{"id":10886,"date":"2018-06-07T17:01:52","date_gmt":"2018-06-07T22:01:52","guid":{"rendered":"https:\/\/rud.is\/b\/?p=10886"},"modified":"2018-06-08T05:25:28","modified_gmt":"2018-06-08T10:25:28","slug":"making-world-tile-grid-grids","status":"publish","type":"post","link":"https:\/\/rud.is\/b\/2018\/06\/07\/making-world-tile-grid-grids\/","title":{"rendered":"Making World Tile Grid-Grids"},"content":{"rendered":"

A colleague asked if I would blog about how I crafted the grid of world tile grids in this post<\/a> and I accepted the challenge. The technique isn’t too hard as it just builds on the initial work<\/a> by Jon Schwabish<\/a> and a handy file<\/a> made by Maarten Lambrechts<\/a>.<\/p>\n

The Premise<\/h3>\n

For this particular use-case, I sifted through our internet scan data and classified a series of device families from their telnet banners then paired that with our country-level attribution data for each IPv4 address. I’m not generally “a fan” of rolling things up at a country level, but since many (most) of these devices are residential or small\/medium-business routers, country-level attribution has some merit.<\/p>\n

But, I’m also not a fan of country-level choropleths when it comes to “cyber” nor am I wont to area-skewed cartograms since most folks still cannot interpret them. Both of those take up a ton of screen real estate, too, espeically if you have more than one of them. Yet, I wanted to show a map-like structure without resorting to Hilbert IPv4 heatmaps since they are neither very readable by a general audience and become skewed when you have to move up from a 1 pixel == 1 Class C network block.<\/p>\n

I think the tile grid is a great compromise since it avoids the “area”and projection skewness confusion that regular global choropleths cause while still preserving geographic & positional proximity. Sure, they’ll take some getting used to by casual readers, but I felt it was the best of all the tradeoffs.<\/p>\n

