README.md
In hrbrmstr/dnshelpers: Tools to Process 'DNS' Response Data
dnshelpers
Tools to Process ‘DNS’ Response Data
Description
The internet domain name system (‘DNS’) supports a wide variety of
response types besides simple ‘IP’ addresses or domain names. Methods
are provided to process a myriad of other response data elements
including ‘DMARC’ (‘TXT’), ‘SPF’ (‘TXT’), ‘MX’ and more.
What’s Inside The Tin
The following functions are implemented:
parse_dmarc: Parse DMARCv1 DNS data reponses into a data frameparse_mta_sts: Parse MTA-STS DNS data reponses into a listparse_mx: Parse MX DNS data reponses into a data frameparse_soa: Parse SOA DNS data reponses into a data frameparse_spf: Parse SPF TXT DNS data reponses into a list
Installation
devtools::install_github("hrbrmstr/dnshelpers")
Usage
library(dnshelpers)
library(gdns)
library(worldtilegrid)
library(tidyverse)
# current verison
packageVersion("dnshelpers")
## [1] '0.1.0'
Basics
targets <- c("senate.gov", "equifax.com", "rapid7.com")
bulk_query(targets, "MX") %>%
bind_cols(parse_mx(.$data))
## # A tibble: 7 x 7
## name type TTL data query preference data1
## <chr> <int> <int> <chr> <chr> <int> <chr>
## 1 equifax.com. 15 309 20 smtp2.equifax.com. equifax.com 20 smtp2.equifax.com.
## 2 equifax.com. 15 309 10 smtp.equifax.com. equifax.com 10 smtp.equifax.com.
## 3 rapid7.com. 15 299 1 aspmx.l.google.com. rapid7.com 1 aspmx.l.google.com.
## 4 rapid7.com. 15 299 10 alt3.aspmx.l.google.com. rapid7.com 10 alt3.aspmx.l.google.com.
## 5 rapid7.com. 15 299 10 alt4.aspmx.l.google.com. rapid7.com 10 alt4.aspmx.l.google.com.
## 6 rapid7.com. 15 299 5 alt1.aspmx.l.google.com. rapid7.com 5 alt1.aspmx.l.google.com.
## 7 rapid7.com. 15 299 5 alt2.aspmx.l.google.com. rapid7.com 5 alt2.aspmx.l.google.com.
bulk_query(targets, "SOA") %>%
bind_cols(parse_soa(.$data))
## # A tibble: 3 x 12
## name type TTL data query mname rname serial refresh retry expire minimum
## <chr> <int> <int> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 senate.gov. 6 10384 sen-dmzp.senate.gov. saa… senate… sen-dmzp… saanoc.saa… 200810… 1800 900 2.59e6 900
## 2 equifax.com. 6 170 ns0.equifax.com. hostmas… equifa… ns0.equi… hostmaster… 201808… 10800 3600 2.42e6 900
## 3 rapid7.com. 6 308 ns-1653.awsdns-14.co.uk.… rapid7… ns-1653.… awsdns-hos… 1 7200 900 1.21e6 86400
bulk_query(sprintf("_dmarc.%s", targets), "TXT") %>%
filter(str_detect(str_to_lower(data), "dmarc1")) %>%
bind_cols(parse_dmarc(.$data))
## # A tibble: 2 x 14
## name type TTL data query v p sp pct rf rua ruf is_valid fo
## <chr> <int> <int> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <lgl> <chr>
## 1 _dmarc.senate.gov. 16 10070 "\"v=DMARC1;\"\"… _dmar… dmar… reje… none 100 afrf mailto:… mailto… TRUE <NA>
## 2 _dmarc.rapid7.com. 16 299 "\"v=DMARC1; p=n… _dmar… dmar… none <NA> <NA> <NA> mailto:… mailto… TRUE 0
bulk_query(targets, "TXT") %>%
filter(str_detect(str_to_lower(data), "v=spf")) %>%
.$data %>%
parse_spf() %>%
str()
