#GROUP 3
#Safira Khoirulanisa Salsabila (2304010001)
#Regita Aftina Rizqi (2304010002)
#Ayunda mulya putri (2304010005)
#Alfian Adi Pratama(2304010008)
#Jeni Anggraeni (2304010021)
#Muhammad Maulana Zafrani(2304010032)
#Dataset
library(readxl)
df <- read_excel("data annova.xlsx")
df
## # A tibble: 243 × 9
## `Country Name` `Country Code` `2015` `2016` `2017` `2018` `2019`
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Aruba ABW -2.69e 7 2.76e 7 1.62e 8 1.44e 8 -7.52e 7
## 2 Africa Eastern a… AFE 2.86e10 1.67e10 1.03e10 1.36e10 1.49e10
## 3 Afghanistan AFG 1.69e 8 9.36e 7 5.15e 7 1.19e 8 2.34e 7
## 4 Africa Western a… AFW 1.66e10 1.56e10 1.77e10 1.50e10 1.28e10
## 5 Angola AGO 1.00e10 -1.80e 8 -7.40e 9 -6.46e 9 -4.10e 9
## 6 Albania ALB 9.90e 8 1.04e 9 1.02e 9 1.20e 9 1.20e 9
## 7 Arab World ARB 2.13e10 4.71e10 3.05e10 4.29e10 3.44e10
## 8 United Arab Emir… ARE 8.55e 9 9.60e 9 1.04e10 1.04e10 1.79e10
## 9 Argentina ARG 1.18e10 3.26e 9 1.15e10 1.17e10 6.65e 9
## 10 Armenia ARM 1.84e 8 3.34e 8 2.53e 8 2.67e 8 1.00e 8
## # ℹ 233 more rows
## # ℹ 2 more variables: `2020` <dbl>, `2021` <dbl>
# Data Scaling Menggunakan Normalization (Normalisasi)
z_score_norm <- function(x) {
return ((x - mean(x, na.rm = TRUE)) / sd(x, na.rm = TRUE))
}
cols_to_scale <- df[, c("2015", "2016", "2017", "2018", "2019", "2020", "2021")]
scaled_cols <- as.data.frame(apply(cols_to_scale, 2, z_score_norm))
final_data <- cbind(scaled_cols, df[, c("Country Name", "Country Code"), drop = FALSE])
print(final_data)
