Tuas Visualisasi Data Sesi UAS
Fathiyya Mufida
2024-05-14
library(ggplot2) ## Warning: package 'ggplot2' was built under R version 4.3.3library(tidyverse) ## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ lubridate 1.9.3 ✔ tibble 3.2.1
## ✔ purrr 1.0.2 ✔ tidyr 1.3.1
## ── 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 errorsData 1
datap10 <- read.csv("C:/semester 4/anreg/World Happiness Report/2019.csv")
datap10## Overall.rank Country.or.region Score GDP.per.capita Social.support
## 1 1 Finland 7.769 1.340 1.587
## 2 2 Denmark 7.600 1.383 1.573
## 3 3 Norway 7.554 1.488 1.582
## 4 4 Iceland 7.494 1.380 1.624
## 5 5 Netherlands 7.488 1.396 1.522
## 6 6 Switzerland 7.480 1.452 1.526
## 7 7 Sweden 7.343 1.387 1.487
## 8 8 New Zealand 7.307 1.303 1.557
## 9 9 Canada 7.278 1.365 1.505
## 10 10 Austria 7.246 1.376 1.475
## 11 11 Australia 7.228 1.372 1.548
## 12 12 Costa Rica 7.167 1.034 1.441
## 13 13 Israel 7.139 1.276 1.455
## 14 14 Luxembourg 7.090 1.609 1.479
## 15 15 United Kingdom 7.054 1.333 1.538
## 16 16 Ireland 7.021 1.499 1.553
## 17 17 Germany 6.985 1.373 1.454
## 18 18 Belgium 6.923 1.356 1.504
## 19 19 United States 6.892 1.433 1.457
## 20 20 Czech Republic 6.852 1.269 1.487
## 21 21 United Arab Emirates 6.825 1.503 1.310
## 22 22 Malta 6.726 1.300 1.520
## 23 23 Mexico 6.595 1.070 1.323
## 24 24 France 6.592 1.324 1.472
## 25 25 Taiwan 6.446 1.368 1.430
## 26 26 Chile 6.444 1.159 1.369
## 27 27 Guatemala 6.436 0.800 1.269
## 28 28 Saudi Arabia 6.375 1.403 1.357
## 29 29 Qatar 6.374 1.684 1.313
## 30 30 Spain 6.354 1.286 1.484
## 31 31 Panama 6.321 1.149 1.442
## 32 32 Brazil 6.300 1.004 1.439
## 33 33 Uruguay 6.293 1.124 1.465
## 34 34 Singapore 6.262 1.572 1.463
## 35 35 El Salvador 6.253 0.794 1.242
## 36 36 Italy 6.223 1.294 1.488
## 37 37 Bahrain 6.199 1.362 1.368
## 38 38 Slovakia 6.198 1.246 1.504
## 39 39 Trinidad & Tobago 6.192 1.231 1.477
## 40 40 Poland 6.182 1.206 1.438
## 41 41 Uzbekistan 6.174 0.745 1.529
## 42 42 Lithuania 6.149 1.238 1.515
## 43 43 Colombia 6.125 0.985 1.410
## 44 44 Slovenia 6.118 1.258 1.523
## 45 45 Nicaragua 6.105 0.694 1.325
## 46 46 Kosovo 6.100 0.882 1.232
## 47 47 Argentina 6.086 1.092 1.432
## 48 48 Romania 6.070 1.162 1.232
## 49 49 Cyprus 6.046 1.263 1.223
## 50 50 Ecuador 6.028 0.912 1.312
## 51 51 Kuwait 6.021 1.500 1.319
## 52 52 Thailand 6.008 1.050 1.409
## 53 53 Latvia 5.940 1.187 1.465
## 54 54 South Korea 5.895 1.301 1.219
## 55 55 Estonia 5.893 1.237 1.528
## 56 56 Jamaica 5.890 0.831 1.478
## 57 57 Mauritius 5.888 1.120 1.402
## 58 58 Japan 5.886 1.327 1.419
## 59 59 Honduras 5.860 0.642 1.236
## 60 60 Kazakhstan 5.809 1.173 1.508
## 61 61 Bolivia 5.779 0.776 1.209
## 62 62 Hungary 5.758 1.201 1.410
## 63 63 Paraguay 5.743 0.855 1.475
## 64 64 Northern Cyprus 5.718 1.263 1.252
## 65 65 Peru 5.697 0.960 1.274
## 66 66 Portugal 5.693 1.221 1.431
## 67 67 Pakistan 5.653 0.677 0.886
## 68 68 Russia 5.648 1.183 1.452
## 69 69 Philippines 5.631 0.807 1.293
## 70 70 Serbia 5.603 1.004 1.383
## 71 71 Moldova 5.529 0.685 1.328
## 72 72 Libya 5.525 1.044 1.303
## 73 73 Montenegro 5.523 1.051 1.361
## 74 74 Tajikistan 5.467 0.493 1.098
## 75 75 Croatia 5.432 1.155 1.266
## 76 76 Hong Kong 5.430 1.438 1.277
## 77 77 Dominican Republic 5.425 1.015 1.401
## 78 78 Bosnia and Herzegovina 5.386 0.945 1.212
## 79 79 Turkey 5.373 1.183 1.360
## 80 80 Malaysia 5.339 1.221 1.171
## 81 81 Belarus 5.323 1.067 1.465
## 82 82 Greece 5.287 1.181 1.156
## 83 83 Mongolia 5.285 0.948 1.531
## 84 84 North Macedonia 5.274 0.983 1.294
## 85 85 Nigeria 5.265 0.696 1.111
## 86 86 Kyrgyzstan 5.261 0.551 1.438
## 87 87 Turkmenistan 5.247 1.052 1.538
## 88 88 Algeria 5.211 1.002 1.160
## 89 89 Morocco 5.208 0.801 0.782
## 90 90 