## List of 3
## $ :List of 4
## ..$ mechasims :List of 1
## .. ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 1 obs. of 3 variables:
## .. .. ..$ qualifier: chr "-"
## .. .. ..$ mechanism: chr "all"
## .. .. ..$ data : chr NA
## ..$ modifiers :List of 1
## .. ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 0 obs. of 0 variables
## ..$ valid : logi TRUE
## ..$ spf_record: chr "\"v=spf1 -all\""
## $ :List of 4
## ..$ mechasims :List of 1
## .. ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 17 obs. of 3 variables:
## .. .. ..$ qualifier: chr [1:17] "+" "+" "+" "+" ...
## .. .. ..$ mechanism: chr [1:17] "ip4" "ip4" "ip4" "ip4" ...
## .. .. ..$ data : chr [1:17] "216.46.96.0/24" "12.104.201.0/24" "12.178.18.150/32" "68.233.76.14/32" ...
## ..$ modifiers :List of 1
## .. ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 0 obs. of 0 variables
## ..$ valid : logi TRUE
## ..$ spf_record: chr "\"v=spf1 ip4:216.46.96.0/24 ip4:12.104.201.0/24 ip4:12.178.18.150/32 ip4:68.233.76.14/32 ip4:198.60.22.202/32 i"| __truncated__
## $ :List of 4
## ..$ mechasims :List of 1
## .. ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 13 obs. of 3 variables:
## .. .. ..$ qualifier: chr [1:13] "+" "+" "+" "+" ...
## .. .. ..$ mechanism: chr [1:13] "ip4" "ip4" "ip4" "ip4" ...
## .. .. ..$ data : chr [1:13] "208.118.237.0/24" "208.118.227.0/25" "64.125.235.5" "64.125.235.6" ...
## ..$ modifiers :List of 1
## .. ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 0 obs. of 0 variables
## ..$ valid : logi TRUE
## ..$ spf_record: chr "\"v=spf1 ip4:208.118.237.0/24 ip4:208.118.227.0/25 ip4:64.125.235.5 ip4:64.125.235.6 ip4:205.201.137.229 ip4:20"| __truncated__
Practical Example (DMARC)
Let’s see how the world’s
colleges
rate DMARC-wise.
We’ll take ^^ JSON and lookup DMARC records via the zdnsr package
(hrbrmstr/zdnsr).
(u_dmarc <- readRDS("ghdat/uinv_dmarc.rds")) # previously read in
## # A tibble: 9,738 x 5
## name country alpha_two_code domains dmarc
## <chr> <chr> <chr> <chr> <chr>
## 1 Cégep de Saint-Jérôme Canada CA cstj.qc.ca _dmarc.cstj.qc.ca
## 2 Lindenwood University United States US lindenwood.edu _dmarc.lindenwood.edu
## 3 DAV Institute of Engineering & Technology India IN davietjal.org _dmarc.davietjal.org
## 4 Lovely Professional University India IN lpu.in _dmarc.lpu.in
## 5 Sullivan University United States US sullivan.edu _dmarc.sullivan.edu
## 6 Florida State College at Jacksonville United States US fscj.edu _dmarc.fscj.edu
## 7 Xavier University United States US xavier.edu _dmarc.xavier.edu
## 8 Tusculum College United States US tusculum.edu _dmarc.tusculum.edu
## 9 Claremont School of Theology United States US cst.edu _dmarc.cst.edu
## 10 Somaiya Vidyavihar India IN somaiya.edu _dmarc.somaiya.edu
## # ... with 9,728 more rows
## Done previously:
# zdnsr::zdns_query(u_dmarc$dmarc, output_file = "somefile.json", query_type = "TXT")
# db <- sergeant::src_drill()
#
# tbl(db, "(
# SELECT
# b.answers.name AS question,
# b.answers.answer AS answer
# FROM (
# SELECT
# FLATTEN(a.data.answers) AS answers
# FROM
# dfs.workspace.`/somefile.json` a
# WHERE
# (a.status = 'NOERROR')
# ) b
# )") %>%
# collect() -> dmarc_recs
(dmarc_recs <- readRDS("ghdat/dmarc-recs.rds")) # what was previously looked up ^^