The Setup<\/h3>\n

Here’s the data:<\/p>\n

\nlibrary(here)\nlibrary(hrbrthemes)\nlibrary(tidyverse)\n\nwtg <- read_csv(\"https:\/\/gist.githubusercontent.com\/maartenzam\/787498bbc07ae06b637447dbd430ea0a\/raw\/9a9dafafb44d8990f85243a9c7ca349acd3a0d07\/worldtilegrid.csv\")\n\nglimpse(wtg)\n\u00a0\n## Observations: 192\n## Variables: 11\n## $ name             \"Afghanistan\", \"Albania\", \"Algeria\", \"Angola\",...\n## $ alpha.2          \"AF\", \"AL\", \"DZ\", \"AO\", \"AQ\", \"AG\", \"AR\", \"AM\"...\n## $ alpha.3          \"AFG\", \"ALB\", \"DZA\", \"AGO\", \"ATA\", \"ATG\", \"ARG...\n## $ country.code     \"004\", \"008\", \"012\", \"024\", \"010\", \"028\", \"032...\n## $ iso_3166.2       \"ISO 3166-2:AF\", \"ISO 3166-2:AL\", \"ISO 3166-2:...\n## $ region           \"Asia\", \"Europe\", \"Africa\", \"Africa\", \"Antarct...\n## $ sub.region       \"Southern Asia\", \"Southern Europe\", \"Northern ...\n## $ region.code      \"142\", \"150\", \"002\", \"002\", NA, \"019\", \"019\", ...\n## $ sub.region.code  \"034\", \"039\", \"015\", \"017\", NA, \"029\", \"005\", ...\n## $ x                22, 15, 13, 13, 15, 7, 6, 20, 24, 15, 21, 4, 2...\n## $ y                8, 9, 11, 17, 23, 4, 14, 6, 19, 6, 7, 2, 9, 8,...\n\nrouters <- read_csv(here::here(\"data\", \"routers.csv\"))\n\nrouters\n\u00a0\n## # A tibble: 453,027 x 3\n##    type     country_name           country_code\n##                                 \n##  1 mikrotik Slovak Republic        SK          \n##  2 mikrotik Czechia                CZ          \n##  3 mikrotik Colombia               CO          \n##  4 mikrotik Bosnia and Herzegovina BA          \n##  5 mikrotik Czechia                CZ          \n##  6 mikrotik Brazil                 BR          \n##  7 mikrotik Vietnam                VN          \n##  8 mikrotik Brazil                 BR          \n##  9 mikrotik India                  IN          \n## 10 mikrotik Brazil                 BR          \n## # ... with 453,017 more rows\n\ndistinct(routers, type) %>% \n  arrange(type) %>% \n  print(n=11)\n\u00a0\n## # A tibble: 11 x 1\n##    type    \n##       \n##  1 asus    \n##  2 dlink   \n##  3 huawei  \n##  4 linksys \n##  5 mikrotik\n##  6 netgear \n##  7 qnap    \n##  8 tplink  \n##  9 ubiquiti\n## 10 upvel   \n## 11 zte\n<\/code><\/pre>\n
So, we have 11 different device families under assault by “VPNFilter” and I wanted to show the global distribution of them. Knowing the compact world tile grid would facet well, I set off to make it happen.<\/p>\n
Let’s get some decent names for facet labels:<\/p>\n
\nreal_names <- read_csv(here::here(\"data\", \"real_names.csv\"))\n\nreal_names\n\u00a0\n## # A tibble: 11 x 2\n##    type     lab             \n##                   \n##  1 asus     Asus Device     \n##  2 dlink    D-Link Devices  \n##  3 huawei   Huawei Devices  \n##  4 linksys  Linksys Devices \n##  5 mikrotik Mikrotik Devices\n##  6 netgear  Netgear Devices \n##  7 qnap     QNAP Devices    \n##  8 tplink   TP-Link Devices \n##  9 ubiquiti Ubiquiti Devices\n## 10 upvel    Upvel Devices   \n## 11 zte      ZTE Devices\n<\/code><\/pre>\n
Next, we need to summarise our scan results and pair it up the world tile grid data and our real names:<\/p>\n
\ncount(routers, country_code, type) %>%  # summarise the data into # of device familes per country\n  left_join(wtg, by = c(\"country_code\" = \"alpha.2\")) %>% # join them up on the common field\n  filter(!is.na(alpha.3)) %>% # we only want countries on the grid and maxmind attributes some things to meta-regions and anonymous proxies\n  left_join(real_names) -> wtg_routers\n\nglimpse(wtg_routers)\n\n## Observations: 629\n## Variables: 14\n## $ country_code     \"AE\", \"AE\", \"AE\", \"AF\", \"AF\", \"AF\", \"AG\", \"AL\"...\n## $ type             \"asus\", \"huawei\", \"mikrotik\", \"huawei\", \"mikro...\n## $ n                1, 12, 70, 12, 264, 27, 1, 941, 2081, 7, 2, 1,...\n## $ name             \"United Arab Emirates\", \"United Arab Emirates\"...\n## $ alpha.3          \"ARE\", \"ARE\", \"ARE\", \"AFG\", \"AFG\", \"AFG\", \"ATG...\n## $ country.code     \"784\", \"784\", \"784\", \"004\", \"004\", \"004\", \"028...\n## $ iso_3166.2       \"ISO 3166-2:AE\", \"ISO 3166-2:AE\", \"ISO 3166-2:...\n## $ region           \"Asia\", \"Asia\", \"Asia\", \"Asia\", \"Asia\", \"Asia\"...\n## $ sub.region       \"Western Asia\", \"Western Asia\", \"Western Asia\"...\n## $ region.code      \"142\", \"142\", \"142\", \"142\", \"142\", \"142\", \"019...\n## $ sub.region.code  \"145\", \"145\", \"145\", \"034\", \"034\", \"034\", \"029...\n## $ x                20, 20, 20, 22, 22, 22, 7, 15, 15, 15, 20, 20,...\n## $ y                10, 10, 10, 8, 8, 8, 4, 9, 9, 9, 6, 6, 6, 6, 1...\n## $ lab              \"Asus Device\", \"Huawei Devices\", \"Mikrotik Dev...\n<\/code><\/pre>\n
