====== Extracting European data from CIA ======
Cyprus, Poseidonia, April 2-6, 2018
> load('C:/Users/batagelj/Downloads/data/CIA/CIA.Rdata')
> e <- match(Eu2,C$ISOalpha2)
> e
[1] 3 6 15 22 28 58 61 62 71 76 77 84 86 87 93 101 107 108
[19] 110 113 123 128 129 130 131 137 146 148 156 166 177 178 193 197 201 202
[37] 208 214 215 234 12 16 21 35 74 83 102 212 145 182 212 227 232 183
[55] 99 NA
> E <- C[e,c(3,6:21)]
> dim(E)
[1] 56 17
> E
ISOalpha3 UrbPop UrbRate BirthRate DeathRate FiBiAge InfMtot InfMmal InfMfem MedAtot MedAmal MedAfem Obesity PhysDens EduExp Pop AreaTot
3 ALB 59.3 1.81 13.2 6.8 24.5 11.9 13.3 10.5 32.9 31.6 34.3 21.7 1.29 3.5 3047987 28748.00
6 AND 84.1 0.09 7.5 7.3 NA 3.6 3.6 3.6 44.3 44.4 44.1 25.6 3.69 3.1 76965 468.00
15 AUT 66.1 0.51 9.5 9.6 29.0 3.4 3.8 3.0 44.0 42.8 45.1 20.1 5.23 5.6 8754413 83871.00
22 BEL 97.9 0.36 11.3 9.7 28.6 3.4 3.8 3.0 41.4 40.2 42.7 22.1 3.01 6.4 11491346 30528.00
28 BIH 40.1 0.38 8.8 10.0 27.0 5.5 5.6 5.4 42.1 40.5 43.5 17.9 1.89 NA 3856181 51197.00
58 HRV 59.6 0.22 8.9 12.2 28.0 9.3 9.0 9.6 43.0 41.1 45.0 24.4 3.13 4.6 4292095 56594.00
61 CZE 73.0 0.30 9.3 10.5 28.1 2.6 2.8 2.5 42.1 40.8 43.4 26.0 3.68 4.1 10674723 78867.00
62 DNK 88.0 0.58 10.5 10.3 29.1 4.0 4.1 3.9 42.2 41.2 43.2 19.7 3.66 8.6 5605948 43094.00
71 EST 67.4 -0.37 10.1 12.6 26.6 3.8 3.7 3.9 42.7 39.4 46.1 21.2 3.43 4.8 1251581 45228.00
76 FIN 84.5 0.46 10.7 10.0 28.8 2.5 2.7 2.4 42.5 40.9 44.3 22.2 3.20 7.2 5518371 338145.00
77 FRA 80.0 0.76 12.2 9.3 28.1 3.2 3.6 2.9 41.4 39.6 43.1 21.6 3.24 5.5 67106161 643801.00
84 DEU 75.7 0.12 8.6 11.7 29.4 3.4 3.7 3.1 47.1 46.0 48.2 22.3 4.13 4.9 80594017 357022.00
86 GIB 100.0 0.01 14.0 8.5 NA 5.9 6.6 5.2 34.7 33.8 35.7 NA NA NA 29396 6.50
87 GRC 78.6 0.31 8.4 11.3 29.8 4.6 5.0 4.1 44.5 43.5 45.6 24.9 6.26 NA 10768477 131957.00
93 GGY 31.7 0.86 9.8 9.0 NA 3.4 3.7 3.1 43.8 42.5 45.1 NA NA NA 66502 78.00
101 HUN 72.1 0.36 9.0 12.8 28.3 4.9 5.2 4.6 42.3 40.4 44.3 26.4 3.32 4.2 9850845 93028.00
107 IRL 63.8 1.45 14.1 6.6 30.7 3.6 4.0 3.3 36.8 36.4 37.1 25.3 2.79 5.3 5011102 70273.00
108 IMN 52.4 0.81 11.0 10.2 NA 4.0 4.0 4.1 44.2 43.3 44.9 NA NA NA 88815 572.00
110 ITA 69.3 0.32 8.6 10.4 30.7 3.3 3.5 3.0 45.5 44.4 46.5 19.9 3.95 4.2 62137802 301340.00
113 JEY 31.7 0.86 12.4 7.8 NA 3.8 4.0 3.5 38.0 36.0 40.7 NA NA NA 98840 116.00
123 LVA 67.4 -0.56 9.7 14.5 27.2 5.2 5.6 4.8 43.6 39.7 46.9 23.6 3.22 4.9 1944643 64589.00
128 LIE 14.3 0.79 10.4 7.4 NA 4.2 4.5 3.9 43.2 41.7 44.5 NA NA 2.6 38244 160.00
129 LTU 66.5 -0.34 9.9 14.6 27.0 3.8 4.3 3.3 43.7 39.7 47.1 26.3 4.33 4.6 2823859 65300.00
130 LUX 90.7 1.46 11.5 7.3 30.1 3.4 3.8 3.0 39.3 38.7 39.9 22.6 2.92 4.1 594130 2586.00
131 MKD 57.3 0.24 11.4 9.2 26.8 7.4 7.6 7.1 37.9 36.8 39.0 22.4 2.80 NA 2103721 25713.00