## # A tibble: 1,250 x 2
## question answer
## * <chr> <chr>
## 1 _dmarc.washjeff.edu v=DMARC1; p=none
## 2 _dmarc.barry.edu v=DMARC1; p=none; rua=mailto:dmpost@barry.edu,mailto:7cc566d7@mxtoolbox.dmarc-report.com; ruf=…
## 3 _dmarc.yhc.edu v=DMARC1; pct=100; p=none
## 4 _dmarc.aacc.edu v=DMARC1;p=none; rua=mailto:DKIM_DMARC@aacc.edu;ruf=mailto:DKIM_DMARC@aacc.edu;fo=1
## 5 _dmarc.sagu.edu v=DMARC1; p=none; rua=mailto:Office365contact@sagu.edu; ruf=mailto:Office365contact@sagu.edu
## 6 _dmarc.colostate.edu v=DMARC1; p=none; pct=100; rua=mailto:re+anahykughvo@dmarc.postmarkapp.com; sp=none; aspf=r;
## 7 _dmarc.wne.edu v=DMARC1;p=quarantine;sp=none;fo=1;ri=86400;pct=50;rua=mailto:dmarcreply@wne.edu
## 8 _dmarc.csuglobal.edu v=DMARC1; p=none;
## 9 _dmarc.devry.edu v=DMARC1; p=none; pct=100; rua=mailto:devry@rua.agari.com; ruf=mailto:devry@ruf.agari.com; fo=1
## 10 _dmarc.sullivan.edu v=DMARC1; p=none; rua=mailto:mcambron@sullivan.edu; ruf=mailto:mcambron@sullivan.edu; fo=1
## # ... with 1,240 more rows
left_join( # join them together
u_dmarc,
bind_cols(
dmarc_recs,
parse_dmarc(dmarc_recs$answer) # parse the raw DMARC records
),
by = c("dmarc"="question")
) %>%
mutate(p = ifelse(is.na(p), "NO_DMARC", p)) %>%
print() -> processed_dmarc
## # A tibble: 9,826 x 17
## name country alpha_two_code domains dmarc answer v p is_valid adkim fo rf rua ruf pct sp
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <lgl> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 Cégep… Canada CA cstj.qc… _dma… <NA> <NA> NO_D… NA <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2 Linde… United … US lindenw… _dma… <NA> <NA> NO_D… NA <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3 DAV I… India IN davietj… _dma… <NA> <NA> NO_D… NA <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4 Lovel… India IN lpu.in _dma… <NA> <NA> NO_D… NA <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5 Sulli… United … US sulliva… _dma… v=DMAR… dmar… none TRUE <NA> 1 <NA> mail… mail… <NA> <NA>
## 6 Flori… United … US fscj.edu _dma… <NA> <NA> NO_D… NA <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7 Xavie… United … US xavier.… _dma… <NA> <NA> NO_D… NA <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8 Tuscu… United … US tusculu… _dma… <NA> <NA> NO_D… NA <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 9 Clare… United … US cst.edu _dma… <NA> <NA> NO_D… NA <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 10 Somai… India IN somaiya… _dma… v=DMAR… dmar… quar… TRUE <NA> <NA> <NA> mail… <NA> <NA> <NA>
## # ... with 9,816 more rows, and 1 more variable: ri <chr>
count(processed_dmarc, alpha_two_code, p) %>% # do a bunch of cleanup & re-org to get policy % by country
spread(p, n) %>%
gather(p, n, -alpha_two_code) %>%
mutate(n = ifelse(is.na(n), 0, n)) %>%
group_by(alpha_two_code) %>%
mutate(pct = n/sum(n)) %>%
ungroup() %>%
select(alpha_two_code, p, pct) %>%
spread(p, pct) %>%
gather(measure, value, -alpha_two_code) %>%
mutate(
measure = factor( # make the DMARC policy ids a bit nicer for the facets
x = measure,
levels = c("NO_DMARC", "none", "quarantine", "reject"),
labels = c("No DMARC", "Policy: None", "Policy: Quarantine", "Policy: Reject")
)
) %>%
print() %>%
ggplot(aes(country = alpha_two_code, fill = value)) +
geom_wtg() +
viridis::scale_fill_viridis(name = NULL, direction = -1, labels=scales::percent) +
facet_wrap(~measure) +
coord_equal() +
labs(
x=NULL, y=NULL, title = "DMARC Usage in Colleges by Country & Policy"
) +
hrbrthemes::theme_ipsum_rc(grid="XY") +
theme_enhance_wtg()
## # A tibble: 816 x 3
## alpha_two_code measure value
## <chr> <fct> <dbl>
## 1 AD No DMARC 1
## 2 AE No DMARC 0.971
## 3 AF No DMARC 0.976
## 4 AG No DMARC 1
## 5 AL No DMARC 1
## 6 AM No DMARC 0.833
## 7 AO No DMARC 1
## 8 AR No DMARC 0.852
## 9 AT No DMARC 0.791
## 10 AU No DMARC 0.627
## # ... with 806 more rows
hrbrmstr/dnshelpers documentation built on May 6, 2019, 8:07 p.m.
