Data Source and Characteristics

The data set presents estimates of international migrant by age, sex and origin. Estimates are presented for 1990, 1995, 2000, 2005, 2010, 2015 and 2019 and are available for all countries and areas of the world. The estimates are based on official statistics on the foreign-born or the foreign population.

Using two data sheets ( Taking only one sheet from main excel file)

  1. Containing main data with total migrant numbers between origin and Destination countries

    Link to Data set : https://www.un.org/en/development/desa/population/migration/data/estimates2/estimates19.asp

  2. Used to map countries to Regions

    Link to country-region Data set : https://unstats.un.org/unsd/methodology/m49/overview/

Variables :

  1. Year - Year of migration

  2. Destination - Destination Country of migrants

  3. Origin - Origin Country of migrants( This is scattered across columns )

  4. Type of Destination or Origin - Indicates whether country is Developed or Developing

Note : Data Source and description taken from UN

Load Data

Before loading : Excluded the top 14 rows ( containing UN logo and other descriptive details before loading)

# Remove old files from environment
rm(list = ls())

# Load libraries
library(tidyverse)
library(kableExtra)
library(ggthemes)

# Read csv files
DF_Migrant <- read.csv("MigrantDataWranglingAndVisualization-1.csv")
DF_Country <- read.csv("MigrantDataWranglingAndVisualization-2.csv")

Showing the Violation of tidy data principle for this data set

Display first few files and view the dataframe

  • The variable should be “Origin Country” of migrants.

  • Instead we have all countries scattered across columns.(Please scroll few columns to the right)

  • Dataset violates tidy data principle “Each variable must have its own column”