Azerbaijan 5.208 1.043 1.147
## 91 91 Lebanon 5.197 0.987 1.224
## 92 92 Indonesia 5.192 0.931 1.203
## 93 93 China 5.191 1.029 1.125
## 94 94 Vietnam 5.175 0.741 1.346
## 95 95 Bhutan 5.082 0.813 1.321
## 96 96 Cameroon 5.044 0.549 0.910
## 97 97 Bulgaria 5.011 1.092 1.513
## 98 98 Ghana 4.996 0.611 0.868
## 99 99 Ivory Coast 4.944 0.569 0.808
## 100 100 Nepal 4.913 0.446 1.226
## 101 101 Jordan 4.906 0.837 1.225
## 102 102 Benin 4.883 0.393 0.437
## 103 103 Congo (Brazzaville) 4.812 0.673 0.799
## 104 104 Gabon 4.799 1.057 1.183
## 105 105 Laos 4.796 0.764 1.030
## 106 106 South Africa 4.722 0.960 1.351
## 107 107 Albania 4.719 0.947 0.848
## 108 108 Venezuela 4.707 0.960 1.427
## 109 109 Cambodia 4.700 0.574 1.122
## 110 110 Palestinian Territories 4.696 0.657 1.247
## 111 111 Senegal 4.681 0.450 1.134
## 112 112 Somalia 4.668 0.000 0.698
## 113 113 Namibia 4.639 0.879 1.313
## 114 114 Niger 4.628 0.138 0.774
## 115 115 Burkina Faso 4.587 0.331 1.056
## 116 116 Armenia 4.559 0.850 1.055
## 117 117 Iran 4.548 1.100 0.842
## 118 118 Guinea 4.534 0.380 0.829
## 119 119 Georgia 4.519 0.886 0.666
## 120 120 Gambia 4.516 0.308 0.939
## 121 121 Kenya 4.509 0.512 0.983
## 122 122 Mauritania 4.490 0.570 1.167
## 123 123 Mozambique 4.466 0.204 0.986
## 124 124 Tunisia 4.461 0.921 1.000
## 125 125 Bangladesh 4.456 0.562 0.928
## 126 126 Iraq 4.437 1.043 0.980
## 127 127 Congo (Kinshasa) 4.418 0.094 1.125
## 128 128 Mali 4.390 0.385 1.105
## 129 129 Sierra Leone 4.374 0.268 0.841
## 130 130 Sri Lanka 4.366 0.949 1.265
## 131 131 Myanmar 4.360 0.710 1.181
## 132 132 Chad 4.350 0.350 0.766
## 133 133 Ukraine 4.332 0.820 1.390
## 134 134 Ethiopia 4.286 0.336 1.033
## 135 135 Swaziland 4.212 0.811 1.149
## 136 136 Uganda 4.189 0.332 1.069
## 137 137 Egypt 4.166 0.913 1.039
## 138 138 Zambia 4.107 0.578 1.058
## 139 139 Togo 4.085 0.275 0.572
## 140 140 India 4.015 0.755 0.765
## 141 141 Liberia 3.975 0.073 0.922
## 142 142 Comoros 3.973 0.274 0.757
## 143 143 Madagascar 3.933 0.274 0.916
## 144 144 Lesotho 3.802 0.489 1.169
## 145 145 Burundi 3.775 0.046 0.447
## 146 146 Zimbabwe 3.663 0.366 1.114
## 147 147 Haiti 3.597 0.323 0.688
## 148 148 Botswana 3.488 1.041 1.145
## 149 149 Syria 3.462 0.619 0.378
## 150 150 Malawi 3.410 0.191 0.560
## 151 151 Yemen 3.380 0.287 1.163
## 152 152 Rwanda 3.334 0.359 0.711
## 153 153 Tanzania 3.231 0.476 0.885
## 154 154 Afghanistan 3.203 0.350 0.517
## 155 155 Central African Republic 3.083 0.026 0.000
## 156 156 South Sudan 2.853 0.306 0.575
## Healthy.life.expectancy Freedom.to.make.life.choices Generosity
## 1 0.986 0.596 0.153
## 2 0.996 0.592 0.252
## 3 1.028 0.603 0.271
## 4 1.026 0.591 0.354
## 5 0.999 0.557 0.322
## 6 1.052 0.572 0.263
## 7 1.009 0.574 0.267
## 8 1.026 0.585 0.330
## 9 1.039 0.584 0.285
## 10 1.016 0.532 0.244
## 11 1.036 0.557 0.332
## 12 0.963 0.558 0.144
## 13 1.029 0.371 0.261
## 14 1.012 0.526 0.194
## 15 0.996 0.450 0.348
## 16 0.999 0.516 0.298
## 17 0.987 0.495 0.261
## 18 0.986 0.473 0.160
## 19 0.874 0.454 0.280
## 20 0.920 0.457 0.046
## 21 0.825 0.598 0.262
## 22 0.999 0.564 0.375
## 23 0.861 0.433 0.074
## 24 1.045 0.436 0.111
## 25 0.914 0.351 0.242
## 26 0.920 0.357 0.187
## 27 0.746 0.535 0.175
## 28 0.795 0.439 0.080
## 29 0.871 0.555 0.220
## 30 1.062 0.362 0.153
## 31 0.910 0.516 0.109
## 32 0.802 0.390 0.099
## 33 0.891 0.523 0.127
## 34 1.141 0.556 0.271
## 35 0.789 0.430 0.093
## 36 1.039 0.231 0.158
## 37 0.871 0.536 0.255
## 38 0.881 0.334 0.121
## 39 0.713 0.489 0.185
## 40 0.884 0.483 0.117
## 41 0.756 0.631 0.322
## 42 0.818 0.291 0.043
## 43 0.841 0.470 0.099
## 44 0.953 0.564 0.144
## 45 0.835 0.435 0.200
## 46 0.758 0.489 0.262
## 47 0.881 0.471 0.066
## 48 0.825 0.462 0.083
## 49 1.042 0.406 0.190