137 MLT 95.6 0.32 10.1 9.4 26.9 3.5 3.9 3.1 41.8 40.8 43.0 28.9 3.91 8.3 416338 316.00
146 MCO 100.0 0.80 6.6 9.8 NA 1.8 2.1 1.6 53.1 51.7 54.5 NA 6.65 1.0 30645 2.00
148 MNE 64.4 0.25 10.0 9.7 26.3 NA NA NA 40.7 39.9 41.8 23.3 2.34 NA 642550 13812.00
156 NLD 91.5 0.72 10.9 8.9 29.6 3.6 3.8 3.3 42.6 41.5 43.6 20.4 3.35 5.6 17084719 41543.00
166 NOR 81.0 1.31 12.2 8.1 28.9 2.5 2.8 2.2 39.2 38.4 40.0 23.1 4.42 7.4 5320045 323802.00
177 POL 60.5 0.02 9.5 10.4 27.4 4.4 4.8 4.0 40.7 39.0 42.4 23.1 2.27 4.9 38476269 312685.00
178 PRT 64.6 0.76 9.0 11.1 30.2 4.3 4.8 3.9 42.2 40.2 44.4 20.8 4.43 5.3 10839514 92090.00
193 SMR 94.2 0.38 8.6 8.7 NA 4.3 4.5 4.1 44.4 43.3 45.4 NA 6.36 2.4 33537 61.00
197 SRB 55.8 -0.29 9.0 13.6 27.9 5.8 6.7 4.9 42.6 40.9 44.3 21.5 2.46 4.2 7111024 77474.00
201 SVK 53.4 -0.09 9.7 9.9 27.6 5.1 5.7 4.5 40.5 38.8 42.3 20.5 3.39 4.1 5445829 49035.00
202 SVN 49.6 0.18 8.2 11.6 29.1 3.9 4.4 3.4 44.5 42.8 46.2 20.2 2.77 5.5 1972126 20273.00
208 ESP 80.0 0.52 9.2 9.1 30.7 3.3 3.6 2.9 42.7 41.5 43.9 23.8 3.82 4.3 48958159 505370.00
214 SWE 86.1 0.86 12.1 9.4 29.1 2.6 2.9 2.3 41.2 40.2 42.2 20.6 4.11 7.7 9960487 450295.00
215 CHE 74.1 1.10 10.5 8.3 30.7 3.6 4.0 3.2 42.4 41.4 43.4 19.5 4.11 5.1 8236303 41277.00
234 GBR 83.1 0.82 12.1 9.4 28.5 4.3 4.7 3.9 40.5 39.3 41.7 27.8 2.81 5.8 65648100 243610.00
12 ARM 62.5 -0.10 12.9 9.4 24.4 12.7 14.1 11.1 35.1 33.3 36.9 20.2 2.80 2.8 3045191 29743.00
16 AZE 55.2 1.38 15.8 7.1 23.2 23.8 24.7 22.9 31.3 29.8 33.0 19.9 3.40 2.6 9961396 86600.00
21 BLR 77.4 -0.04 10.3 13.2 25.7 3.6 4.0 3.1 40.0 37.1 43.1 24.5 4.07 4.9 9549747 207600.00
35 BGR 74.6 -0.40 8.7 14.5 26.7 8.4 9.5 7.3 42.7 40.9 44.7 25.0 4.00 4.1 7101510 110879.00
74 FRO 42.4 0.87 14.3 8.8 NA 5.4 5.7 5.1 37.6 37.1 38.3 NA 2.63 NA 50730 1393.00
83 GEO 54.0 -0.09 12.3 10.9 24.5 15.2 17.3 12.9 38.1 35.3 40.9 21.7 4.78 2.0 4926330 69700.00
102 ISL 94.3 1.10 13.7 6.4 27.4 2.1 2.2 1.9 36.5 35.9 37.1 21.9 3.79 7.8 339747 103000.00
212 SJM NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
145 MDA 45.2 -0.36 11.5 12.6 24.0 12.0 13.7 10.1 36.7 34.9 38.6 18.9 2.54 7.5 3474121 33851.00
182 ROU 54.9 0.05 8.9 12.0 26.7 9.4 10.7 8.0 41.1 39.7 42.6 22.5 2.67 2.9 21529967 238391.00
212.1 SJM NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
227 TUR 74.4 1.54 15.7 6.0 22.3 17.6 18.8 16.3 30.9 30.5 31.4 32.1 1.75 4.8 80845215 783562.00
232 UKR 70.1 -0.35 10.3 14.4 24.9 7.8 8.7 6.9 40.6 37.4 43.7 24.1 3.00 6.0 44033874 603550.00
183 RUS 74.2 -0.15 11.0 13.5 24.6 6.8 7.6 5.9 39.6 36.6 42.5 23.1 3.31 3.9 142257519 17098242.00
99 VAT 100.0 0.10 NA NA NA NA NA NA NA NA NA NA NA NA 1000 0.44
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
Problematic parts of countries attached in variable ''reduced''. Included also missing countries: Cyprus, Vatican and Kosovo.