head(DF_Migrant, n = 50) %>% 
  kable() %>% 
  kable_styling(bootstrap_options = c("bordered","striped","hover","condensed","responsive")) %>% scroll_box(width="100%",height="300px")
Year Sort.order Major.area..region..country.or.area.of.destination Notes Code Type.of.data..a. Total Other.South Other.North Afghanistan Albania Algeria American.Samoa Andorra Angola Anguilla Antigua.and.Barbuda Argentina Armenia Aruba Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia..Plurinational.State.of. Bonaire..Sint.Eustatius.and.Saba Bosnia.and.Herzegovina Botswana Brazil British.Virgin.Islands Brunei.Darussalam Bulgaria Burkina.Faso Burundi Cabo.Verde Cambodia Cameroon Canada Cayman.Islands Central.African.Republic Chad Channel.Islands Chile China China..Hong.Kong.SAR China..Macao.SAR Colombia Comoros Congo Cook.Islands Costa.Rica Côte.d.Ivoire Croatia Cuba Curaçao Cyprus Czechia Dem..People.s.Republic.of.Korea Democratic.Republic.of.the.Congo Denmark Djibouti Dominica Dominican.Republic Ecuador Egypt El.Salvador Equatorial.Guinea Eritrea Estonia Eswatini Ethiopia Falkland.Islands..Malvinas. Faroe.Islands Fiji Finland France French.Guiana French.Polynesia Gabon Gambia Georgia Germany Ghana Gibraltar Greece Greenland Grenada Guadeloupe Guam Guatemala Guinea Guinea.Bissau Guyana Haiti Holy.See Honduras Hungary Iceland India Indonesia Iran..Islamic.Republic.of. Iraq Ireland Isle.of.Man Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Kiribati Kuwait Kyrgyzstan Lao.People.s.Democratic.Republic Latvia Lebanon Lesotho Liberia Libya Liechtenstein Lithuania Luxembourg Madagascar Malawi Malaysia Maldives Mali Malta Marshall.Islands Martinique Mauritania Mauritius Mayotte Mexico Micronesia..Fed..States.of. Monaco Mongolia Montenegro Montserrat Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands New.Caledonia New.Zealand Nicaragua Niger Nigeria Niue North.Macedonia Northern.Mariana.Islands Norway Oman Pakistan Palau Panama Papua.New.Guinea Paraguay Peru Philippines Poland Portugal Puerto.Rico Qatar Republic.of.Korea Republic.of.Moldova Réunion Romania Russian.Federation Rwanda Saint.Helena Saint.Kitts.and.Nevis Saint.Lucia Saint.Pierre.and.Miquelon Saint.Vincent.and.the.Grenadines Samoa San.Marino Sao.Tome.and.Principe Saudi.Arabia Senegal Serbia Seychelles Sierra.Leone Singapore Sint.Maarten..Dutch.part. Slovakia Slovenia Solomon.Islands Somalia South.Africa South.Sudan Spain Sri.Lanka State.of.Palestine Sudan Suriname Sweden Switzerland Syrian.Arab.Republic Tajikistan Thailand Timor.Leste Togo Tokelau Tonga Trinidad.and.Tobago Tunisia Turkey Turkmenistan Turks.and.Caicos.Islands Tuvalu Uganda Ukraine United.Arab.Emirates United.Kingdom United.Republic.of.Tanzania United.States.of.America United.States.Virgin.Islands Uruguay Uzbekistan Vanuatu Venezuela..Bolivarian.Republic.of. Viet.Nam Wallis.and.Futuna.Islands Western.Sahara Yemen Zambia Zimbabwe X X.1 X.2 X.3 X.4 X.5 X.6 X.7 X.8 X.9 X.10 X.11 X.12 X.13 X.14 X.15 X.16 X.17 X.18 X.19 X.20 X.21 X.22 X.23 X.24 X.25 X.26 X.27 X.28 X.29 X.30 X.31 X.32 X.33 X.34 X.35 X.36 X.37 X.38 X.39 X.40 X.41 X.42 X.43 X.44 X.45 X.46 X.47
1990 1990001 WORLD 900 153,011,473 6,548,526 2,366,800 6,823,350 180,284 921,727 2,041 3,792 824,942 2,047 21,753 430,169 899,649 10,596 303,696 506,088 1,634,081 25,182 12,820 5,451,831 84,931 1,767,606 365,360 36,117 234,314 71,702 28,465 224,693 3,206 861,766 26,053 500,392 3,094 26,323 613,093 1,021,332 337,118 91,368 355,430 115,853 998,163 373 46,362 336,802 18,726 493,026 4,231,648 551,080 95,648 1,009,935 40,083 96,372 17,488 69,711 366,348 425,807 835,796 43,190 174,378 277,260 39,784 436,526 201,761 5,308 42,437 466,216 214,008 1,322,178 1,242,075 36,178 170,603 113,905 35,181 1,689,955 260 7,520 90,166 250,765 1,215,895 2,844 3,149 15,352 36,280 919,454 2,929,448 371,162 11,994 1,022,459 9,510 43,249 5,828 1,376 348,332 352,763 55,409 233,731 528,873 31 156,594 387,514 17,621 6,623,177 1,638,365 631,339 1,506,702 917,286 10,735 281,597 3,351,006 589,010 608,921 313,997 2,971,639 250,340 4,053 81,611 522,578 483,021 215,134 509,323 191,339 516,886 76,256 3,428 341,050 36,141 59,424 143,437 562,762 2,193 647,436 110,746 1,426 11,041 134,488 110,708 1,835 4,395,365 7,714 4,479 24,466 77,384 7,188 1,748,251 2,222,369 685,310 16,079 1,419 748,060 723,638 4,151 388,173 442,126 149,779 446,806 5,860 432,296 2,525 138,536 12,535 3,343,328 2,958 134,743 3,111 297,979 314,854 2,033,684 1,510,415 1,873,457 1,200,821 12,204 1,624,729 625,683 3,087 813,066 12,662,893 547,718 884 20,714 22,005 485 37,049 74,861 1,419 13,977 107,166 370,263 742,547 35,633 61,854 156,468 14,823 133,006 91,496 2,212 848,067 308,303 514,943 1,439,019 885,951 1,813,063 584,940 179,870 206,848 326,276 621,881 537,701 311,308 11,261 193,830 1,684 32,666 197,521 465,576 2,640,033 259,987 2,311 2,350 311,602 5,545,760 79,545 3,794,333 203,070 1,739,233 2,362 237,486 1,428,020 5,060 185,946 1,237,873 6,484 168,239 455,492 85,203 204,365 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990002 UN development groups NA .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990003 More developed regions b 901 82,767,216 3,385,103 1,077,179 119,386 177,986 867,015 1,027 3,737 167,381 540 14,561 223,308 654,111 4,639 245,256 471,950 1,056,543 23,376 1,783 161,091 79,563 1,570,292 338,069 32,183 14,838 71,550 554 43,272 2,900 854,349 2,843 282,969 158 6,623 122,758 6,386 4,620 80,227 237,274 50,166 938,586 269 9,114 3,608 18,625 191,694 1,460,345 516,749 14,382 365,140 18,946 61,595 17,441 48,040 49,237 422,110 774,534 39,067 163,985 268,472 15,643 95,528 194,363 3,466 23,814 387,566 165,601 269,821 507,652 8,443 25,565 110,168 546 116,900 237 7,518 85,997 247,088 881,133 61 324 8,717 12,656 801,710 2,488,935 127,620 11,920 890,569 8,997 23,826 162 43 245,297 14,712 16,106 206,865 294,766 10 114,335 360,160 17,533 1,232,954 309,159 518,437 145,177 908,952 10,735 169,372 2,789,415 575,132 430,558 70,531 2,833,828 154,625 989 16,425 483,043 251,777 200,654 370,568 365 18,354 25,322 3,250 310,298 35,779 43,500 11,584 188,217 304 51,354 110,502 1,115 316 13,255 101,617 0 4,350,586 2,764 4,200 24,300 77,143 5,174 1,567,742 78,957 44,698 1,304 465 7,177 660,809 1,268 371,488 179,003 3,433 148,202 5,821 380,767 274 130,212 714 447,344 12 90,144 1,845 14,571 228,073 1,349,642 1,346,970 1,475,456 1,180,927 904 1,382,392 567,165 125 661,082 7,566,200 8,427 539 9,886 10,108 433 18,424 59,558 1,376 5,881 24,905 130,627 738,976 28,990 19,718 90,554 13,911 132,447 89,464 1,115 67,402 224,304 1 917,001 260,120 35,600 15,083 160,380 197,121 288,203 126,353 471,233 206,019 10,514 19,358 1,523 29,974 182,904 416,484 2,548,456 249,213 221 1,171 71,129 4,668,356 5,600 3,462,531 61,033 889,414 70 56,838 1,078,563 1,017 114,991 1,085,310 884 333 11,457 26,062 40,957 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990004 Less developed