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dnshelpers
Tools to Process ‘DNS’ Response Data
Description
The internet domain name system (‘DNS’) supports a wide variety of response types besides simple ‘IP’ addresses or domain names. Methods are provided to process a myriad of other response data elements including ‘DMARC’ (‘TXT’), ‘SPF’ (‘TXT’), ‘MX’ and more.
What’s Inside The Tin
The following functions are implemented:
parse_dmarc: Parse DMARCv1 DNS data reponses into a data frameparse_mta_sts: Parse MTA-STS DNS data reponses into a listparse_mx: Parse MX DNS data reponses into a data frameparse_soa: Parse SOA DNS data reponses into a data frameparse_spf: Parse SPF TXT DNS data reponses into a list
Installation
devtools::install_github("hrbrmstr/dnshelpers")
Usage
library(dnshelpers)
library(gdns)
library(worldtilegrid)
library(tidyverse)
# current verison
packageVersion("dnshelpers")
## [1] '0.1.0'
Basics
targets <- c("senate.gov", "equifax.com", "rapid7.com")
bulk_query(targets, "MX") %>%
bind_cols(parse_mx(.$data))
## # A tibble: 7 x 7
## name type TTL data query preference data1
## <chr> <int> <int> <chr> <chr> <int> <chr>
## 1 equifax.com. 15 309 20 smtp2.equifax.com. equifax.com 20 smtp2.equifax.com.
## 2 equifax.com. 15 309 10 smtp.equifax.com. equifax.com 10 smtp.equifax.com.
## 3 rapid7.com. 15 299 1 aspmx.l.google.com. rapid7.com 1 aspmx.l.google.com.
## 4 rapid7.com. 15 299 10 alt3.aspmx.l.google.com. rapid7.com 10 alt3.aspmx.l.google.com.
## 5 rapid7.com. 15 299 10 alt4.aspmx.l.google.com. rapid7.com 10 alt4.aspmx.l.google.com.
## 6 rapid7.com. 15 299 5 alt1.aspmx.l.google.com. rapid7.com 5 alt1.aspmx.l.google.com.
## 7 rapid7.com. 15 299 5 alt2.aspmx.l.google.com. rapid7.com 5 alt2.aspmx.l.google.com.
bulk_query(targets, "SOA") %>%
bind_cols(parse_soa(.$data))
## # A tibble: 3 x 12
## name type TTL data query mname rname serial refresh retry expire minimum
## <chr> <int> <int> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 senate.gov. 6 10384 sen-dmzp.senate.gov. saa… senate… sen-dmzp… saanoc.saa… 200810… 1800 900 2.59e6 900
## 2 equifax.com. 6 170 ns0.equifax.com. hostmas… equifa… ns0.equi… hostmaster… 201808… 10800 3600 2.42e6 900
## 3 rapid7.com. 6 308 ns-1653.awsdns-14.co.uk.… rapid7… ns-1653.… awsdns-hos… 1 7200 900 1.21e6 86400
bulk_query(sprintf("_dmarc.%s", targets), "TXT") %>%
filter(str_detect(str_to_lower(data), "dmarc1")) %>%
bind_cols(parse_dmarc(.$data))
## # A tibble: 2 x 14
## name type TTL data query v p sp pct rf rua ruf is_valid fo
## <chr> <int> <int> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <lgl> <chr>
## 1 _dmarc.senate.gov. 16 10070 "\"v=DMARC1;\"\"… _dmar… dmar… reje… none 100 afrf mailto:… mailto… TRUE <NA>
## 2 _dmarc.rapid7.com. 16 299 "\"v=DMARC1; p=n… _dmar… dmar… none <NA> <NA> <NA> mailto:… mailto… TRUE 0
bulk_query(targets, "TXT") %>%
filter(str_detect(str_to_lower(data), "v=spf")) %>%
.$data %>%
parse_spf() %>%
str()