## 2015 2016 2017 2018 2019 2020
## 1 -0.28075130 -0.26826008 -0.282662756 -0.28449585 -0.305358414 -0.30344104
## 2 -0.18817118 -0.21565134 -0.240248951 -0.17896070 -0.226107202 -0.20568023
## 3 -0.28011820 -0.26805120 -0.283125517 -0.28468974 -0.304837393 -0.30451416
## 4 -0.22705998 -0.21885274 -0.209320697 -0.16830844 -0.237229220 -0.22500934
## 5 -0.24827671 -0.26891503 -0.314201365 -0.33628238 -0.326611345 -0.31813260
## 6 -0.27746848 -0.26504386 -0.279073657 -0.27617704 -0.298616599 -0.29685675
## 7 -0.21175277 -0.11941757 -0.155984281 0.05070570 -0.123485831 -0.06972968
## 8 -0.25304794 -0.23796765 -0.240143621 -0.20414202 -0.210537691 -0.16052492
## 9 -0.24268688 -0.25803544 -0.235293198 -0.19369496 -0.269836528 -0.26921766
## 10 -0.28006981 -0.26729135 -0.282286069 -0.28353305 -0.304431259 -0.30418365
## 11 -0.28029622 -0.26803905 -0.282709420 -0.28403930 -0.304253653 -0.30383858
## 12 -0.12921644 -0.13243385 -0.082257051 0.19053143 -0.100288914 -0.18982080
## 13 -0.30642356 -0.35988138 -0.226941019 -0.51007276 -0.370432814 -0.39469741
## 14 -0.26759200 -0.25411493 -0.271377613 -0.27461867 -0.297016544 -0.30093331
## 15 -0.28064071 -0.26834705 -0.282418496 -0.28557456 -0.304801022 -0.30445433
## 16 -0.34368895 -0.08635536 -0.438906211 -0.61203881 -0.417901094 -0.50285668
## 17 -0.28018082 -0.26793038 -0.282502363 -0.28410411 -0.303808340 -0.30334719
## 18 -0.27991552 -0.26711170 -0.283329778 -0.28352081 -0.304100135 -0.30532388
## 19 -0.27152081 -0.26096890 -0.275787702 -0.26662633 -0.294881731 -0.29355570
## 20 -0.27349014 -0.26363937 -0.274966277 -0.27142636 -0.293227220 -0.27856197
## 21 -0.28045490 -0.26757751 -0.277391101 -0.27264726 -0.299986182 -0.29720795
## 22 -0.28041878 -0.26711240 -0.282069799 -0.28177120 -0.302413640 -0.30145902
## 23 -0.27942723 -0.26735659 -0.281215219 -0.28090515 -0.302595115 -0.30110629
## 24 -0.27532811 -0.26440332 -0.278015902 -0.27443427 -0.298234789 -0.29451299
## 25 -0.28047355 -0.26824369 -0.283207713 -0.28466928 -0.304464945 -0.30405597
## 26 -0.28112663 -0.26857880 -0.284542478 -0.28487819 -0.304937839 -0.30115528
## 27 -0.27887317 -0.26728648 -0.280368133 -0.28325441 -0.306105418 -0.31279242
## 28 -0.07158196 -0.03335582 0.004043584 0.32781761 0.060454527 -0.02730220
## 29 -0.27844078 -0.26658387 -0.282958857 -0.28373124 -0.303823330 -0.30270896
## 30 -0.28011128 -0.26882341 -0.281388360 -0.28157663 -0.302989290 -0.30051021
## 31 -0.28064365 -0.26830964 -0.283409572 -0.28560606 -0.304892295 -0.30462833
## 32 -0.27944188 -0.26789643 -0.282253415 -0.28338319 -0.304466546 -0.30437777
## 33 -0.28065479 -0.26832427 -0.283311772 -0.28548559 -0.304825790 -0.30459555
## 34 -0.08692915 -0.16016786 -0.177550038 0.04864993 -0.046422104 -0.09358228
## 35 -0.20641882 0.09539390 -0.161346234 -0.43960933 0.497982764 1.23154761
## 36 0.10366560 0.25986815 0.313242672 -1.54101560 -0.258607328 -2.05814441
## 37 -0.22328534 -0.23240697 -0.261491451 -0.22330753 -0.233229141 -0.22166010
## 38 0.50091435 0.28517188 0.409196610 1.55822539 0.682871137 1.52863433
## 39 -0.27906771 -0.26651944 -0.279272836 -0.28075962 -0.300476793 -0.29944234
## 40 -0.27842201 -0.26624735 -0.279942662 -0.27962380 -0.299547606 -0.29971572
## 41 -0.27689960 -0.26539816 -0.278968435 -0.27458285 -0.297824368 -0.29375299
## 42 -0.26684204 -0.26818715 -0.264913376 -0.25176861 -0.312502780 -0.31897829
## 43 -0.24313406 -0.22451504 -0.226181666 -0.19697527 -0.231062973 -0.25056281
## 44 -0.28064854 -0.26833593 -0.283324160 -0.28558234 -0.304938309 -0.30458005
## 45 -0.28035421 -0.26794771 -0.282874461 -0.28481437 -0.304311694 -0.30411816
## 46 -0.27111914 -0.26005888 -0.271138062 -0.26197419 -0.290597678 -0.28936974
## 47 -0.27477055 -0.26209208 -0.277158123 -0.26398239 -0.288145581 -0.28644989
## 48 -0.28019155 -0.26792629 -0.282620099 -0.28463097 -0.303887458 -0.30347962
## 49 -0.02067156 -0.25985708 -0.272883756 -0.28426440 -0.299253681 -0.25478611
## 50 -0.18713168 -0.24143606 -0.223237598 -0.29433814 -0.025860157 -0.49317520
## 51 -0.27517433 -0.23402710 -0.236470179 -0.22031000 -0.248162682 -0.24290481
## 52 -0.07888932 -0.06356339 0.171397719 1.02468517 0.085810908 0.97793699
## 53 -0.28019995 -0.26784116 -0.282652439 -0.28429299 -0.304036592 -0.30346160
## 54 -0.28064136 -0.26821354 -0.283245904 -0.28501262 -0.304628019 -0.30444266
## 55 -0.27471223 -0.24366337 -0.268291278 -0.21686274 -0.325043782 -0.29523142
## 56 -0.27347105 -0.26038889 -0.268330395 -0.26409300 -0.290015709 -0.28677859
## 57 -0.28240137 -0.26316545 -0.278208046 -0.27412343 -0.297664805 -0.29631929
## 58 0.70761288 0.42750537 0.717917978 2.15453504 1.073777983 1.88368920
## 59 0.40725407 