20251MBI012
Isabelle IZABAYO
2026-05-24
#make use about the workind directory
getwd()## [1] "C:/Users/Isabelle.Izabayo/Desktop/PYTHONCLASSES"setwd("C:/Users/Isabelle.Izabayo/Desktop/PYTHONCLASSES")##4.1.load data to R,HW1
wp<- read.csv("world_population.csv")
CO2<-read.csv("CO2_emission.csv")
head(CO2)## Country.Name country_code Region
## 1 Aruba ABW Latin America & Caribbean
## 2 Afghanistan AFG South Asia
## 3 Angola AGO Sub-Saharan Africa
## 4 Albania ALB Europe & Central Asia
## 5 Andorra AND Europe & Central Asia
## 6 United Arab Emirates ARE Middle East & North Africa
## Indicator.Name X1990 X1991 X1992
## 1 CO2 emissions (metric tons per capita) NA NA NA
## 2 CO2 emissions (metric tons per capita) 0.1917451 0.1676816 0.09595774
## 3 CO2 emissions (metric tons per capita) 0.5536620 0.5445386 0.54355722
## 4 CO2 emissions (metric tons per capita) 1.8195416 1.2428102 0.68369983
## 5 CO2 emissions (metric tons per capita) 7.5218317 7.2353792 6.96307870
## 6 CO2 emissions (metric tons per capita) 30.1951886 31.7784962 29.08092584
## X1993 X1994 X1995 X1996 X1997 X1998
## 1 NA NA NA NA NA NA
## 2 0.08472111 0.07554583 0.06846796 0.06258803 0.05682662 0.05269086
## 3 0.70898423 0.83680440 0.91214149 1.07216847 1.08663697 1.09182531
## 4 0.63830704 0.64535519 0.60543625 0.61236736 0.46692147 0.57215370
## 5 6.72417752 6.54157891 6.73347949 6.99159455 7.30744115 7.63953851
## 6 29.27567777 30.84933296 31.12501806 30.92802588 30.48633262 29.66358052
## X1999 X2000 X2001 X2002 X2003 X2004
## 1 NA NA NA NA NA NA
## 2 0.04015697 0.0365737 0.03378536 0.04557366 0.05151838 0.04165539
## 3 1.10985966 0.9880774 0.94182891 0.89557767 0.92486944 0.93026295
## 4 0.95535931 1.0262131 1.05549588 1.23237878 1.33898498 1.40405869
## 5 7.92319165 7.9522863 7.72154906 7.56623988 7.24241557 7.34426233
## 6 28.88710798 27.0351591 29.43026994 28.50146173 27.96926982 27.03893822
## X2005 X2006 X2007 X2008 X2009 X2010
## 1 NA NA NA NA NA NA
## 2 0.06041878 0.06658329 0.06531235 0.1284166 0.1718624 0.2436140
## 3 0.81353929 0.82184008 0.81175351 0.8886580 0.9394040 0.9761842
## 4 1.33820940 1.33999574 1.39393137 1.3843112 1.4414936 1.5276237
## 5 7.35378001 6.79054277 6.53104692 6.4393039 6.1566875 6.1571978
## 6 25.38238104 22.93510429 21.37028576 22.0114692 19.8323489 19.0397698
## X2011 X2012 X2013 X2014 X2015 X2016 X2017
## 1 NA NA NA NA NA NA NA
## 2 0.2965062 0.2592953 0.1856237 0.1462356 0.1728967 0.1497893 0.1316946
## 3 0.9855223 0.9506959 1.0362939 1.0997791 1.1350441 1.0318113 0.8133007
## 4 1.6694232 1.5032405 1.5336300 1.6683374 1.6037751 1.5576644 1.7887861
## 5 5.8508861 5.9446542 5.9428004 5.8071277 6.0261818 6.0806003 6.1041339
## 6 18.5094574 19.2078011 20.0556476 20.0516980 21.0776420 21.4806686 20.7690223
## X2018 X2019 X2019.1
## 1 NA NA NA
## 2 0.1632953 0.1598244 0.1598244
## 3 0.7776749 0.7921371 0.7921371
## 4 1.7827389 1.6922483 1.6922483
## 5 6.3629754 6.4812174 6.4812174
## 6 18.3906781 19.3295633 19.3295633##4.2.Exploratory data analysis
variable.names(wp)## [1] "Rank" "CCA3"
## [3] "Country.Territory" "Capital"
## [5] "Continent" "X2022.Population"
## [7] "X2020.Population" "X2015.Population"
## [9] "X2010.Population" "X2000.Population"
## [11] "X1990.Population" "X1980.Population"
## [13] "X1970.Population" "Area..km.."
## [15] "Density..per.km.." "Growth.Rate"
## [17] "World.Population.Percentage"head(wp)## Rank CCA3 Country.Territory Capital Continent X2022.Population
## 1 36 AFG Afghanistan Kabul Asia 41128771
## 2 138 ALB Albania Tirana Europe 2842321
## 3 34 DZA Algeria Algiers Africa 44903225
## 4 213 ASM American Samoa Pago Pago Oceania 44273
## 5 203 AND Andorra Andorra la Vella Europe 79824
## 6 42 AGO Angola Luanda Africa 35588987
## X2020.Population X2015.Population X2010.Population X2000.Population
## 1 38972230 33753499 28189672 19542982
## 2 2866849 2882481 2913399 3182021
## 3 43451666 39543154 35856344 30774621
## 4 46189 51368 54849 58230
## 5 77700 71746 71519 66097
## 6 33428485 28127721 23364185 16394062
## X1990.Population X1980.Population X1970.Population Area..km..
## 1 10694796 12486631 10752971 652230
## 2 3295066 2941651 2324731 28748
## 3 25518074 18739378 13795915 2381741
## 4 47818 32886 27075 199
## 5 53569 35611 19860 468
## 6 11828638 8330047 6029700 1246700
## Density..per.km.. Growth.Rate World.Population.Percentage
## 1 63.0587 1.0257 0.52
## 2 98.8702 0.9957 0.04
## 3 18.8531 1.0164 0.56
## 4 222.4774 0.9831 0.00
## 5 170.5641 1.0100 0.00
## 6 28.5466 1.0315 0.45tail(wp,n=10)## Rank CCA3 Country.Territory Capital Continent X2022.Population
## 225 43 UZB Uzbekistan Tashkent Asia 34627652
## 226 181 VUT Vanuatu Port-Vila Oceania 326740
## 227 234 VAT Vatican City Vatican City Europe 510
## 228 51 VEN Venezuela Caracas South America 28301696
## 229 16 VNM Vietnam Hanoi Asia 98186856
## 230 226 WLF Wallis and Futuna Mata-Utu Oceania 11572
## 231 172 ESH Western Sahara El Aaiún Africa 575986
## 232 46 YEM Yemen Sanaa Asia 33696614
## 233 63 ZMB Zambia Lusaka Africa 20017675
## 234 74 ZWE Zimbabwe Harare Africa 16320537
## X2020.Population X2015.Population X2010.Population X2000.Population
## 225 33526656 30949417 28614227 24925554
## 226 311685 276438 245453 192074
## 227 520 564 596 651
## 228 28490453 30529716 28715022 24427729
## 229 96648685 92191398 87411012 79001142
## 230 11655 12182 13142 14723
## 231 556048 491824 413296 270375
## 232 32284046 28516545 24743946 18628700
## 233 18927715 16248230 13792086 9891136
## 234 15669666 14154937 12839771 11834676
## X1990.Population X1980.Population X1970.Population Area..km..
## 225 20579100 15947129 12011361 447400
## 226 150882 118156 87019 12189
## 227 700 733 752 1
## 228 19750579 15210443 11355475 916445
## 229 66912613 52968270 41928849 331212
## 230 13454 11315 9377 142
## 231 178529 116775 76371 266000
## 232 13375121 9204938 6843607 527968
## 233 7686401 5720438 4281671 752612
## 234 10113893 7049926 5202918 390757
## Density..per.km.. Growth.Rate World.Population.Percentage
## 225 77.3975 1.0160 0.43
## 226 26.8061 1.0238 0.00
## 227 510.0000 0.9980 0.00
## 228 30.8820 1.0036 0.35
## 229 296.4472 1.0074 1.23
## 230 81.4930 0.9953 0.00
## 231 2.1654 1.0184 0.01
## 232 63.8232 1.0217 0.42
## 233 26.5976 1.0280 0.25
## 234 41.7665 1.0204 0.20length(wp)## [1] 17##data type
str(wp)## 'data.frame': 234 obs. of 17 variables:
## $ Rank : int 36 138 34 213 203 42 224 201 33 140 ...
## $ CCA3 : chr "AFG" "ALB" "DZA" "ASM" ...
## $ Country.Territory : chr "Afghanistan" "Albania" "Algeria" "American Samoa" ...
## $ Capital : chr "Kabul" "Tirana" "Algiers" "Pago Pago" ...
## $ Continent : chr "Asia" "Europe" "Africa" "Oceania" ...
## $ X2022.Population : int 41128771 2842321 44903225 44273 79824 35588987 15857 93763 45510318 2780469 ...
## $ X2020.Population : int 38972230 2866849 43451666 46189 77700 33428485 15585 92664 45036032 2805608 ...
## $ X2015.Population : int 33753499 2882481 39543154 51368 71746 28127721 14525 89941 43257065 2878595 ...
## $ X2010.Population : int 28189672 2913399 35856344 54849 71519 23364185 13172 85695 41100123 2946293 ...
## $ X2000.Population : int 19542982 3182021 30774621 58230 66097 16394062 11047 75055 37070774 3168523 ...
## $ X1990.Population : int 10694796 3295066 25518074 47818 53569 11828638 8316 63328 32637657 3556539 ...
## $ X1980.Population : int 12486631 2941651 18739378 32886 35611 8330047 6560 64888 28024803 3135123 ...
## $ X1970.Population : int 10752971 2324731 13795915 27075 19860 6029700 6283 64516 23842803 2534377 ...
## $ Area..km.. : int 652230 28748 2381741 199 468 1246700 91 442 2780400 29743 ...
## $ Density..per.km.. : num 63.1 98.9 18.9 222.5 170.6 ...
## $ Growth.Rate : num 1.026 0.996 1.016 0.983 1.01 ...
## $ World.Population.Percentage: num 0.52 0.04 0.56 0 0 0.45 0 0 0.57 0.03 ...##shape of dataset
dim(wp)## [1] 234 17##check duplicates
duplicated(wp)## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [97] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [145] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [157] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [169] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [181] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [193] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [205] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [217] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [229] FALSE FALSE FALSE FALSE FALSE FALSE##check missing values
