Two dirt rides a week. Sullivan, Nike tower, down Mandeville fire road. This #wearespooky is incredible, great climbing, stiff yet ultra comfortable, loves mud, ruts, gravel, birms, descending, and all the other things. I am so happy with this build. Thank you Spooky Cycles
New bike day. Spooky
Distribution of 2FA codes.
My brain has been playing tricks on me for the last few years. I had come to the conclusion that I had seen many of the 2FA codes before. A few minutes later with R and GGPLOT. I believe my assumptions about repeated 2FA codes have been debunked.
I collected all 2FA codes sent to me for the last year.
wc -l Codes.txt
383 Codes.txt
2FA codes delivered to me are always 6 digits. Over the last ~year, I have received 383 2FA codes from my bank
Using egrep to collect the codes from erroneous text
Getting these out of the collected file is easy with egrep.
EG: cat Codes.txt | egrep -o ‘[0-9]{6}’
reading these into R is trivial via “read table”
read.table(“~/codes.txt”)
converting to a data frame
2fa_TABLE <- as.data.frame(table(as.numeric(strsplit(as.character(read.table(“codes.txt”)), “”)[[1]])))
Applying column names to the data frame
colnames(2fa_TABLE) <- c(“Number”, “Unique_Count”)
Plotting with GGPLOT
ggplot(2fa_TABLE) +
geom_histogram(aes(x=Number, y=Unique_Count), stat=“identity”) +
theme_bw()
The Plot