0.40536460 0.605276398 1.52684067 0.804369210 1.11561132
## 60 1.71203856 1.40486196 2.057101207 4.42208098 2.505342243 3.98503432
## 61 -0.13782963 -0.10224724 -0.134312641 -0.03128735 -0.115986740 -0.11115786
## 62 3.46574643 4.06491665 3.371252974 -2.43548008 2.367996595 0.79678655
## 63 -0.27639260 -0.26593016 -0.280708710 -0.27472628 -0.299787411 -0.29667300
## 64 -0.25829841 -0.24270569 -0.252432079 -0.22174874 -0.257364950 -0.26220589
## 65 2.39995712 1.72432156 1.644082080 -0.38223716 1.015670208 -0.39286003
## 66 -0.28050519 -0.26818179 -0.283109104 -0.28514811 -0.305281529 -0.30482805
## 67 -0.20626247 -0.12837540 -0.151652684 0.17436470 -0.168245835 -0.02691936
## 68 -0.28297520 -0.26541907 -0.276099012 -0.27595754 -0.288738076 -0.27737513
## 69 -0.27218171 -0.25524325 -0.266581282 -0.25926039 -0.291497228 -0.28724813
## 70 2.49684706 2.13550719 1.840161867 -0.53628073 1.797483900 1.21705557
## 71 -0.20938172 -0.20195663 -0.180978666 -0.10797500 -0.207888477 -0.20758933
## 72 -0.22419335 -0.25213995 -0.211795911 -0.36857429 -0.222492000 -0.32263326
## 73 -0.28000120 -0.26710855 -0.281723468 -0.28194808 -0.303258561 -0.30287358
## 74 -0.14135831 -0.15567175 -0.100889298 0.32239793 -0.022349443 -0.16426126
## 75 -0.28052978 -0.26441357 -0.277858499 -0.27480641 -0.296756544 -0.29217027
## 76 -0.13425254 0.75961683 0.238148285 -0.48221633 -0.200415835 0.83301663
## 77 -0.27506002 -0.26309726 -0.275287133 -0.27539523 -0.297649079 -0.30057733
## 78 -0.27035436 -0.25732323 -0.269760985 -0.26217434 -0.284465722 -0.29101618
## 79 -0.28049243 -0.26322813 -0.280930858 -0.28285903 -0.304726484 -0.30333030
## 80 -0.28043203 -0.26812636 -0.283072097 -0.28498499 -0.304585529 -0.30323447
## 81 -0.28060449 -0.26827132 -0.283275049 -0.28546550 -0.304582490 -0.30445605
## 82 -0.27682861 -0.26279526 -0.281735022 -0.28676035 -0.300621893 -0.30467388
## 83 -0.27656624 -0.25981182 -0.268992762 -0.25404246 -0.278550326 -0.28066984
## 84 -0.28016625 -0.26800115 -0.282701048 -0.28416753 -0.303859010 -0.30358444
## 85 -0.27677805 -0.26567203 -0.279175508 -0.27838955 -0.298776465 -0.29733044
## 86 -0.28021976 -0.26783276 -0.281974863 -0.27636517 -0.296006198 -0.29522830
## 87 6.82459760 6.87995669 6.642030704 2.42130515 6.086894157 4.68789803
## 88 0.30390481 0.15232730 0.242320195 0.47573705 0.003006801 0.54317593
## 89 -0.27641205 -0.26471921 -0.279377380 -0.27430804 -0.299915714 -0.30289796
## 90 -0.18427678 -0.19032453 -0.159098673 -0.06276568 -0.181007710 -0.16310304
## 91 -0.28055728 -0.26696417 -0.281464671 -0.27533126 -0.284150679 -0.29553953
## 92 -0.28032317 -0.26801543 -0.281776629 -0.28480300 -0.304564839 -0.30442699
## 93 -0.29767331 -0.04794790 -0.333958531 -0.79064541 0.215174735 0.91272283
## 94 1.68297158 1.47128426 1.902740523 4.43777334 2.711360716 3.58650335
## 95 1.84445481 1.61678071 2.119679862 4.79869761 2.938509289 3.85460600
## 96 -0.11880974 -0.12559330 -0.067850331 0.07497546 -0.076634804 -0.03688874
## 97 -0.23973138 -0.23625294 -0.223581240 -0.19827643 -0.260842492 -0.26410304
## 98 -0.21678453 -0.25398194 -0.197773333 -0.13725695 -0.172931968 -0.16566519
## 99 -0.15974284 -0.15768759 -0.127609602 -0.01237495 -0.120753340 -0.07739384
## 100 -0.13852859 -0.12772626 -0.116605560 0.04483424 -0.037609021 0.16176270
## 101 0.48476602 0.05743831 -0.040114764 0.24290541 -0.551302210 -0.10029110
## 102 -0.27404367 -0.25768170 -0.262401693 -0.26700786 -0.296994981 -0.29488398
## 103 -0.30512661 -0.28813443 -0.304335233 -0.32395622 -0.321207962 -0.32532523
## 104 -0.27696404 -0.27171986 -0.312543438 -0.29051281 -0.307900562 -0.31097346
## 105 -0.24405236 -0.23042959 -0.212866516 -0.11682021 -0.213243189 -0.15266857
## 106 -0.23769884 -0.18719606 -0.236871980 0.06156437 -0.116055029 -0.42815564
## 107 -0.27767712 -0.26541207 -0.279632371 -0.27954903 -0.301445954 -0.30268722
## 108 -0.27549611 -0.26343527 -0.274872709 -0.27813430 -0.301106273 -0.29909912
## 109 -0.26370158 -0.13881053 -0.204899205 -0.08720192 -0.093868456 0.14888184
## 110 -0.25942032 -0.21386896 -0.263493078 -0.28285486 -0.285252687 -0.25239341
## 111 -0.27866298 -0.26686211 -0.277724764 -0.27960285 -0.302478557 -0.30151912
## 112 -0.27696957 -0.26638865 -0.283787794 -0.28449525 -0.302827601 -0.30751755
## 113 -0.27477744 -0.26051599 -0.271708874 -0.26041995 -0.285610965 -0.27834390
## 114 -0.28066713 -0.26834151 -0.283337237 -0.28563582 -0.304963977 -0.30458912
## 115 -0.28025019 -0.26796319 -0.283140151 -0.28531259 -0.304631086 -0.30458757
## 116 -0.26740962 -0.23006173 -0.208609498 -0.19003998 -0.254067598 -0.24109752
## 117 -0.27974517 -0.26742356 -0.282869035 -0.28579458 -0.302237266 -0.30865182
## 118 