colSums(is.na(wp))## Rank CCA3
## 0 0
## Country.Territory Capital
## 0 0
## Continent X2022.Population
## 0 0
## X2020.Population X2015.Population
## 0 0
## X2010.Population X2000.Population
## 0 0
## X1990.Population X1980.Population
## 0 0
## X1970.Population Area..km..
## 0 0
## Density..per.km.. Growth.Rate
## 0 0
## World.Population.Percentage
## 0sum(is.na(wp))## [1] 0#HW4:use of sapply():is a function used to apply another function to each element of a list or vector ##apply family (apply(), lapply(), sapply()):avoid writting loops #lapply():always return list #sapply():always return simplified result #use of split():used to divide dataset into smaller
continents_list <- split(wp, wp$Continent)
continents_list## $Africa
## Rank CCA3 Country.Territory Capital Continent X2022.Population
## 3 34 DZA Algeria Algiers Africa 44903225
## 6 42 AGO Angola Luanda Africa 35588987
## 22 77 BEN Benin Porto-Novo Africa 13352864
## 27 144 BWA Botswana Gaborone Africa 2630296
## 32 58 BFA Burkina Faso Ouagadougou Africa 22673762
## 33 78 BDI Burundi Bujumbura Africa 12889576
## 35 53 CMR Cameroon Yaounde Africa 27914536
## 37 171 CPV Cape Verde Praia Africa 593149
## 39 117 CAF Central African Republic Bangui Africa 5579144
## 40 69 TCD Chad N'Djamena Africa 17723315
## 44 163 COM Comoros Moroni Africa 836774
## 53 160 DJI Djibouti Djibouti Africa 1120849
## 56 15 COD DR Congo Kinshasa Africa 99010212
## 58 14 EGY Egypt Cairo Africa 110990103
## 60 152 GNQ Equatorial Guinea Malabo Africa 1674908
## 61 132 ERI Eritrea Asmara Africa 3684032
## 63 159 SWZ Eswatini Mbabane Africa 1201670
## 64 12 ETH Ethiopia Addis Ababa Africa 123379924
## 72 146 GAB Gabon Libreville Africa 2388992
## 73 142 GMB Gambia Banjul Africa 2705992
## 76 47 GHA Ghana Accra Africa 33475870
## 85 75 GIN Guinea Conakry Africa 13859341
## 86 149 GNB Guinea-Bissau Bissau Africa 2105566
## 101 52 CIV Ivory Coast Yamoussoukro Africa 28160542
## 107 27 KEN Kenya Nairobi Africa 54027487
## 114 147 LSO Lesotho Maseru Africa 2305825
## 115 121 LBR Liberia Monrovia Africa 5302681
## 116 107 LBY Libya Tripoli Africa 6812341
## 121 50 MDG Madagascar Antananarivo Africa 29611714
## 122 62 MWI Malawi Lilongwe Africa 20405317
## 125 59 MLI Mali Bamako Africa 22593590
## 129 126 MRT Mauritania Nouakchott Africa 4736139
## 130 157 MUS Mauritius Port Louis Africa 1299469
## 131 182 MYT Mayotte Mamoudzou Africa 326101
## 139 40 MAR Morocco Rabat Africa 37457971
## 140 48 MOZ Mozambique Maputo Africa 32969517
## 142 145 NAM Namibia Windhoek Africa 2567012
## 149 54 NER Niger Niamey Africa 26207977
## 150 6 NGA Nigeria Abuja Africa 218541212
## 169 114 COG Republic of the Congo Brazzaville Africa 5970424
## 170 161 REU Reunion Saint-Denis Africa 974052
## 173 76 RWA Rwanda Kigali Africa 13776698
## 182 187 STP Sao Tome and Principe São Tomé Africa 227380
## 184 72 SEN Senegal Dakar Africa 17316449
## 186 196 SYC Seychelles Victoria Africa 107118
## 187 102 SLE Sierra Leone Freetown Africa 8605718
## 193 70 SOM Somalia Mogadishu Africa 17597511
## 194 24 ZAF South Africa Pretoria Africa 59893885
## 196 86 SSD South Sudan Juba Africa 10913164
## 199 32 SDN Sudan Khartoum Africa 46874204
## 206 22 TZA Tanzania Dodoma Africa 65497748
## 209 100 TGO Togo Lomé Africa 8848699
## 213 79 TUN Tunisia Tunis Africa 12356117
## 218 31 UGA Uganda Kampala Africa 47249585
## 231 172 ESH Western Sahara El Aaiún Africa 575986
## 233 63 ZMB Zambia Lusaka Africa 20017675
## 234 74 ZWE Zimbabwe Harare Africa 16320537
## X2020.Population X2015.Population X2010.Population X2000.Population
## 3 43451666 39543154 35856344 30774621
## 6 33428485 28127721 23364185 16394062
## 22 12643123 10932783 9445710 6998023
## 27 2546402 2305171 2091664 1726985
## 32 21522626 18718019 16116845 11882888
## 33 12220227 10727148 9126605 6307659
## 35 26491087 23012646 19878036 15091594
## 37 582640 552166 521212 458251
## 39 5343020 4819333 4660067 3759170
## 40 16644701 14140274 11894727 8259137
## 44 806166 730216 656024 536758
## 53 1090156 1006259 919199 742033
## 56 92853164 78656904 66391257 48616317
## 58 107465134 97723799 87252413 71371371
## 60 1596049 1346973 1094524 684977
## 61 3555868 3340006 3147727 2392880
## 63 1180655 1133936 1099920 1030496
## 64 117190911 102471895 89237791 67031867
## 72 2292573 2028517 1711105 1272935
## 73 2573995 2253133 1937275 1437539
## 76 32180401 28870939 25574719 19665502
## 85 13205153 11625998 10270728 8336967
## 86 2015828 1788919 1567220 1230849
## 101 26811790 23596741 21120042 16799670
## 107 51985780 46851488 41517895 30851606
## 114 2254100 2118521 2022747 1998630
## 115 5087584 4612329 4019956 2895224
## 116 6653942 6192235 6491988 5154790
## 121 28225177 24850912 21731053 16216431
## 122 19377061 16938942 14718422 11229387
## 125 21224040 18112907 15529181 11239101
## 129 4498604 3946220 3419461 2695003
## 130 1297828 1293153 1283330 1215930
## 131 305587 249545 211786 159215
## 139 36688772 34680458 32464865 28554415
## 140 31178239 26843246 23073723 17768505
## 142 2489098 2282704 2099271 1819141
## 149 24333639 20128124 16647543 11622665
## 150 208327405 183995785 160952853 122851984
## 169 5702174 5064386 4437884 3134030
## 170 957822 922495 890130 785424
## 173 13146362 11642959 10309031 8109989
## 182 218641 201124 182138 143714
## 184 16436119 14356181 12530121 9704287
## 186 105530 99240 92409 80060
## 187 8233969 7314773 6436698 4584067
## 193 16537016 13763906 12026649 8721465
## 194 58801927 55876504 51784921 46813266
## 196 10606227 11194299 9714419 6114440
## 199 44440486 38171178 33739933 26298773
## 206 61704518 52542823 45110527 34463704
## 209 8442580 7473229 6571855 5008035
## 213 12161723 11557779 10895063 9893316
## 218 44404611 37477356 32341728 24020697
## 231 556048 491824 413296 270375
## 233 18927715 16248230 13792086 9891136
## 234 15669666 14154937 12839771 11834676
## X1990.Population X1980.Population X1970.Population Area..km..
## 3 25518074 18739378 13795915 2381741
## 6 11828638 8330047 6029700 1246700
## 22 5133419 3833939 3023443 112622
## 27 1341474 938578 592244 582000
## 32 9131361 6932967 5611666 272967
## 33 5483793 4312834 3497834 27834
## 35 11430520 8519891 6452787 475442
## 37 364563 317234 287262 4033
## 39 2809221 2415276 2067356 622984
## 40 5827069 4408230 3667394 1284000
## 44 431119 328328 242351 1862
## 53 577173 324121 144379 23200
## 56 35987541 26708686 20151733 2344858
## 58 57214630 43748556 34781986 1002450
## 60 465549 282509 316955 28051
## 61 2149960 1657982 1272748 117600
## 63 854011 598564 442865 17364
## 64 47878073 34945469 28308246 1104300
## 72 983028 749078 597192 267668
## 73 1040616 718586 528731 10689
## 76 15446982 11865246 8861895 238533
## 85 6354145 4972609 4222374 245857
## 86 973551 831462 591663 36125
## 101 11910540 8303809 5477086 322463
## 107 23162269 16187124 11473087 580367
## 114 1798997 1407672 1023481 30355
## 115 2209731 1932169 1463563 111369
## 116 4236983 2962720 1909177 1759540
## 121 11882762 8948162 6639751 587041
## 122 9539665 6267369 4625141 118484
## 125 8945026 7372581 6153587 1240192
## 129 2006027 1506694 1122198 1030700
## 130 1090290 954865 830115 2040
## 131 92659 52233 35383 374
## 139 24570814 19678444 15274351 446550
## 140 13303459 11413587 8411676 801590
## 142 1369011 975994 754467 825615
## 149 8370647 6173177 4669708 1267000
## 150 95214257 72951439 55569264 923768
## 169 2385435 1829256 1396989 342000
## 170 658992 551674 473925 2511
## 173 7319962 5247532 3896367 26338
## 182 120343 97210 77583 964
## 184 7536001 5703869 4367744 196722
## 186 71057 65290 54379 452
## 187 4325388 3367477 2778557 71740
## 193 6999096 5892224 3720977 637657
## 194 39877570 29463549 22368306 1221037
## 196 4750817 4192011 3342410 619745
## 199 21090886 16673586 11305206 1886068
## 206 26206012 19297659 13618192 945087
## 209 3875947 2838110 2197383 56785
## 213 8440023 6578156 5047404 163610
## 218 17586630 13284026 10317212 241550
## 231 178529 116775 76371 266000
## 233 7686401 5720438 4281671 752612
## 234 10113893 7049926 5202918 390757
## Density..per.km.. Growth.Rate World.Population.Percentage
## 3 18.8531 1.0164 0.56
## 6 28.5466 1.0315 0.45
## 22 118.5635 1.0274 0.17
## 27 4.5194 1.0162 0.03
## 32 83.0641 1.0259 0.28
## 33 463.0874 1.0270 0.16
## 35 58.7128 1.0263 0.35
## 37 147.0739 1.0089 0.01
## 39 8.9555 1.0224 0.07
## 40 13.8032 1.0316 0.22
## 44 449.3953 1.0184 0.01
## 53 48.3125 1.0138 0.01
## 56 42.2244 1.0325 1.24
## 58 110.7188 1.0158 1.39
## 60 59.7094 1.0247 0.02
## 61 31.3268 1.0176 0.05
## 63 69.2047 1.0079 0.02
## 64 111.7268 1.0257 1.55
## 72 8.9252 1.0204 0.03
## 73 253.1567 1.0250 0.03
## 76 140.3406 1.0196 0.42
## 85 56.3716 1.0242 0.17
## 86 58.2856 1.0218 0.03
## 101 87.3295 1.0248 0.35
## 107 93.0919 1.0193 0.68
## 114 75.9620 1.0107 0.03
## 115 47.6136 1.0210 0.07
## 116 3.8717 1.0114 0.09
## 121 50.4423 1.0241 0.37
## 122 172.2200 1.0259 0.26
## 125 18.2178 1.0314 0.28
## 129 4.5951 1.0263 0.06
## 130 636.9946 1.0004 0.02
## 131 871.9278 1.0319 0.00
## 139 83.8830 1.0103 0.47
## 140 41.1302 1.0278 0.41
## 142 3.1092 1.0146 0.03
## 149 20.6851 1.0378 0.33
## 150 236.5759 1.0241 2.74
## 169 17.4574 1.0231 0.07
## 170 387.9140 1.0082 0.01
## 173 523.0731 1.0234 0.17
## 182 235.8714 1.0192 0.00
## 184 88.0250 1.0261 0.22
## 186 236.9867 1.0061 0.00
## 187 119.9570 1.0220 0.11
## 193 27.5971 1.0312 0.22