## 50 0.868 0.498 0.126
## 51 0.808 0.493 0.142
## 52 0.828 0.557 0.359
## 53 0.812 0.264 0.075
## 54 1.036 0.159 0.175
## 55 0.874 0.495 0.103
## 56 0.831 0.490 0.107
## 57 0.798 0.498 0.215
## 58 1.088 0.445 0.069
## 59 0.828 0.507 0.246
## 60 0.729 0.410 0.146
## 61 0.706 0.511 0.137
## 62 0.828 0.199 0.081
## 63 0.777 0.514 0.184
## 64 1.042 0.417 0.191
## 65 0.854 0.455 0.083
## 66 0.999 0.508 0.047
## 67 0.535 0.313 0.220
## 68 0.726 0.334 0.082
## 69 0.657 0.558 0.117
## 70 0.854 0.282 0.137
## 71 0.739 0.245 0.181
## 72 0.673 0.416 0.133
## 73 0.871 0.197 0.142
## 74 0.718 0.389 0.230
## 75 0.914 0.296 0.119
## 76 1.122 0.440 0.258
## 77 0.779 0.497 0.113
## 78 0.845 0.212 0.263
## 79 0.808 0.195 0.083
## 80 0.828 0.508 0.260
## 81 0.789 0.235 0.094
## 82 0.999 0.067 0.000
## 83 0.667 0.317 0.235
## 84 0.838 0.345 0.185
## 85 0.245 0.426 0.215
## 86 0.723 0.508 0.300
## 87 0.657 0.394 0.244
## 88 0.785 0.086 0.073
## 89 0.782 0.418 0.036
## 90 0.769 0.351 0.035
## 91 0.815 0.216 0.166
## 92 0.660 0.491 0.498
## 93 0.893 0.521 0.058
## 94 0.851 0.543 0.147
## 95 0.604 0.457 0.370
## 96 0.331 0.381 0.187
## 97 0.815 0.311 0.081
## 98 0.486 0.381 0.245
## 99 0.232 0.352 0.154
## 100 0.677 0.439 0.285
## 101 0.815 0.383 0.110
## 102 0.397 0.349 0.175
## 103 0.508 0.372 0.105
## 104 0.571 0.295 0.043
## 105 0.551 0.547 0.266
## 106 0.469 0.389 0.130
## 107 0.874 0.383 0.178
## 108 0.805 0.154 0.064
## 109 0.637 0.609 0.232
## 110 0.672 0.225 0.103
## 111 0.571 0.292 0.153
## 112 0.268 0.559 0.243
## 113 0.477 0.401 0.070
## 114 0.366 0.318 0.188
## 115 0.380 0.255 0.177
## 116 0.815 0.283 0.095
## 117 0.785 0.305 0.270
## 118 0.375 0.332 0.207
## 119 0.752 0.346 0.043
## 120 0.428 0.382 0.269
## 121 0.581 0.431 0.372
## 122 0.489 0.066 0.106
## 123 0.390 0.494 0.197
## 124 0.815 0.167 0.059
## 125 0.723 0.527 0.166
## 126 0.574 0.241 0.148
## 127 0.357 0.269 0.212
## 128 0.308 0.327 0.153
## 129 0.242 0.309 0.252
## 130 0.831 0.470 0.244
## 131 0.555 0.525 0.566
## 132 0.192 0.174 0.198
## 133 0.739 0.178 0.187
## 134 0.532 0.344 0.209
## 135 0.000 0.313 0.074
## 136 0.443 0.356 0.252
## 137 0.644 0.241 0.076
## 138 0.426 0.431 0.247
## 139 0.410 0.293 0.177
## 140 0.588 0.498 0.200
## 141 0.443 0.370 0.233
## 142 0.505 0.142 0.275
## 143 0.555 0.148 0.169
## 144 0.168 0.359 0.107
## 145 0.380 0.220 0.176
## 146 0.433 0.361 0.151
## 147 0.449 0.026 0.419
## 148 0.538 0.455 0.025
## 149 0.440 0.013 0.331
## 150 0.495 0.443 0.218
## 151 0.463 0.143 0.108
## 152 0.614 0.555 0.217
## 153 0.499 0.417 0.276
## 154 0.361 0.000 0.158
## 155 0.105 0.225 0.235
## 156 0.295 0.010 0.202
## Perceptions.of.corruption
## 1 0.393
## 2 0.410
## 3 0.341
## 4 0.118
## 5 0.298
## 6 0.343
## 7 0.373
## 8 0.380
## 9 0.308
## 10 0.226
## 11 0.290
## 12 0.093
## 13 0.082
## 14 0.316
## 15 0.278
## 16 0.310
## 17 0.265
## 18 0.210
## 19 0.128
## 20 0.036
## 21 0.182
## 22 0.151
## 23 0.073
## 24 0.183
## 25 0.097
## 26 0.056
## 27 0.078
## 28 0.132
## 29 0.167
## 30 0.079
## 31 0.054
## 32 0.086
## 33 0.150
## 34 0.453
## 35 0.074
## 36 0.030
## 37 0.110
## 38 0.014
## 39 0.016
## 40 0.050
## 41 0.240
## 42 0.042
## 43 0.034
## 44 0.057
## 45 0.127
## 46 0.006
## 47 0.050
## 48 0.005
## 49 0.041
## 50 0.087
## 51 0.097
## 52 0.028
## 53 0.064
## 54 0.056
## 55 0.161
## 56 0.028
## 57 0.060
## 58 0.140
## 59 0.078
## 60 0.096
## 61 0.064
## 62 0.020
## 63 0.080
## 64 0.162
## 65 0.027
## 66 0.025
## 67 0.098
## 68 0.031
## 69 0.107
## 70 0.039
## 71 0.000
## 72 0.152
## 73 0.080
## 74 0.144
## 75 0.022
## 76 0.287
## 77 0.101
## 78 0.006
## 79 0.106
## 80 0.024
## 81 0.142
## 82 0.034
## 83 0.038
## 84 0.034
## 85 0.041
## 86 0.023
## 87 0.028
## 88 0.114
## 89 0.076
## 90 0.182
## 91 0.027