Then, plot it:<\/p>\n
\nggplot(wtg_routers, aes(x, y, fill=n, group=lab)) +\n  geom_tile(color=\"#b2b2b2\", size=0.125) +\n  scale_y_reverse() +\n  viridis::scale_fill_viridis(name=\"# Devices\", trans=\"log10\", na.value=\"white\", label=scales::comma) +\n  facet_wrap(~lab, ncol=3) +\n  coord_equal() +\n  labs(\n    x=NULL, y=NULL,\n    title = \"World Tile Grid Per-country Concentration of\\nSeriously Poorly Configured Network Devices\",\n    subtitle = \"Device discovery based on in-scope 'VPNFilter' vendor device banner strings\",\n    caption = \"Source: Rapid7 Project Sonar & Censys\"\n  ) +\n  theme_ipsum_rc(grid=\"\") +\n  theme(panel.background = element_rect(fill=\"#969696\", color=\"#969696\")) +\n  theme(axis.text=element_blank()) +\n  theme(legend.direction=\"horizontal\") +\n  theme(legend.key.width = unit(2, \"lines\")) +\n  theme(legend.position=c(0.85, 0.1))\n<\/code><\/pre>\n
\u00a0
\n\"\"<\/p>\n
Doh! We forgot to ensure we had data for every country. Let’s try that again:<\/p>\n
\ncount(routers, country_code, type) %>%\n  complete(country_code, type) %>%\n  filter(!is.na(country_code)) %>%\n  left_join(wtg, c(\"country_code\" = \"alpha.2\")) %>%\n  filter(!is.na(alpha.3)) %>%\n  left_join(real_names) %>%\n  complete(country_code, type, x=unique(wtg$x), y=unique(wtg$y)) %>%\n  filter(!is.na(lab)) %>%\n  ggplot(aes(x, y, fill=n, group=lab)) +\n  geom_tile(color=\"#b2b2b2\", size=0.125) +\n  scale_y_reverse() +\n  viridis::scale_fill_viridis(name=\"# Devices\", trans=\"log10\", na.value=\"white\", label=scales::comma) +\n  facet_wrap(~lab, ncol=3) +\n  coord_equal() +\n  labs(\n    x=NULL, y=NULL,\n    title = \"World Tile Grid Per-country Concentration of\\nSeriously Poorly Configured Network Devices\",\n    subtitle = \"Device discovery based on in-scope 'VPNFilter' vendor device banner strings\",\n    caption = \"Source: Rapid7 Project Sonar & Censys\"\n  ) +\n  theme_ipsum_rc(grid=\"\") +\n  theme(panel.background = element_rect(fill=\"#969696\", color=\"#969696\")) +\n  theme(axis.text=element_blank()) +\n  theme(legend.direction=\"horizontal\") +\n  theme(legend.key.width = unit(2, \"lines\")) +\n  theme(legend.position=c(0.85, 0.1))\n<\/code><\/pre>\n
\u00a0
\n\"\"<\/p>\n
That’s better.<\/p>\n
We take advantage of ggplot2’s ability to facet and just ensure we have complete (even if NA<\/code>) tiles for each panel.<\/p>\n
Once consumers start seeing these used more they’ll be able to pick up key markers (or one of us will come up with a notation that makes key markers more visible) and be able to get specific information from the chart. I just wanted to show regional and global differences between vendors (and really give MikroTik users a swift kick in the patootie for being so bad with their kit).<\/p>\n
FIN<\/h3>\n
You can find the RStudio project (code + data) here: (http:\/\/rud.is\/dl\/tile-grid-grid.zip<\/a>)<\/p>\n","protected":false},"excerpt":{"rendered":"
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The core [MetricsGraphics](http:\/\/metricsgraphicsjs.org) JavaScript library has been updated to version 2.1.0 (from 1.1.0). Two blog-worthy features since releasing version 0.5 are `mjs_grid` (which is a `grid.arrange`-like equivalent for `metricsgraphics` plots and `mjs_add_rollover` which lets you add your own custom rollover text to\u2026","rel":"","context":"In "Charts & Graphs"","block_context":{"text":"Charts & Graphs","link":"https:\/\/rud.is\/b\/category\/charts-graphs\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":12195,"url":"https:\/\/rud.is\/b\/2019\/05\/18\/mapping-tornado-alley-with-r\/","url_meta":{"origin":10886,"position":2},"title":"Mapping Tornado Alley with R","author":"hrbrmstr","date":"2019-05-18","format":false,"excerpt":"I caught a re-tweet of this tweet by @harry_stevens: THREAD: I wrote a post on @observablehq about a map I made today. 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