## 194 49.0517 1.0084 0.75
## 196 17.6091 1.0153 0.14
## 199 24.8529 1.0267 0.59
## 206 69.3034 1.0300 0.82
## 209 155.8281 1.0236 0.11
## 213 75.5218 1.0076 0.15
## 218 195.6100 1.0304 0.59
## 231 2.1654 1.0184 0.01
## 233 26.5976 1.0280 0.25
## 234 41.7665 1.0204 0.20
##
## $Asia
## Rank CCA3 Country.Territory Capital Continent
## 1 36 AFG Afghanistan Kabul Asia
## 10 140 ARM Armenia Yerevan Asia
## 14 91 AZE Azerbaijan Baku Asia
## 16 154 BHR Bahrain Manama Asia
## 17 8 BGD Bangladesh Dhaka Asia
## 24 165 BTN Bhutan Thimphu Asia
## 30 175 BRN Brunei Bandar Seri Begawan Asia
## 34 73 KHM Cambodia Phnom Penh Asia
## 42 1 CHN China Beijing Asia
## 74 131 GEO Georgia Tbilisi Asia
## 90 104 HKG Hong Kong Hong Kong Asia
## 93 2 IND India New Delhi Asia
## 94 4 IDN Indonesia Jakarta Asia
## 95 17 IRN Iran Tehran Asia
## 96 35 IRQ Iraq Baghdad Asia
## 99 98 ISR Israel Jerusalem Asia
## 103 11 JPN Japan Tokyo Asia
## 105 83 JOR Jordan Amman Asia
## 106 66 KAZ Kazakhstan Nursultan Asia
## 109 129 KWT Kuwait Kuwait City Asia
## 110 110 KGZ Kyrgyzstan Bishkek Asia
## 111 103 LAO Laos Vientiane Asia
## 113 119 LBN Lebanon Beirut Asia
## 120 167 MAC Macau Concelho de Macau Asia
## 123 45 MYS Malaysia Kuala Lumpur Asia
## 124 174 MDV Maldives Malé Asia
## 136 134 MNG Mongolia Ulaanbaatar Asia
## 141 26 MMR Myanmar Nay Pyi Taw Asia
## 144 49 NPL Nepal Kathmandu Asia
## 152 56 PRK North Korea Pyongyang Asia
## 156 127 OMN Oman Muscat Asia
## 157 5 PAK Pakistan Islamabad Asia
## 159 122 PSE Palestine Ramallah Asia
## 164 13 PHL Philippines Manila Asia
## 168 143 QAT Qatar Doha Asia
## 183 41 SAU Saudi Arabia Riyadh Asia
## 188 113 SGP Singapore Singapore Asia
## 195 29 KOR South Korea Seoul Asia
## 198 61 LKA Sri Lanka Colombo Asia
## 203 60 SYR Syria Damascus Asia
## 204 57 TWN Taiwan Taipei Asia
## 205 95 TJK Tajikistan Dushanbe Asia
## 207 20 THA Thailand Bangkok Asia
## 208 155 TLS Timor-Leste Dili Asia
## 214 18 TUR Turkey Ankara Asia
## 215 111 TKM Turkmenistan Ashgabat Asia
## 220 97 ARE United Arab Emirates Abu Dhabi Asia
## 225 43 UZB Uzbekistan Tashkent Asia
## 229 16 VNM Vietnam Hanoi Asia
## 232 46 YEM Yemen Sanaa Asia
## X2022.Population X2020.Population X2015.Population X2010.Population
## 1 41128771 38972230 33753499 28189672
## 10 2780469 2805608 2878595 2946293
## 14 10358074 10284951 9863480 9237202
## 16 1472233 1477469 1362142 1213645
## 17 171186372 167420951 157830000 148391139
## 24 782455 772506 743274 705516
## 30 449002 441725 421437 396053
## 34 16767842 16396860 15417523 14363532
## 42 1425887337 1424929781 1393715448 1348191368
## 74 3744385 3765912 3771132 3836831
## 90 7488865 7500958 7399838 7132438
## 93 1417173173 1396387127 1322866505 1240613620
## 94 275501339 271857970 259091970 244016173
## 95 88550570 87290193 81790841 75373855
## 96 44496122 42556984 37757813 31264875
## 99 9038309 8757489 8007778 7328445
## 103 123951692 125244761 127250933 128105431
## 105 11285869 10928721 9494246 6931258
## 106 19397998 18979243 17835909 16627837
## 109 4268873 4360444 3908743 2943356
## 110 6630623 6424874 5914980 5483774
## 111 7529475 7319399 6787419 6323418
## 113 5489739 5662923 6398940 4995800
## 120 695168 676283 615239 557297
## 123 33938221 33199993 31068833 28717731
## 124 523787 514438 435582 361575
## 136 3398366 3294335 2964749 2702520
## 141 54179306 53423198 51483949 49390988
## 144 30547580 29348627 27610325 27161567
## 152 26069416 25867467 25258015 24686435
## 156 4576298 4543399 4191776 2881914
## 157 235824862 227196741 210969298 194454498
## 159 5250072 5019401 4484614 3992278
## 164 115559009 112190977 103031365 94636700
## 168 2695122 2760385 2414573 1713504
## 183 36408820 35997107 32749848 29411929
## 188 5975689 5909869 5650018 5163590
## 195 51815810 51844690 50994401 48813042
## 198 21832143 21715079 21336697 20668557
## 203 22125249 20772595 19205178 22337563
## 204 23893394 23821464 23512136 23083083
## 205 9952787 9543207 8524063 7621779
## 207 71697030 71475664 70294397 68270489
## 208 1341296 1299995 1205813 1088486
## 214 85341241 84135428 79646178 73195345
## 215 6430770 6250438 5766431 5267970
## 220 9441129 9287289 8916899 8481771
## 225 34627652 33526656 30949417 28614227
## 229 98186856 96648685 92191398 87411012
## 232 33696614 32284046 28516545 24743946
## X2000.Population X1990.Population X1980.Population X1970.Population
## 1 19542982 10694796 12486631 10752971
## 10 3168523 3556539 3135123 2534377
## 14 8190337 7427836 6383060 5425317
## 16 711442 517418 362595 222555
## 17 129193327 107147651 83929765 67541860
## 24 587207 558442 415257 298894
## 30 333926 261928 187921 133343
## 34 12118841 8910808 6198959 6708525
## 42 1264099069 1153704252 982372466 822534450
## 74 4265172 5391636 5145843 4800426
## 90 6731195 5838574 4978544 3955072
## 93 1059633675 870452165 696828385 557501301
## 94 214072421 182159874 148177096 115228394
## 95 65544383 55793629 38520664 28449705
## 96 24628858 17658381 13653369 9811347
## 99 6116958 4803254 3744608 2907307
## 103 126803861 123686321 117624196 105416839
## 105 5056174 3480587 2216903 1557374
## 106 15236253 16866563 14172710 12265305
## 109 1934901 1674938 1493870 802786
## 110 4935182 4394734 3691209 3016384
## 111 5430853 4314443 3297519 2675283
## 113 4320642 3593700 2963702 2381791
## 120 431896 350227 245332 247284
## 123 22945150 17517054 13215707 10306508
## 124 282507 224957 164887 123243
## 136 2450979 2161433 1697780 1293880
## 141 45538332 40099553 33465781 27284112
## 144 24559500 19616530 15600442 12501285
## 152 23367059 20799523 17973650 14996879
## 156 2344253 1804524 1017462 670693
## 157 154369924 115414069 80624057 59290872
## 159 3139954 2124609 1453620 1118241
## 164 77958223 61558898 48419546 37435586
## 168 645937 441675 277450 118007
## 183 21547390 16004763 10171710 6106191
## 188 4053602 3022209 2400729 2061831
## 195 46788591 44120039 38170501 32601143
## 198 18776371 17204094 14943645 12388769
## 203 16307654 12408996 8898954 6319199
## 204 22194731 20586174 18100281 14957870
## 205 6272998 5417860 4045965 2993019
## 207 63066603 55228410 45737753 35791728
## 208 878360 758106 642224 554021
## 214 64113547 54324142 44089069 35540990
## 215 4569132 3720278 2862903 2201432
## 220 3275333 1900151 1014048 298084
## 225 24925554 20579100 15947129 12011361
## 229 79001142 66912613 52968270 41928849
## 232 18628700 13375121 9204938 6843607
## Area..km.. Density..per.km.. Growth.Rate World.Population.Percentage
## 1 652230 63.0587 1.0257 0.52
## 10 29743 93.4831 0.9962 0.03
## 14 86600 119.6082 1.0044 0.13
## 16 765 1924.4876 1.0061 0.02
## 17 147570 1160.0350 1.0108 2.15
## 24 38394 20.3796 1.0064 0.01
## 30 5765 77.8841 1.0081 0.01
## 34 181035 92.6221 1.0108 0.21
## 42 9706961 146.8933 1.0000 17.88
## 74 69700 53.7214 0.9964 0.05
## 90 1104 6783.3922 0.9992 0.09
## 93 3287590 431.0675 1.0068 17.77
## 94 1904569 144.6529 1.0064 3.45
## 95 1648195 53.7258 1.0071 1.11
## 96 438317 101.5158 1.0221 0.56
## 99 20770 435.1617 1.0155 0.11
## 103 377930 327.9753 0.9947 1.55
## 105 89342 126.3221 1.0123 0.14
## 106 2724900 7.1188 1.0105 0.24
## 109 17818 239.5821 1.0044 0.05
## 110 199951 33.1612 1.0158 0.08
## 111 236800 31.7968 1.0141 0.09
## 113 10452 525.2334 0.9816 0.07
## 120 30 23172.2667 1.0125 0.01
## 123 330803 102.5934 1.0109 0.43
## 124 300 1745.9567 1.0045 0.01
## 136 1564110 2.1727 1.0151 0.04
## 141 676578 80.0784 1.0071 0.68
## 144 147181 207.5511 1.0171 0.38
## 152 120538 216.2755 1.0038 0.33
## 156 309500 14.7861 1.0123 0.06
## 157 881912 267.4018 1.0191 2.96
## 159 6220 844.0630 1.0227 0.07
## 164 342353 337.5434 1.0147 1.45
## 168 11586 232.6189 1.0026 0.03
## 183 2149690 16.9368 1.0128 0.46
## 188 710 8416.4634 1.0058 0.07
## 195 100210 517.0722 0.9997 0.65
## 198 65610 332.7563 1.0027 0.27
## 203 185180 119.4797 1.0376 0.28
## 204 36193 660.1662 1.0014 0.30
## 205 143100 69.5513 1.0208 0.12
## 207 513120 139.7276 1.0013 0.90
## 208 14874 90.1772 1.0154 0.02
## 214 783562 108.9145 1.0067 1.07
## 215 488100 13.1751 1.0140 0.08
## 220 83600 112.9322 1.0081 0.12
## 225 447400 77.3975 1.0160 0.43
## 229 331212 296.4472 1.0074 1.23
## 232 527968 63.8232 1.0217 0.42
##
## $Europe
## Rank CCA3 Country.Territory Capital Continent
## 2 138 ALB Albania Tirana Europe
## 5 203 AND Andorra Andorra la Vella Europe
## 13 99 AUT Austria Vienna Europe
## 19 96 BLR Belarus Minsk Europe
## 20 81 BEL Belgium Brussels Europe
## 26 137 BIH Bosnia and Herzegovina Sarajevo Europe
## 31 108 BGR Bulgaria Sofia Europe
## 47 130 HRV Croatia Zagreb Europe
## 50 158 CYP Cyprus Nicosia Europe
## 51 88 CZE Czech Republic Prague Europe
## 52 115 DNK Denmark Copenhagen Europe
## 62 156 EST Estonia Tallinn Europe
## 66 209 FRO Faroe Islands Tórshavn Europe
## 68 118 FIN Finland Helsinki Europe
## 69 23 FRA France Paris Europe
## 75 19 DEU Germany Berlin Europe
## 77 219 GIB Gibraltar Gibraltar Europe
## 78 90 GRC Greece Athens Europe
## 84 207 GGY Guernsey Saint Peter Port Europe
## 91 94 HUN Hungary Budapest Europe
## 92 179 ISL Iceland Reykjavík Europe
## 97 125 IRL Ireland Dublin Europe
## 98 202 IMN Isle of Man Douglas Europe
## 100 25 ITA Italy Rome Europe
## 104 195 JEY Jersey Saint Helier Europe
## 112 151 LVA Latvia Riga Europe
## 117 216 LIE Liechtenstein Vaduz Europe
## 118 141 LTU Lithuania Vilnius Europe
## 119 168 LUX Luxembourg Luxembourg Europe
## 126 173 MLT Malta Valletta