0.18763688 0.20293476 0.334102223 0.95406955 0.413462364 0.35185828
## 119 -0.27718368 -0.26538892 -0.276277130 -0.27497158 -0.300969953 -0.29759612
## 120 -0.27369070 -0.26022319 -0.272817244 -0.26477167 -0.294890462 -0.29296455
## 121 -0.27991301 -0.26736133 -0.282306533 -0.28461367 -0.304503130 -0.29926081
## 122 -0.28017280 -0.26783605 -0.282965284 -0.28526967 -0.304562145 -0.30388980
## 123 0.75928751 0.42750537 0.563557294 1.36207089 0.735696386 0.79678655
## 124 -0.15658178 -0.18636460 -0.194987260 -0.14137673 -0.199402005 -0.13876007
## 125 -0.23593003 -0.22870126 -0.230722998 -0.19110235 -0.233562745 -0.21237327
## 126 -0.27846942 -0.26550989 -0.277613634 -0.27296277 -0.301033649 -0.30146282
## 127 0.11335460 0.08590500 0.175569629 0.56960674 0.281399241 0.53592991
## 128 1.65390460 1.37323230 1.865193330 4.36715772 2.500059718 3.48505910
## 129 -0.28029882 -0.26809661 -0.283164587 -0.28530614 -0.304772270 -0.30440524
## 130 1.99624905 1.39537307 1.602362976 3.78654042 1.977089749 2.65176707
## 131 -0.27731547 -0.26462273 -0.277567709 -0.27542805 -0.286819257 -0.27195944
## 132 -0.13393387 -0.21273921 -0.397524767 -0.93949986 0.561373063 -0.23331543
## 133 -0.27803927 -0.26728630 -0.278375988 -0.28229779 -0.299073168 -0.29772375
## 134 -0.27909320 -0.26246667 -0.276244082 -0.27205437 -0.270574148 -0.34965216
## 135 -0.27015866 -0.26153621 -0.272159333 -0.25781695 -0.295870727 -0.29432812
## 136 -0.27993500 -0.26807066 -0.282716727 -0.28319735 -0.302206878 -0.30347979
## 137 -0.27960496 -0.26663656 -0.281401171 -0.28082470 -0.302455466 -0.30201068
## 138 -0.27970212 -0.26690289 -0.281430576 -0.28111013 -0.299884323 -0.30141473
## 139 -0.16495859 -0.06733122 -0.056795821 0.25510829 -0.002829774 0.10915577
## 140 -0.16358934 -0.14530941 -0.145191296 0.01140745 -0.146768849 -0.07618731
## 141 -0.28068190 -0.26835689 -0.283316166 -0.28555033 -0.304933745 -0.30458607
## 142 1.60868930 1.33211374 1.810958495 4.27300356 2.426104369 3.39086087
## 143 -0.27970655 -0.26660958 -0.281752103 -0.28053677 -0.302058276 -0.30455239
## 144 -0.27977463 -0.26721970 -0.281001123 -0.28196036 -0.300422856 -0.30071810
## 145 -0.26890623 -0.25956672 -0.267110760 -0.25042829 -0.281912326 -0.27157030
## 146 -0.26747505 -0.25797872 -0.263297517 -0.27175327 -0.295792734 -0.29078887
## 147 -0.25134062 -0.22516005 -0.213470407 -0.16524590 -0.231367294 -0.23123817
## 148 -0.27840418 -0.26763017 -0.281001466 -0.28181632 -0.302755099 -0.30076121
## 149 -0.28035986 -0.28149379 -0.277106216 -0.27031480 -0.292054007 -0.29215139
## 150 -0.26817100 -0.25845299 -0.273665551 -0.27246048 -0.287109638 -0.28150825
## 151 -0.27904407 -0.26748964 -0.280886522 -0.27956261 -0.309628462 -0.29788444
## 152 -0.27996541 -0.26714921 -0.281338012 -0.28201359 -0.302615176 -0.30298019
## 153 -0.27973516 -0.26798127 -0.282964205 -0.28502259 -0.304669286 -0.30278084
## 154 -0.24882916 -0.22574178 -0.244256095 -0.22046840 -0.256599926 -0.27519822
## 155 1.56347399 1.34160264 1.410455099 1.73084134 1.623160577 0.90547681
## 156 -0.27795519 -0.26721258 -0.282170387 -0.28378791 -0.305893279 -0.30569629
## 157 -0.27677631 -0.26578579 -0.281301852 -0.28291760 -0.301139225 -0.30046539
## 158 -0.27895445 -0.26739412 -0.281927441 -0.28197019 -0.301172678 -0.30199484
## 159 -0.27076825 -0.25742469 -0.273273796 -0.27954411 -0.292784281 -0.28732437
## 160 -0.27754140 -0.26521874 -0.279020915 -0.27905488 -0.302303918 -0.29919899
## 161 0.93368940 0.60463148 0.551041563 -2.52963424 -0.975841697 -1.90597804
## 162 -0.25717162 -0.32739529 -0.258723717 -0.33007011 -0.218698324 -0.33433609
## 163 -0.28049688 -0.26801196 -0.282521711 -0.28509125 -0.303980786 -0.30369060
## 164 -0.28090145 -0.26199564 -0.273369496 -0.26588202 -0.289558482 -0.27464499
## 165 5.27435860 5.77291847 5.265300282 1.40130179 4.528549298 3.11551220
## 166 -0.28768071 -0.26117972 -0.271331486 -0.24168212 -0.294720211 -0.29074177
## 167 -0.16781480 -0.22810040 -0.212961419 -0.21997366 0.029611629 -0.42142058
## 168 -0.27526125 -0.26019942 -0.272927423 -0.27199803 -0.293159868 -0.28970308
## 169 -0.26413086 -0.25174686 -0.266310513 -0.24630708 -0.282813283 -0.32264090
## 170 -0.25696889 -0.24682246 -0.252412430 -0.23954892 -0.279734981 -0.29980100
## 171 -0.26245190 -0.24215929 -0.240551553 -0.20756835 -0.259154322 -0.25517484
## 172 -0.28055279 -0.26814271 -0.283141353 -0.28523257 -0.304724836 -0.30429584
## 173 -0.28056750 -0.26847094 -0.284088394 -0.28322088 -0.303188360 -0.30378939
## 174 -0.23028818 -0.21220457 -0.233281759 -0.13494881 -0.211888221 -0.16583965
## 175 -0.18983761 -0.22647754 -0.239441672 -0.22315630 -0.252566947 -0.22297018
## 176 -0.27656276 -0.24508421 -0.238768470 -0.22406504 -0.250445146 -0.27568309
## 177 -0.27858910 -0.26596045 -0.280893184 -0.28384590 -0.302798499 -0.30317015
## 178 -0.28032557 -0.26740949 -0.282573086 -0.28397370 -0.304264369 -0.30403030
## 179 -0.27959707 -0.26658862 -0.281184005 -0.28067969 -0.302587853 -0.30209638
## 180 4.98368879 5.74128881 5.599053112 1.48760977 5.188864916 3.63722547
## 181 -0.28058049 -0.26815256 -0.282964878 -0.28558026 -0.304893776 -0.30472080
## 182 -0.27720590 -0.26589940 -0.279227054 -0.30278066 -0.319818855 -0.32224542
## 183 -0.26671966 -0.24857225 -0.258505506 -0.22800795 -0.266052898 -0.27850495
## 184 -0.25853169 -0.16542778 -0.164201431 -0.21669933 -0.136053508 -0.23592451
## 185 -0.28014101 -0.26746239 -0.282197300 -0.28275363 -0.303570814 -0.30350229
## 186 -0.12008953 -0.10724958 -0.090038116 0.09613786 -0.005551869 0.19505024
## 187 -0.26784064 -0.19890483 -0.279109934 -0.19036542 -0.288694986 -0.29286043
## 188 -0.27508242 -0.26498256 -0.278896182 -0.27671525 -0.300601070 -0.29941318
## 189 -0.27934301 -0.26685301 -0.280886206 -0.27897453 -0.299332702 -0.29123442
## 190 -0.05531608 -0.06160606 0.142194346 0.35783192 0.254986616 0.28038364
## 191 -0.28056042 -0.26822911 -0.283161603 -0.28543075 -0.304787830 -0.30454307
## 192 -0.27984920 -0.26790912 -0.281614490 -0.28366180 -0.303152292 -0.30335676
## 193 -0.27906728 -0.26683068 -0.281238697 -0.28238838 -0.301282875 -0.30180732
## 194 -0.27968590 -0.26730345 -0.281801077 -0.28242560 -0.302599740 -0.30073876
## 195 -0.27309693 -0.26089776 -0.271264433 -0.25367802 -0.282411465 -0.27935007
## 196 -0.13490867 -0.16637304 -0.167052302 -0.06403674 -0.159733459 -0.12666150
## 197 -0.28066400 -0.26837205 -0.283334587 -0.28515498 -0.304972703 -0.30448133
## 198 -0.13456667 -0.16615686 -0.166229137 -0.06164230 -0.158375394 -0.12608143
## 199 -0.16085345 -0.22008665 -0.204622524 -0.19338204 0.048800252 -0.40075058
## 200 -0.28057430 -0.26827343 -0.283197797 -0.28544090 -0.304833235 -0.30426676
## 201 -0.28007130 -0.26761447 -0.282939693 -0.28459930 -0.304516449 -0.30460060
## 202 -0.27575414 -0.25334567 -0.265711762 -0.26796626 -0.292917869 -0.31288094
## 203 -0.27507669 -0.26377271 -0.278349953 -0.27355834 -0.293595478 -0.30093970
## 204 -0.24908621 -0.21941470 -0.181941354 -0.29123022 -0.221061535 -0.17042810
## 205 -0.28056276 -0.26826229 -0.283581012 -0.28538259 -0.304285019 -0.30432708
## 206 -0.28057479 -0.26821579 -0.283072473 -0.28600280 -0.304570817 -0.30444751
## 207 -0.28032248 -0.26813105 -0.282517347 -0.28323241 -0.303602963 -0.30402807
## 208 -0.28089020 -0.26829475 -0.283248446 -0.28529552 -0.304818603 -0.30438772
## 209 -0.27885703 -0.26757330 -0.281824516 -0.28201062 -0.301967744 -0.30056709
## 210 0.70761288 0.42750537 0.717917978 2.15453504 1.073777983 1.88368920
## 211 -0.04409415 0.08274204 0.069970271 0.27043316 0.217445875 0.15751222
## 212 -0.27983168 -0.26849370 -0.282971053 -0.28704679 -0.303134872 -0.30503715
## 213 -0.25183140 -0.25732054 -0.248775526 -0.17776369 -0.275808315 -0.34045763
## 214 -0.27919818 -0.26758300 -0.282565258 -0.28389392 -0.303836856 -0.30383619
## 215 -0.27083672 -0.26125219 -0.274638139 -0.27302086 -0.295169170 -0.29420110
## 216 0.28452682 0.26303112 0.384165148 1.07960842 0.519112863 0.44897770
## 217 -0.28052562 -0.26832990 -0.283312493 -0.28525081 -0.306223525 -0.30977235
## 218 -0.25167953 -0.22609778 -0.214237832 -0.16689905 -0.232063953 -0.23181600
## 219 -0.28064398 -0.26829464 -0.283405893 -0.28544473 -0.304993296 -0.30458043
## 220 -0.12008953 -0.10724958 -0.090038116 0.09613786 -0.005551869 0.19505024
## 221 -0.13456667 -0.16615686 -0.166229137 -0.06164230 -0.158375394 -0.12608143
## 222 -0.28009338 -0.26842187 -0.285304991 -0.29112063 -0.303989188 -0.29695657
## 223 -0.27753002 -0.26637806 -0.279957357 -0.27784825 -0.300655702 -0.30009332
## 224 -0.21845145 -0.22458759 -0.236656834 -0.18794191 -0.254740064 -0.24899495
## 225 -0.28066351 -0.26834628 -0.283339260 -0.28562450 -0.304959450 -0.30460741
## 226 -0.27580053 -0.26561430 -0.279428511 -0.27800368 -0.298530953 -0.29776960
## 227 -0.27828211 -0.26636814 -0.279991702 -0.27734635 -0.298077872 -0.29597461
## 228 -0.28130396 -0.25529050 -0.267987881 -0.24659210 -0.274343514 -0.30240535
## 229 1.21789988 0.97786151 1.352048354 3.42561616 1.839744100 2.55032282
## 230 -0.27203146 -0.26997821 -0.272132635 -0.27208002 -0.297196978 -0.30078163
## 231 1.36969412 1.23089882 1.306157340 1.40130179 1.364316855 0.68809628
## 232 -0.27730176 -0.26308757 -0.275838167 -0.28072544 -0.292724149 -0.29208522
## 233 -0.28026440 -0.26812416 -0.282650506 -0.28531293 -0.304597388 -0.30413538
## 234 -0.27111760 -0.26332760 -0.284663007 -0.27900467 -0.312182240 -0.29371013
## 235 -0.06520471 -0.11328930 -0.118091261 -0.01579901 -0.098396545 -0.01752356
## 236 -0.24255444 -0.22849385 -0.224516575 -0.16401106 -0.219806726 -0.19012106
## 237 -0.28056430 -0.26820749 -0.283181050 -0.28533196 -0.304681847 -0.30430990
## 238 8.76239635 8.52469918 8.769704994 7.08193590 8.886632378 8.46307323
## 239 -0.28057771 -0.26833925 -0.283302066 -0.28549571 -0.304972538 -0.30457607
## 240 -0.27955558 -0.26757614 -0.282143861 -0.28312750 -0.303455078 -0.30174868
## 241 -0.27575171 -0.26134028 -0.274752301 -0.24192784 -0.277935111 -0.28175744
## 242 -0.27555300 -0.26625077 -0.278720038 -0.28242216 -0.302066374 -0.30283138
## 243 -0.27937520 -0.26726228 -0.282058952 -0.27999434 -0.303643038 -0.30351863
## 2021 Country Name
## 1 -0.30557200 Aruba
## 2 -0.08922704 Africa Eastern and Southern
## 3 -0.30602216 Afghanistan
## 4 -0.23829106 Africa Western and Central
## 5 -0.32346246 Angola
## 6 -0.30124735 Albania
## 7 -0.05223705 Arab World
## 8 -0.22373134 United Arab Emirates
## 9 -0.27956729 Argentina
## 10 -0.30464373 Armenia
## 11 -0.30494899 Antigua and Barbuda
## 12 -0.19528565 Australia
## 13 -0.23525273 Austria
## 14 -0.31291052 Azerbaijan
## 15 -0.30601508 Burundi
## 16 -0.18846309 Belgium
## 17 -0.30472528 Benin
## 18 -0.30642296 Burkina Faso
## 19 -0.29923350 Bangladesh
## 20 -0.29614269 Bulgaria
## 21 -0.29901269 Bahrain
## 22 -0.30457749 Bahamas, The
## 23 -0.30317673 Bosnia and Herzegovina
## 24 -0.30119898 Belarus
## 25 -0.30560408 Belize
## 26 -0.30590628 Bermuda
## 27 -0.30377831 Bolivia
## 28 -0.12100638 Brazil
## 29 -0.30515275 Barbados
## 30 -0.30528820 Brunei Darussalam
## 31 -0.30607724 Bhutan
## 32 -0.30737593 Botswana
## 33 -0.30608271 Central African Republic
## 34 -0.07044937 Canada
## 35 0.17217967 Central Europe and the Baltics
## 36 -0.77243111 Switzerland
## 37 -0.24561370 Chile
## 38 1.06497635 China
## 39 -0.30055444 Cote d'Ivoire
## 40 -0.30226392 Cameroon
## 41 -0.29941757 Congo, Dem. Rep.
## 42 -0.30738053 Congo, Rep.
## 43 -0.26799566 Colombia
## 44 -0.30608823 Comoros
## 45 -0.30573975 Cabo Verde
## 46 -0.29178444 Costa Rica
## 47 -0.29168722 Caribbean small states
## 48 -0.30542307 Curacao
## 49 -0.30932395 Cayman Islands
## 50 -0.27531931 Cyprus
## 51 -0.25472482 Czechia
## 52 0.10043708 Germany
## 53 -0.30543966 Djibouti
## 54 -0.30599132 Dominica
## 55 -0.23835819 Denmark
## 56 -0.29275337 Dominican Republic
## 57 -0.30263993 Algeria
## 58 1.43564641 East Asia & Pacific (excluding high income)
## 59 0.77800599 Early-demographic dividend
## 60 2.93426942 East Asia & Pacific
## 61 -0.14950067 Europe & Central Asia (excluding high income)
## 62 1.89400185 Europe & Central Asia
## 63 -0.30351928 Ecuador
## 64 -0.28568832 Egypt, Arab Rep.
## 65 1.30810402 Euro area
## 66 -0.30622874 Eritrea
## 67 -0.07816211 Spain
## 68 -0.27605048 Estonia
## 69 -0.28912740 Ethiopia
## 70 1.97770154 European Union
## 71 -0.19193491 Fragile and conflict affected situations
## 72 -0.21174244 Finland
## 73 -0.30447056 Fiji
## 74 0.08454428 France
## 75 -0.30000926 Gabon
## 76 -0.28249995 United Kingdom
## 77 -0.30101027 Georgia
## 78 -0.29600616 Ghana
## 79 -0.30531666 Guinea
## 80 -0.30510058 Gambia, The
## 81 -0.30603042 Guinea-Bissau
## 82 -0.30386592 Equatorial Guinea
## 83 -0.28166207 Greece
## 84 -0.30549755 Grenada
## 85 -0.29165067 Guatemala
## 86 -0.29719024 Guyana
## 87 5.91158694 High income
## 88 0.23993656 Hong Kong SAR, China
## 89 -0.30291445 Honduras
## 90 -0.17547896 Heavily indebted poor countries (HIPC)
## 91 -0.28744970 Croatia
## 92 -0.30589981 Haiti
## 93 -0.17346887 Hungary
## 94 2.86252683 IBRD only
## 95 3.06978320 IDA & IBRD total
## 96 -0.09761782 IDA total
## 97 -0.26680747 IDA blend
## 98 -0.22155531 Indonesia
## 99 -0.13691462 IDA only
## 100 -0.12783478 India
## 101 0.05990378 Ireland
## 102 -0.30042465 Iran, Islamic Rep.
## 103 -0.31661576 Iraq
## 104 -0.30424991 Iceland
## 105 -0.23057566 Israel
## 106 -0.20685092 Italy
## 107 -0.30482693 Jamaica
## 108 -0.30362584 Jordan
## 109 -0.16649652 Japan
## 110 -0.28790312 Kazakhstan
## 111 -0.30425750 Kenya
## 112 -0.30520269 Kyrgyz Republic
## 113 -0.29222024 Cambodia
## 114 -0.30610016 Kiribati
## 115 -0.30600889 St. Kitts and Nevis
## 116 -0.21817815 Korea, Rep.