## 92 0.028
## 93 0.100
## 94 0.073
## 95 0.167
## 96 0.037
## 97 0.004
## 98 0.040
## 99 0.090
## 100 0.089
## 101 0.130
## 102 0.082
## 103 0.093
## 104 0.055
## 105 0.164
## 106 0.055
## 107 0.027
## 108 0.047
## 109 0.062
## 110 0.066
## 111 0.072
## 112 0.270
## 113 0.056
## 114 0.102
## 115 0.113
## 116 0.064
## 117 0.125
## 118 0.086
## 119 0.164
## 120 0.167
## 121 0.053
## 122 0.088
## 123 0.138
## 124 0.055
## 125 0.143
## 126 0.089
## 127 0.053
## 128 0.052
## 129 0.045
## 130 0.047
## 131 0.172
## 132 0.078
## 133 0.010
## 134 0.100
## 135 0.135
## 136 0.060
## 137 0.067
## 138 0.087
## 139 0.085
## 140 0.085
## 141 0.033
## 142 0.078
## 143 0.041
## 144 0.093
## 145 0.180
## 146 0.089
## 147 0.110
## 148 0.100
## 149 0.141
## 150 0.089
## 151 0.077
## 152 0.411
## 153 0.147
## 154 0.025
## 155 0.035
## 156 0.091Visualisasi Korelasi
library(tidyverse) #Include beberapa packages termasuk ggplot
library(dplyr)
library(reshape2)##
## Attaching package: 'reshape2'## The following object is masked from 'package:tidyr':
##
## smithsggplot(datap10, aes(x = Score, y = GDP.per.capita)) +
geom_point() +
labs(title = "World Happiness", x = "Score", y = "GDP per capita") +
theme_classic()
ggplot(datap10, aes(x = Score, y = Social.support)) +
geom_point() +
labs(title = "World Happiness", x = "Score", y = "Social Support") +
theme_classic()
data_numerik <- select_if(datap10, is.numeric)
str(data_numerik)## 'data.frame': 156 obs. of 8 variables:
## $ Overall.rank : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Score : num 7.77 7.6 7.55 7.49 7.49 ...
## $ GDP.per.capita : num 1.34 1.38 1.49 1.38 1.4 ...
## $ Social.support : num 1.59 1.57 1.58 1.62 1.52 ...
## $ Healthy.life.expectancy : num 0.986 0.996 1.028 1.026 0.999 ...
## $ Freedom.to.make.life.choices: num 0.596 0.592 0.603 0.591 0.557 0.572 0.574 0.585 0.584 0.532 ...
## $ Generosity : num 0.153 0.252 0.271 0.354 0.322 0.263 0.267 0.33 0.285 0.244 ...
## $ Perceptions.of.corruption : num 0.393 0.41 0.341 0.118 0.298 0.343 0.373 0.38 0.308 0.226 ...data_melt <- cor(data_numerik[sapply(data_numerik,is.numeric)])
data_melt <- melt(data_melt)
ggplot(data_melt, aes(x = Var1, y = Var2, fill = value)) +
geom_tile() +
labs(title = "Correlation Heatmap",
x = "Variable 1",
y = "Variable 2")
Data 2
datasony <- read.csv("C:/Users/Aminah/Downloads/SONY.csv")
datasony## Date Open High Low Close Adj.Close Volume
## 1 2023-05-15 94.62 95.12 94.57 94.67 94.67 538700
## 2 2023-05-16 93.90 94.26 93.54 93.81 93.81 504000
## 3 2023-05-17 95.55 95.89 95.01 95.32 95.32 922100
## 4 2023-05-18 99.07 99.15 98.35 98.89 98.89 1810100
## 5 2023-05-19 98.06 98.49 97.62 98.01 98.01 711400
## 6 2023-05-22 98.12 98.49 97.46 97.60 97.60 886000
## 7 2023-05-23 95.57 95.57 94.56 94.97 94.97 980600
## 8 2023-05-24 94.44 94.94 94.01 94.23 94.23 657300
## 9 2023-05-25 95.46 95.55 94.60 95.04 95.04 614500
## 10 2023-05-26 95.59 96.63 95.37 95.98 95.98 622500
## 11 2023-05-30 96.09 96.22 94.75 94.76 94.76 765100
## 12 2023-05-31 94.21 94.49 92.87 93.70 93.70 640100
## 13 2023-06-01 96.19 97.05 96.16 96.66 96.66 968700
## 14 2023-06-02 98.05 98.86 97.93 98.01 98.01 676400
## 15 2023-06-05 98.99 99.10 98.39 98.50 98.50 672400
## 16 2023-06-06 99.00 100.24 98.79 100.05 100.05 754100
## 17 2023-06-07 98.30 98.80 97.63 97.68 97.68 736100
## 18 2023-06-08 96.21 97.41 95.92 97.41 97.41 686100
## 19 2023-06-09 98.54 99.05 98.00 98.44 98.44 516800
## 20 2023-06-12 98.81 99.55 98.50 99.25 99.25 1193900
## 21 2023-06-13 100.23 100.94 100.04 100.19 100.19 620700
## 22 2023-06-14 100.71 100.89 99.51 99.87 99.87 515100
## 23 2023-06-15 98.79 99.75 98.55 99.67 99.67 590100
## 24 2023-06-16 98.48 98.66 97.46 97.57 97.57 778000
## 25 2023-06-20 97.00 97.01 96.21 96.31 