Europe
## 134 135 MDA Moldova Chisinau Europe
## 135 217 MCO Monaco Monaco Europe
## 137 169 MNE Montenegro Podgorica Europe
## 145 71 NLD Netherlands Amsterdam Europe
## 153 150 MKD North Macedonia Skopje Europe
## 155 120 NOR Norway Oslo Europe
## 165 37 POL Poland Warsaw Europe
## 166 92 PRT Portugal Lisbon Europe
## 171 64 ROU Romania Bucharest Europe
## 172 9 RUS Russia Moscow Europe
## 181 218 SMR San Marino San Marino Europe
## 185 105 SRB Serbia Belgrade Europe
## 190 116 SVK Slovakia Bratislava Europe
## 191 148 SVN Slovenia Ljubljana Europe
## 197 30 ESP Spain Madrid Europe
## 201 87 SWE Sweden Stockholm Europe
## 202 101 CHE Switzerland Bern Europe
## 219 38 UKR Ukraine Kiev Europe
## 221 21 GBR United Kingdom London Europe
## 227 234 VAT Vatican City Vatican City Europe
## X2022.Population X2020.Population X2015.Population X2010.Population
## 2 2842321 2866849 2882481 2913399
## 5 79824 77700 71746 71519
## 13 8939617 8907777 8642421 8362829
## 19 9534954 9633740 9700609 9731427
## 20 11655930 11561717 11248303 10877947
## 26 3233526 3318407 3524324 3811088
## 31 6781953 6979175 7309253 7592273
## 47 4030358 4096868 4254815 4368682
## 50 1251488 1237537 1187280 1129686
## 51 10493986 10530953 10523798 10464749
## 52 5882261 5825641 5677796 5550849
## 62 1326062 1329444 1314657 1331535
## 66 53090 52415 48816 48410
## 68 5540745 5529468 5479461 5363271
## 69 64626628 64480053 63809769 62444567
## 75 83369843 83328988 82073226 81325090
## 77 32649 32709 32520 31262
## 78 10384971 10512232 10806641 11033783
## 84 63301 62794 61629 60782
## 91 9967308 9750573 9844246 9986825
## 92 372899 366669 331060 318333
## 97 5023109 4946119 4665760 4524585
## 98 84519 84046 83593 83828
## 100 59037474 59500579 60232906 59822450
## 104 110778 108319 100561 96151
## 112 1850651 1897052 1991955 2101530
## 117 39327 38756 37355 35926
## 118 2750055 2820267 2963765 3139019
## 119 647599 630399 569408 507070
## 126 533286 515357 456579 418755
## 134 3272996 3084847 3277388 3678186
## 135 36469 36922 36760 33178
## 137 627082 629048 633966 631044
## 145 17564014 17434557 17041107 16617116
## 153 2093599 2111072 2107962 2093828
## 155 5434319 5379839 5190356 4889741
## 165 39857145 38428366 38553146 38597353
## 166 10270865 10298192 10365435 10588401
## 171 19659267 19442038 19906079 20335211
## 172 144713314 145617329 144668389 143242599
## 181 33660 34007 33570 31608
## 185 7221365 7358005 7519496 7653748
## 190 5643453 5456681 5424444 5396424
## 191 2119844 2117641 2080862 2057286
## 197 47558630 47363807 46431342 46572772
## 201 10549347 10368969 9849349 9381729
## 202 8740472 8638613 8281732 7822435
## 219 39701739 43909666 44982564 45683020
## 221 67508936 67059474 65224364 62760039
## 227 510 520 564 596
## X2000.Population X1990.Population X1980.Population X1970.Population
## 2 3182021 3295066 2941651 2324731
## 5 66097 53569 35611 19860
## 13 8010428 7678729 7547561 7465301
## 19 10256483 10428525 9817257 9170786
## 20 10264343 9959560 9828986 9629376
## 26 4179350 4494310 4199820 3815561
## 31 8097691 8767778 8980606 8582950
## 47 4548434 4873707 4680144 4492638
## 50 948237 788500 679327 640804
## 51 10234710 10301192 10270060 9795744
## 52 5340655 5144623 5125392 4922963
## 62 1396877 1570674 1476983 1361999
## 66 45660 47479 43054 38416
## 68 5176209 4986545 4779418 4606621
## 69 58665453 56412897 53713830 50523586
## 75 81551677 79370196 77786703 78294583
## 77 27741 27317 28734 26685
## 78 11038109 10302255 9307148 8544873
## 84 59114 57727 52860 52656
## 91 10202055 10375989 10698679 10315366
## 92 281462 255019 228263 204468
## 97 3768950 3485374 3391387 2937637
## 98 75562 68865 64022 55298
## 100 56966397 56756561 56329482 53324036
## 104 86192 82874 75124 68347
## 112 2392530 2689391 2572037 2397414
## 117 33026 28765 25003 21089
## 118 3599637 3785847 3521206 3210147
## 119 435628 381267 363741 339342
## 126 399212 365392 333587 315414
## 134 4251573 4480199 4103240 3711140
## 135 32465 30329 27076 24270
## 137 633324 621442 589324 530268
## 145 15899135 14944548 14130387 13037686
## 153 2037936 2044174 1907023 1656783
## 155 4491202 4241636 4085776 3875546
## 165 38504431 38064255 35521429 32482943
## 166 10300626 10007346 9785252 8683631
## 171 21919876 22836234 22125224 19922618
## 172 146844839 148005704 138257420 130093010
## 181 26823 23132 21346 18169
## 185 7935022 7987529 7777010 7193533
## 190 5376690 5261305 4973883 4522867
## 191 1984339 1986024 1901570 1741286
## 197 40741651 38889889 37491666 33792617
## 201 8871043 8548406 8311763 8027702
## 202 7182059 6711693 6319113 6181227
## 219 48879755 51589817 49973920 47279086
## 221 58850043 57210442 56326328 55650166
## 227 651 700 733 752
## Area..km.. Density..per.km.. Growth.Rate World.Population.Percentage
## 2 28748 98.8702 0.9957 0.04
## 5 468 170.5641 1.0100 0.00
## 13 83871 106.5877 1.0020 0.11
## 19 207600 45.9295 0.9955 0.12
## 20 30528 381.8111 1.0038 0.15
## 26 51209 63.1437 0.9886 0.04
## 31 110879 61.1654 0.9849 0.09
## 47 56594 71.2153 0.9927 0.05
## 50 9251 135.2814 1.0059 0.02
## 51 78865 133.0627 0.9984 0.13
## 52 43094 136.4984 1.0048 0.07
## 62 45227 29.3201 0.9980 0.02
## 66 1393 38.1120 1.0038 0.00
## 68 338424 16.3722 1.0009 0.07
## 69 551695 117.1419 1.0015 0.81
## 75 357114 233.4544 0.9995 1.05
## 77 6 5441.5000 0.9994 0.00
## 78 131990 78.6800 0.9942 0.13
## 84 78 811.5513 1.0037 0.00
## 91 93028 107.1431 1.0265 0.12
## 92 103000 3.6204 1.0069 0.00
## 97 70273 71.4799 1.0073 0.06
## 98 572 147.7605 1.0030 0.00
## 100 301336 195.9191 0.9966 0.74
## 104 116 954.9828 1.0106 0.00
## 112 64559 28.6660 0.9876 0.02
## 117 160 245.7937 1.0074 0.00
## 118 65300 42.1142 0.9869 0.03
## 119 2586 250.4250 1.0129 0.01
## 126 316 1687.6139 1.0124 0.01
## 134 33846 96.7026 1.0691 0.04
## 135 2 18234.5000 0.9941 0.00
## 137 13812 45.4012 0.9988 0.01
## 145 41850 419.6897 1.0036 0.22
## 153 25713 81.4218 0.9954 0.03
## 155 323802 16.7828 1.0058 0.07
## 165 312679 127.4698 1.0404 0.50
## 166 92090 111.5307 0.9981 0.13
## 171 238391 82.4665 1.0171 0.25
## 172 17098242 8.4636 0.9973 1.81
## 181 61 551.8033 0.9975 0.00
## 185 88361 81.7257 0.9897 0.09
## 190 49037 115.0856 1.0359 0.07
## 191 20273 104.5649 1.0002 0.03
## 197 505992 93.9909 1.0015 0.60
## 201 450295 23.4276 1.0079 0.13
## 202 41284 211.7157 1.0056 0.11
## 219 603500 65.7858 0.9120 0.50
## 221 242900 277.9289 1.0034 0.85
## 227 1 510.0000 0.9980 0.00
##
## $`North America`
## Rank CCA3 Country.Territory Capital Continent
## 7 224 AIA Anguilla The Valley North America
## 8 201 ATG Antigua and Barbuda Saint John’s North America
## 11 198 ABW Aruba Oranjestad North America
## 15 176 BHS Bahamas Nassau North America
## 18 186 BRB Barbados Bridgetown North America
## 21 177 BLZ Belize Belmopan North America
## 23 206 BMU Bermuda Hamilton North America
## 29 221 VGB British Virgin Islands Road Town North America
## 36 39 CAN Canada Ottawa North America
## 38 205 CYM Cayman Islands George Town North America
## 46 124 CRI Costa Rica San José North America
## 48 85 CUB Cuba Havana North America
## 49 189 CUW Curacao Willemstad North America
## 54 204 DMA Dominica Roseau North America
## 55 84 DOM Dominican Republic Santo Domingo North America
## 59 112 SLV El Salvador San Salvador North America
## 79 208 GRL Greenland Nuuk North America
## 80 193 GRD Grenada Saint George's North America
## 81 178 GLP Guadeloupe Basse-Terre North America
## 83 68 GTM Guatemala Guatemala City North America
## 88 82 HTI Haiti Port-au-Prince North America
## 89 89 HND Honduras Tegucigalpa North America
## 102 139 JAM Jamaica Kingston North America
## 128 180 MTQ Martinique Fort-de-France North America
## 132 10 MEX Mexico Mexico City North America
## 138 230 MSR Montserrat Brades North America
## 148 106 NIC Nicaragua Managua North America
## 160 128 PAN Panama Panama City North America
## 167 136 PRI Puerto Rico San Juan North America
## 174 228 BLM Saint Barthelemy Gustavia North America
## 175 211 KNA Saint Kitts and Nevis Basseterre North America
## 176 190 LCA Saint Lucia Castries North America
## 177 220 MAF Saint Martin Marigot North America
## 178 229 SPM Saint Pierre and Miquelon Saint-Pierre North America
## 179 199 VCT Saint Vincent and the Grenadines Kingstown North America
## 189 214 SXM Sint Maarten Philipsburg North America
## 212 153 TTO Trinidad and Tobago Port-of-Spain North America
## 216 212 TCA Turks and Caicos Islands Cockburn Town North America
## 222 3 USA United States Washington, D.C. North America
## 223 200 VIR United States Virgin Islands Charlotte Amalie North America
## X2022.Population X2020.Population X2015.Population X2010.Population
## 7 15857 15585 14525 13172
## 8 93763 92664 89941 85695
## 11 106445 106585 104257 100341
## 15 409984 406471 392697 373272
## 18 281635 280693 278083 274711
## 21 405272 394921 359871 322106
## 23 64184 64031 63144 63447
## 29 31305 30910 29366 27556
## 36 38454327 37888705 35732126 33963412
## 38 68706 67311 60911 54074
## 46 5180829 5123105 4895242 4622252
## 48 11212191 11300698 11339894 11290417
## 49 191163 189288 169572 159380
## 54 72737 71995 70007 68755
## 55 11228821 10999664 10405832 9775755
## 59 6336392 6292731 6231066 6114034
## 79 56466 56026 55895 56351
## 80 125438 123663 118980 114039
## 81 395752 395642 399089 403072
## 83 17843908 17362718 16001107 14543121
## 88 11584996 11306801 