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()
