100% Stacked Chicklets

September 27, 2019
By

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I posted a visualization of email safety status (a.k.a. DMARC) of the Fortune 500 (2017 list) the other day on Twitter and received this spiffy request from @MarkAltosaar:

There are many ways to achieve this result. I’ll show one here and walk through the process starting with the data (this is the 2018 DMARC evaluation run):

library(hrbrthemes) # CRAN or fav social coding site using hrbrmstr/pkgname
library(ggchicklet) # fav social coding site using hrbrmstr/pkgname
library(tidyverse)

f500_dmarc <- read_csv("https://rud.is/dl/f500-industry-dmarc.csv.gz", col_types = "cc")

f500_dmarc
## # A tibble: 500 x 2
##    industry               p         
##                           
##  1 Retailing              Reject    
##  2 Technology             None      
##  3 Health Care            Reject    
##  4 Wholesalers            None      
##  5 Retailing              Quarantine
##  6 Motor Vehicles & Parts None      
##  7 Energy                 None      
##  8 Wholesalers            None      
##  9 Retailing              None      
## 10 Telecommunications     Quarantine
## # … with 490 more rows

The p column is the DMARC classification for each organization (org names have been withheld to protect the irresponsible) and comes from the p=… value in the DMARC DNS TXT record field. It has a limited set of values, so let’s enumerate them and assign some colors:

dmarc_levels <- c("No DMARC", "None", "Quarantine", "Reject")
dmarc_cols <- set_names(c(ft_cols$slate, "#a6dba0", "#5aae61", "#1b7837"), dmarc_levels)

We want the aggregate value of each p, thus we need to do count counting:

(dmarc_summary <- count(f500_dmarc, industry, p))
## # A tibble: 63 x 3
##    industry            p              n
##                         
##  1 Aerospace & Defense No DMARC       9
##  2 Aerospace & Defense None           3
##  3 Aerospace & Defense Quarantine     1
##  4 Apparel             No DMARC       4
##  5 Apparel             None           1
##  6 Business Services   No DMARC       9
##  7 Business Services   None           7
##  8 Business Services   Reject         4
##  9 Chemicals           No DMARC      12
## 10 Chemicals           None           2
## # … with 53 more rows

We’re also going to want to sort the industries by those with the most DMARC (sorted bars/chicklets FTW!). We’ll need a factor for that, so let’s make one:

(dmarc_summary %>% 
  filter(p != "No DMARC") %>% # we don't care abt this `p` value
  count(industry, wt=n, sort=TRUE) -> industry_levels)
## # A tibble: 21 x 2
##    industry                      n
##                         
##  1 Financials                   54
##  2 Technology                   25
##  3 Health Care                  24
##  4 Retailing                    23
##  5 Wholesalers                  16
##  6 Energy                       12
##  7 Transportation               12
##  8 Business Services            11
##  9 Industrials                   8
## 10 Food, Beverages & Tobacco     6
## # … with 11 more rows

Now, we can make the chart:

dmarc_summary %>% 
  mutate(p = factor(p, levels = rev(dmarc_levels))) %>% 
  mutate(industry = factor(industry, rev(industry_levels$industry))) %>% 
  ggplot(aes(industry, n)) +
  geom_chicklet(aes(fill = p)) +
  scale_fill_manual(name = NULL, values = dmarc_cols) +
  scale_y_continuous(expand = c(0,0), position = "right") +
  coord_flip() +
  labs(
    x = NULL, y = NULL,
    title = "DMARC Status of Fortune 500 (2017 List; 2018 Measurement) Primary Email Domains"
  ) +
  theme_ipsum_rc(grid = "X") +
  theme(legend.position = "top")

Doh! We rly want them to be 100% width. Thankfully, {ggplot2} has a position_fill() we can use instead of position_dodge():

dmarc_summary %>% 
  mutate(p = factor(p, levels = rev(dmarc_levels))) %>% 
  mutate(industry = factor(industry, rev(industry_levels$industry))) %>% 
  ggplot(aes(industry, n)) +
  geom_chicklet(aes(fill = p), position = position_fill()) +
  scale_fill_manual(name = NULL, values = dmarc_cols) +
  scale_y_continuous(expand = c(0,0), position = "right") +
  coord_flip() +
  labs(
    x = NULL, y = NULL,
    title = "DMARC Status of Fortune 500 (2017 List; 2018 Measurement) Primary Email Domains"
  ) +
  theme_ipsum_rc(grid = "X") +
  theme(legend.position = "top")

Doh! Even though we forgot to use reverse = TRUE in the call to position_fill() everything is out of order. Kinda. It’s in the order we told it to be in, but that’s not right b/c we need it ordered by the in-industry percentages. If each industry had the same number of organizations, there would not have been an issue. Unfortunately, the folks who make up these lists care not about our time. Let’s re-compute the industry factor by computing the percents:

(dmarc_summary %>% 
  group_by(industry) %>% 
  mutate(pct = n/sum(n)) %>% 
  ungroup() %>% 
  filter(p != "No DMARC") %>% 
  count(industry, wt=pct, sort=TRUE) -> industry_levels)
## # A tibble: 21 x 2
##    industry               n
##                  
##  1 Transportation     0.667
##  2 Technology         0.641
##  3 Wholesalers        0.615
##  4 Financials         0.614
##  5 Health Care        0.6  
##  6 Business Services  0.55 
##  7 Food & Drug Stores 0.5  
##  8 Retailing          0.5  
##  9 Industrials        0.444
## 10 Telecommunications 0.375
## # … with 11 more rows

Now, we can go back to using position_fill() as before:

dmarc_summary %>% 
  mutate(p = factor(p, levels = rev(dmarc_levels))) %>% 
  mutate(industry = factor(industry, rev(industry_levels$industry))) %>% 
  ggplot(aes(industry, n)) +
  geom_chicklet(aes(fill = p), position = position_fill(reverse = TRUE)) +
  scale_fill_manual(name = NULL, values = dmarc_cols) +
  scale_y_percent(expand = c(0, 0.001), position = "right") +
  coord_flip() +
  labs(
    x = NULL, y = NULL,
    title = "DMARC Status of Fortune 500 (2017 List; 2018 Measurement) Primary Email Domains"
  ) +
  theme_ipsum_rc(grid = "X") +
  theme(legend.position = "top")

FIN

As noted, this is one way to handle this situation. I’m not super happy with the final visualization here as it doesn’t have the counts next to the industry labels and I like to have the ordering by both count and more secure configuration (so, conditional on higher prevalence of Quarantine or Reject when there are ties). That is an exercise left to the reader 😎.

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