10563757 9842880
## 89 10432860 10121763 9294505 8450933
## 102 2827377 2820436 2794445 2733896
## 128 367507 370391 383515 392181
## 132 127504125 125998302 120149897 112532401
## 138 4390 4500 5059 4938
## 148 6948392 6755895 6298598 5855734
## 160 4408581 4294396 3957099 3623617
## 167 3252407 3271564 3497335 3717922
## 174 10967 10681 9643 8988
## 175 47657 47642 47790 47403
## 176 179857 179237 175623 170935
## 177 31791 32552 35020 36458
## 178 5862 5906 5978 6052
## 179 103948 104632 106482 109308
## 189 44175 43621 40205 33034
## 212 1531044 1518147 1460177 1410296
## 216 45703 44276 36538 29726
## 222 338289857 335942003 324607776 311182845
## 223 99465 100442 102803 106142
## X2000.Population X1990.Population X1980.Population X1970.Population
## 7 11047 8316 6560 6283
## 8 75055 63328 64888 64516
## 11 89101 65712 62267 59106
## 15 325014 270679 223752 179129
## 18 264657 258868 253575 241397
## 21 240406 182589 145133 120905
## 23 61371 57470 53565 52019
## 29 20104 15617 11109 9581
## 36 30683313 27657204 24511510 21434577
## 38 39658 26027 17100 10533
## 46 3979193 3158253 2414303 1855697
## 48 11105791 10626680 9809107 8869636
## 49 141424 155446 156851 150385
## 54 68346 69481 72978 68895
## 55 8540791 7129004 5755800 4475871
## 59 5958482 5367179 4508992 3619090
## 79 56184 55599 50106 45434
## 80 107432 99047 94838 98794
## 81 424067 391951 334234 318310
## 83 11735894 9084780 6987767 5453208
## 88 8360225 6925331 5646676 4680812
## 89 6656725 5053234 3777990 2782753
## 102 2612205 2392030 2135546 1859091
## 128 432543 374271 333786 326428
## 132 97873442 81720428 67705186 50289306
## 138 5138 10805 11452 11402
## 148 5123222 4227820 3303309 2444767
## 160 3001731 2449968 1956987 1516188
## 167 3827108 3543776 3214568 2737619
## 174 7082 5168 2983 2417
## 175 45461 40636 43097 44968
## 176 159500 142301 121633 103090
## 177 29610 28127 7776 5802
## 178 6274 6324 6106 5537
## 179 113813 112487 107480 98459
## 189 30489 27845 12243 6260
## 212 1332203 1266518 1127852 988890
## 216 18744 11709 7598 5665
## 222 282398554 248083732 223140018 200328340
## 223 108185 100685 96640 63446
## Area..km.. Density..per.km.. Growth.Rate World.Population.Percentage
## 7 91 174.2527 1.0066 0.00
## 8 442 212.1335 1.0058 0.00
## 11 180 591.3611 0.9991 0.00
## 15 13943 29.4043 1.0051 0.01
## 18 430 654.9651 1.0015 0.00
## 21 22966 17.6466 1.0131 0.01
## 23 54 1188.5926 1.0000 0.00
## 29 151 207.3179 1.0059 0.00
## 36 9984670 3.8513 1.0078 0.48
## 38 264 260.2500 1.0084 0.00
## 46 51100 101.3861 1.0052 0.06
## 48 109884 102.0366 0.9961 0.14
## 49 444 430.5473 1.0043 0.00
## 54 751 96.8535 1.0045 0.00
## 55 48671 230.7087 1.0100 0.14
## 59 21041 301.1450 1.0035 0.08
## 79 2166086 0.0261 1.0040 0.00
## 80 344 364.6453 1.0066 0.00
## 81 1628 243.0909 0.9992 0.00
## 83 108889 163.8725 1.0134 0.22
## 88 27750 417.4773 1.0120 0.15
## 89 112492 92.7431 1.0150 0.13
## 102 10991 257.2447 0.9999 0.04
## 128 1128 325.8041 0.9965 0.00
## 132 1964375 64.9082 1.0063 1.60
## 138 102 43.0392 0.9939 0.00
## 148 130373 53.2962 1.0143 0.09
## 160 75417 58.4561 1.0132 0.06
## 167 8870 366.6750 0.9989 0.04
## 174 21 522.2381 1.0098 0.00
## 175 261 182.5939 1.0011 0.00
## 176 616 291.9756 1.0011 0.00
## 177 53 599.8302 0.9951 0.00
## 178 242 24.2231 0.9964 0.00
## 179 389 267.2185 0.9963 0.00
## 189 34 1299.2647 1.0030 0.00
## 212 5130 298.4491 1.0035 0.02
## 216 948 48.2099 1.0131 0.00
## 222 9372610 36.0935 1.0038 4.24
## 223 347 286.6427 0.9937 0.00
##
## $Oceania
## Rank CCA3 Country.Territory Capital Continent X2022.Population
## 4 213 ASM American Samoa Pago Pago Oceania 44273
## 12 55 AUS Australia Canberra Oceania 26177413
## 45 223 COK Cook Islands Avarua Oceania 17011
## 67 162 FJI Fiji Suva Oceania 929766
## 71 183 PYF French Polynesia Papeete Oceania 306279
## 82 191 GUM Guam Hagåtña Oceania 171774
## 108 192 KIR Kiribati Tarawa Oceania 131232
## 127 215 MHL Marshall Islands Majuro Oceania 41569
## 133 194 FSM Micronesia Palikir Oceania 114164
## 143 225 NRU Nauru Yaren Oceania 12668
## 146 185 NCL New Caledonia Nouméa Oceania 289950
## 147 123 NZL New Zealand Wellington Oceania 5185288
## 151 232 NIU Niue Alofi Oceania 1934
## 154 210 NFK Northern Mariana Islands Saipan Oceania 49551
## 158 222 PLW Palau Ngerulmud Oceania 18055
## 161 93 PNG Papua New Guinea Port Moresby Oceania 10142619
## 180 188 WSM Samoa Apia Oceania 222382
## 192 166 SLB Solomon Islands Honiara Oceania 724273
## 210 233 TKL Tokelau Nukunonu Oceania 1871
## 211 197 TON Tonga Nuku‘alofa Oceania 106858
## 217 227 TUV Tuvalu Funafuti Oceania 11312
## 226 181 VUT Vanuatu Port-Vila Oceania 326740
## 230 226 WLF Wallis and Futuna Mata-Utu Oceania 11572
## X2020.Population X2015.Population X2010.Population X2000.Population
## 4 46189 51368 54849 58230
## 12 25670051 23820236 22019168 19017963
## 45 17029 17695 17212 15897
## 67 920422 917200 905169 832509
## 71 301920 291787 283788 250927
## 82 169231 167978 164905 160188
## 108 126463 116707 107995 88826
## 127 43413 49410 53416 54224
## 133 112106 109462 107588 111709
## 143 12315 11185 10241 10377
## 146 286403 283032 261426 221537
## 147 5061133 4590590 4346338 3855266
## 151 1942 1847 1812 2074
## 154 49587 51514 54087 80338
## 158 17972 17794 18540 19726
## 161 9749640 8682174 7583269 5508297
## 180 214929 203571 194672 184008
## 192 691191 612660 540394 429978
## 210 1827 1454 1367 1666
## 211 105254 106122 107383 102603
## 217 11069 10877 10550 9638
## 226 311685 276438 245453 192074
## 230 11655 12182 13142 14723
## X1990.Population X1980.Population X1970.Population Area..km..
## 4 47818 32886 27075 199
## 12 17048003 14706322 12595034 7692024
## 45 17123 17651 20470 236
## 67 780430 644582 527634 18272
## 71 211089 163591 117891 4167
## 82 138263 110286 88300 549
## 108 75124 60813 57437 811
## 127 46047 31988 23969 181
## 133 98603 76299 58989 702
## 143 9598 7635 6663 21
## 146 177264 148599 110982 18575
## 147 3397389 3147168 2824061 270467
## 151 2533 3637 5185 260
## 154 48002 17613 10143 464
## 158 15293 12252 11366 459
## 161 3864972 3104788 2489059 462840
## 180 168186 164905 142771 2842
## 192 324171 233668 172833 28896
## 210 1669 1647 1714 12
## 211 98727 96708 86484 747
## 217 9182 7731 5814 26
## 226 150882 118156 87019 12189
## 230 13454 11315 9377 142
## Density..per.km.. Growth.Rate World.Population.Percentage
## 4 222.4774 0.9831 0.00
## 12 3.4032 1.0099 0.33
## 45 72.0805 1.0005 0.00
## 67 50.8847 1.0056 0.01
## 71 73.5011 1.0074 0.00
## 82 312.8852 1.0073 0.00
## 108 161.8150 1.0183 0.00
## 127 229.6630 0.9886 0.00
## 133 162.6268 1.0091 0.00
## 143 603.2381 1.0125 0.00
## 146 15.6097 1.0075 0.00
## 147 19.1716 1.0108 0.07
## 151 7.4385 0.9985 0.00
## 154 106.7909 1.0014 0.00
## 158 39.3355 1.0017 0.00
## 161 21.9139 1.0194 0.13
## 180 78.2484 1.0165 0.00
## 192 25.0648 1.0232 0.01
## 210 155.9167 1.0119 0.00
## 211 143.0495 1.0079 0.00
## 217 435.0769 1.0096 0.00
## 226 26.8061 1.0238 0.00
## 230 81.4930 0.9953 0.00
##
## $`South America`
## Rank CCA3 Country.Territory Capital Continent X2022.Population
## 9 33 ARG Argentina Buenos Aires South America 45510318
## 25 80 BOL Bolivia Sucre South America 12224110
## 28 7 BRA Brazil Brasilia South America 215313498
## 41 65 CHL Chile Santiago South America 19603733
## 43 28 COL Colombia Bogota South America 51874024
## 57 67 ECU Ecuador Quito South America 18001000
## 65 231 FLK Falkland Islands Stanley South America 3780
## 70 184 GUF French Guiana Cayenne South America 304557
## 87 164 GUY Guyana Georgetown South America 808726
## 162 109 PRY Paraguay Asunción South America 6780744
## 163 44 PER Peru Lima South America 34049588
## 200 170 SUR Suriname Paramaribo South America 618040
## 224 133 URY Uruguay Montevideo South America 3422794
## 228 51 VEN Venezuela Caracas South America 28301696
## X2020.Population X2015.Population X2010.Population X2000.Population
## 9 45036032 43257065 41100123 37070774
## 25 11936162 11090085 10223270 8592656
## 28 213196304 205188205 196353492 175873720
## 41 19300315 17870124 17004162 15351799
## 43 50930662 47119728 44816108 39215135
## 57 17588595 16195902 14989585 12626507
## 65 3747 3408 3187 3080
## 70 290969 257026 228453 164351
## 87 797202 755031 747932 759051
## 162 6618695 6177950 5768613 5123819
## 163 33304756 30711863 29229572 26654439
## 200 607065 575475 546080 478998
## 224 3429086 3402818 3352651 3292224
## 228 28490453 30529716 28715022 24427729
## X1990.Population X1980.Population X1970.Population Area..km..
## 9 32637657 28024803 23842803 2780400
## 25 7096194 5736088 4585693 1098581
## 28 150706446 122288383 96369875 8515767
## 41 13342868 11469828 9820481 756102
## 43 32601393 26176195 20905254 1141748
## 57 10449837 8135845 6172215 276841
## 65 2332 2240 2274 12173
## 70 113931 66825 46484 83534
## 87 747116 778176 705261 214969
## 162 4059195 3078912 2408787 406752
## 163 22109099 17492406 13562371 1285216
## 200 412756 375112 379918 163820
## 224 3117012 2953750 2790265 181034
## 228 19750579 15210443 11355475 916445
## Density..per.km.. Growth.Rate World.Population.Percentage
## 9 16.3683 1.0052 0.57
## 25 11.1272 1.0120 0.15
## 28 25.2841 1.0046 2.70
## 41 25.9274 1.0057 0.25
## 43 45.4339 1.0069 0.65
## 57 65.0229 1.0114 0.23
## 65 0.3105 1.0043 0.00
## 70 3.6459 1.0239 0.00
## 87 3.7621 1.0052 0.01
## 162 16.6705 1.0115 0.09
## 163 26.4933 1.0099 0.43
## 200 3.7727 1.0082 0.01
## 224 18.9069 0.9990 0.04
## 228 30.8820 1.0036 0.35avg_pop_by_continent <- lapply(continents_list, function(sub_df) {
mean(sub_df$`2022 Population`, na.rm = TRUE)