## List of 3
## $ :List of 4
## ..$ mechasims :List of 1
## .. ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 1 obs. of 3 variables:
## .. .. ..$ qualifier: chr "-"
## .. .. ..$ mechanism: chr "all"
## .. .. ..$ data : chr NA
## ..$ modifiers :List of 1
## .. ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 0 obs. of 0 variables
## ..$ valid : logi TRUE
## ..$ spf_record: chr "\"v=spf1 -all\""
## $ :List of 4
## ..$ mechasims :List of 1
## .. ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 17 obs. of 3 variables:
## .. .. ..$ qualifier: chr [1:17] "+" "+" "+" "+" ...
## .. .. ..$ mechanism: chr [1:17] "ip4" "ip4" "ip4" "ip4" ...
## .. .. ..$ data : chr [1:17] "216.46.96.0/24" "12.104.201.0/24" "12.178.18.150/32" "68.233.76.14/32" ...
## ..$ modifiers :List of 1
## .. ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 0 obs. of 0 variables
## ..$ valid : logi TRUE
## ..$ spf_record: chr "\"v=spf1 ip4:216.46.96.0/24 ip4:12.104.201.0/24 ip4:12.178.18.150/32 ip4:68.233.76.14/32 ip4:198.60.22.202/32 i"| __truncated__
## $ :List of 4
## ..$ mechasims :List of 1
## .. ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 13 obs. of 3 variables:
## .. .. ..$ qualifier: chr [1:13] "+" "+" "+" "+" ...
## .. .. ..$ mechanism: chr [1:13] "ip4" "ip4" "ip4" "ip4" ...
## .. .. ..$ data : chr [1:13] "208.118.237.0/24" "208.118.227.0/25" "64.125.235.5" "64.125.235.6" ...
## ..$ modifiers :List of 1
## .. ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 0 obs. of 0 variables
## ..$ valid : logi TRUE
## ..$ spf_record: chr "\"v=spf1 ip4:208.118.237.0/24 ip4:208.118.227.0/25 ip4:64.125.235.5 ip4:64.125.235.6 ip4:205.201.137.229 ip4:20"| __truncated__
Practical Example (DMARC)
Let’s see how the world’s colleges rate DMARC-wise.
We’ll take ^^ JSON and lookup DMARC records via the zdnsr package
(hrbrmstr/zdnsr).
(u_dmarc <- readRDS("ghdat/uinv_dmarc.rds")) # previously read in
## # A tibble: 9,738 x 5
## name country alpha_two_code domains dmarc
## <chr> <chr> <chr> <chr> <chr>
## 1 Cégep de Saint-Jérôme Canada CA cstj.qc.ca _dmarc.cstj.qc.ca
## 2 Lindenwood University United States US lindenwood.edu _dmarc.lindenwood.edu
## 3 DAV Institute of Engineering & Technology India IN davietjal.org _dmarc.davietjal.org
## 4 Lovely Professional University India IN lpu.in _dmarc.lpu.in
## 5 Sullivan University United States US sullivan.edu _dmarc.sullivan.edu
## 6 Florida State College at Jacksonville United States US fscj.edu _dmarc.fscj.edu
## 7 Xavier University United States US xavier.edu _dmarc.xavier.edu
## 8 Tusculum College United States US tusculum.edu _dmarc.tusculum.edu
## 9 Claremont School of Theology United States US cst.edu _dmarc.cst.edu
## 10 Somaiya Vidyavihar India IN somaiya.edu _dmarc.somaiya.edu
## # ... with 9,728 more rows
## Done previously:
# zdnsr::zdns_query(u_dmarc$dmarc, output_file = "somefile.json", query_type = "TXT")
# db <- sergeant::src_drill()
#
# tbl(db, "(
# SELECT
# b.answers.name AS question,
# b.answers.answer AS answer
# FROM (
# SELECT
# FLATTEN(a.data.answers) AS answers
# FROM
# dfs.workspace.`/somefile.json` a
# WHERE
# (a.status = 'NOERROR')
# ) b
# )") %>%
# collect() -> dmarc_recs
(dmarc_recs <- readRDS("ghdat/dmarc-recs.rds")) # what was previously looked up ^^
