
panel.background = element_rect(fill="white"),
plot.margin=grid::unit(c(1,1,0,0), "mm"),
panel.grid=element_blank(),
axis.ticks=element_blank(),
axis.title=element_blank(),
axis.text=element_blank())
ggsave(file=paste0("plot",stringr::str_pad(id, 4, pad = "0"),".png"), width = 1, height = 1)
id=id+1
}
system('"C:\\Program Files\\ImageMagick-6.9.3-Q16\\convert.exe" -delay 10 -loop 0 -duplicate 1,-2-1 *.png zooming.gif')
# cleaning up
file.remove(list.files(pattern=".png"))

The Coaster Maker by Shiny

The word you invented is well formed and could be used in the Italian language (The Accademia della Crusca regarding to the word “Petaloso”, recently invented by an eight-year-old boy)

Are you tired of your old coasters? Do you like to make things by your own? Do you have a PC and a printer at home? If you answered yes to all these questions, just follow these simple instructions:

  • Install R and RStudio in your PC
  • Open RStudio and create a new Shiny Web App multiple file (ui.R/server.R)
  • Substitute sample code of each file by the code below
  • Press Run App
  • Press buttom Get your coaster! until you obtain a image you like
  • Print the image
  • Cut out the image
  • Place on the coaster your favorite drinking

These are some examples:

This is the code of ui.R

#
# This is the user-interface definition of a Shiny web application. You can
# run the application by clicking 'Run App' above.
#
# Find out more about building applications with Shiny here:
#
#    http://shiny.rstudio.com/
#
library(shiny)
shinyUI(fluidPage(
  titlePanel("The coaster maker"),
  sidebarLayout(
    sidebarPanel(
      #helpText(),
 
      # adding the new div tag to the sidebar
      tags$div(class="header", checked=NA,
               tags$p("This coasters are generated by hypocycloid curves.The curve is formed by the locus of a point,
                      attached to a circle, that rolls on the inside of another circle.
                      In the curve's equation the first part denotes the relative position between the two circles,
                      the second part denotes the rotation of the rolling circle.")),
      tags$div(class="header", checked=NA,
               HTML(
"More info <a href=\"http://www.2dcurves.com/roulette/rouletteh.html#rhodon\">here</a>")),
      actionButton('rerun','Get your coaster!')
    ),
    mainPanel(
      plotOutput("HarmPlot")
    )
  )
))

This is the code of server.R

# This is the server logic of a Shiny web application. You can run the
# application by clicking 'Run App' above.
#
# Find out more about building applications with Shiny here:
#
#    http://shiny.rstudio.com/
#
library(shiny)
library(ggplot2)
CreateDS = function ()
{
  t=seq(-31*pi, 31*pi, 0.002)
  a=sample(seq(from=1/31, to=29/31, by=2/31), 1)
  b=runif(1, min = 1, max = 3)
  data.frame(x=(1-a)*cos(a*t)+a*b*cos((1-a)*t), y=(1-a)*sin(a*t)-a*b*sin((1-a)*t))
}
shinyServer(function(input, output) {
  dat<-reactive({if (input$rerun) dat=CreateDS() else dat=CreateDS()})
    output$HarmPlot<-renderPlot({
    ggplot(dat())+
      geom_point(data=data.frame(x=0,y=0), aes(x,y), color=rgb(rbeta(1, .5, .5), rbeta(1, .5, .5), rbeta(1, .5, .5)) , shape=19, fill="yellow", size=220)+
      geom_polygon(aes(x, y), fill=rgb(rbeta(1, 2, 2), rbeta(1, 2, 2), rbeta(1, 2, 2))) +
      theme(legend.position="none",
            panel.background = element_rect(fill="white"),
            panel.grid=element_blank(),
            axis.ticks=element_blank(),
            axis.title=element_blank(),
            axis.text=element_blank())
  }, height = 500, width = 500)
})