})## Warning in mean.default(sub_df$`2022 Population`, na.rm = TRUE): argument is
## not numeric or logical: returning NA
## Warning in mean.default(sub_df$`2022 Population`, na.rm = TRUE): argument is
## not numeric or logical: returning NA
## Warning in mean.default(sub_df$`2022 Population`, na.rm = TRUE): argument is
## not numeric or logical: returning NA
## Warning in mean.default(sub_df$`2022 Population`, na.rm = TRUE): argument is
## not numeric or logical: returning NA
## Warning in mean.default(sub_df$`2022 Population`, na.rm = TRUE): argument is
## not numeric or logical: returning NA
## Warning in mean.default(sub_df$`2022 Population`, na.rm = TRUE): argument is
## not numeric or logical: returning NAavg_pop_by_continent## $Africa
## [1] NA
##
## $Asia
## [1] NA
##
## $Europe
## [1] NA
##
## $`North America`
## [1] NA
##
## $Oceania
## [1] NA
##
## $`South America`
## [1] NA##check quantitative variables(numeric)
Quantitative<- sapply(wp[sapply(wp,is.numeric)],sum)
Quantitative## Rank X2022.Population
## 2.749500e+04 7.973413e+09
## X2020.Population X2015.Population
## 7.839251e+09 7.424810e+09
## X2010.Population X2000.Population
## 6.983785e+09 6.147056e+09
## X1990.Population X1980.Population
## 5.314192e+09 4.442400e+09
## X1970.Population Area..km..
## 3.694137e+09 1.360592e+08
## Density..per.km.. Growth.Rate
## 1.057977e+05 2.362411e+02
## World.Population.Percentage
## 9.993000e+01#detect outliers
boxplot(Quantitative,las=2,main="quantitative variables")
##4.3Generating new Variable
#HW3:use of “group_by” :help to perform calculations within each group instead of on the whole dataset. ##use of %>%(pipe operator):pass the result of one operation directly into the next function, making code easier to read and write. ##all these are from from the magrittr package, commonly used in library(dplyr) to manipulate and transform dataset. #there are several function of this library to manipulate data such as: #mutate(),select(),arrange(),filter(),..
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, uniont<-8
wp<- wp%>%
mutate(X2030.population=X2022.Population*exp(Growth.Rate*t))
duplicated(colnames(wp))## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [13] FALSE FALSE FALSE FALSE FALSE FALSE##4.4 Value extraction and plot ##4.4.1.Based on 2022 population, extract top ten countries ##with high population number
library(dplyr)
top10 <- wp %>%
arrange(desc(X2022.Population)) %>%
select(Country.Territory,
X2022.Population) %>%
head(10)
top10## Country.Territory X2022.Population
## 1 China 1425887337
## 2 India 1417173173
## 3 United States 338289857
## 4 Indonesia 275501339
## 5 Pakistan 235824862
## 6 Nigeria 218541212
## 7 Brazil 215313498
## 8 Bangladesh 171186372
## 9 Russia 144713314
## 10 Mexico 127504125barplot(top10$X2022.Population,names.arg = top10$Country.Territory,
las=3,main = "top ten countries population",xlab = "",
ylab = "population")
##4.4.2.trend in their population number since 1990-2022
library(dplyr)
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats 1.0.1 ✔ readr 2.2.0
## ✔ ggplot2 4.0.3 ✔ stringr 1.6.0
## ✔ lubridate 1.9.5 ✔ tibble 3.3.1
## ✔ purrr 1.2.2 ✔ tidyr 1.3.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(stringr)
top10_long <- wp %>%
arrange(desc(X2022.Population))%>%
select(Country.Territory, X2022.Population, X2015.Population, X2010.Population,
X2000.Population, X1990.Population) %>%
pivot_longer(
cols = ends_with("Population"),
names_to = "Year",
values_to = "Population"
) %>%
# Clean the "Year" string (e.g., "X1990.Population" -> 1990)
mutate(Year = as.numeric(str_extract(Year, "\\d+")))
# Check the result
trend_pop<-head(top10_long,20)
print(trend_pop)## # A tibble: 20 × 3
## Country.Territory Year Population
## <chr> <dbl> <int>
## 1 China 2022 1425887337
## 2 China 2015 1393715448
## 3 China 2010 1348191368
## 4 China 2000 1264099069
## 5 China 1990 1153704252
## 6 India 2022 1417173173
## 7 India 2015 1322866505
## 8 India 2010 1240613620
## 9 India 2000 1059633675
## 10 India 1990 870452165
## 11 United States 2022 338289857
## 12 United States 2015 324607776
## 13 United States 2010 311182845
## 14 United States 2000 282398554
## 15 United States 1990 248083732
## 16 Indonesia 2022 275501339
## 17 Indonesia 2015 259091970
## 18 Indonesia 2010 244016173
## 19 Indonesia 2000 214072421
## 20 Indonesia 1990 182159874##trend graph
library(ggplot2)
ggplot(trend_pop, aes(x = Year, y = Population, color = Country.Territory)) +
geom_line(linewidth = 1) +
geom_point() +
scale_y_continuous(labels = scales::comma) +
labs(
title = "Population Trend of Top 10 Countries (1990-2022)",
x = "Year",
y = "Total Population",
color = "Country"
) +
theme_minimal()
##4.4.3.The most populous countries and ##emission trend since
1990-2019)
library(dplyr)
library(tidyverse)
str(CO2)## 'data.frame': 215 obs. of 35 variables:
## $ Country.Name : chr "Aruba" "Afghanistan" "Angola" "Albania" ...
## $ country_code : chr "ABW" "AFG" "AGO" "ALB" ...
## $ Region : chr "Latin America & Caribbean" "South Asia" "Sub-Saharan Africa" "Europe & Central Asia" ...
## $ Indicator.Name: chr "CO2 emissions (metric tons per capita)" "CO2 emissions (metric tons per capita)" "CO2 emissions (metric tons per capita)" "CO2 emissions (metric tons per capita)" ...
## $ X1990 : num NA 0.192 0.554 1.82 7.522 ...
## $ X1991 : num NA 0.168 0.545 1.243 7.235 ...
## $ X1992 : num NA 0.096 0.544 0.684 6.963 ...
## $ X1993 : num NA 0.0847 0.709 0.6383 6.7242 ...
## $ X1994 : num NA 0.0755 0.8368 0.6454 6.5416 ...
## $ X1995 : num NA 0.0685 0.9121 0.6054 6.7335 ...
## $ X1996 : num NA 0.0626 1.0722 0.6124 6.9916 ...
## $ X1997 : num NA 0.0568 1.0866 0.4669 7.3074 ...
## $ X1998 : num NA 0.0527 1.0918 0.5722 7.6395 ...
## $ X1999 : num NA 0.0402 1.1099 0.9554 7.9232 ...
## $ X2000 : num NA 0.0366 0.9881 1.0262 7.9523 ...
## $ X2001 : num NA 0.0338 0.9418 1.0555 7.7215 ...
## $ X2002 : num NA 0.0456 0.8956 1.2324 7.5662 ...
## $ X2003 : num NA 0.0515 0.9249 1.339 7.2424 ...
## $ X2004 : num NA 0.0417 0.9303 1.4041 7.3443 ...
## $ X2005 : num NA 0.0604 0.8135 1.3382 7.3538 ...
## $ X2006 : num NA 0.0666 0.8218 1.34 6.7905 ...
## $ X2007 : num NA 0.0653 0.8118 1.3939 6.531 ...
## $ X2008 : num NA 0.128 0.889 1.384 6.439 ...
## $ X2009 : num NA 0.172 0.939 1.441 6.157 ...
## $ X2010 : num NA 0.244 0.976 1.528 6.157 ...
## $ X2011 : num NA 0.297 0.986 1.669 5.851 ...
## $ X2012 : num NA 0.259 0.951 1.503 5.945 ...
## $ X2013 : num NA 0.186 1.036 1.534 5.943 ...
## $ X2014 : num NA 0.146 1.1 1.668 5.807 ...
## $ X2015 : num NA 0.173 1.135 1.604 6.026 ...
## $ X2016 : num NA 0.15 1.03 1.56 6.08 ...
## $ X2017 : num NA 0.132 0.813 1.789 6.104 ...
## $ X2018 : num NA 0.163 0.778 1.783 6.363 ...
## $ X2019 : num NA 0.16 0.792 1.692 6.481 ...
## $ X2019.1 : num NA 0.16 0.792 1.692 6.481 ...sum(colSums(is.na(CO2))==nrow(CO2))## [1] 0colnames(CO2)## [1] "Country.Name" "country_code" "Region" "Indicator.Name"
## [5] "X1990" "X1991" "X1992" "X1993"
## [9] "X1994" "X1995" "X1996" "X1997"
## [13] "X1998" "X1999" "X2000" "X2001"
## [17] "X2002" "X2003" "X2004" "X2005"
## [21] "X2006" "X2007" "X2008" "X2009"
## [25] "X2010" "X2011" "X2012" "X2013"
## [29] "X2014" "X2015" "X2016" "X2017"
## [33] "X2018" "X2019" "X2019.1"dim(CO2)## [1] 215 35##trend sice 1990-2019
top10_pop <- trend_pop$Country.Territory
print(top10_pop)## [1] "China" "China" "China" "China"
## [5] "China" "India" "India" "India"
## [9] "India" "India" "United States" "United States"
## [13] "United States" "United States" "United States" "Indonesia"
## [17] "Indonesia" "Indonesia" "Indonesia" "Indonesia"#extract
top10_pop_co2<-CO2 %>%
filter(Country.Name %in% top10_pop)%>%
pivot_longer(
cols =starts_with("X"),
names_to = "Year",
values_to="CO2")%>%
mutate(Year = as.numeric(str_extract(Year, "\\d+")))
print(top10_pop_co2,n=70)## # A tibble: 124 × 6