regions c 902 70,244,257 3,163,423 1,289,621 6,703,964 2,298 54,712 1,014 55 657,561 1,507 7,192 206,861 245,538 5,957 58,440 34,138 577,538 1,806 11,037 5,290,740 5,368 197,314 27,291 3,934 219,476 152 27,911 181,421 306 7,417 23,210 217,423 2,936 19,700 490,335 1,014,946 332,498 11,141 118,156 65,687 59,577 104 37,248 333,194 101 301,332 2,771,303 34,331 81,266 644,795 21,137 34,777 47 21,671 317,111 3,697 61,262 4,123 10,393 8,788 24,141 340,998 7,398 1,842 18,623 78,650 48,407 1,052,357 734,423 27,735 145,038 3,737 34,635 1,573,055 23 2 4,169 3,677 334,762 2,783 2,825 6,635 23,624 117,744 440,513 243,542 74 131,890 513 19,423 5,666 1,333 103,035 338,051 39,303 26,866 234,107 21 42,259 27,354 88 5,390,223 1,329,206 112,902 1,361,525 8,334 0 112,225 561,591 13,878 178,363 243,466 137,811 95,715 3,064 65,186 39,535 231,244 14,480 138,755 190,974 498,532 50,934 178 30,752 362 15,924 131,853 374,545 1,889 596,082 244 311 10,725 121,233 9,091 1,835 44,779 4,950 279 166 241 2,014 180,509 2,143,412 640,612 14,775 954 740,883 62,829 2,883 16,685 263,123 146,346 298,604 39 51,529 2,251 8,324 11,821 2,895,984 2,946 44,599 1,266 283,408 86,781 684,042 163,445 398,001 19,894 11,300 242,337 58,518 2,962 151,984 5,096,693 539,291 345 10,828 11,897 52 18,625 15,303 43 8,096 82,261 239,636 3,571 6,643 42,136 65,914 912 559 2,032 1,097 780,665 83,999 514,942 522,018 625,831 1,777,463 569,857 19,490 9,727 38,073 495,528 66,468 105,289 747 174,472 161 2,692 14,617 49,092 91,577 10,774 2,090 1,179 240,473 877,404 73,945 331,802 142,037 849,819 2,292 180,648 349,457 4,043 70,955 152,563 5,600 167,906 444,035 59,141 163,408 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990005 Least developed countries d 941 11,060,221 482,753 239,756 0 0 5,622 0 0 608,108 0 0 526 0 0 1,465 190 0 0 89 2,046 0 0 973 0 41,733 0 16,805 0 0 0 652 1,631 0 0 0 43,162 329,604 10,796 14,322 33,380 1,876 0 26,440 208,161 0 333 245,923 54 0 828 8,549 25,168 0 274 308,481 0 1,493 0 5 0 78 257,210 189 904 0 2,406 0 30,582 0 355 134,471 0 102 1,432,950 0 0 721 0 38,541 0 0 5,273 13,064 0 4,776 58,990 0 40 0 0 0 0 0 200,952 37,630 0 0 0 0 10 0 462,470 75,246 0 5,020 1 0 0 2,401 0 9,729 0 0 82,931 1,054 29 0 58,843 0 7,337 2,954 477,733 3,757 0 0 0 10,810 47,615 92,880 0 132,257 0 0 0 107,801 113 0 306 0 0 0 0 0 3,711 1,265,353 226,295 3,080 928 10,025 548 292 487 0 55,934 114,276 0 0 0 4,249 0 17,315 0 0 1,160 0 351 818 0 8,194 1,546 0 354 0 957 0 1,465 532,395 0 0 0 0 0 21 0 3,219 2,132 196,154 0 6,426 39,039 10,678 0 0 15 83 757,421 21,041 498,608 1,778 115 2,697 244,607 0 1,170 686 2,393 40,537 32,076 0 32,868 0 24 0 221 6 0 0 377 149,308 84 62 15,760 72,693 38,316 0 286 2,027 9 2,510 71,579 0 0 357 26,254 75,122 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990006 Less developed regions, excluding least developed countries 934 59,184,036 2,680,670 1,049,865 6,703,964 2,298 49,090 1,014 55 49,453 1,507 7,192 206,335 245,538 5,957 56,975 33,948 577,538 1,806 10,948 5,288,694 5,368 197,314 26,318 3,934 177,743 152 11,106 181,421 306 7,417 22,558 215,792 2,936 19,700 490,335 971,784 2,894 345 103,834 32,307 57,701 104 10,808 125,033 101 300,999 2,525,380 34,277 81,266 643,967 12,588 9,609 47 21,397 8,630 3,697 59,769 4,123 10,388 8,788 24,063 83,788 7,209 938 18,623 76,244 48,407 1,021,775 734,423 27,380 10,567 3,737 34,533 140,105 23 2 3,448 3,677 296,221 2,783 2,825 1,362 10,560 117,744 435,737 184,552 74 131,850 513 19,423 5,666 1,333 103,035 137,099 1,673 26,866 234,107 21 42,259 27,344 88 4,927,753 1,253,960 112,902 1,356,505 8,333 0 112,225 559,190 13,878 168,634 243,466 137,811 12,784 2,010 65,157 39,535 172,401 14,480 131,418 188,020 20,799 47,177 178 30,752 362 5,114 84,238 281,665 1,889 463,825 244 311 10,725 13,432 8,978 1,835 44,473 4,950 279 166 241 2,014 176,798 878,059 414,317 11,695 26 730,858 62,281 2,591 16,198 263,123 90,412 184,328 39 51,529 2,251 4,075 11,821 2,878,669 2,946 44,599 106 283,408 86,430 683,224 163,445 389,807 18,348 11,300 241,983 58,518 2,005 151,984 5,095,228 6,896 345 10,828 11,897 52 18,625 15,282 43 4,877 80,129 43,482 3,571 217 3,097 55,236 912 559 2,017 1,014 23,244 62,958 16,334 520,240 625,716 1,774,766 325,250 19,490 8,557 37,387 493,135 25,931 73,213 747 141,604 161 2,668 14,617 48,871 91,571 10,774 2,090 802 91,165 877,320 73,883 316,042 69,344 811,503 2,292 180,362 347,430 4,034 68,445 80,984 5,600 167,906 443,678 32,887 88,286 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990007 World Bank income groups NA .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990008 High-income countries e 1503 77,802,868 3,803,597 1,223,239 269,933 131,448 886,589 68 3,748 166,885 1,364 19,773 297,255 77,381 5,113 258,651 477,594 33,510 24,353 2,420 1,038,903 83,075 378,521 342,837 32,424 14,521 71,624 557 197,715 2,973 806,947 2,649 338,336 2,979 6,626 105,794 5,885 4,464 80,229 237,574 49,459 957,542 295 8,995 16,007 18,629 420,298 3,410,668 544,143 83,390 389,561 18,933 61,187 17,443 53,398 49,136 365,642 796,921 39,810 163,475 257,181 6,256 95,708 193,540 3,466 33,406 430,991 170,425 990,463 510,982 8,417 33,706 33,851 547 224,332 242 7,520 86,031 244,953 946,977 67 3,068 8,719 12,654 84,449 2,392,512 127,386 11,956 852,952 9,038 41,961 210 1,258 246,425 14,279 15,766 218,135 309,142 31 116,522 350,633 17,528 3,225,452 1,263,593 560,279 260,920 909,672 10,735 164,597 3,138,225 585,336 453,983 221,420 58,640 154,305 988 37,265 7,121 252,745 83,481 445,184 376 18,361 44,248 3,422 199,383 35,895 43,415 11,575 433,379 171 51,168 109,940 1,300 326 13,264 101,606 0 4,358,976 7,622 4,034 2,803 54,113 6,971 1,727,036 78,883 133,959 1,204 465 198,230 672,951 3,016 374,480 184,512 3,411 150,630 5,823 380,039 2,140 129,287 700 1,366,281 2,577 92,723 1,848 273,632 254,579 1,882,009 1,439,656 1,493,766 1,186,679 964 1,400,764 62,455 125 785,992 1,701,262 8,251 539 17,764 18,008 446 36,013 59,557 1,387 5,893 40,163 130,540 703,566 28,992 19,720 98,656 14,127 126,572 86,731 1,115 72,440 234,358 5,082 1,200,417 571,108 62,685 267,318 161,130 198,993 293,717 463,692 9,541 234,818 10,514 19,361 1,523 29,975 188,682 459,395 2,573,354 46,253 2,307 1,146 71,050 1,015,865 52,769 3,545,103 60,825 1,261,938 1,567 199,100 65,671 4,376 124,640 1,046,349 6,472 223 430,024 25,738 40,771 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990009 Middle-income countries e 1517 65,124,366 1,877,678 987,475 6,544,932 48,836 30,513 1,973 44 174,224 515 1,487 132,407 822,011 1,107 44,800 28,206 1,600,314 785 10,400 4,412,305 1,714 1,388,192 21,287 3,693 178,551 78 11,103 26,978 233 54,819 20,379 154,538 115 19,659 507,299 972,285 3,050 8,162 117,856 33,374 38,291 78 28,838 308,857 97 72,352 775,344 6,782 12,258 618,759 1,872 10,543 45 16,039 8,911 60,165 36,846 1,013 10,898 20,079 33,232 138,969 7,851 938 612 20,823 43,583 323,025 731,093 27,711 67,216 80,054 