Numerate biochemists can make good passwords :-)

The great thing about programming skills is that they are very transferable. As part of my learning, I joined the Cardiff R User Group which allowed me to learn from professionals from other industries. For 2020, our user group is planning to host monthly workshops playing with the Tidy Tuesday datasets.

The dataset for this month is about passwords. Because my interest is data visualisation, I have created this graph. Basically my conclusion is that nerdy passwords with numbers are a good way to go. Other and better password advice is available.

Here is the code:
# START
library(tidyverse)
passwords <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-01-14/passwords.csv')

passwords <- passwords[1:500,]

# frequency of passwords
# interesting plot about the frequency of passwords...
ggplot(passwords, aes(str_length(password))) + geom_histogram()

# plot rank v strength
p <- ggplot(passwords, aes(rank, strength, colour = factor(category))) +
    geom_point()

p

p + facet_wrap(~category) + theme(legend.position = "none")

# pays be nerdy and numeric...

# numbers are probably the key...
# want to see if they have numbers
# colour these by number
passwords$digit <- str_detect(passwords$password, "[1234567890]")

p1 <- ggplot(passwords, aes(rank, strength, colour = digit)) +
    geom_point()

p1 + facet_wrap(~category) 

# look at one category - pays to be a nerd? 
passwords %>%
    filter(category == "nerdy-pop") %>%
    ggplot(aes(rank, strength, colour = digit)) + geom_point()

# what are the better passwords
# filter on nerdy-pop, simple-alphanumeric, sport and password-related
good_words_in <- c("nerdy-pop", "simple-alphanumeric", "sport", "password-related")

passwords %>%
    filter(category %in% good_words_in) %>%  # the %in% is important!!  
    ggplot(aes(rank, strength, colour = digit)) + 
    geom_point() + facet_wrap(~category)

# https://stackoverflow.com/questions/15624656/label-points-in-geom-point
passwords %>%
    filter(category %in% good_words_in) %>%  # the %in% is important!!  
    ggplot(aes(rank, strength, colour = digit)) + 
    geom_point() +
    geom_text(aes(label=ifelse(strength>20,as.character(password),''),
        vjust=1)) +
    facet_wrap(~category)
# works but labels are not the best :-(

# try ggrepel
# https://cran.r-project.org/web/packages/ggrepel/vignettes/ggrepel.html
library(ggrepel)

passwords %>%
    filter(category %in% good_words_in) %>%  # the %in% is important!!  
    ggplot(aes(rank, strength, colour = digit, label = password)) + 
    geom_point() + geom_text_repel()
# this works but is a mess!!!!
# need to remove labels for strength < 20. 

# create a data set with strength greater than 20
passwords %>%
    filter(strength >20) -> better_passwords

# overlay these labels with a different dataset...
passwords %>%
    filter(category %in% good_words_in) %>% 
    ggplot(aes(rank, strength, colour = digit)) + 
    geom_point() -> graphs

graphs

graphs + facet_wrap(~category)

g_label <- graphs + geom_text_repel(data = better_passwords, 
    aes(rank, strength, label=password))
g_label
# works nicely 

g_label + facet_wrap(~category) 
# works nicely

link <- "https://github.com/rfordatascience/tidytuesday/blob/master/data/2020/2020-01-14/readme.md"
# add some styling...
g_label + facet_wrap(~category) + 
    labs(x = "Password Rank",
        y = "Password Strength", 
        title = "Exploring Passwords for Tidy Tuesday",
        subtitle = link) +
    theme_bw()

# this is my final plot, I think!!

Some resources:

• Tidy Tuesday on Github 
• Tidy Tuesday on Twitter
• Examples from ggrepel
• Another article using geom_point()