## Country.Name country_code Region Indicator.Name Year CO2
## <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 China CHN East Asia & Pacific CO2 emissions (met… 1990 1.91
## 2 China CHN East Asia & Pacific CO2 emissions (met… 1991 2.00
## 3 China CHN East Asia & Pacific CO2 emissions (met… 1992 2.08
## 4 China CHN East Asia & Pacific CO2 emissions (met… 1993 2.24
## 5 China CHN East Asia & Pacific CO2 emissions (met… 1994 2.32
## 6 China CHN East Asia & Pacific CO2 emissions (met… 1995 2.56
## 7 China CHN East Asia & Pacific CO2 emissions (met… 1996 2.52
## 8 China CHN East Asia & Pacific CO2 emissions (met… 1997 2.55
## 9 China CHN East Asia & Pacific CO2 emissions (met… 1998 2.61
## 10 China CHN East Asia & Pacific CO2 emissions (met… 1999 2.52
## 11 China CHN East Asia & Pacific CO2 emissions (met… 2000 2.65
## 12 China CHN East Asia & Pacific CO2 emissions (met… 2001 2.77
## 13 China CHN East Asia & Pacific CO2 emissions (met… 2002 2.98
## 14 China CHN East Asia & Pacific CO2 emissions (met… 2003 3.43
## 15 China CHN East Asia & Pacific CO2 emissions (met… 2004 3.95
## 16 China CHN East Asia & Pacific CO2 emissions (met… 2005 4.47
## 17 China CHN East Asia & Pacific CO2 emissions (met… 2006 4.91
## 18 China CHN East Asia & Pacific CO2 emissions (met… 2007 5.31
## 19 China CHN East Asia & Pacific CO2 emissions (met… 2008 5.44
## 20 China CHN East Asia & Pacific CO2 emissions (met… 2009 5.80
## 21 China CHN East Asia & Pacific CO2 emissions (met… 2010 6.34
## 22 China CHN East Asia & Pacific CO2 emissions (met… 2011 6.90
## 23 China CHN East Asia & Pacific CO2 emissions (met… 2012 7.05
## 24 China CHN East Asia & Pacific CO2 emissions (met… 2013 7.32
## 25 China CHN East Asia & Pacific CO2 emissions (met… 2014 7.29
## 26 China CHN East Asia & Pacific CO2 emissions (met… 2015 7.15
## 27 China CHN East Asia & Pacific CO2 emissions (met… 2016 7.12
## 28 China CHN East Asia & Pacific CO2 emissions (met… 2017 7.23
## 29 China CHN East Asia & Pacific CO2 emissions (met… 2018 7.49
## 30 China CHN East Asia & Pacific CO2 emissions (met… 2019 7.61
## 31 China CHN East Asia & Pacific CO2 emissions (met… 2019 7.61
## 32 Indonesia IDN East Asia & Pacific CO2 emissions (met… 1990 0.819
## 33 Indonesia IDN East Asia & Pacific CO2 emissions (met… 1991 0.880
## 34 Indonesia IDN East Asia & Pacific CO2 emissions (met… 1992 0.914
## 35 Indonesia IDN East Asia & Pacific CO2 emissions (met… 1993 0.970
## 36 Indonesia IDN East Asia & Pacific CO2 emissions (met… 1994 1.03
## 37 Indonesia IDN East Asia & Pacific CO2 emissions (met… 1995 1.14
## 38 Indonesia IDN East Asia & Pacific CO2 emissions (met… 1996 1.18
## 39 Indonesia IDN East Asia & Pacific CO2 emissions (met… 1997 1.29
## 40 Indonesia IDN East Asia & Pacific CO2 emissions (met… 1998 1.28
## 41 Indonesia IDN East Asia & Pacific CO2 emissions (met… 1999 1.34
## 42 Indonesia IDN East Asia & Pacific CO2 emissions (met… 2000 1.33
## 43 Indonesia IDN East Asia & Pacific CO2 emissions (met… 2001 1.41
## 44 Indonesia IDN East Asia & Pacific CO2 emissions (met… 2002 1.41
## 45 Indonesia IDN East Asia & Pacific CO2 emissions (met… 2003 1.52
## 46 Indonesia IDN East Asia & Pacific CO2 emissions (met… 2004 1.53
## 47 Indonesia IDN East Asia & Pacific CO2 emissions (met… 2005 1.51
## 48 Indonesia IDN East Asia & Pacific CO2 emissions (met… 2006 1.59
## 49 Indonesia IDN East Asia & Pacific CO2 emissions (met… 2007 1.64
## 50 Indonesia IDN East Asia & Pacific CO2 emissions (met… 2008 1.60
## 51 Indonesia IDN East Asia & Pacific CO2 emissions (met… 2009 1.64
## 52 Indonesia IDN East Asia & Pacific CO2 emissions (met… 2010 1.72
## 53 Indonesia IDN East Asia & Pacific CO2 emissions (met… 2011 1.94
## 54 Indonesia IDN East Asia & Pacific CO2 emissions (met… 2012 1.94
## 55 Indonesia IDN East Asia & Pacific CO2 emissions (met… 2013 1.78
## 56 Indonesia IDN East Asia & Pacific CO2 emissions (met… 2014 1.90
## 57 Indonesia IDN East Asia & Pacific CO2 emissions (met… 2015 1.89
## 58 Indonesia IDN East Asia & Pacific CO2 emissions (met… 2016 1.84
## 59 Indonesia IDN East Asia & Pacific CO2 emissions (met… 2017 1.95
## 60 Indonesia IDN East Asia & Pacific CO2 emissions (met… 2018 2.16
## 61 Indonesia IDN East Asia & Pacific CO2 emissions (met… 2019 2.29
## 62 Indonesia IDN East Asia & Pacific CO2 emissions (met… 2019 2.29
## 63 India IND South Asia CO2 emissions (met… 1990 0.645
## 64 India IND South Asia CO2 emissions (met… 1991 0.681
## 65 India IND South Asia CO2 emissions (met… 1992 0.689
## 66 India IND South Asia CO2 emissions (met… 1993 0.702
## 67 India IND South Asia CO2 emissions (met… 1994 0.725
## 68 India IND South Asia CO2 emissions (met… 1995 0.765
## 69 India IND South Asia CO2 emissions (met… 1996 0.788
## 70 India IND South Asia CO2 emissions (met… 1997 0.819
## # ℹ 54 more rowssum(duplicated(top10_pop_co2))## [1] 4##HW.5.visualize trend by graph
library(ggplot2)
ggplot(top10_pop_co2,aes(x=reorder(Country.Name,CO2),y=CO2))+
geom_col(fill = "steelblue")+
coord_flip()+
scale_y_continuous(labels = scales::comma)+
labs(title = "CO2 Emission for populous countries",
x="Country",
y="CO2",
color="Country")+
theme_minimal()## Ignoring unknown labels:
## • colour : "Country"
##4.5.1.Correlation with heatmap
library(corrplot)## corrplot 0.95 loadeddf_corr <- read.csv("world_population.csv", check.names = FALSE)
df_co2 <- read.csv("CO2_emission.csv", check.names = FALSE)
head(df_corr)## Rank CCA3 Country/Territory Capital Continent 2022 Population
## 1 36 AFG Afghanistan Kabul Asia 41128771
## 2 138 ALB Albania Tirana Europe 2842321
## 3 34 DZA Algeria Algiers Africa 44903225
## 4 213 ASM American Samoa Pago Pago Oceania 44273
## 5 203 AND Andorra Andorra la Vella Europe 79824
## 6 42 AGO Angola Luanda Africa 35588987
## 2020 Population 2015 Population 2010 Population 2000 Population
## 1 38972230 33753499 28189672 19542982
## 2 2866849 2882481 2913399 3182021
## 3 43451666 39543154 35856344 30774621
## 4 46189 51368 54849 58230
## 5 77700 71746 71519 66097
## 6 33428485 28127721 23364185 16394062
## 1990 Population 1980 Population 1970 Population Area (km²) Density (per km²)
## 1 10694796 12486631 10752971 652230 63.0587
## 2 3295066 2941651 2324731 28748 98.8702
## 3 25518074 18739378 13795915 2381741 18.8531
## 4 47818 32886 27075 199 222.4774
## 5 53569 35611 19860 468 170.5641
## 6 11828638 8330047 6029700 1246700 28.5466
## Growth Rate World Population Percentage
## 1 1.0257 0.52
## 2 0.9957 0.04
## 3 1.0164 0.56
## 4 0.9831 0.00
## 5 1.0100 0.00
## 6 1.0315 0.45# Select relevant columns
# Use backticks for column names containing spaces or special characters
cols_to_analyze <- df_corr[, c("Area (km²)",
"Density (per km²)",
"Growth Rate",
"World Population Percentage")]
#Calculate the correlation matrix
# 'use = "complete.obs"' handles any missing values by excluding those rows
correlation_matrix <- cor(cols_to_analyze, use = "complete.obs")
#Display numerical values
print("Numerical Correlation Matrix:")## [1] "Numerical Correlation Matrix:"print(correlation_matrix)## Area (km²) Density (per km²) Growth Rate
## Area (km²) 1.00000000 -0.06312785 -0.01397017
## Density (per km²) -0.06312785 1.00000000 -0.06975328
## Growth Rate -0.01397017 -0.06975328 1.00000000
## World Population Percentage 0.45328363 -0.02764600 -0.02092954
## World Population Percentage
## Area (km²) 0.45328363
## Density (per km²) -0.02764600
## Growth Rate -0.02092954
## World Population Percentage 1.00000000###Create the heatmap plot
corrplot(correlation_matrix,
method = "color", # Use solid colors for cells
type = "lower", # Only show the lower triangle (cuts out redundant mirror data)
addCoef.col = "black", # Overlay the numerical correlation coefficients
tl.col = "black", # Color of text labels (variable names)
tl.srt = 45, # Rotate text labels by 45 degrees
diag = FALSE, # Hide the diagonal 1.00 correlations to reduce clutter
title = "Population Metrics Correlation",
mar = c(0, 0, 2, 0))# Adjust margins so the title doesn't get cut off
##4.5.2.Merge the World Population Dataset and CO2 Emissions
df_pop_sub <- df_corr %>%
select(`Country/Territory`, CCA3, Population_2022 = `2022 Population`)
head(df_pop_sub,n=20)## Country/Territory CCA3 Population_2022
## 1 Afghanistan AFG 41128771
## 2 Albania ALB 2842321
## 3 Algeria DZA 44903225
## 4 American Samoa ASM 44273
## 5 Andorra AND 79824
## 6 Angola AGO 35588987
## 7 Anguilla AIA 15857
## 8 Antigua and Barbuda ATG 93763
## 9 Argentina ARG 45510318
## 10 Armenia ARM 2780469
## 11 Aruba ABW 106445
## 12 Australia AUS 26177413
## 13 Austria AUT 8939617
## 14 Azerbaijan AZE 10358074
## 15 Bahamas BHS 409984
## 16 Bahrain BHR 1472233
## 17 Bangladesh BGD 171186372
## 18 Barbados BRB 281635
## 19 Belarus BLR 9534954
## 20 Belgium BEL 11655930head(df_co2)## Country Name country_code Region
## 1 Aruba ABW Latin America & Caribbean
## 2 Afghanistan AFG South Asia
## 3 Angola AGO Sub-Saharan Africa
## 4 Albania ALB Europe & Central Asia
## 5 Andorra AND Europe & Central Asia
## 6 United Arab Emirates ARE Middle East & North Africa
## Indicator Name 1990 1991 1992
## 1 CO2 emissions (metric tons per capita) NA NA NA
## 2 CO2 emissions (metric tons per capita) 0.1917451 0.1676816 0.09595774
## 3 CO2 emissions (metric tons per capita) 0.5536620 0.5445386 0.54355722
## 4 CO2 emissions (metric tons per capita) 1.8195416 1.2428102 0.68369983
## 5 CO2 emissions (metric tons per capita) 7.5218317 7.2353792 6.96307870
## 6 CO2 emissions (metric tons per capita) 30.1951886 31.7784962 29.08092584
## 1993 1994 1995 1996 1997 1998
## 1 NA NA NA NA NA NA
## 2 0.08472111 0.07554583 0.06846796 0.06258803 0.05682662 0.05269086
## 3 0.70898423 0.83680440 0.91214149 1.07216847 1.08663697 1.09182531
## 4 0.63830704 0.64535519 0.60543625 0.61236736 0.46692147 0.57215370
## 5 6.72417752 6.54157891 6.73347949 6.99159455 7.30744115 7.63953851
## 6 29.27567777 30.84933296 31.12501806 30.92802588 30.48633262 29.66358052
## 1999 2000 2001 2002 2003 2004
## 1 NA NA NA NA NA NA
## 2 0.04015697 0.0365737 0.03378536 0.04557366 0.05151838 0.04165539
## 3 1.10985966 0.9880774 0.94182891 0.89557767 0.92486944 0.93026295
## 4 0.95535931 1.0262131 1.05549588 1.23237878 1.33898498 1.40405869
## 5 7.92319165 7.9522863 7.72154906 7.56623988 7.24241557 7.34426233
## 6 28.88710798 27.0351591 29.43026994 28.50146173 27.96926982 27.03893822
## 2005 2006 2007 2008 2009 2010
## 1 NA NA NA NA NA NA
## 2 0.06041878 0.06658329 0.06531235 0.1284166 0.1718624 0.2436140
## 3 0.81353929 0.82184008 0.81175351 0.8886580 0.9394040 0.9761842
## 4 1.33820940 1.33999574 1.39393137 1.3843112 1.4414936 1.5276237
## 5 7.35378001 6.79054277 6.53104692 6.4393039 6.1566875 6.1571978
## 6 25.38238104 22.93510429 21.37028576 22.0114692 19.8323489 19.0397698
## 2011 2012 2013 2014 2015 2016 2017
## 1 NA NA NA NA NA NA NA
## 2 0.2965062 0.2592953 0.1856237 0.1462356 0.1728967 0.1497893 0.1316946
## 3 0.9855223 0.9506959 1.0362939 1.0997791 1.1350441 1.0318113 0.8133007
## 4 1.6694232 1.5032405 1.5336300 1.6683374 1.6037751 1.5576644 1.7887861
## 5 5.8508861 5.9446542 5.9428004 5.8071277 6.0261818 6.0806003 6.1041339
## 6 18.5094574 19.2078011 20.0556476 20.0516980 21.0776420 21.4806686 20.7690223
## 2018 2019 2019
## 1 NA NA NA
## 2 0.1632953 0.1598244 0.1598244
## 3 0.7776749 0.7921371 0.7921371
## 4 1.7827389 1.6922483 1.6922483
## 5 6.3629754 6.4812174 6.4812174
## 6 18.3906781 19.3295633 19.3295633colnames(df_co2) <- make.unique(colnames(df_co2))
df_co2_sub <- df_co2 %>%