34,583 988,373 18 0 4,033 5,812 113,929 942 0 1,448 10,562 834,133 532,171 185,578 38 169,467 472 1,283 141 118 101,907 139,916 2,557 12,369 192,927 0 40,072 36,871 93 3,016,218 373,868 69,247 1,236,917 7,613 0 117,000 210,131 3,004 154,359 92,577 2,911,898 19,655 3,065 44,346 495,415 229,356 131,653 56,475 188,009 20,792 30,763 6 141,667 246 774 31,753 129,256 2,022 471,871 806 126 342 13,141 7,981 0 36,073 92 445 21,663 23,271 82 17,810 437,105 551,351 14,505 954 549,830 43,051 292 10,996 257,614 90,532 205,268 28 52,257 385 9,238 11,778 1,965,180 381 42,020 1,253 24,347 59,528 150,559 70,701 375,745 12,573 11,240 217,880 561,157 1,143 27,074 10,596,628 7,118 314 265 1,012 39 952 15,148 32 7,977 65,144 136,917 38,981 215 3,095 57,812 400 6,434 4,750 1,097 119,222 52,988 11,253 236,886 314,645 1,503,109 73,015 1,443 6,696 31,714 155,736 487,623 76,258 747 141,705 161 2,643 8,750 6,181 65,498 212,650 4 1,143 166,211 4,525,821 26,776 223,677 76,598 466,493 514 38,100 1,341,474 532 58,342 190,102 12 168,016 25,400 14,403 123,378 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990010 Upper-middle-income countries e 1502 33,285,549 957,059 376,006 3,152,957 48,298 4,719 1,954 42 37,413 515 1,428 109,480 766,027 1,098 18,679 27,153 1,469,433 742 1,555 35,223 1,643 888,069 19,076 2,185 16,883 78 13 26,638 220 54,796 19,646 142,143 115 3,268 504,566 2,794 2,897 57 74,856 19,111 26,977 10 260 2,438 97 66,718 209,531 6,511 12,258 616,794 1,441 8,582 45 7,805 3,058 60,041 34,979 903 10,789 19,173 30,697 15,750 7,296 695 606 20,573 42,569 281,241 603,623 26,921 2,068 67,065 34,532 5,355 18 0 913 5,158 63,786 942 0 375 141 743,620 463,516 9,699 31 167,820 472 1,282 141 5 89,126 2,130 573 12,368 192,862 0 13,961 36,005 91 59,646 282,088 59,120 1,232,207 7,339 0 12,468 199,367 2,924 124,952 24,288 2,469,371 4,789 2,074 8,583 449,222 165,155 107,560 47,670 187,710 309 4,015 4 106,140 239 724 14,606 15,830 191 18,043 800 0 342 678 5,434 0 23,665 92 381 21,314 23,271 82 7,596 347,974 234,724 11,297 33 21,409 40,498 0 7,699 110,853 331 28,397 28 52,098 282 4,699 2,135 31,752 0 40,739 84 23,111 51,622 143,309 54,810 367,428 12,271 1,047 203,242 368,601 1,143 20,544 3,568,415 2,867 314 264 1,012 39 952 15,137 32 2,390 27,211 13,417 38,981 200 743 32,900 368 6,359 4,750 690 8,740 20,011 5,452 229,844 31,528 1,447,038 56,982 1,414 5,888 28,992 135,316 438,397 40,354 0 7,116 161 2,572 8,746 2,835 60,445 176,548 4 766 5,737 3,970,020 5,563 171,954 2,887 364,483 494 37,509 1,176,874 369 57,468 82,875 12 168,016 19,772 13,173 88,433 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990011 Lower-middle-income countries e 1501 31,838,817 920,619 611,469 3,391,975 538 25,794 19 2 136,811 0 59 22,927 55,984 9 26,121 1,053 130,881 43 8,845 4,377,082 71 500,123 2,211 1,508 161,668 0 11,090 340 13 23 733 12,395 0 16,391 2,733 969,491 153 8,105 43,000 14,263 11,314 68 28,578 306,419 0 5,634 565,813 271 0 1,965 431 1,961 0 8,234 5,853 124 1,867 110 109 906 2,535 123,219 555 243 6 250 1,014 41,784 127,470 790 65,148 12,989 51 983,018 0 0 3,120 654 50,143 0 0 1,073 10,421 90,513 68,655 175,879 7 1,647 0 1 0 113 12,781 137,786 1,984 1 65 0 26,111 866 2 2,956,572 91,780 10,127 4,710 274 0 104,532 10,764 80 29,407 68,289 442,527 14,866 991 35,763 46,193 64,201 24,093 8,805 299 20,483 26,748 2 35,527 7 50 17,147 113,426 1,831 453,828 6 126 0 12,463 2,547 0 12,408 0 64 349 0 0 10,214 89,131 316,627 3,208 921 528,421 2,553 292 3,297 146,761 90,201 176,871 0 159 103 4,539 9,643 1,933,428 381 1,281 1,169 1,236 7,906 7,250 15,891 8,317 302 10,193 14,638 192,556 0 6,530 7,028,213 4,251 0 1 0 0 0 11 0 5,587 37,933 123,500 0 15 2,352 24,912 32 75 0 407 110,482 32,977 5,801 7,042 283,117 56,071 16,033 29 808 2,722 20,420 49,226 35,904 747 134,589 0 71 4 3,346 5,053 36,102 0 377 160,474 555,801 21,213 51,723 73,711 102,010 20 591 164,600 163 874 107,227 0 0 5,628 1,230 34,945 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990012 Low-income countries e 1500 9,790,238 850,549 145,493 8,485 0 4,370 0 0 483,833 0 0 466 257 0 105 288 257 0 0 623 0 893 917 0 41,242 0 16,805 0 0 0 3,025 1,263 0 38 0 43,162 329,604 2,977 0 33,020 1,792 0 8,529 11,938 0 333 44,584 97 0 828 8,549 24,642 0 274 308,301 0 1,408 0 5 0 296 201,849 370 904 0 2,406 0 8,690 0 50 69,681 0 51 477,250 0 0 0 0 38,090 0 0 5,185 13,064 872 4,640 58,198 0 40 0 0 0 0 0 198,073 36,563 0 0 0 0 10 0 381,415 845 1,813 8,865 1 0 0 2,338 0 503 0 1,101 76,380 0 0 20,042 0 0 7,231 2,954 477,733 1,245 0 0 0 10,810 100,109 127 0 124,203 0 0 0 107,801 113 0 306 0 0 0 0 0 3,405 1,706,381 0 370 0 0 603 0 166 0 55,836 90,908 0 0 0 9 57 11,867 0 0 0 0 438 1,116 58 2,432 1,546 0 6,085 2,071 957 0 365,003 532,349 0 0 0 0 0 0 0 107 1,859 101,901 0 6,426 39,039 0 0 0 15 0 656,405 20,925 498,608 1,701 198 247,269 244,607 0 1,159 756 2,268 40,537 232 0 32,764 0 0 0 0 1,181 1,084 0 0 74,341 4,074 0 24,077 65,647 9,756 37 286 20,875 0 2,510 1,422 0 0 68 45,062 40,216 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990013 No income group available e NA 294,001 16,702 10,593 0 0 255 0 0 0 168 493 41 0 4,376 140 0 0 44 0 0 142 0 319 0 0 0 0 0 0 0 0 6,255 0 0 0 0 0 0 0 0 538 0 0 0 0 43 1,052 58 0 787 10,729 0 0 0 0 0 621 2,367 0 0 0 0 0 0 8,419 11,996 0 0 0 0 0 0 0 0 0 0 102 0 116,899 1,835 81 0 0 0 125 0 0 0 0 5 5,477 0 0 495 523 3,227 26,804 0 0 0 0 92 59 0 0 0 0 0 312 670 76 0 0 0 0 0 0 920 0 433 0 0 0 0 0 0 4,425 0 0 0 194 0 0 10,373 282 1,008 1,835 10 0 0 0 0 135 0 0 0 0 0 0 7,033 843 2,531 0 0 0 9 0 0 2 0 0 0 0 10 0 309 0 0 1,514 23 0 0 0 862 0 0 0 31 2,685 2,985 0 84 156 0 0 0 905 0 0 0 0 296 0 0 0 0 32 0 15 0 0 0 17,297 0 89 185 0 0 0 0 0 48 89 0 0 0 0 61 0 0 0 1,476 0 1,046 244 0 0 152 454 0 0 0 0 0 0 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990014 Geographic regions NA .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990015 Africa 903 15,689,666 807,899 258,638 1,037 504 31,807 0 0 651,580 0 707 1,084 784 0 7,318 7,204 24 85 1,706 3,078 30 1,061 10,934 0 219,473 61 0 158 0 236 23,203 3,307 23 88 3,952 1,014,919 332,476 11,070 0 65,623 6,033 0 37,225 320,620 0 719 21,617 1,877 0 183 21,133 34,554 0 0 316,953 603 1,780 0 1,047 1,315 511 340,767 2,493 1,841 0 0 9 33,380 618 27,732 136,373 82 34,614 1,449,227 0 0 47 1,419 143,740 0 0 6,625 23,613 0 97,653 243,344 33 8,267 0 0 0 0 97 338,046 39,300 54 0 0 0 483 16 34,437 8,035 1,242 7,546 6,204 0 3,626 37,590 192 7,162 15,394 545 87,373 0 9,647 48 33 199 21,383 190,957 498,523 16,291 0 192 83 15,895 131,850 12,001 60 596,072 163 0 0 120,979 6,540 1,800 153 0 249 1 0 0 14,297 2,142,332 220 14,768 23 152 23,225 0 1,594 106 146,295 293,246 21 206 28 1,145 1,715 4,923 0 59 59 116 203 4,536 3,475 29,457 0 1,106 3,100 48 2,100 1,689 46,505 539,287 220 26 0 0 0 0 0 8,084 23,326 239,474 1,964 6,641 42,134 694 0 272 15 0 738,124 72,864 509,162 6,390 2,660 242,419 292,614 0 2,932 