96.31 509200
## 26 2023-06-21 95.09 95.43 94.27 94.68 94.68 559600
## 27 2023-06-22 93.59 94.07 93.30 93.82 93.82 701900
## 28 2023-06-23 91.24 91.49 90.80 91.14 91.14 1150100
## 29 2023-06-26 90.63 91.16 90.19 90.37 90.37 694700
## 30 2023-06-27 89.80 90.38 89.22 90.07 90.07 1313700
## 31 2023-06-28 90.70 91.55 90.56 91.29 91.29 807900
## 32 2023-06-29 90.11 90.30 89.58 89.96 89.96 664700
## 33 2023-06-30 90.05 90.79 90.00 90.04 90.04 939100
## 34 2023-07-03 91.54 92.10 91.34 91.55 91.55 759700
## 35 2023-07-05 91.43 91.77 91.13 91.43 91.43 835300
## 36 2023-07-06 90.66 90.84 89.84 90.49 90.49 759700
## 37 2023-07-07 90.64 91.86 90.64 91.04 91.04 726000
## 38 2023-07-10 90.40 90.87 90.40 90.73 90.73 672200
## 39 2023-07-11 89.92 90.40 89.29 89.64 89.64 1363400
## 40 2023-07-12 89.65 92.53 89.64 91.24 91.24 2313700
## 41 2023-07-13 93.66 94.13 93.38 93.81 93.81 1503900
## 42 2023-07-14 93.22 93.22 92.34 92.39 92.39 915700
## 43 2023-07-17 92.53 94.29 92.37 93.57 93.57 769700
## 44 2023-07-18 94.86 95.42 94.58 95.10 95.10 967100
## 45 2023-07-19 94.76 95.00 94.13 94.57 94.57 539100
## 46 2023-07-20 93.29 93.86 93.18 93.25 93.25 650700
## 47 2023-07-21 93.82 93.96 93.28 93.39 93.39 425700
## 48 2023-07-24 93.39 93.92 93.26 93.66 93.66 386200
## 49 2023-07-25 93.03 93.79 92.80 93.69 93.69 437300
## 50 2023-07-26 93.39 94.07 93.20 93.74 93.74 374700
## 51 2023-07-27 94.70 95.19 93.59 93.76 93.76 686100
## 52 2023-07-28 93.77 94.17 93.40 93.47 93.47 679800
## 53 2023-07-31 93.81 94.36 93.38 93.60 93.60 644800
## 54 2023-08-01 93.50 93.92 93.28 93.49 93.49 435900
## 55 2023-08-02 91.89 92.18 90.91 91.07 91.07 667500
## 56 2023-08-03 90.02 90.20 89.52 90.03 90.03 571900
## 57 2023-08-04 91.07 91.36 90.06 90.19 90.19 558500
## 58 2023-08-07 90.66 90.73 90.12 90.44 90.44 653700
## 59 2023-08-08 90.09 90.58 89.42 89.82 89.82 907000
## 60 2023-08-09 90.31 90.31 82.70 83.85 83.85 2456800
## 61 2023-08-10 86.95 87.00 85.31 85.44 85.44 1609600
## 62 2023-08-11 85.16 85.79 84.63 85.58 85.58 775700
## 63 2023-08-14 83.58 84.00 82.90 83.97 83.97 1146300
## 64 2023-08-15 83.42 83.72 82.92 83.53 83.53 570300
## 65 2023-08-16 82.73 83.19 82.15 82.24 82.24 894500
## 66 2023-08-17 83.50 83.50 82.23 82.40 82.40 699300
## 67 2023-08-18 82.02 83.01 81.76 82.66 82.66 614600
## 68 2023-08-21 82.40 82.68 82.18 82.58 82.58 575000
## 69 2023-08-22 82.65 82.81 81.79 81.90 81.90 745400
## 70 2023-08-23 82.10 82.78 82.10 82.61 82.61 877700
## 71 2023-08-24 82.23 82.37 80.88 81.05 81.05 881000
## 72 2023-08-25 81.41 81.69 80.34 81.10 81.10 719900
## 73 2023-08-28 81.73 82.25 81.73 82.07 82.07 458800
## 74 2023-08-29 81.68 82.84 81.45 82.61 82.61 538800
## 75 2023-08-30 82.50 82.85 82.19 82.35 82.35 556900
## 76 2023-08-31 83.14 83.45 83.01 83.19 83.19 657000
## 77 2023-09-01 85.80 85.97 84.74 85.26 85.26 1157600
## 78 2023-09-05 84.65 85.19 84.42 84.54 84.54 518300
## 79 2023-09-06 85.82 86.09 85.35 85.50 85.50 830000
## 80 2023-09-07 84.97 85.36 84.75 85.23 85.23 594900
## 81 2023-09-08 84.25 84.65 84.04 84.22 84.22 488900
## 82 2023-09-11 85.10 85.63 84.82 85.20 85.20 627500
## 83 2023-09-12 85.20 85.65 84.98 85.41 85.41 369100
## 84 2023-09-13 84.38 84.93 84.25 84.43 84.43 563200
## 85 2023-09-14 85.93 86.89 85.79 86.50 86.50 652700
## 86 2023-09-15 87.36 87.50 86.76 86.76 86.76 490200
## 87 2023-09-18 86.37 86.89 86.05 86.76 86.76 439400
## 88 2023-09-19 86.62 86.97 86.29 86.54 86.54 363000
## 89 2023-09-20 85.69 85.97 84.77 84.85 84.85 505600
## 90 2023-09-21 82.85 83.41 82.53 82.64 82.64 709300
## 91 2023-09-22 82.98 83.13 82.33 82.38 82.38 505000
## 92 2023-09-25 83.05 83.39 82.73 83.27 83.27 608000
## 93 2023-09-26 82.39 82.60 81.08 81.30 81.30 592100
## 94 2023-09-27 83.20 83.30 82.31 82.55 