> load('C:/Users/batagelj/Downloads/data/CIA/CIA.Rdata')
> dim(C)
[1] 248 21
> fmt <- c("numeric","character","character","character")
> CE <- read.csv("alpha2.csv",sep="\t",header=TRUE,skip=2,colClasses=fmt)
> CE$NAME <- str_trim(CE$NAME)
> str(CE)
'data.frame': 57 obs. of 4 variables:
$ n : num 1 2 3 4 5 6 7 8 9 10 ...
$ NAME : chr "Albania" "Andorra" "Austria" "Belgium" ...
$ alpha2 : chr "AL" "AD" "AT" "BE" ...
$ reduced: chr "AL" "AD" "AT" "BE" ...
> CE$alpha2
[1] "AL" "AD" "AT" "BE" "BA" "HR" "CZ" "DK" "EE" "FI" "FR" "DE" "GI" "GR" "GG"
[16] "HU" "IE" "IM" "IT" "JE" "LV" "LI" "LT" "LU" "MK" "MT" "MC" "ME" "NL" "NO"
[31] "PL" "PT" "SM" "RS" "SK" "SI" "ES" "SE" "CH" "GB" "AM" "AZ" "BY" "BG" "FO"
[46] "GE" "IS" "SJ" "MD" "RO" "SJ" "TR" "UA" "RU" "CY" "VA" "XK"
> Eur2 <- CE$reduced
> Eur2
[1] "AL" "AD" "AT" "BE" "BA" "HR" "CZ" "DK" "EE" "FI" "FR" "DE" "GB" "GR" "GB"
[16] "HU" "IE" "GB" "IT" "GB" "LV" "LI" "LT" "LU" "MK" "MT" "MC" "ME" "NL" "NO"
[31] "PL" "PT" "SM" "RS" "SK" "SI" "ES" "SE" "CH" "GB" "AM" "AZ" "BY" "BG" "FO"
[46] "GE" "IS" "NO" "MD" "RO" "NO" "TR" "UA" "RU" "CY" "VA" "RS"
> er <- match(Eur2,C$ISOalpha2)
> er
[1] 3 6 15 22 28 58 61 62 71 76 77 84 234 87 234 101 107 234 110
[20] 234 123 128 129 130 131 137 146 148 156 166 177 178 193 197 201 202 208 214
[39] 215 234 12 16 21 35 74 83 102 166 145 182 166 227 232 183 60 99 197
> E3 <- C[er,c(3,6:21)]
> E3
ISOalpha3 UrbPop UrbRate BirthRate DeathRate FiBiAge InfMtot InfMmal InfMfem MedAtot MedAmal MedAfem Obesity PhysDens EduExp Pop AreaTot
3 ALB 59.3 1.81 13.2 6.8 24.5 11.9 13.3 10.5 32.9 31.6 34.3 21.7 1.29 3.5 3047987 28748.00
6 AND 84.1 0.09 7.5 7.3 NA 3.6 3.6 3.6 44.3 44.4 44.1 25.6 3.69 3.1 76965 468.00
15 AUT 66.1 0.51 9.5 9.6 29.0 3.4 3.8 3.0 44.0 42.8 45.1 20.1 5.23 5.6 8754413 83871.00
22 BEL 97.9 0.36 11.3 9.7 28.6 3.4 3.8 3.0 41.4 40.2 42.7 22.1 3.01 6.4 11491346 30528.00
28 BIH 40.1 0.38 8.8 10.0 27.0 5.5 5.6 5.4 42.1 40.5 43.5 17.9 1.89 NA 3856181 51197.00
58 HRV 59.6 0.22 8.9 12.2 28.0 9.3 9.0 9.6 43.0 41.1 45.0 24.4 3.13 4.6 4292095 56594.00
61 CZE 73.0 0.30 9.3 10.5 28.1 2.6 2.8 2.5 42.1 40.8 43.4 26.0 3.68 4.1 10674723 78867.00
62 DNK 88.0 0.58 10.5 10.3 29.1 4.0 4.1 3.9 42.2 41.2 43.2 19.7 3.66 8.6 5605948 43094.00
71 EST 67.4 -0.37 10.1 12.6 26.6 3.8 3.7 3.9 42.7 39.4 46.1 21.2 3.43 4.8 1251581 45228.00
76 FIN 84.5 0.46 10.7 10.0 28.8 2.5 2.7 2.4 42.5 40.9 44.3 22.2 3.20 7.2 5518371 338145.00