12,232 25,911 180 3,994 11 174,468 0 0 26 4,073 3,817 79 0 0 238,388 2,570 2,023 165,322 141,874 52,040 20 111 328 0 322 156 0 167,905 17,828 58,056 163,304 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990016 Asia 935 48,209,949 2,005,398 961,154 6,702,728 1,291 20,924 0 0 581 0 0 28,270 243,388 9 35,094 14,070 577,509 0 9,278 5,287,899 0 195,573 8,936 0 2 0 27,895 111 0 7,148 2 75,906 18 19,610 483,744 0 1 12 118,677 14 32,681 0 4 12,561 0 2,644 2,842,685 29,981 81,233 1,368 0 121 0 35 0 93 104 0 9,210 467 22,346 178 3,223 1 0 3 107 1,013,469 3 0 8,640 3,444 18 123,669 0 0 2,386 1,878 42,813 0 0 0 0 117,706 265,317 705 1 114,719 0 0 11 58 9 0 0 0 1 0 1 15,922 18 5,350,529 1,315,257 111,858 1,353,689 1,570 0 99,111 9,781 4 61,052 226,702 137,253 8,237 0 55,446 39,483 229,221 13,801 83,639 0 0 34,364 0 28,272 14 2 2 366,794 1,827 0 18 0 0 244 2,550 0 2,292 0 0 158 0 0 163,288 3 640,337 1 0 740,561 21,053 0 7,816 3 39 4,768 0 37,872 0 6,431 10,072 2,891,169 3 5 9 2 13,247 684,653 107,815 4,741 9 10,157 901,419 57,455 0 136,664 5,024,473 0 0 0 0 0 0 0 0 0 57,423 18 665 2 1 66,254 0 126 39 0 42,507 10,038 5,780 5,124 624,222 1,533,729 277,219 29 3,763 13,209 443,703 66,286 107,697 736 0 0 0 4 44,331 83,256 10,692 0 0 2,063 868,519 71,876 137,217 89 221,975 37 4,333 349,088 0 710 157,530 0 0 426,193 1,080 76 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990017 Europe 908 49,608,231 432,964 871,477 82,736 170,977 857,978 963 3,404 163,553 434 618 103,854 610,471 727 137,808 334,710 1,046,377 504 993 130,797 22,022 1,544,226 277,088 1,257 14,672 61,553 529 9,915 2,843 751,548 2,613 133,363 80 757 109,550 6,318 4,116 65,791 75,272 46,635 152,316 170 9,094 3,487 18,625 84,097 261,274 76,599 984 69,128 18,927 61,374 10 3,027 46,145 349,479 35,454 38,636 125,778 147,698 15,487 90,298 119,143 3,321 2,830 37,008 13,517 135,397 6,300 8,433 8,532 92,057 328 66,841 159 7,240 820 198,461 688,603 7 35 8,659 11,139 794,255 1,468,166 98,718 11,310 484,693 8,997 1,464 28 3 10,782 13,422 16,069 22,309 30,031 10 3,086 165,727 11,567 533,424 208,724 258,799 87,217 652,060 10,004 59,461 1,598,890 141,726 104,641 34,460 2,824,054 119,390 51 4,175 480,853 55,168 156,919 149,947 245 6,711 19,446 3,234 269,095 33,050 42,183 10,931 53,437 224 51,219 33,708 3 8 13,215 76,307 0 32,399 17 4,033 24,240 75,846 1,297 1,535,101 77,880 12,279 872 2 4,319 310,778 20 49,307 3,420 3,370 88,671 0 322,536 268 76,958 655 323,236 12 3,171 579 3,535 58,787 174,506 705,505 1,089,715 316 548 69,923 553,934 11 525,028 7,270,336 8,076 508 958 1,420 11 1,221 114 1,376 5,881 9,904 127,725 688,901 3,894 12,165 40,205 13,798 119,464 69,900 63 44,672 101,596 1 813,826 171,524 6,380 8,118 156,939 129,013 220,892 73,660 469,388 68,281 996 19,226 3 213 18,854 409,534 2,448,405 248,513 25 74 54,922 4,450,625 2,165 779,773 34,268 546,592 36 19,244 1,062,226 156 68,927 286,822 884 333 5,842 19,235 25,028 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990018 Latin America and the Caribbean 904 7,161,371 344,362 64,989 199 503 863 0 55 5,400 1,507 6,485 180,164 1,366 5,948 3,056 13,173 5 1,721 53 71 5,338 680 7,823 3,934 1 91 16 181,152 306 33 5 194,655 2,895 2 2,639 27 21 59 168 50 25,285 104 19 13 101 297,969 50,072 2,465 33 643,244 4 102 11 21,636 158 3,001 59,378 4,123 136 7,006 1,284 53 2,132 0 18,623 78,647 48,291 5,876 733,802 3 25 211 3 159 23 2 7 812 113,595 2,783 18 10 11 38 80,796 91 40 8,904 513 19,423 5,655 6 102,929 5 3 26,812 234,106 21 42,258 10,949 54 6,508 511 1,039 290 1,164 0 9,488 514,797 13,682 106,383 1,370 13 105 0 93 4 1,475 480 33,733 17 9 279 178 2,288 265 27 1 186 2 10 63 0 10,725 10 1 0 43,120 10 30 7 241 2,014 2,445 1,077 55 6 0 14 19,290 3 464 263,014 12 783 2 13,451 8 1,108 34 666 1 44,535 4 283,290 83,613 2,106 52,514 364,122 18,768 37 19,244 1,015 862 13,631 26,114 4 125 10,802 11,897 52 18,625 0 43 12 1,512 144 942 0 1 89 912 161 1,978 0 34 1,097 0 511,194 37 1,315 24 19,461 3,618 13,505 25,914 2 324 0 4 0 1 14,587 142 4,504 3 2,090 8 22 6,315 46 36,002 36 578,299 2,235 176,204 41 211 69,923 260 12 1 14 5 28 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990019 Northern America 905 27,610,408 2,923,653 198,963 33,765 6,025 8,372 0 0 3,518 106 13,922 103,223 43,252 3,886 55,694 113,299 10,067 22,694 297 25,617 57,240 25,806 55,959 30,892 149 9,646 0 32,848 57 80,489 77 89,517 72 4,246 11,453 38 489 14,416 136,066 3,463 747,342 96 14 106 0 77,626 942,103 377,459 11,699 293,671 19 87 0 44,777 3,058 23,393 738,719 396 14,171 102,819 106 4,975 64,250 111 20,978 350,535 150,999 93,194 492,578 10 15,761 15,380 101 48,570 0 261 31,287 38,477 172,949 48 53 38 1,499 7,438 885,412 27,310 201 257,786 0 22,273 125 0 234,298 1,257 24 184,130 264,692 0 111,125 165,039 5,624 617,202 55,685 240,408 51,791 197,169 0 102,155 918,065 432,744 302,020 33,896 9,477 30,285 0 11,331 2,089 185,437 33,591 138,763 77 11,580 4,608 0 36,385 2,604 1,180 293 49,300 0 111 19,387 1,086 288 24 5,095 0 4,316,624 2,741 130 24 0 3,877 31,657 691 22,172 167 0 2,382 220,571 58 22,597 174,858 48 58,379 0 17,507 0 50,272 14 116,063 0 86,855 106 10,759 155,209 1,031,071 565,580 366,307 1,180,565 259 600,119 13,216 63 123,542 285,987 307 0 8,911 8,672 422 17,181 11,471 0 0 14,364 2,753 5,763 22,486 7,433 18,919 113 10,286 12,337 0 21,954 58,469 0 87,222 38,432 26,467 5,747 3,373 61,306 54,921 47,342 1,804 112,507 0 120 0 10,764 163,130 6,539 66,805 675 196 0 15,282 205,056 2,874 1,333,457 25,343 242,567 34 25,799 16,092 0 45,458 652,259 0 0 5,394 4,293 6,988 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990020 Oceania 909 4,731,848 34,250 11,579 2,885 984 1,783 1,078 333 310 0 21 13,574 388 26 64,726 23,632 99 178 493 4,369 301 260 4,620 34 17 351 25 509 0 22,312 153 3,644 6 1,620 1,755 30 15 20 25,247 68 34,506 3 6 15 0 29,971 113,897 62,699 1,699 2,341 0 134 17,467 236 34 49,238 361 35 24,036 17,955 50 255 10,520 34 6 23 1,085 40,862 8,774 0 1,272 2,731 117 1,489 78 17 55,619 9,718 54,195 6 3,043 20 18 17 132,104 994 409 148,090 0 89 9 1,309 217 33 13 426 43 0 124 29,394 342 81,077 50,153 17,993 6,169 59,119 731 7,756 271,883 662 27,663 2,175 297 4,950 4,002 919 101 11,687 10,144 81,858 43 63 1,268 16 4,818 125 137 360 81,044 80 24 57,407 337 20 16 20,215 35 777 4,946 37 36 1,297 0 1,463 386 10,247 265 1,394 632 128,721 4,070 306,395 725 15 959 5,837 40,724 2,221 2,622 45 7,271 2,942 118 2,354 277 3,795 136,812 75,526 19,115 1,163 97 30,924 15 51 12,512 9,478 44 31 17 16 0 22 63,276 0 0 637 149 44,312 2,610 120 30,307 0 2,697 7,227 2,149 776 64,239 0 15,263 49,076 2,753 1,218 68 6,216 11,517 5,351 41 18,505 9,518 12 1,681 21,688 920 957 33,246 25 0 2,268 925 12,675 561 1,342,562 1,460 97,760 0 11,795 245 4,693 606 140,846 5,588 0 221 2,534 8,941 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990021 Sustainable Development Goal (SDG) regions f NA .