Alan,
I downloaded the ISC file and a trivial R script [1] makes this
a little more readable but not palatable :-)-O
ISO HOSTS ALL DUPLICATES SLD THREELD
1 ZA 6197417 6268058 70641 48 22616
2 EG 4989280 5004756 15476 39 798
3 MA 153009 157364 4355 1075 1795
4 KE 142034 313717 171683 17 1514
5 MG 117130 130093 12963 243 26852
6 AO 104001 104243 242 74 1481
7 RE 97696 97734 38 376 97308
8 MZ 97258 97817 559 26 9680
9 NA 79258 79786 528 57 70490
10 CI 71311 71852 541 244 15026
11 MU 51689 52266 577 273 51050
12 GH 45780 48225 2445 13 236
13 CM 36060 36329 269 170 36014
14 TZ 34901 35182 281 7 576
15 ZW 29158 41208 12050 11 1903
16 UG 25570 28502 2932 58 421
17 LY 25519 26290 771 232 590
18 ZM 19880 20388 508 29 17513
19 LS 16179 16197 18 10 99
20 CV 14315 14320 5 51 77
21 GM 7996 8010 14 31 7981
22 BW 5420 5576 156 63 904
23 BJ 2766 2773 7 50 2753
24 CD 2655 2675 20 123 2628
25 NG 2563 10476 7913 154 606
26 GQ 2265 2569 304 259 2141
27 ML 2067 2129 62 922 1157
28 GA 1969 2023 54 796 1621
29 ST 1754 2158 404 410 1259
30 TN 1520 1805 285 207 893
31 CF 1350 1419 69 580 900
32 SL 1300 1310 10 30 1286
33 SD 1263 1279 16 55 126
34 MW 1158 1215 57 69 244
35 SZ 1125 1140 15 14 191
36 DZ 1008 10355 9347 259 835
37 TG 973 1063 90 46 957
38 SH 801 1086 285 385 599
39 SN 417 437 20 77 404
40 ER 414 622 208 6 60
41 BI 317 331 14 67 160
42 RW 304 341 37 46 176
43 SC 298 1924 1626 178 231
44 LR 282 916 634 4 12
45 NE 282 288 6 164 189
46 ET 244 260 16 20 174
47 SO 200 215 15 141 138
48 DJ 162 165 3 89 123
49 BF 115 140 25 43 110
50 GW 92 93 1 91 40
51 YT 57 118 61 35 40
52 CG 39 41 2 18 35
53 MR 23 25 2 17 21
54 KM 13 13 0 9 11
55 GN 13 13 0 3 11
56 TD 1 1 0 1 1
57 SS NA NA NA NA NA
[1]
library(rvest)
library(tidyverse)
library(xml2)
html_table(xml2::read_html("https://www.countrycallingcodes.com/iso-country-codes/africa-codes.php";),fill=TRUE)[[2]]
%>%
as_tibble(.name_repair = "unique") %>%
mutate(X2=case_when(str_detect(X2,regex("^\\w{2}$")) ~ toupper(X2)),
X2=replace_na(X2, "NA")) %>%
rename(ISO = X2) %>%
distinct(ISO) %>%
left_join(read_table2("bynum.txt", col_names = F, skip = 5) %>%
rename(ISO=1, HOSTS=2, ALL=3, DUPLICATES=4, SLD=5, THREELE=6,
NET=7) %>%
mutate(ISO = toupper(ISO))) %>%
select(-NET) %>%
arrange(desc(HOSTS)) %>%
print(n = Inf)
On 2020-08-07 12:47 , Alan Levin wrote:
[...
> I do not wish to single out any... so please I use only as one simple
> example, there are many many questions.
>
> I believe Nigeria is one of our largest populations and economies...
> yet the .ng domain has less than 2600 live hosts in 2019
> ng 2563 10476 7913 154 606 Nigeria
>
> compared with Egypt
> eg 4989280 5004756 15476 39 798 Egypt
> almost 5 million
>
> 2600 and 5 million are clearly vastly different numbers!
>
> Sincerely
>
> Alan
[...]
--
Dr. Eberhard W. Lisse \ / Obstetrician & Gynaecologist
[email protected] / * | Telephone: +264 81 124 6733 (cell)
PO Box 8421 Bachbrecht \ / If this email is signed with GPG/PGP
10007, Namibia ;____/ Sect 20 of Act No. 4 of 2019 may apply
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