## 117 -0.30718972 Kuwait
## 118 0.17616537 Latin America & Caribbean (excluding high income)
## 119 -0.30183195 Lao PDR
## 120 -0.30371370 Lebanon
## 121 -0.30396960 Liberia
## 122 -0.30567025 St. Lucia
## 123 0.42327874 Latin America & Caribbean
## 124 -0.19696506 Least developed countries: UN classification
## 125 -0.23623442 Low income
## 126 -0.30374358 Sri Lanka
## 127 0.12833697 Lower middle income
## 128 2.59947066 Low & middle income
## 129 -0.30615358 Lesotho
## 130 2.58751356 Late-demographic dividend
## 131 -0.29426029 Lithuania
## 132 -0.20597317 Luxembourg
## 133 -0.29135643 Latvia
## 134 -0.28344825 Macao SAR, China
## 135 -0.29708006 Morocco
## 136 -0.30456531 Moldova
## 137 -0.30467924 Madagascar
## 138 -0.30354214 Maldives
## 139 0.03918618 Middle East & North Africa
## 140 -0.16498868 Mexico
## 141 -0.30610228 Marshall Islands
## 142 2.52772807 Middle income
## 143 -0.30332921 North Macedonia
## 144 -0.30355365 Mali
## 145 -0.28712812 Malta
## 146 -0.29786740 Myanmar
## 147 -0.26650395 Middle East & North Africa (excluding high income)
## 148 -0.30333923 Montenegro
## 149 -0.29744181 Mongolia
## 150 -0.28499841 Mozambique
## 151 -0.30183857 Mauritania
## 152 -0.30509514 Mauritius
## 153 -0.30558814 Malawi
## 154 -0.22541316 Malaysia
## 155 1.82624496 North America
## 156 -0.30275514 Namibia
## 157 -0.30294077 New Caledonia
## 158 -0.30373346 Niger
## 159 -0.29289881 Nigeria
## 160 -0.30124132 Nicaragua
## 161 -0.76445971 Netherlands
## 162 -0.24959087 Norway
## 163 -0.30532178 Nepal
## 164 -0.28800954 New Zealand
## 165 4.35716414 OECD members
## 166 -0.27105707 Oman
## 167 -0.20965776 Other small states
## 168 -0.29754698 Pakistan
## 169 -0.30071318 Panama
## 170 -0.27763730 Peru
## 171 -0.25834219 Philippines
## 172 -0.30597896 Palau
## 173 -0.30614697 Papua New Guinea
## 174 -0.16193355 Poland
## 175 -0.23511108 Pre-demographic dividend
## 176 -0.27481268 Portugal
## 177 -0.30488536 Paraguay
## 178 -0.30469542 West Bank and Gaza
## 179 -0.30400463 Pacific island small states
## 180 4.59630611 Post-demographic dividend
## 181 -0.30620640 French Polynesia
## 182 -0.31046226 Qatar
## 183 -0.25931926 Romania
## 184 -0.14488273 Russian Federation
## 185 -0.30525972 Rwanda
## 186 -0.10659225 South Asia
## 187 -0.21398717 Saudi Arabia
## 188 -0.30402027 Sudan
## 189 -0.29578878 Senegal
## 190 0.23993656 Singapore
## 191 -0.30599299 Solomon Islands
## 192 -0.30525815 Sierra Leone
## 193 -0.30291394 El Salvador
## 194 -0.30370887 Somalia
## 195 -0.28776935 Serbia
## 196 -0.02186004 Sub-Saharan Africa (excluding high income)
## 197 -0.30583524 South Sudan
## 198 -0.02141382 Sub-Saharan Africa
## 199 -0.19314106 Small states
## 200 -0.30586912 Sao Tome and Principe
## 201 -0.30663320 Suriname
## 202 -0.29526447 Slovak Republic
## 203 -0.29725902 Slovenia
## 204 -0.08753315 Sweden
## 205 -0.30565442 Eswatini
## 206 -0.30599739 Sint Maarten (Dutch part)
## 207 -0.30565805 Seychelles
## 208 -0.30598992 Turks and Caicos Islands
## 209 -0.30329395 Chad
## 210 1.43564641 East Asia & Pacific (IDA & IBRD countries)
## 211 0.23196516 Europe & Central Asia (IDA & IBRD countries)
## 212 -0.30664721 Togo
## 213 -0.24568595 Thailand
## 214 -0.30576932 Tajikistan
## 215 -0.30097334 Turkmenistan
## 216 0.26783646 Latin America & the Caribbean (IDA & IBRD countries)
## 217 -0.30777442 Timor-Leste
## 218 -0.26791281 Middle East & North Africa (IDA & IBRD countries)
## 219 -0.30608911 Tonga
## 220 -0.10659225 South Asia (IDA & IBRD)
## 221 -0.02141382 Sub-Saharan Africa (IDA & IBRD countries)
## 222 -0.30983003 Trinidad and Tobago
## 223 -0.30392245 Tunisia
## 224 -0.25470868 Turkiye
## 225 -0.30610348 Tuvalu
## 226 -0.30135927 Tanzania
## 227 -0.29953488 Uganda
## 228 -0.27440202 Ukraine
## 229 2.09727252 Upper middle income
## 230 -0.29236347 Uruguay
## 231 1.59108869 United States
## 232 -0.29701572 Uzbekistan
## 233 -0.30545518 St. Vincent and the Grenadines
## 234 -0.30226206 Venezuela, RB
## 235 -0.14922117 British Virgin Islands
## 236 -0.24368822 Viet Nam
## 237 -0.30593267 Vanuatu
## 238 8.82114757 World
## 239 -0.30606860 Samoa
## 240 -0.30410772 Kosovo
## 241 -0.14405056 South Africa
## 242 -0.30453304 Zambia
## 243 -0.30510785 Zimbabwe
## Country Code
## 1 ABW
## 2 AFE
## 3 AFG
## 4 AFW
## 5 AGO
## 6 ALB
## 7 ARB
## 8 ARE
## 9 ARG
## 10 ARM
## 11 ATG
## 12 AUS
## 13 AUT
## 14 AZE
## 15 BDI
## 16 BEL
## 17 BEN
## 18 BFA
## 19 BGD
## 20 BGR
## 21 BHR
## 22 BHS
## 23 BIH
## 24 BLR
## 25 BLZ
## 26 BMU
## 27 BOL
## 28 BRA
## 29 BRB
## 30 BRN
## 31 BTN
## 32 BWA
## 33 CAF
## 34 CAN
## 35 CEB
## 36 CHE
## 37 CHL
## 38 CHN
## 39 CIV
## 40 CMR
## 41 COD
## 42 COG
## 43 COL
## 44 COM
## 45 CPV
## 46 CRI
## 47 CSS
## 48 CUW
## 49 CYM
## 50 CYP
## 51 CZE
## 52 DEU
## 53 DJI
## 54 DMA
## 55 DNK
## 56 DOM
## 57 DZA
## 58 EAP
## 59 EAR
## 60 EAS
## 61 ECA
## 62 ECS
## 63 ECU
## 64 EGY
## 65 EMU
## 66 ERI
## 67 ESP
## 68 EST
## 69 ETH
## 70 EUU
## 71 FCS
## 72 FIN
## 73 FJI
## 74 FRA
## 75 GAB
## 76 GBR
## 77 GEO
## 78 GHA
## 79 GIN
## 