77 FRA 80.0 0.76 12.2 9.3 28.1 3.2 3.6 2.9 41.4 39.6 43.1 21.6 3.24 5.5 67106161 643801.00
84 DEU 75.7 0.12 8.6 11.7 29.4 3.4 3.7 3.1 47.1 46.0 48.2 22.3 4.13 4.9 80594017 357022.00
234 GBR 83.1 0.82 12.1 9.4 28.5 4.3 4.7 3.9 40.5 39.3 41.7 27.8 2.81 5.8 65648100 243610.00
87 GRC 78.6 0.31 8.4 11.3 29.8 4.6 5.0 4.1 44.5 43.5 45.6 24.9 6.26 NA 10768477 131957.00
234.1 GBR 83.1 0.82 12.1 9.4 28.5 4.3 4.7 3.9 40.5 39.3 41.7 27.8 2.81 5.8 65648100 243610.00
101 HUN 72.1 0.36 9.0 12.8 28.3 4.9 5.2 4.6 42.3 40.4 44.3 26.4 3.32 4.2 9850845 93028.00
107 IRL 63.8 1.45 14.1 6.6 30.7 3.6 4.0 3.3 36.8 36.4 37.1 25.3 2.79 5.3 5011102 70273.00
234.2 GBR 83.1 0.82 12.1 9.4 28.5 4.3 4.7 3.9 40.5 39.3 41.7 27.8 2.81 5.8 65648100 243610.00
110 ITA 69.3 0.32 8.6 10.4 30.7 3.3 3.5 3.0 45.5 44.4 46.5 19.9 3.95 4.2 62137802 301340.00
234.3 GBR 83.1 0.82 12.1 9.4 28.5 4.3 4.7 3.9 40.5 39.3 41.7 27.8 2.81 5.8 65648100 243610.00
123 LVA 67.4 -0.56 9.7 14.5 27.2 5.2 5.6 4.8 43.6 39.7 46.9 23.6 3.22 4.9 1944643 64589.00
128 LIE 14.3 0.79 10.4 7.4 NA 4.2 4.5 3.9 43.2 41.7 44.5 NA NA 2.6 38244 160.00
129 LTU 66.5 -0.34 9.9 14.6 27.0 3.8 4.3 3.3 43.7 39.7 47.1 26.3 4.33 4.6 2823859 65300.00
130 LUX 90.7 1.46 11.5 7.3 30.1 3.4 3.8 3.0 39.3 38.7 39.9 22.6 2.92 4.1 594130 2586.00
131 MKD 57.3 0.24 11.4 9.2 26.8 7.4 7.6 7.1 37.9 36.8 39.0 22.4 2.80 NA 2103721 25713.00
137 MLT 95.6 0.32 10.1 9.4 26.9 3.5 3.9 3.1 41.8 40.8 43.0 28.9 3.91 8.3 416338 316.00
146 MCO 100.0 0.80 6.6 9.8 NA 1.8 2.1 1.6 53.1 51.7 54.5 NA 6.65 1.0 30645 2.00
148 MNE 64.4 0.25 10.0 9.7 26.3 NA NA NA 40.7 39.9 41.8 23.3 2.34 NA 642550 13812.00
156 NLD 91.5 0.72 10.9 8.9 29.6 3.6 3.8 3.3 42.6 41.5 43.6 20.4 3.35 5.6 17084719 41543.00
166 NOR 81.0 1.31 12.2 8.1 28.9 2.5 2.8 2.2 39.2 38.4 40.0 23.1 4.42 7.4 5320045 323802.00
177 POL 60.5 0.02 9.5 10.4 27.4 4.4 4.8 4.0 40.7 39.0 42.4 23.1 2.27 4.9 38476269 312685.00
178 PRT 64.6 0.76 9.0 11.1 30.2 4.3 4.8 3.9 42.2 40.2 44.4 20.8 4.43 5.3 10839514 92090.00
193 SMR 94.2 0.38 8.6 8.7 NA 4.3 4.5 4.1 44.4 43.3 45.4 NA 6.36 2.4 33537 61.00
197 SRB 55.8 -0.29 9.0 13.6 27.9 5.8 6.7 4.9 42.6 40.9 44.3 21.5 2.46 4.2 7111024 77474.00
201 SVK 53.4 -0.09 9.7 9.9 27.6 5.1 5.7 4.5 40.5 38.8 42.3 20.5 3.39 4.1 5445829 49035.00
202 SVN 49.6 0.18 8.2 11.6 29.1 3.9 4.4 3.4 44.5 42.8 46.2 20.2 2.77 5.5 1972126 20273.00
208 ESP 80.0 0.52 9.2 9.1 30.7 3.3 3.6 2.9 42.7 41.5 43.9 23.8 3.82 4.3 48958159 505370.00