## # A tibble: 1,250 x 2
## question answer
## * <chr> <chr>
## 1 _dmarc.washjeff.edu v=DMARC1; p=none
## 2 _dmarc.barry.edu v=DMARC1; p=none; rua=mailto:dmpost@barry.edu,mailto:7cc566d7@mxtoolbox.dmarc-report.com; ruf=…
## 3 _dmarc.yhc.edu v=DMARC1; pct=100; p=none
## 4 _dmarc.aacc.edu v=DMARC1;p=none; rua=mailto:DKIM_DMARC@aacc.edu;ruf=mailto:DKIM_DMARC@aacc.edu;fo=1
## 5 _dmarc.sagu.edu v=DMARC1; p=none; rua=mailto:Office365contact@sagu.edu; ruf=mailto:Office365contact@sagu.edu
## 6 _dmarc.colostate.edu v=DMARC1; p=none; pct=100; rua=mailto:re+anahykughvo@dmarc.postmarkapp.com; sp=none; aspf=r;
## 7 _dmarc.wne.edu v=DMARC1;p=quarantine;sp=none;fo=1;ri=86400;pct=50;rua=mailto:dmarcreply@wne.edu
## 8 _dmarc.csuglobal.edu v=DMARC1; p=none;
## 9 _dmarc.devry.edu v=DMARC1; p=none; pct=100; rua=mailto:devry@rua.agari.com; ruf=mailto:devry@ruf.agari.com; fo=1
## 10 _dmarc.sullivan.edu v=DMARC1; p=none; rua=mailto:mcambron@sullivan.edu; ruf=mailto:mcambron@sullivan.edu; fo=1
## # ... with 1,240 more rows
left_join( # join them together
u_dmarc,
bind_cols(
dmarc_recs,
parse_dmarc(dmarc_recs$answer) # parse the raw DMARC records
),
by = c("dmarc"="question")
) %>%
mutate(p = ifelse(is.na(p), "NO_DMARC", p)) %>%
print() -> processed_dmarc
## # A tibble: 9,826 x 17
## name country alpha_two_code domains dmarc answer v p is_valid adkim fo rf rua ruf pct sp
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <lgl> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 Cégep… Canada CA cstj.qc… _dma… <NA> <NA> NO_D… NA <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2 Linde… United … US lindenw… _dma… <NA> <NA> NO_D… NA <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3 DAV I… India IN davietj… _dma… <NA> <NA> NO_D… NA <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4 Lovel… India IN lpu.in _dma… <NA> <NA> NO_D… NA <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5 Sulli… United … US sulliva… _dma… v=DMAR… dmar… none TRUE <NA> 1 <NA> mail… mail… <NA> <NA>
## 6 Flori… United … US fscj.edu _dma… <NA> <NA> NO_D… NA <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7 Xavie… United … US xavier.… _dma… <NA> <NA> NO_D… NA <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8 Tuscu… United … US tusculu… _dma… <NA> <NA> NO_D… NA <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 9 Clare… United … US cst.edu _dma… <NA> <NA> NO_D… NA <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 10 Somai… India IN somaiya… _dma… v=DMAR… dmar… quar… TRUE <NA> <NA> <NA> mail… <NA> <NA> <NA>
## # ... with 9,816 more rows, and 1 more variable: ri <chr>
count(processed_dmarc, alpha_two_code, p) %>% # do a bunch of cleanup & re-org to get policy % by country
spread(p, n) %>%
gather(p, n, -alpha_two_code) %>%
mutate(n = ifelse(is.na(n), 0, n)) %>%
group_by(alpha_two_code) %>%
mutate(pct = n/sum(n)) %>%
ungroup() %>%
select(alpha_two_code, p, pct) %>%
spread(p, pct) %>%
gather(measure, value, -alpha_two_code) %>%
mutate(
measure = factor( # make the DMARC policy ids a bit nicer for the facets
x = measure,
levels = c("NO_DMARC", "none", "quarantine", "reject"),
labels = c("No DMARC", "Policy: None", "Policy: Quarantine", "Policy: Reject")
)
) %>%
print() %>%
ggplot(aes(country = alpha_two_code, fill = value)) +
geom_wtg() +
viridis::scale_fill_viridis(name = NULL, direction = -1, labels=scales::percent) +
facet_wrap(~measure) +
coord_equal() +
labs(
x=NULL, y=NULL, title = "DMARC Usage in Colleges by Country & Policy"
) +
hrbrthemes::theme_ipsum_rc(grid="XY") +
theme_enhance_wtg()
## # A tibble: 816 x 3
## alpha_two_code measure value
## <chr> <fct> <dbl>
## 1 AD No DMARC 1
## 2 AE No DMARC 0.971
## 3 AF No DMARC 0.976
## 4 AG No DMARC 1
## 5 AL No DMARC 1
## 6 AM No DMARC 0.833
## 7 AO No DMARC 1
## 8 AR No DMARC 0.852
## 9 AT No DMARC 0.791
## 10 AU No DMARC 0.627
## # ... with 806 more rows
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