82.55 733900
## 95 2023-09-28 81.71 83.18 81.70 82.78 82.78 878900
## 96 2023-09-29 82.81 83.08 82.07 82.41 82.41 622700
## 97 2023-10-02 81.66 82.05 81.20 81.65 81.65 473400
## 98 2023-10-03 81.49 82.05 80.78 81.21 81.21 494000
## 99 2023-10-04 81.42 81.93 80.75 81.82 81.82 513800
## 100 2023-10-05 82.66 83.16 82.26 82.86 82.86 501200
## 101 2023-10-06 82.30 83.35 81.80 82.97 82.97 379600
## 102 2023-10-09 82.41 83.63 82.29 83.50 83.50 322600
## 103 2023-10-10 84.29 85.18 84.12 84.61 84.61 429100
## 104 2023-10-11 85.34 85.98 85.33 85.46 85.46 620100
## 105 2023-10-12 86.75 87.27 86.15 86.58 86.58 789200
## 106 2023-10-13 85.75 85.85 84.89 85.15 85.15 560200
## 107 2023-10-16 84.79 85.47 84.51 85.40 85.40 485300
## 108 2023-10-17 84.90 86.38 84.88 85.71 85.71 424900
## 109 2023-10-18 85.12 85.29 83.98 84.10 84.10 385800
## 110 2023-10-19 84.15 84.92 83.70 83.84 83.84 434900
## 111 2023-10-20 83.71 83.96 83.18 83.19 83.19 343100
## 112 2023-10-23 82.61 83.63 82.05 83.11 83.11 335100
## 113 2023-10-24 83.72 84.33 83.68 84.22 84.22 491700
## 114 2023-10-25 83.78 84.15 82.30 82.70 82.70 738500
## 115 2023-10-26 81.57 81.82 80.55 80.97 80.97 633000
## 116 2023-10-27 80.56 80.95 79.62 80.04 80.04 688500
## 117 2023-10-30 80.93 82.05 80.93 81.74 81.74 1441300
## 118 2023-10-31 82.82 83.27 82.55 83.05 83.05 660800
## 119 2023-11-01 84.34 85.21 84.02 85.03 85.03 816700
## 120 2023-11-02 85.65 86.79 85.65 86.56 86.56 965300
## 121 2023-11-03 87.39 87.96 87.39 87.42 87.42 934500
## 122 2023-11-06 86.32 87.31 86.32 86.69 86.69 1129300
## 123 2023-11-07 87.00 88.05 86.87 87.76 87.76 725300
## 124 2023-11-08 88.64 88.86 87.23 87.54 87.54 1376000
## 125 2023-11-09 85.62 85.62 80.77 81.73 81.73 3518100
## 126 2023-11-10 85.36 86.28 83.87 85.94 85.94 1889100
## 127 2023-11-13 83.57 83.60 82.51 83.33 83.33 1109700
## 128 2023-11-14 84.38 85.34 84.32 85.07 85.07 649600
## 129 2023-11-15 87.62 88.21 86.97 87.12 87.12 1110200
## 130 2023-11-16 86.69 87.63 86.56 87.60 87.60 582700
## 131 2023-11-17 87.58 87.69 87.00 87.65 87.65 429400
## 132 2023-11-20 86.98 87.62 86.77 87.54 87.54 493000
## 133 2023-11-21 87.37 87.48 86.71 86.79 86.79 541000
## 134 2023-11-22 87.38 87.99 87.07 87.59 87.59 469900
## 135 2023-11-24 86.22 86.78 86.03 86.64 86.64 389800
## 136 2023-11-27 86.31 87.12 86.16 86.95 86.95 564800
## 137 2023-11-28 86.70 87.61 86.40 87.23 87.23 1169300
## 138 2023-11-29 86.90 87.08 86.04 86.10 86.10 606900
## 139 2023-11-30 86.61 86.66 85.77 85.95 85.95 813000
## 140 2023-12-01 85.37 86.36 85.37 86.14 86.14 648300
## 141 2023-12-04 85.27 85.53 84.62 85.32 85.32 1027800
## 142 2023-12-05 85.88 86.69 85.86 86.40 86.40 817300
## 143 2023-12-06 89.00 89.54 88.40 88.40 88.40 2176600
## 144 2023-12-07 87.98 88.62 87.37 88.51 88.51 853100
## 145 2023-12-08 88.37 89.48 88.37 89.30 89.30 647900
## 146 2023-12-11 89.29 89.65 89.11 89.33 89.33 717100
## 147 2023-12-12 89.41 90.54 89.19 90.39 90.39 689400
## 148 2023-12-13 90.58 91.86 89.79 91.79 91.79 1503400
## 149 2023-12-14 91.30 92.56 91.14 92.42 92.42 1340800
## 150 2023-12-15 93.25 93.70 92.63 92.69 92.69 702300
## 151 2023-12-18 91.57 92.41 91.42 92.21 92.21 869900
## 152 2023-12-19 91.66 92.66 91.45 91.62 91.62 1035800
## 153 2023-12-20 91.91 92.00 90.08 90.08 90.08 784400
## 154 2023-12-21 92.08 92.89 91.34 92.76 92.76 751800
## 155 2023-12-22 91.75 92.30 91.51 92.14 92.14 531900
## 156 2023-12-26 92.23 93.65 92.21 93.47 93.47 660400
## 157 2023-12-27 93.62 94.05 93.46 94.01 94.01 585400
## 158 2023-12-28 93.98 94.84 93.91 94.65 94.65 445700
## 159 2023-12-29 94.60 94.91 94.31 94.69 94.69 302500
## 160 2024-01-02 93.50 94.00 92.96 93.30 93.30 667500
## 161 2024-01-03 92.60 92.80 92.21 92.38 92.38 569800
## 162 2024-01-04 90.97 91.83 90.72 