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990022 SUB-SAHARAN AFRICA 947 13,286,341 744,422 247,002 123 78 6,727 0 0 651,557 0 693 762 94 0 5,294 4,444 0 74 233 2,648 30 113 7,385 0 219,427 61 0 158 0 147 23,203 2,780 23 23 3,881 1,014,852 332,429 11,070 0 65,488 4,756 0 19,263 123,280 0 698 18,094 1,877 0 118 19,472 33,168 0 0 315,601 479 1,780 0 949 1,065 394 324,530 1,418 904 0 0 0 1,663 0 27,732 70,202 68 34,614 504,747 0 0 47 692 110,678 0 0 6,625 23,577 0 51,856 242,520 26 3,231 0 0 0 0 90 336,696 38,507 54 0 0 0 10 16 29,089 1,326 725 219 6,093 0 2,631 13,577 192 1,551 54 0 80,749 0 119 0 33 199 9,964 190,957 498,483 6,958 0 192 83 15,847 131,827 925 56 593,691 163 0 0 118,648 6,540 1,800 82 0 11 0 0 0 4,990 2,142,318 211 14,389 23 152 19,109 0 1,480 106 145,923 267,662 21 59 28 737 33 3,432 0 59 59 116 203 1,930 1,905 29,413 0 0 2,012 0 2,100 818 4,321 539,228 220 26 0 0 0 0 0 8,084 2,736 235,531 1,964 6,626 41,976 427 0 201 15 0 731,909 72,131 504,409 1,857 1,701 232 229,704 0 1,553 5,388 314 0 265 11 174,468 0 0 26 401 850 0 0 0 163,864 2,314 237 150,671 141,273 20,299 20 111 0 0 296 156 0 0 440 58,030 163,266 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990023 Eastern Africa 910 5,959,183 259,087 105,685 0 0 35 0 0 139,108 0 0 0 0 0 148 0 0 0 0 131 0 0 794 0 0 0 0 0 0 0 3,494 0 0 0 0 0 309,500 2,500 0 10 545 0 0 0 0 0 2,130 91 0 0 19,278 15,858 0 0 68 0 164 0 0 0 51 215,992 170 904 0 0 0 279 0 0 69,681 0 51 501,546 0 0 0 0 47,793 0 0 0 29 0 1,239 513 0 40 0 0 0 0 0 0 0 0 0 0 0 10 0 15,828 2 0 0 30 0 0 810 0 237 0 0 76,838 0 0 0 0 0 0 2,954 0 49 0 0 0 15,325 117,182 36 2 220 0 0 0 39 1,379 1,800 0 0 0 0 0 0 0 1,748,879 0 2,849 0 27 208 0 0 0 0 2,264 0 0 0 9 0 1,163 0 0 0 0 0 61 0 1,667 0 0 59 0 957 0 408 512,371 0 0 0 0 0 0 0 0 0 173 0 6,469 0 0 0 0 0 0 728,536 27,397 391,000 85 325 0 178,918 0 29 66 0 0 5 0 105 0 0 0 0 0 0 0 0 139,632 0 0 24,610 139,041 1,789 0 0 0 0 0 0 0 0 357 45,024 75,045 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990024 Burundi 108 B R 333,110 50,676 8,943 642 36,654 321 186 221,943 1,833 11,912 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990025 Comoros 174 B 14,079 847 672 543 87 10,810 957 163 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990026 Djibouti 262 B R 122,221 5,484 1,827 13,405 101,216 289 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990027 Eritrea 232 I 11,848 737 345 247 618 8 27 407 1,284 108 39 187 114 3,537 991 1,438 97 933 131 175 34 218 91 82 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990028 Ethiopia 231 B R 1,155,390 22,075 7,358 19 48 904 69,681 71 616,940 384,266 53,857 103 68 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990029 Kenya 404 B R 298,089 65,948 35,411 99 394 0 26,695 1,043 119 3,874 7,875 197 5,801 814 83,355 66,464 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990030 Madagascar 450 C 23,917 2,851 3,563 1,481 8,179 6,672 1,171 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990031 Malawi 454 B R 1,127,724 19,158 11,744 705 735 1,338 1,362 1,075,410 2,249 211 1,276 769 3,279 5,637 3,851 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990032 Mauritius 1 480 C 3,613 292 75 71 76 36 346 91 788 57 515 30 38 23 56 36 1 34 43 184 15 9 66 694 37 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990033 Mayotte 175 B 15,229 1,354 1,142 10,288 1,359 1,086 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990034 Mozambique 508 B R 122,332 2,148 6,443 14,337 615 2,500 2,315 900 9,087 2,875 22,975 1,964 516 1,667 6,212 7,397 2,722 1,948 35,711 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990035 Réunion 638 B 57,210 6,776 6,330 441 37,516 3,339 1,008 1,800 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990036 Rwanda 646 B R 160,024 19,265 954 26,393 62,526 955 34,106 15,825 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990037 Seychelles 690 B 3,721 493 281 67 11 32 58 20 180 40 1,276 2 22 25 34 2 258 27 25 23 61 79 11 83 11 316 5 5 274 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990038 Somalia 706 I R 478,294 12,196 6,098 0 460,000 .. NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990039 South Sudan 728 B R .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990040 Uganda 800 B R 544,273 19,697 359 35 49 44 48,841 98 137 68 144 51 65,659 170 44 0 162 241 29 226 29 40 10 1,313 390 68 32,265 49 184 39 226 208 115 9 216 59 295 214,634 58 378 0 59 124,116 29 105 1,034 31,528 485 194 84 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990041 United Republic of Tanzania 2 834 B R 574,025 2,845 1,577 717 80 232,210 184 343 15,007 19,354 51 605 4,968 360 33,613 79 7,218 113 144,353 370 407 68,609 214 467 409 19,706 682 808 18,188 488 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990042 Zambia 894 B R 279,463 6,228 2,905 123,807 81 469 0 10 227 100 116 27,297 235 65 311 484 4,284 146 433 17,073 220 41,455 2,360 160 0 115 6,328 349 1,987 6,930 459 34,829 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990043 Zimbabwe 716 B R 634,621 20,017 9,658 2,945 69,618 482,855 11,426 19,136 18,966 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990044 Middle Africa 911 1,461,155 125,389 52,108 0 0 34 0 0 479,606 0 0 0 0 0 9 20 0 0 0 0 0 0 154 0 20,498 0 0 0 0 0 0 0 0 0 0 2,161 20,255 7,819 0 50,319 109 0 18,683 110,186 0 0 390 0 0 0 0 9,817 0 0 1,054 0 164 0 5 0 50 95,519 1 0 0 0 0 67 0 27,682 0 0 0 0 0 0 0 0 37,928 0 0 1,338 0 0 205 1,968 0 0 0 0 0 0 0 1,670 0 0 0 0 0 0 0 0 0 0 0 1 0 5 7 0 10 0 0 0 0 0 0 0 0 1,131 0 0 1,096 0 0 0 0 0 0 0 22,084 0 0 0 965 0 0 0 0 0 0 0 0 221 340 0 345 0 0 19 0 0 0 2,614 127,445 0 0 0 0 0 0 0 0 0 0 0 0 0 5,946 0 0 60 0 0 0 953 23,083 0 0 0 0 0 0 0 5,590 0 12,899 0 0 0 0 0 0 15 0 0 2,522 113,409 324 0 0 47,518 0 10 150 9 0 0 0 7,868 0 0 0 69 6 0 0 0 18,418 0 0 0 0 696 0 0 0 0 0 0 0 0 0 119 0 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990045 Angola 24 B R 33,517 2,953 2,805 3,569 12 410 5 12,178 131 97 345 5,298 46 3,112 2,437 119 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990046 Cameroon 120 B 265,967 40,119 13,373 0 95,527 11,808 105,140 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990047 Central African Republic 140 C 67,234 7,445 2,482 9 20 0 19 10,010 5 13,173 1 67 10,537 1 7 351 19 163 88 15 81 0 22,094 10 150 5 6 476 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990048 Chad 148 B R 74,342 4,986 245 30,992 8,067 715 98 886 292 1,096 2,614 14,822 9,529 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990049 Congo 178 B 129,391 5,934 1,978 10,334 4,357 3,464 10,616 3,239 283 164 67,316 485 8,574 970 178 358 4,678 965 442 790 0 3,468 747 51 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1990 1990050 Democratic Republic of the Congo 180 B R 754,194 60,792 30,396 468,462 20,203 3,647 22,972 113,409 15,895 18,418 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Other Observations from viewing the dataset

