**Ripples**, and kindly contributed to R-bloggers)

Now I’m gonna tell my momma that I’m a traveller, I’m gonna follow the sun (The Sun, Parov Stelar)

Inspired by this book I read recently, I decided to do this experiment. The idea is comparing *how easy* is to find sequences of numbers inside Pi, e, Golden Ratio (Phi) and a randomly generated number. For example, since Pi is 3.1415926535897932384… the 4-size sequence 5358 can be easily found at the begining as well as the 5-size sequence 79323. I considered interesting comparing Pi with a random generated number. What I though before doing the experiment is that it would be easier finding sequences inside the andom one. Why? Because despite of being irrational and transcendental I thought there should be some kind of *residual pattern* in Pi that should make more difficult to find random sequences inside it than do it inside a randomly generated number.

- I downloaded Pi, e and Phi from the Internet and extract first 100.000 digits of all of them. I generate a random 100.000 number
*on the fly*. - I generate a representative sample of 4-size sequences
- I look for each of these sequences inside first 5.000 digits of Pi, e, Phi and the randomly generated one. I repeat searching for first 10.000, first 15.000 and so on until I search into the whole 100.000 -size number
- I store how many sequences I find for each searching
- I repeat this for 5 and 6-size sequences.

At first sight, is equally easy (or difficult), to find random sequences inside all numbers: my hypothesis was wrong.

As you can see here, 100.000 digits is more than enough to find 4-size sequences. In fact, from 45.000 digits I reach 100% of successful matches:

I only find 60% of 5-size sequences inside 100.000 digits of numbers:

And only 10% of 6-size sequences:

Why these four numbers are so equal in order to find random sequences inside them? I don’t know. What I know is that if you want to find your telephone number inside Pi, you will probably need an enormous number of digits.

library(rvest) library(stringr) library(reshape2) library(ggplot2) library(extrafont);windowsFonts(Comic=windowsFont("Comic Sans MS")) library(dplyr) library(magrittr) library(scales) p = html("http://www.geom.uiuc.edu/~huberty/math5337/groupe/digits.html") f = html("http://www.goldennumber.net/wp-content/uploads/2012/06/Phi-To-100000-Places.txt") e = html("http://apod.nasa.gov/htmltest/gifcity/e.2mil") p %>% html_text() %>% substr(., regexpr("3.14",.), regexpr("Go to Historical",.)) %>% gsub("[^0-9]", "", .) %>% substr(., 1, 100000) -> p f %>% html_text() %>% substr(., regexpr("1.61",.), nchar(.)) %>% gsub("[^0-9]", "", .) %>% substr(., 1, 100000) -> f e %>% html_text() %>% substr(., regexpr("2.71",.), nchar(.)) %>% gsub("[^0-9]", "", .) %>% substr(., 1, 100000) -> e r = paste0(sample(0:9, 100000, replace = TRUE), collapse = "") results=data.frame(Cut=numeric(0), Pi=numeric(0), Phi=numeric(0), e=numeric(0), Random=numeric(0)) bins=20 dgts=6 samp=min(10^dgts*2/100, 10000) for (i in 1:bins) { cut=100000/bins*i p0=substr(p, start=0, stop=cut) f0=substr(f, start=0, stop=cut) e0=substr(e, start=0, stop=cut) r0=substr(r, start=0, stop=cut) sample(0:(10^dgts-1), samp, replace = FALSE) %>% str_pad(dgts, pad = "0") -> comb comb %>% sapply(function(x) grepl(x, p0)) %>% sum() -> p1 comb %>% sapply(function(x) grepl(x, f0)) %>% sum() -> f1 comb %>% sapply(function(x) grepl(x, e0)) %>% sum() -> e1 comb %>% sapply(function(x) grepl(x, r0)) %>% sum() -> r1 results=rbind(results, data.frame(Cut=cut, Pi=p1, Phi=f1, e=e1, Random=r1)) } results=melt(results, id.vars=c("Cut") , variable.name="number", value.name="matches") opts=theme( panel.background = element_rect(fill="darkolivegreen1"), panel.border = element_rect(colour="black", fill=NA), axis.line = element_line(size = 0.5, colour = "black"), axis.ticks = element_line(colour="black"), panel.grid.major = element_line(colour="white", linetype = 1), panel.grid.minor = element_blank(), axis.text.y = element_text(colour="black"), axis.text.x = element_text(colour="black"), text = element_text(size=20, family="Comic"), legend.text = element_text(size=25), legend.key = element_blank(), legend.position = c(.75,.2), legend.background = element_blank(), plot.title = element_text(size = 30)) ggplot(results, aes(x = Cut, y = matches/samp, color = number))+ geom_line(size=1.5, alpha=.8)+ scale_color_discrete(name = "")+ scale_x_continuous(breaks=seq(100000/bins, 100000, by=100000/bins))+ scale_y_continuous(labels = percent)+ theme(axis.text.x = element_text(angle = 90, vjust=.5, hjust = 1))+ labs(title=paste0("Finding ",dgts, "-size strings into 100.000-digit numbers"), x="Cut Position", y="% of Matches")+opts

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**Ripples**.

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