91.05 91.05 911000
## 163 2024-01-05 90.95 92.30 90.83 91.22 91.22 722400
## 164 2024-01-08 91.21 92.50 91.21 92.40 92.40 415400
## 165 2024-01-09 92.13 92.38 91.85 92.11 92.11 529400
## 166 2024-01-10 95.11 96.35 95.07 96.15 96.15 1096200
## 167 2024-01-11 98.30 98.55 96.37 97.31 97.31 1223300
## 168 2024-01-12 99.77 100.88 99.45 99.75 99.75 1522700
## 169 2024-01-16 98.59 99.03 98.12 98.16 98.16 853400
## 170 2024-01-17 97.06 97.68 97.02 97.41 97.41 809300
## 171 2024-01-18 97.08 97.94 97.00 97.85 97.85 660000
## 172 2024-01-19 98.18 99.10 97.94 99.02 99.02 436000
## 173 2024-01-22 99.82 100.33 99.34 99.59 99.59 574300
## 174 2024-01-23 98.88 99.00 98.23 98.75 98.75 541900
## 175 2024-01-24 98.84 99.77 97.79 97.95 97.95 586000
## 176 2024-01-25 97.25 97.67 96.76 97.02 97.02 549300
## 177 2024-01-26 95.30 95.81 94.87 95.39 95.39 898100
## 178 2024-01-29 96.90 97.30 96.49 97.22 97.22 611000
## 179 2024-01-30 98.01 98.21 97.31 97.40 97.40 474600
## 180 2024-01-31 99.27 99.47 97.63 97.75 97.75 925300
## 181 2024-02-01 98.03 99.04 97.81 98.89 98.89 624200
## 182 2024-02-02 97.91 98.76 97.80 98.56 98.56 405700
## 183 2024-02-05 97.40 97.76 96.89 97.59 97.59 515300
## 184 2024-02-06 96.41 96.98 96.13 96.96 96.96 751300
## 185 2024-02-07 97.26 97.67 96.96 97.26 97.26 589700
## 186 2024-02-08 96.17 96.47 95.47 96.40 96.40 628500
## 187 2024-02-09 95.91 96.29 95.60 96.00 96.00 819600
## 188 2024-02-12 96.23 97.52 95.89 96.16 96.16 981800
## 189 2024-02-13 96.95 97.20 95.42 95.86 95.86 1115800
## 190 2024-02-14 95.18 95.42 89.35 91.75 91.75 3357100
## 191 2024-02-15 91.25 92.18 90.88 91.99 91.99 1475400
## 192 2024-02-16 90.18 90.25 88.70 88.84 88.84 1433400
## 193 2024-02-20 88.27 88.58 87.60 87.85 87.85 919100
## 194 2024-02-21 87.86 87.90 86.76 86.89 86.89 1159900
## 195 2024-02-22 88.25 89.04 87.86 88.32 88.32 1721300
## 196 2024-02-23 88.31 88.57 87.67 87.91 87.91 1202400
## 197 2024-02-26 86.56 86.59 85.92 86.07 86.07 1654800
## 198 2024-02-27 86.18 86.24 85.27 85.79 85.79 876800
## 199 2024-02-28 85.32 85.48 84.89 85.16 85.16 580500
## 200 2024-02-29 86.21 86.50 85.62 85.81 85.81 1146800
## 201 2024-03-01 87.62 88.45 87.27 88.02 88.02 1166000
## 202 2024-03-04 86.63 86.93 86.16 86.48 86.48 561700
## 203 2024-03-05 86.12 86.51 85.56 85.66 85.66 536400
## 204 2024-03-06 86.76 87.10 86.39 86.77 86.77 735500
## 205 2024-03-07 87.21 87.33 86.79 86.90 86.90 1088500
## 206 2024-03-08 87.65 88.00 87.00 87.21 87.21 663400
## 207 2024-03-11 86.49 86.93 86.33 86.76 86.76 554900
## 208 2024-03-12 87.46 88.22 87.07 87.75 87.75 480300
## 209 2024-03-13 87.81 88.16 87.58 87.84 87.84 442500
## 210 2024-03-14 87.98 88.22 86.81 87.10 87.10 648000
## 211 2024-03-15 87.35 87.67 87.12 87.38 87.38 560600
## 212 2024-03-18 88.89 89.52 88.89 89.03 89.03 881900
## 213 2024-03-19 89.00 89.20 88.55 89.00 89.00 640200
## 214 2024-03-20 88.75 89.74 88.35 89.62 89.62 583000
## 215 2024-03-21 88.90 89.58 88.64 88.72 88.72 600600
## 216 2024-03-22 88.74 88.85 88.36 88.62 88.62 298800
## 217 2024-03-25 86.26 86.47 85.73 85.96 85.96 723500
## 218 2024-03-26 86.60 87.12 86.45 86.72 86.72 568800
## 219 2024-03-27 86.90 86.90 86.16 86.54 86.54 655200
## 220 2024-03-28 86.19 86.35 85.73 85.74 85.74 661000
## 221 2024-04-01 86.00 86.34 85.79 86.17 86.17 398000
## 222 2024-04-02 85.50 85.50 84.67 85.33 85.33 540900
## 223 2024-04-03 85.43 86.19 85.41 85.77 85.77 939900
## 224 2024-04-04 86.09 86.10 84.52 84.55 84.55 607700
## 225 2024-04-05 84.50 85.20 84.29 84.82 84.82 411300
## 226 2024-04-08 84.59 84.76 84.13 84.32 84.32 551100
## 227 2024-04-09 84.87 85.13 84.22 84.64 84.64 513500
## 228 2024-04-10 84.12 84.42 83.83 84.13 84.13 438700
## 229 2024-04-11 84.25 84.31 83.42 84.06 84.06 382800
## 230 2024-04-12 