Renaming some columns and drop rows with empty values in ‘TypeOfData’ column

Need to drop these before Tidy

  • TypeOfData - specifies what type of region is there in Destination column ( If its empty those rows are not that of any country. So need to filter out those ) .
  • Top few rows which contained totals ( not to any specific country also had to be removed )
# Rename columns for ease of use
colnames(DF_Migrant)[3] <- "Destination_Country"
colnames(DF_Migrant)[6] <- "TypeOfData"

# Dropping unnecessary rows 
DF_Migrant <- DF_Migrant %>% filter(TypeOfData != '')

# Removing columns which are not needed
DF_Migrant <- DF_Migrant[-c(4,5,6,7,8,9)]

Tidy the data using pivot_longer and display the result

Please note : Top rows might have empty values for n_Migrants.( Will be cleaned up in next step)

# Converting data frame to tibble and using pivot_longer for transformation

tib <- tibble(DF_Migrant,)
tib <- tib %>%
  pivot_longer(-c(Year,Sort.order,Destination_Country),
               names_to = "Origin_Country", 
               values_to = "n_Migrants")
DF_Migrant <- data.frame(tib)

# Display the data after tidying

head(DF_Migrant, n = 50) %>% kable() %>%
  kable_styling(bootstrap_options = c("bordered","striped","hover","condensed","responsive")) %>% scroll_box(width="100%",height="300px")
Year Sort.order Destination_Country Origin_Country n_Migrants
1990 1990024 Burundi Afghanistan
1990 1990024 Burundi Albania
1990 1990024 Burundi Algeria
1990 1990024 Burundi American.Samoa
1990 1990024 Burundi Andorra
1990 1990024 Burundi Angola
1990 1990024 Burundi Anguilla
1990 1990024 Burundi Antigua.and.Barbuda
1990 1990024 Burundi Argentina
1990 1990024 Burundi Armenia
1990 1990024 Burundi Aruba
1990 1990024 Burundi Australia
1990 1990024 Burundi Austria
1990 1990024 Burundi Azerbaijan
1990 1990024 Burundi Bahamas
1990 1990024 Burundi Bahrain
1990 1990024 Burundi Bangladesh
1990 1990024 Burundi Barbados
1990 1990024 Burundi Belarus
1990 1990024 Burundi Belgium 642
1990 1990024 Burundi Belize
1990 1990024 Burundi Benin
1990 1990024 Burundi Bermuda
1990 1990024 Burundi Bhutan
1990 1990024 Burundi Bolivia..Plurinational.State.of.
1990 1990024 Burundi Bonaire..Sint.Eustatius.and.Saba
1990 1990024 Burundi Bosnia.and.Herzegovina
1990 1990024 Burundi Botswana
1990 1990024 Burundi Brazil
1990 1990024 Burundi British.Virgin.Islands
1990 1990024 Burundi Brunei.Darussalam
1990 1990024 Burundi Bulgaria
1990 1990024 Burundi Burkina.Faso
1990 1990024 Burundi Burundi
1990 1990024 Burundi Cabo.Verde
1990 1990024 Burundi Cambodia
1990 1990024 Burundi Cameroon
1990 1990024 Burundi Canada
1990 1990024 Burundi Cayman.Islands
1990 1990024 Burundi Central.African.Republic
1990 1990024 Burundi Chad
1990 1990024 Burundi Channel.Islands
1990 1990024 Burundi Chile
1990 1990024 Burundi China
1990 1990024 Burundi China..Hong.Kong.SAR
1990 1990024 Burundi China..Macao.SAR
1990 1990024 Burundi Colombia
1990 1990024 Burundi Comoros
1990 1990024 Burundi Congo
1990 1990024 Burundi Cook.Islands