214 SWE 86.1 0.86 12.1 9.4 29.1 2.6 2.9 2.3 41.2 40.2 42.2 20.6 4.11 7.7 9960487 450295.00
215 CHE 74.1 1.10 10.5 8.3 30.7 3.6 4.0 3.2 42.4 41.4 43.4 19.5 4.11 5.1 8236303 41277.00
234.4 GBR 83.1 0.82 12.1 9.4 28.5 4.3 4.7 3.9 40.5 39.3 41.7 27.8 2.81 5.8 65648100 243610.00
12 ARM 62.5 -0.10 12.9 9.4 24.4 12.7 14.1 11.1 35.1 33.3 36.9 20.2 2.80 2.8 3045191 29743.00
16 AZE 55.2 1.38 15.8 7.1 23.2 23.8 24.7 22.9 31.3 29.8 33.0 19.9 3.40 2.6 9961396 86600.00
21 BLR 77.4 -0.04 10.3 13.2 25.7 3.6 4.0 3.1 40.0 37.1 43.1 24.5 4.07 4.9 9549747 207600.00
35 BGR 74.6 -0.40 8.7 14.5 26.7 8.4 9.5 7.3 42.7 40.9 44.7 25.0 4.00 4.1 7101510 110879.00
74 FRO 42.4 0.87 14.3 8.8 NA 5.4 5.7 5.1 37.6 37.1 38.3 NA 2.63 NA 50730 1393.00
83 GEO 54.0 -0.09 12.3 10.9 24.5 15.2 17.3 12.9 38.1 35.3 40.9 21.7 4.78 2.0 4926330 69700.00
102 ISL 94.3 1.10 13.7 6.4 27.4 2.1 2.2 1.9 36.5 35.9 37.1 21.9 3.79 7.8 339747 103000.00
166.1 NOR 81.0 1.31 12.2 8.1 28.9 2.5 2.8 2.2 39.2 38.4 40.0 23.1 4.42 7.4 5320045 323802.00
145 MDA 45.2 -0.36 11.5 12.6 24.0 12.0 13.7 10.1 36.7 34.9 38.6 18.9 2.54 7.5 3474121 33851.00
182 ROU 54.9 0.05 8.9 12.0 26.7 9.4 10.7 8.0 41.1 39.7 42.6 22.5 2.67 2.9 21529967 238391.00
166.2 NOR 81.0 1.31 12.2 8.1 28.9 2.5 2.8 2.2 39.2 38.4 40.0 23.1 4.42 7.4 5320045 323802.00
227 TUR 74.4 1.54 15.7 6.0 22.3 17.6 18.8 16.3 30.9 30.5 31.4 32.1 1.75 4.8 80845215 783562.00
232 UKR 70.1 -0.35 10.3 14.4 24.9 7.8 8.7 6.9 40.6 37.4 43.7 24.1 3.00 6.0 44033874 603550.00
183 RUS 74.2 -0.15 11.0 13.5 24.6 6.8 7.6 5.9 39.6 36.6 42.5 23.1 3.31 3.9 142257519 17098242.00
60 CYP 66.8 0.84 11.3 6.8 28.8 7.9 9.2 6.4 36.8 35.5 38.3 21.8 2.50 6.4 1221549 9251.00
99 VAT 100.0 0.10 NA NA NA NA NA NA NA NA NA NA NA NA 1000 0.44
197.1 SRB 55.8 -0.29 9.0 13.6 27.9 5.8 6.7 4.9 42.6 40.9 44.3 21.5 2.46 4.2 7111024 77474.00
> E2 <- C[er,c(2,6:21)]; E2$reduced <- CE$reduced; E2$alpha2 <- CE$alpha2
> save(E2,ascii=TRUE,file='C:/Users/batagelj/Downloads/data/CIA/CIAeurope.Rdata')
ISO 2 letter country code names ''Europe.nam'' and reduced partition ''EuropeReduced.clu'':
> nam <- file("Europe.nam","w")
> dim(CE)
[1] 57 4
> n <- nrow(CE)
> cat("*vertices",n,"\n ",file=nam)
> cat(paste(1:n,' "',CE[,3],'"\n',sep=''),file=nam)
> close(nam)
> clu <- file("EuropeReduced.clu","w")
> cat("*vertices",n,"\n",paste(as.integer(as.factor(CE[,4])),'\n',sep=''),file=clu)
> close(clu)