84.33 84.57 83.69 83.99 83.99 777400
## 231 2024-04-15 83.55 83.67 82.12 82.30 82.30 791400
## 232 2024-04-16 83.50 83.63 82.90 82.97 82.97 853200
## 233 2024-04-17 82.28 82.28 81.36 81.58 81.58 580000
## 234 2024-04-18 82.07 82.57 81.85 81.88 81.88 447900
## 235 2024-04-19 81.51 82.16 81.19 81.30 81.30 597800
## 236 2024-04-22 81.24 81.73 80.89 81.56 81.56 611200
## 237 2024-04-23 81.15 81.90 81.03 81.81 81.81 809000
## 238 2024-04-24 82.54 83.11 82.22 82.71 82.71 644700
## 239 2024-04-25 81.28 82.49 81.14 82.44 82.44 552900
## 240 2024-04-26 81.79 82.43 81.71 82.33 82.33 440600
## 241 2024-04-29 82.87 83.13 82.49 82.99 82.99 708500
## 242 2024-04-30 82.89 83.14 82.49 82.57 82.57 464300
## 243 2024-05-01 82.81 83.86 82.53 82.89 82.89 393900
## 244 2024-05-02 84.04 84.63 83.44 84.08 84.08 657100
## 245 2024-05-03 85.25 85.57 84.20 84.54 84.54 631200
## 246 2024-05-06 85.01 85.13 84.50 84.84 84.84 746500
## 247 2024-05-07 82.24 82.33 81.59 81.65 81.65 946000
## 248 2024-05-08 78.26 78.85 77.66 78.35 78.35 1716800
## 249 2024-05-09 76.85 77.50 76.63 77.47 77.47 1648700Visualisasi Time Series
opensony <- data.frame(date = as.Date(datasony$Date), value = datasony$Open)
closesony <- data.frame(date = as.Date(datasony$Date), value = datasony$Close)combined_data <- rbind(opensony,closesony)
combined_data$series <- factor(rep(c("Open", "Close"), each = nrow(opensony)))
# Buat plot menggunakan ggplot
ggplot(combined_data, aes(x = date, y = value, color = series)) +
geom_line() +
labs(title = "Time Series Saahm Sony Corporation",
x = "Date",
y = "Value",
color = "Series") +
theme_minimal()
Visualisasi Geospasial
library("ggplot2")
library("sf")## Warning: package 'sf' was built under R version 4.3.3## Linking to GEOS 3.11.2, GDAL 3.8.2, PROJ 9.3.1; sf_use_s2() is TRUElibrary("rnaturalearth")## Warning: package 'rnaturalearth' was built under R version 4.3.3library("rnaturalearthdata")## Warning: package 'rnaturalearthdata' was built under R version 4.3.3##
## Attaching package: 'rnaturalearthdata'## The following object is masked from 'package:rnaturalearth':
##
## countries110library(sf)
library(ggspatial)## Warning: package 'ggspatial' was built under R version 4.3.3library(maps)## Warning: package 'maps' was built under R version 4.3.3##
## Attaching package: 'maps'## The following object is masked from 'package:purrr':
##
## mapdataworld <- map_data("world")
data3 <- datap10 %>% rename(region = Country.or.region)
data4 <- left_join(dataworld, data3, by = "region")
data4_vac <- data4 %>% filter(!is.na(Score))
head(data4_vac)## long lat group order region subregion Overall.rank Score
## 1 74.89131 37.23164 2 12 Afghanistan <NA> 154 3.203
## 2 74.84023 37.22505 2 13 Afghanistan <NA> 154 3.203
## 3 74.76738 37.24917 2 14 Afghanistan <NA> 154 3.203
## 4 74.73896 37.28564 2 15 Afghanistan <NA> 154 3.203
## 5 74.72666 37.29072 2 16 Afghanistan <NA> 154 3.203
## 6 74.66895 37.26670 2 17 Afghanistan <NA> 154 3.203
## GDP.per.capita Social.support Healthy.life.expectancy
## 1 0.35 0.517 0.361
## 2 0.35 0.517 0.361
## 3 0.35 0.517 0.361
## 4 0.35 0.517 0.361
## 5 0.35 0.517 0.361
## 6 0.35 0.517 0.361
## Freedom.to.make.life.choices Generosity Perceptions.of.corruption
## 1 0 0.158 0.025
## 2 0 0.158 0.025
## 3 0 0.158 0.025
## 4 0 0.158 0.025
## 5 0 0.158 0.025
## 6 0 0.158 0.025ggplot(data4_vac, aes(x = long, y = lat, group = group)) +
geom_polygon(aes(fill = Score), color = "black") +
scale_fill_gradient(name = "happiness score", low = "lightblue", high = "darkgreen", na.value = "yellow") +
labs(title = "Happiness Score Distribution") +
theme(axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
axis.title.y = element_blank(),
axis.title.x = element_blank(),
rect = element_blank())