Some more clean up with data

# Clean up 
DF_Migrant$n_Migrants <- str_replace_all(DF_Migrant$n_Migrants, "\\,","")
DF_Migrant$Origin_Country <- str_replace_all(DF_Migrant$Origin_Country, "\\."," ")
DF_Migrant <- DF_Migrant %>% filter(n_Migrants != '')
DF_Migrant$n_Migrants <- as.numeric(DF_Migrant$n_Migrants)
DF_Migrant <- DF_Migrant %>% filter(n_Migrants > 0)


# Display the data after clean up
head(DF_Migrant) %>% kable() %>%
  kable_styling(bootstrap_options = c("bordered","striped","hover","condensed","responsive")) %>% scroll_box(width="100%",height="300px")
Year Sort.order Destination_Country Origin_Country n_Migrants
1990 1990024 Burundi Belgium 642
1990 1990024 Burundi Democratic Republic of the Congo 36654
1990 1990024 Burundi France 321
1990 1990024 Burundi Kenya 186
1990 1990024 Burundi Rwanda 221943
1990 1990024 Burundi Uganda 1833

Joining with the UN Country-Region mapping data frame

### Join and Find out Continent of Destination and Origin Countries

DF_Migrant <- DF_Migrant %>% inner_join(DF_Country, by = c("Destination_Country" = "Country.or.Area"))
DF_Migrant <- DF_Migrant[c(1,2,3,4,5,9,20)]
colnames(DF_Migrant)[6] <- "Destination_Region"
colnames(DF_Migrant)[7] <- "Destination_Country_Type"

DF_Migrant <- DF_Migrant %>% inner_join(DF_Country, by = c("Origin_Country" = "Country.or.Area"))
DF_Migrant <- DF_Migrant[c(1,2,3,4,5,6,7,11,22)]
colnames(DF_Migrant)[8] <- "Origin_Region"
colnames(DF_Migrant)[9] <- "Origin_Country_Type"

Display and show data after tidy and other clean ups

head(DF_Migrant) %>% kable() %>% kable_styling(bootstrap_options = c("bordered","striped","hover","condensed","responsive")) %>% scroll_box(width="100%",height="300px")
Year Sort.order Destination_Country Origin_Country n_Migrants Destination_Region Destination_Country_Type Origin_Region Origin_Country_Type
1990 1990024 Burundi Belgium 642 Africa Developing Europe Developed
1990 1990024 Burundi Democratic Republic of the Congo 36654 Africa Developing Africa Developing
1990 1990024 Burundi France 321 Africa Developing Europe Developed
1990 1990024 Burundi Kenya 186 Africa Developing Africa Developing
1990 1990024 Burundi Rwanda 221943 Africa Developing Africa Developing
1990 1990024 Burundi Uganda 1833 Africa Developing Africa Developing

Visualization of migrant count by year

# Create summary table ( By year )
DF_Summary_1 <- DF_Migrant %>% 
  group_by(Year) %>% 
  summarise(Migrant_Count = sum(n_Migrants)) %>% 
  mutate(Migrant_Count = Migrant_Count/1000000) 

# create the plot using ggplot 
ggplot(data=DF_Summary_1, 
    aes(x=Year,y= Migrant_Count, group=1)) +
    geom_line() +
  geom_point(colour = "Blue") +
  theme_economist() +
    scale_color_gdocs() +
    ggtitle("Immigration Trend (1990-2019)") +
    geom_text(aes(label=paste0(round(Migrant_Count,0),"M")), vjust=-2, color="black", 
        position = position_dodge(0.9), size=1.75)

Visualization by Destination_Country for 2019

# Create summary ( by Destination country for year 2019 ) table
DF_Summary_2 <- DF_Migrant %>% 
  filter(Year == 2019) %>%
  group_by(Destination_Country) %>% 
  summarise(Migrant_Count = sum(n_Migrants))  %>% 
  mutate(Migrant_Count = Migrant_Count/1000000) %>%
  arrange(desc(Migrant_Count))

# Take top destinations from sorted summary table
topDestinations <- head(DF_Summary_2)

# Create plot
ggplot(topDestinations, 
    aes(x = Destination_Country, y = Migrant_Count)) +
    geom_bar(stat = "identity", position = "dodge") +
    geom_text(aes(label=paste(round(Migrant_Count,1),"M")), vjust=-0.7,color="black", position = position_dodge(1), size=2.5) +
    theme_economist() +
    scale_color_gdocs() +
    theme(axis.text.x=element_text(angle = 30, vjust = 0.5)) +
    theme(plot.title = element_text(hjust = 0.5), legend.position = "bottom") +
    ggtitle("Top Destinations of Migration (2019)") +
    xlab("Destination Country") +
    ylab ("Migrant Count(in Millions)")

Additional Plot

Visualization by Year and Destination Region

# Create summary by year and destination region
DF_Summary_3 <- DF_Migrant %>% 
  group_by(Year, Destination_Region) %>%
  summarise(Migrant_Count = sum(n_Migrants))  %>% 
  mutate(Migrant_Count = Migrant_Count/1000000)

# Create plot from above table
ggplot(DF_Summary_3) +
  aes(x = Destination_Region, y = Migrant_Count, fill = as.character(Year)) +
    geom_bar(stat = "identity", position = "dodge") +
    geom_text(aes(label=paste(round(Migrant_Count,1),"M")), vjust=-0.7,color="black", position = position_dodge(1), size=2) +
  theme_economist () +
    scale_fill_brewer(palette = "Blues") +
    theme(axis.text.x=element_text(angle = 0, vjust = 0.5)) +
    theme(plot.title = element_text(hjust = 0.5), legend.position = "bottom") +
    ggtitle("Migration Trend by Destination_Region (1990-2019)") +
    xlab("Destination_Region") +
    ylab ("Migrant Count(in Millions)")

Patterns or Observations from Visualizations

Plot 1. Immigration across countries has seen steady increase in the past 3 decades

Plot 2. From latest data ( year = 2019 )United states is the popular destination and Germany is second

Observations from Additional Plot

From Plot 3.

  • From 1990 to 2019 Immigration numbers are nearly doubled. Americas has more than double immigrants in 2019 compared to 1990s.

  • Africas has less immigration count increase compared to other regions