European union membership
> load('C:/Users/batagelj/Downloads/data/CIA/CIAeurope.Rdata')
> eUnion2 <- c("AT","BE","BG","HR","CY","CZ","DK","EE","FI",
+ "FR","DE","GR","HU","IE","IT","LV","LT","LU","MT","NL",
+ "PL","PT","RO","SK","SI","ES","SE","GB")
> eu <- match(eUnion2,E2$alpha2)
> eUnion <- rep(0,nrow(E2)); eUnion[eu] <- 1; E2$eUnion <- eUnion
> save(E2,ascii=TRUE,file='C:/Users/batagelj/Downloads/data/CIA/CIAeurope.Rdata')
> clu <- file("EuUnion.clu","w"); n <- nrow(E2)
> cat("*vertices",n,"\n",paste(eUnion,'\n',sep=''),file=clu)
> close(clu)
===== Extract data for Natural Earth map =====
In this map only Gibraltar ("GIB") and Svalbard+Jan Mayen are missing.
> map <- readShapeSpatial("ne_50m_admin_0_countries", proj4string = CRS("+proj=longlat"))
> names(map)
[1] "scalerank" "featurecla" "LABELRANK" "SOVEREIGNT" "SOV_A3" "ADM0_DIF"
[7] "LEVEL" "TYPE" "ADMIN" "ADM0_A3" "GEOU_DIF" "GEOUNIT"
[13] "GU_A3" "SU_DIF" "SUBUNIT" "SU_A3" "BRK_DIFF" "NAME"
[19] "NAME_LONG" "BRK_A3" "BRK_NAME" "BRK_GROUP" "ABBREV" "POSTAL"
[25] "FORMAL_EN" "FORMAL_FR" "NAME_CIAWF" "NOTE_ADM0" "NOTE_BRK" "NAME_SORT"
[31] "NAME_ALT" "MAPCOLOR7" "MAPCOLOR8" "MAPCOLOR9" "MAPCOLOR13" "POP_EST"
[37] "POP_RANK" "GDP_MD_EST" "POP_YEAR" "LASTCENSUS" "GDP_YEAR" "ECONOMY"
[43] "INCOME_GRP" "WIKIPEDIA" "FIPS_10_" "iso2" "ISO_A3" "ISO_A3_EH"
[49] "ISO_N3" "UN_A3" "WB_A2" "WB_A3" "WOE_ID" "WOE_ID_EH"
[55] "WOE_NOTE" "ADM0_A3_IS" "ADM0_A3_US" "ADM0_A3_UN" "ADM0_A3_WB" "CONTINENT"
[61] "REGION_UN" "SUBREGION" "REGION_WB" "NAME_LEN" "LONG_LEN" "ABBREV_LEN"
[67] "TINY" "HOMEPART" "MIN_ZOOM" "MIN_LABEL" "MAX_LABEL" "id"
> load('C:/Users/batagelj/Downloads/data/CIA/CIAeurope.Rdata')
> fmt <- c("numeric","character","character","character")
> CE <- read.csv("C:/Users/batagelj/Documents/papers/2018/CRoNoS/shape/europe/alpha2.csv",sep="\t",header=TRUE,skip=2,colClasses=fmt)
> library(stringr)
> CE$NAME <- str_trim(CE$NAME)
> Eu2 <- unique(CE$alpha2)
> length(Eu2)
[1] 56
> Eu3 <- C$ISOalpha3[match(Eu2,C$ISOalpha2)]
> length(Eu3)
[1] 56
> i <- match(Eu3,map$ISO_A3)
> Eu3[which(is.na(i))]
[1] "FRA" "GIB" "NOR" "SJM" "XKX"
> cbind(as.character(map$NAME),as.character(map$ISO_A3))
> ma3 <- as.character(map$ISO_A3)
> length(ma3)
[1] 241
> as.character(map$NAME)[ma3=="-99"]
[1] "Ashmore and Cartier Is." "N. Cyprus" "France"
[4] "Indian Ocean Ter." "Siachen Glacier" "Kosovo"
[7] "Norway" "Somaliland"