80 GMB
## 81 GNB
## 82 GNQ
## 83 GRC
## 84 GRD
## 85 GTM
## 86 GUY
## 87 HIC
## 88 HKG
## 89 HND
## 90 HPC
## 91 HRV
## 92 HTI
## 93 HUN
## 94 IBD
## 95 IBT
## 96 IDA
## 97 IDB
## 98 IDN
## 99 IDX
## 100 IND
## 101 IRL
## 102 IRN
## 103 IRQ
## 104 ISL
## 105 ISR
## 106 ITA
## 107 JAM
## 108 JOR
## 109 JPN
## 110 KAZ
## 111 KEN
## 112 KGZ
## 113 KHM
## 114 KIR
## 115 KNA
## 116 KOR
## 117 KWT
## 118 LAC
## 119 LAO
## 120 LBN
## 121 LBR
## 122 LCA
## 123 LCN
## 124 LDC
## 125 LIC
## 126 LKA
## 127 LMC
## 128 LMY
## 129 LSO
## 130 LTE
## 131 LTU
## 132 LUX
## 133 LVA
## 134 MAC
## 135 MAR
## 136 MDA
## 137 MDG
## 138 MDV
## 139 MEA
## 140 MEX
## 141 MHL
## 142 MIC
## 143 MKD
## 144 MLI
## 145 MLT
## 146 MMR
## 147 MNA
## 148 MNE
## 149 MNG
## 150 MOZ
## 151 MRT
## 152 MUS
## 153 MWI
## 154 MYS
## 155 NAC
## 156 NAM
## 157 NCL
## 158 NER
## 159 NGA
## 160 NIC
## 161 NLD
## 162 NOR
## 163 NPL
## 164 NZL
## 165 OED
## 166 OMN
## 167 OSS
## 168 PAK
## 169 PAN
## 170 PER
## 171 PHL
## 172 PLW
## 173 PNG
## 174 POL
## 175 PRE
## 176 PRT
## 177 PRY
## 178 PSE
## 179 PSS
## 180 PST
## 181 PYF
## 182 QAT
## 183 ROU
## 184 RUS
## 185 RWA
## 186 SAS
## 187 SAU
## 188 SDN
## 189 SEN
## 190 SGP
## 191 SLB
## 192 SLE
## 193 SLV
## 194 SOM
## 195 SRB
## 196 SSA
## 197 SSD
## 198 SSF
## 199 SST
## 200 STP
## 201 SUR
## 202 SVK
## 203 SVN
## 204 SWE
## 205 SWZ
## 206 SXM
## 207 SYC
## 208 TCA
## 209 TCD
## 210 TEA
## 211 TEC
## 212 TGO
## 213 THA
## 214 TJK
## 215 TKM
## 216 TLA
## 217 TLS
## 218 TMN
## 219 TON
## 220 TSA
## 221 TSS
## 222 TTO
## 223 TUN
## 224 TUR
## 225 TUV
## 226 TZA
## 227 UGA
## 228 UKR
## 229 UMC
## 230 URY
## 231 USA
## 232 UZB
## 233 VCT
## 234 VEN
## 235 VGB
## 236 VNM
## 237 VUT
## 238 WLD
## 239 WSM
## 240 XKX
## 241 ZAF
## 242 ZMB
## 243 ZWE
#Normality test
normality_results <- lapply(df[, c("2015", "2016", "2017", "2018", "2019", "2020", "2021")], function(x) {
test_result <- shapiro.test(x)
data.frame(
W = test_result$statistic,
p_value = format(test_result$p.value, scientific = FALSE) # Mencegah notasi ilmiah
)
})
final_results <- do.call(rbind, normality_results)
print(final_results)
## W p_value
## 2015 0.3079370 0.00000000000000000000000000002877851
## 2016 0.2877426 0.00000000000000000000000000001353288
## 2017 0.3123475 0.00000000000000000000000000003401292
## 2018 0.4513711 0.00000000000000000000000001082795
## 2019 0.3564934 0.0000000000000000000000000001902077
## 2020 0.4169773 0.000000000000000000000000002357283
## 2021 0.3553163 0.0000000000000000000000000001814574
#Normal Probability Plot
library(ggplot2)
par(mfrow=c(2, 4))
for (year in c("2015", "2016", "2017", "2018", "2019", "2020", "2021")) {
qqnorm(df[[year]], main=paste("Q-Q Plot for", year), pch=19)
qqline(df[[year]], col="red")
}
par(mfrow=c(1, 1))
#Mengubah bentuk data ke long (formatnya atau tampilannya)
library(reshape2)
df_long <- melt(df, id.vars = c("Country Name", "Country Code"),
measure.vars = c("2015", "2016", "2017", "2018", "2019", "2020", "2021"),
variable.name = "Year", value.name = "Value")
head(df_long)
## Country Name Country Code Year Value
## 1 Aruba ABW 2015 -26881397
## 2 Africa Eastern and Southern AFE 2015 28638672651
## 3 Afghanistan AFG 2015 169146608
## 4 Africa Western and Central AFW 2015 16597546704
## 5 Angola AGO 2015 10028215163
## 6 Albania ALB 2015 989578335
#Analisis dengan Kruskal Wallis Test karena data tidak normal
kruskal_country <- kruskal.test(Value ~ `Country Name`, data = df_long)
print(kruskal_country)
##
## Kruskal-Wallis rank sum test
##
## data: Value by Country Name
## Kruskal-Wallis chi-squared = 1394.5, df = 242, p-value < 2.2e-16
kruskal_year <- kruskal.test(Value ~ Year, data = df_long)
print(kruskal_year)
##
## Kruskal-Wallis rank sum test
##
## data: Value by Year
## Kruskal-Wallis chi-squared = 10.711, df = 6, p-value = 0.09774
#Kesimpulan hasil analisis:
#Uji Kruskal-Wallis untuk variabel Value berdasarkan Country Name:
#Chi-squared = 1394.5, df = 242, p-value < 2.2e-16
#Kesimpulan: Perbedaan nilai Foregin direct invesment (net inflow) antar negara sangat signifikan (p-value < 0.05), artinya terdapat perbedaan nyata dalam data antara negara-negara yang dianalisis.
#Uji Kruskal-Wallis untuk variabel Value berdasarkan Year:
#Chi-squared = 10.711, df = 6, p-value = 0.09774
#Kesimpulan: P-value sebesar 0.09774 lebih besar dari 0.05, yang berarti tidak ada perbedaan signifikan antar nilai Foregin Direct Invesment
#berdasarkan tahun. Artinya, variasi antar tahun tidak cukup kuat untuk disimpulkan sebagai perbedaan nyata dalam data.
#Sumber data: https://data.worldbank.org/indicator/BX.KLT.DINV.CD.WD?end=2023&start=1970&view=chart (World Bank)