select(country_code, CO2_Per_Capita_2019 = `2019`)
head(df_co2_sub,n=20)## country_code CO2_Per_Capita_2019
## 1 ABW NA
## 2 AFG 0.15982437
## 3 AGO 0.79213707
## 4 ALB 1.69224832
## 5 AND 6.48121743
## 6 ARE 19.32956328
## 7 ARG 3.74065029
## 8 ARM 2.08606068
## 9 ASM NA
## 10 ATG 5.35447646
## 11 AUS 15.23826715
## 12 AUT 7.29398425
## 13 AZE 3.54239783
## 14 BDI 0.06244267
## 15 BEL 8.09558395
## 16 BEN 0.61858375
## 17 BFA 0.24604625
## 18 BGD 0.55652945
## 19 BGR 5.61085728
## 20 BHR 20.26610279##merge two dataset HW2 ##INNER JOIN is exclusive:It only keeps rows where there is a match in both tables. ##OUTER JOIN is inclusive:It keeps matched rows plus unmatched rows from one or both tables (filling in the missing gaps with NULL). #so here we use INNER join because we need match=“country_code”on both tables
merged_df <- inner_join(df_pop_sub, df_co2_sub, by = c("CCA3" = "country_code"))
head(merged_df,n=20)## Country/Territory CCA3 Population_2022 CO2_Per_Capita_2019
## 1 Afghanistan AFG 41128771 0.1598244
## 2 Albania ALB 2842321 1.6922483
## 3 Algeria DZA 44903225 3.9776505
## 4 American Samoa ASM 44273 NA
## 5 Andorra AND 79824 6.4812174
## 6 Angola AGO 35588987 0.7921371
## 7 Antigua and Barbuda ATG 93763 5.3544765
## 8 Argentina ARG 45510318 3.7406503
## 9 Armenia ARM 2780469 2.0860607
## 10 Aruba ABW 106445 NA
## 11 Australia AUS 26177413 15.2382672
## 12 Austria AUT 8939617 7.2939843
## 13 Azerbaijan AZE 10358074 3.5423978
## 14 Bahamas BHS 409984 7.2916611
## 15 Bahrain BHR 1472233 20.2661028
## 16 Bangladesh BGD 171186372 0.5565295
## 17 Barbados BRB 281635 4.3550820
## 18 Belarus BLR 9534954 6.1222378
## 19 Belgium BEL 11655930 8.0955840
## 20 Belize BLZ 405272 1.6395500Calculate the Total National CO2 Emissions
merged_df <- merged_df %>%
mutate(Total_CO2_2019 = CO2_Per_Capita_2019 * Population_2022)
head(merged_df,n=20)## Country/Territory CCA3 Population_2022 CO2_Per_Capita_2019 Total_CO2_2019
## 1 Afghanistan AFG 41128771 0.1598244 6573380.0
## 2 Albania ALB 2842321 1.6922483 4809912.9
## 3 Algeria DZA 44903225 3.9776505 178609333.5
## 4 American Samoa ASM 44273 NA NA
## 5 Andorra AND 79824 6.4812174 517356.7
## 6 Angola AGO 35588987 0.7921371 28191355.9
## 7 Antigua and Barbuda ATG 93763 5.3544765 502051.8
## 8 Argentina ARG 45510318 3.7406503 170238184.3
## 9 Armenia ARM 2780469 2.0860607 5800227.0
## 10 Aruba ABW 106445 NA NA
## 11 Australia AUS 26177413 15.2382672 398898412.6
## 12 Austria AUT 8939617 7.2939843 65205425.6
## 13 Azerbaijan AZE 10358074 3.5423978 36692418.8
## 14 Bahamas BHS 409984 7.2916611 2989464.4
## 15 Bahrain BHR 1472233 20.2661028 29836425.3
## 16 Bangladesh BGD 171186372 0.5565295 95270258.3
## 17 Barbados BRB 281635 4.3550820 1226543.5
## 18 Belarus BLR 9534954 6.1222378 58375255.5
## 19 Belgium BEL 11655930 8.0955840 94361559.9
## 20 Belize BLZ 405272 1.6395500 664463.7# Statistical Analysis (Correlations)
# Population vs Per Capita CO2
pearson_per_capita <- cor(merged_df$Population_2022,
merged_df$CO2_Per_Capita_2019,
method = "pearson")
spearman_per_capita <- cor(merged_df$Population_2022,
merged_df$CO2_Per_Capita_2019,
method = "spearman")
# Population vs Total CO2
pearson_total <- cor(merged_df$Population_2022,
merged_df$Total_CO2_2019,
method = "pearson")
spearman_total <- cor(merged_df$Population_2022,
merged_df$Total_CO2_2019,
method = "spearman")
cat(sprintf("Population vs Per Capita CO2 ->
Pearson: %.4f, Spearman: %.4f\n",
pearson_per_capita,
spearman_per_capita))## Population vs Per Capita CO2 ->
## Pearson: NA, Spearman: NAcat(sprintf("Population vs Total CO2 ->
Pearson: %.4f, Spearman: %.4f\n",
pearson_total, spearman_total))## Population vs Total CO2 ->
## Pearson: NA, Spearman: NA#to decide if i need to apply logarithm on my data
hist(merged_df$Population_2022) # Is it heavily bunched up 
##on the left with a long tail to the right?
plot(merged_df$Population_2022, merged_df$Total_CO2_2019) 
# Is it a straight line, or a curve/fan?
# 5. Regression Analysis (Log-Log Model)
# Filter out 0 values before log transformation to avoid -Inf errors
merged_df_log <- merged_df %>%
filter(Total_CO2_2019 > 0 & CO2_Per_Capita_2019 > 0) %>%
mutate(
Log_Population = log10(Population_2022),
Log_Total_CO2 = log10(Total_CO2_2019)
)
# Fit the Linear Model: log10(Total_CO2) = beta_0 + beta_1 * log10(Population)
log_model <- lm(Log_Total_CO2 ~ Log_Population, data = merged_df_log)
# Display regression statistics (R², slope, p-values)
print(summary(log_model))##
## Call:
## lm(formula = Log_Total_CO2 ~ Log_Population, data = merged_df_log)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.6300 -0.3920 0.1216 0.4493 1.1769
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.89576 0.32144 2.787 0.00587 **
## Log_Population 0.91274 0.04666 19.563 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6091 on 189 degrees of freedom
## Multiple R-squared: 0.6694, Adjusted R-squared: 0.6677
## F-statistic: 382.7 on 1 and 189 DF, p-value: < 2.2e-16# 6. Visualizations
# Plot 1: Population vs Per-Capita CO2 (Linear scale)
plot1 <- ggplot(merged_df, aes(x = Population_2022, y = CO2_Per_Capita_2019)) +
geom_point(alpha = 0.7) +
geom_smooth(method = "lm", color = "red", linetype = "dashed", se = FALSE) +
labs(
title = "Population vs. Per Capita CO2 Emissions (2019)",
x = "2022 Population (Linear Scale)",
y = "CO2 Emissions (metric tons per capita)"
) +
theme_minimal()
print(plot1)## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 23 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 23 rows containing missing values or values outside the scale range
## (`geom_point()`).
##HW6.>to create my own function of doing summarystatistics
get_summary_stat<-function(df) {
# 1. Handle Numeric Columns
numeric_summary <- df %>%
select(where(is.numeric)) %>%
pivot_longer(cols = everything(), names_to = "variable", values_to = "value") %>%
group_by(variable) %>%
summarise(
total_count = n(),
missing_count = sum(is.na(value)),
mean = mean(value, na.rm = TRUE),
sd = sd(value, na.rm = TRUE),
variance = var(value, na.rm = TRUE),
min = min(value, na.rm = TRUE),
q1 = quantile(value, 0.25, na.rm = TRUE),
median = median(value, na.rm = TRUE),
q2 = quantile(value, 0.75, na.rm = TRUE),
max = max(value, na.rm = TRUE),
.groups = "drop"
)
# 2. Handle Categorical Columns (Factors and Characters)
categorical_summary <- df %>%
select(where(is.character) | where(is.factor)) %>%
pivot_longer(cols = everything(), names_to = "variable", values_to = "value") %>%
# Convert value to character so factors and strings play nice together
mutate(value = as.character(value)) %>%
group_by(variable) %>%
summarise(
total_count = n(),
missing_count = sum(is.na(value)),
unique_count = n_distinct(value, na.rm = TRUE),
# Find the most frequent value (mode)
most_frequent = if(all(is.na(value))) NA_character_ else names(sort(table(value), decreasing = TRUE))[1],
.groups = "drop"
)
# Return both summaries as a named list
return(list(
numeric = numeric_summary,
categorical = categorical_summary
))
}
summary_f<- get_summary_stat(wp)
summary_f$numeric## # A tibble: 14 × 11
## variable total_count missing_count mean sd variance min q1
## <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Area..k… 234 0 5.81e+ 5 1.76e+ 6 3.10e+12 1 e+0 2.65e+3
## 2 Density… 234 0 4.52e+ 2 2.07e+ 3 4.27e+ 6 2.61e-2 3.84e+1
## 3 Growth.… 234 0 1.01e+ 0 1.34e- 2 1.79e- 4 9.12e-1 1.00e+0
## 4 Rank 234 0 1.17e+ 2 6.77e+ 1 4.58e+ 3 1 e+0 5.92e+1
## 5 World.P… 234 0 4.27e- 1 1.71e+ 0 2.94e+ 0 0 1 e-2
## 6 X1970.P… 234 0 1.58e+ 7 6.78e+ 7 4.60e+15 7.52e+2 1.56e+5
## 7 X1980.P… 234 0 1.90e+ 7 8.18e+ 7 6.69e+15 7.33e+2 2.30e+5
## 8 X1990.P… 234 0 2.27e+ 7 9.78e+ 7 9.57e+15 7 e+2 2.64e+5
## 9 X2000.P… 234 0 2.63e+ 7 1.12e+ 8 1.25e+16 6.51e+2 3.27e+5
## 10 X2010.P… 234 0 2.98e+ 7 1.24e+ 8 1.54e+16 5.96e+2 3.93e+5
## 11 X2015.P… 234 0 3.17e+ 7 1.30e+ 8 1.70e+16 5.64e+2 4.05e+5
## 12 X2020.P… 234 0 3.35e+ 7 1.36e+ 8 1.84e+16 5.2 e+2 4.15e+5
## 13 X2022.P… 234 0 3.41e+ 7 1.37e+ 8 1.87e+16 5.1 e+2 4.20e+5
## 14 X2030.p… 234 0 1.09e+11 4.21e+11 1.78e+23 1.50e+6 1.36e+9
## # ℹ 3 more variables: median <dbl>, q2 <dbl>, max <dbl> summary_f$categorical## # A tibble: 4 × 5
## variable total_count missing_count unique_count most_frequent
## <chr> <int> <int> <int> <chr>
## 1 CCA3 234 0 234 ABW
## 2 Capital 234 0 234 Abu Dhabi
## 3 Continent 234 0 6 Africa
## 4 Country.Territory 234 0 234 Afghanistan#HW7.use of trace() and recover() ##trace():“modify or inspect a function while it runs” ##recover(): “investigate an error after it happens