> ma3[c(73,120,163)] <- c("FRA","XKX","NOR")
> i <- match(Eu3,ma3)
> Eu3[which(is.na(i))]
[1] "GIB" "SJM"
> j <- match(Eu3,C$ISOalpha3)
> j
[1] 3 6 15 22 28 58 61 62 71 76 77 84 86 87 93 101 107 108 110
[20] 113 123 128 129 130 131 137 146 148 156 166 177 178 193 197 201 202 208 214
[39] 215 234 12 16 21 35 74 83 102 212 145 182 227 232 183 60 99 248
> CE[c(13,48),]
n NAME alpha2 reduced
13 13 Gibraltar (UK) GI GB
48 48 Jan Mayen (Norway) SJ NO
> C[j,c(3,6:19)]
> j[48] <- 166 # imputing NOR data to SJM
> EE <- C[j,6:19]
> rownames(EE) <- Eu3
> EE
Inspecting the data table we see that almost all data are missing for Vatican and Svalbard+Jan Mayen, and that the variables ''UrbPop'', ''BirthRate'', ''DeathRate'', ''MedAtot'' are complete. There are two options: remove VAT and SJM from the data set or impute the data. I decided to "guess" the missing Vatican data and impute the NOR data to SJM.
> CIAeu <- EE[,c(1,3,4,9)]
> CIAeu
UrbPop BirthRate DeathRate MedAtot
ALB 59.3 13.2 6.8 32.9
AND 84.1 7.5 7.3 44.3
AUT 66.1 9.5 9.6 44.0
BEL 97.9 11.3 9.7 41.4
BIH 40.1 8.8 10.0 42.1
HRV 59.6 8.9 12.2 43.0
CZE 73.0 9.3 10.5 42.1
DNK 88.0 10.5 10.3 42.2
EST 67.4 10.1 12.6 42.7
FIN 84.5 10.7 10.0 42.5
FRA 80.0 12.2 9.3 41.4
DEU 75.7 8.6 11.7 47.1
GIB 100.0 14.0 8.5 34.7
GRC 78.6 8.4 11.3 44.5
GGY 31.7 9.8 9.0 43.8
HUN 72.1 9.0 12.8 42.3
IRL 63.8 14.1 6.6 36.8
IMN 52.4 11.0 10.2 44.2
ITA 69.3 8.6 10.4 45.5
JEY 31.7 12.4 7.8 38.0
LVA 67.4 9.7 14.5 43.6
LIE 14.3 10.4 7.4 43.2
LTU 66.5 9.9 14.6 43.7
LUX 90.7 11.5 7.3 39.3
MKD 57.3 11.4 9.2 37.9
MLT 95.6 10.1 9.4 41.8
MCO 100.0 6.6 9.8 53.1
MNE 64.4 10.0 9.7 40.7
NLD 91.5 10.9 8.9 42.6
NOR 81.0 12.2 8.1 39.2
POL 60.5 9.5 10.4 40.7
PRT 64.6 9.0 11.1 42.2
SMR 94.2 8.6 8.7 44.4
SRB 55.8 9.0 13.6 42.6
SVK 53.4 9.7 9.9 40.5
SVN 49.6 8.2 11.6 44.5
ESP 80.0 9.2 9.1 42.7
SWE 86.1 12.1 9.4 41.2
CHE 74.1 10.5 8.3 42.4
GBR 83.1 12.1 9.4 40.5
ARM 62.5 12.9 9.4 35.1
AZE 55.2 15.8 7.1 31.3
BLR 77.4 10.3 13.2 40.0
BGR 74.6 8.7 14.5 42.7
FRO 42.4 14.3 8.8 37.6
GEO 54.0 12.3 10.9 38.1
ISL 94.3 13.7 6.4 36.5
SJM 81.0 12.2 8.1 39.2
MDA 45.2 11.5 12.6 36.7
ROU 54.9 8.9 12.0 41.1
TUR 74.4 15.7 6.0 30.9
UKR 70.1 10.3 14.4 40.6
RUS 74.2 11.0 13.5 39.6
CYP 66.8 11.3 6.8 36.8
VAT 100.0 NA NA NA
XKX 56.0 14.0 7.0 29.1
> CIAeu["VAT",2:4] <- c(0,0,55)
> CIAeu["VAT",]
UrbPop BirthRate DeathRate MedAtot
VAT 100 0 0 55
> save(CIAeu,ascii=TRUE,file='C:/Users/batagelj/Downloads/data/CIA/CIAeu.Rdata')
The second option would go as follows:
> Eu3c <- Eu3[Eu3!="VAT"]
> j <- match(Eu3c,C$ISOalpha3)
> Ceu <- C[j,c(3,6,8,9,14)]
> Ceu
==== Linkings ====
There are three different lists (orderings) of countries:
* countries in the data table
* countries in the constraints network
* countries in the map
After merging countries Svalbard and Jan Mayen into SJM the network order is determined by vector ''A3net''. We reorder the data table in the same order - obtaining the table ''EuCIA'':
> load('C:/Users/batagelj/Downloads/data/CIA/CIAeu.Rdata')
> A3net <- c("SJM","ALB","AND","AUT","BEL","BIH","HRV","CZE","DNK","EST","FIN","FRA","DEU",
+ "GIB","GRC","GGY","HUN","IRL","IMN","ITA","JEY","LVA","LIE","LTU","LUX","MKD","MLT",
+ "MCO","MNE","NLD","NOR","POL","PRT","SMR","SRB","SVK","SVN","ESP","SWE","CHE","GBR",
+ "ARM","AZE","BLR","BGR","FRO","GEO","ISL","MDA","ROU","TUR","UKR","RUS","CYP","VAT","XKX")
> A3df <- rownames(CIAeu)
> P <- match(A3net,A3df)
> EuCIA <- CIAeu[P,]
> head(EuCIA)
Let's draw the map of European countries.
> setwd("C:/Users/batagelj/Documents/papers/2018/CRoNoS/shape/nEarth")
> library(maptools)
> gpclibPermit()
> library(RColorBrewer)
> library(reshape)
> map <- readShapeSpatial("ne_50m_admin_0_countries", proj4string = CRS("+proj=longlat"))
> plot(map,xlim=c(-24,46),ylim=c(31,79))
> A3map <- as.character(map$ISO_A3)
> n <- length(A3map)
> P <- match(A3net,A3map)
> Q <- match(A3map,A3net)
> I <- 2 - is.na(Q)
> C <- c("yellow","blue")[I]
> plot(map,xlim=c(-24,46),ylim=c(31,79),col=C)
{{notes:da:pics:eupairs.png}}
There are some "[[https://github.com/nvkelso/natural-earth-vector/issues/112|problems]]" with ISO_A3 country codes:
> i <- which(A3map=="-99")
> cbind(i,as.character(map$NAME[i]))
i
[1,] "13" "Ashmore and Cartier Is."
[2,] "55" "N. Cyprus"
[3,] "73" "France"
[4,] "100" "Indian Ocean Ter."
[5,] "112" "Siachen Glacier"
[6,] "120" "Kosovo"
[7,] "163" "Norway"
[8,] "198" "Somaliland"
> plot(map,xlim = c(-24, 46), ylim = c(31, 79),col=map$MAPCOLOR7)
> plot(map,xlim = c(-24, 46), ylim = c(31, 79),col=map$MAPCOLOR8)
> plot(map,xlim = c(-24, 46), ylim = c(31, 79),col=map$MAPCOLOR9)
> A3map[i] <- c("ATC","CYN","FRA","IOA","KAS","XKX","NOR","SOL")
> map$CONTINENT
> Eu <- which(map$CONTINENT=="Europe")
> A3map[Eu]
> cbind(Eu,as.character(A3map[Eu]),as.character(map$NAME[Eu]))
\\
====== ======
[[notes:da:ciaeu|Back to CIA Europe data]]